The Bioinformatics CRO Podcast

Episode 74 with Phillip Meade

Dr. Phillip Meade, a leadership and culture advisor at Gallaher Edge, discusses his experience evaluating organizational culture and how to diagnose culture problems and build lasting habits for high-performance organizations.

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Phillip Meade

Phillip Meade is a leadership and cultural advisor at Gallaher Edge, which provides executive coaching, leadership development, strategic guidance and culture management services for businesses and organizations.

Transcript of Episode 74: Phillip Meade

Disclaimer: Transcripts are automated and may contain errors.

Grant Belgard: Welcome back to the Bioinformatics CRO podcast. Today I’m talking with Dr. Philip Meade, a leadership and culture advisor at Gallaher Edge, whose career has included extensive work inside NASA, particularly around organizational culture and return-to-flight moments after major setbacks. He’s collaborated across public and private sectors and co-authored a book on building high-performing cultures. Today we’ll translate those lessons for labs, universities, biotechs, and pharma, how to evaluate the strength of a culture, diagnose problems, and build habits that last, plus common pitfalls to avoid. Dr. Meade, thanks for joining us.

Phillip Meade: Good morning. Thank you for having me. I’m happy to be here.

Grant Belgard: So we’ll cover three arcs today, your current work and lens, how you got there, including time with NASA, and practical advice for leaders and teams in the life sciences. So to kick us off, in your current work at Gallaher Edge, what kinds of culture or leadership challenges are you most often being asked to help with right now?

Phillip Meade: The thing that we see most often is companies asking us to come in and help them because either they are in the process of growing and scaling or they want to grow and scale and they’ve hit a ceiling and they’re having trouble doing that. And so culture typically is one of those things that either is an enabler for scaling or it ends up being a roadblock that keeps them from being able to do the scaling that they’re wanting to do.

Grant Belgard: When you first meet an executive team, what signals, good or bad, do you look for the first hour?

Phillip Meade: There’s a few things that we typically see that demonstrates what we’re looking for in terms of a high-performing culture. Openness is one of them. Is every member of the executive team truly engaged and contributing or is there one or two key members that are really the ones that are doing everything and everybody else is sort of sitting there waiting and seeing what they do and hanging back? Another one is self-awareness. Are they really aware that when we’re talking about culture that they’re a part of it, that culture starts with them and so that this work is really about them and they’re a piece of it and they’re involved? Or are they talking about everybody else needs to change and this culture is about out there? And then another piece of it that’s very important is a willingness to be vulnerable.

Phillip Meade: Do they show that and demonstrate that willingness to actually let the guard down and take the armor off and be vulnerable as human beings? Or are they armored up and trying to present themselves that way?

Grant Belgard: How do you decide whether a client needs structural changes, leadership, behavioral changes, or both?

Phillip Meade: You know, it’s usually all of the above. It’s just a question of how much of each and how do we set those dials in there. When we talk about organizational culture and how is that created, people take cues for how they behave and what they believe about how they should behave. They take that from the leaders and what the leaders do and what the leaders pay attention to and what the leaders say and do and all of that, as well as from the structure. And so we really want to be intentional about all of that and be intentional about how do we design the behaviors that we want from the leaders and what are the leaders saying and doing, as well as how are we creating the structures and the experiences within the organization that people are seeing and responding to. And so it’s really a total design that we’re looking for from that perspective.

Grant Belgard: Many leaders feel they already talk about culture. What separates talk from traction?

Phillip Meade: I just touched on it a little bit in my previous answer, but first and foremost, it’s an intentional design. I think a lot of people think they’re doing culture just because they do things that are culture adjacent. Like they do things that are around, you know, employees being happy or feeling good in the workplace, but they haven’t done the work to intentionally design what is the culture that they want? How do they create that culture? What are the beliefs that they’re intentionally trying to create in their employees around that culture? And how are they creating those beliefs through the specific experiences that they’re creating? And what experiences are those? How are they doing those experiences? So if you haven’t intentionally designed that, then it is kind of just talk.

Phillip Meade: And so you want to have that level of intentionality to the design of what you’re doing so that you know, let’s just take the silly ping pong table in the break room. If you want to have a ping pong table in the break room, that’s great. Do you know why you have that ping pong table in the break room? You should know exactly why you have that ping pong table there, what that experience is designed to do. Is it what beliefs are you trying to create in your employees? And then what beliefs those are creating? What do those beliefs drive from a behavioral perspective from your employees? And how do those behaviors then help to create that culture and ultimately drive the strategy of your organization? So that’s the whole flow that you want to have from a design perspective. And if you don’t have that level of understanding, then you haven’t really designed your culture.

Phillip Meade: You’ve just bought a ping pong table and put it into your break room. And so it’s there’s nothing wrong with the ping pong table. It’s neither good nor bad, but you haven’t designed a culture around it.

Grant Belgard: What’s your go to way to align executive intent with middle management behaviors?

Phillip Meade: So you want to have first the senior leaders to demonstrate those behaviors, because if the senior leaders aren’t truly living it, it’s going to be very difficult to just look at the middle managers and say, you know, do what I say, not what I do. That never works. Secondly, you’re going to want to communicate those expectations clearly. It needs to be crystal clear so that they understand what is exactly expected of them. You’re going to want to align the systems and processes so that they have the ability to do what you’re asking them to do and that it fits into how they do their jobs and they’re rewarded for it. And then finally, you’re going to want to provide them with if it’s if it’s skills based, you’re going to provide them with training.

Phillip Meade: And if it’s really is behavioral, you’re going to provide them with some behavioral change workshops that will support the behavioral change that you want from them.

Grant Belgard: If a team has strong technical results, but shows strain, missed handoffs, creeping burnout, how do you frame the problem without pathologizing people?

Phillip Meade: This is one of the things that we typically focus on with all of the organizations that we work with, because blame is actually one of the greatest drivers of organizational dysfunction. I mean, you see it in a lot of a lot of organizations, and it’s a huge waste of time and energy. We like to focus on contributions. And so in any time that there’s an issue that happens, there are many things that contribute to it. If you think about blame, blame is typically a game that we play where we try to figure out who was mostly responsible, and then we assign blame to them so that we can say it was their fault. And from an organizational standpoint, if you’re trying to think about how do we become most effective, that doesn’t make us most effective. We really want to figure out how do we diagnose how this happened? How do we correct that?

Phillip Meade: And how do we move forward and prevent this from happening in the future? So the way that we do that is we try to identify all the contributors to the situation, and then we figure out how do we prevent those contributions or shift those contributions so that this doesn’t happen in the future. And so we want to approach it from that standpoint so that people aren’t afraid that if I admit that I contributed to this, either through my action or inaction in some way, I’m not going to be in danger of becoming the person who is blamed as a result. And so we come together and we look. Everybody contributed in multiple ways through action and inaction. The system contributed to it. There were environmental contributors. We really look at exactly all the things that contributed to it, and then we say, okay, how can we shift those contributions in the future and get a different result?

Phillip Meade: And so that’s the way we want to start approaching things differently from now on. How do you design for sustainability so the workout lives the initial consulting period? You really want to embed it within the fabric of the organization. And that’s where, when we talk about true culture change is not a short-term project, this is why. Because oftentimes it can take a little while to really go through the whole process of getting it really embedded. But you want to build it into everything you’re doing.

Phillip Meade: Once you really understand the culture that you’re trying to create and what that looks like and have it well-defined, and you understand the behaviors that you’re looking for, and you understand the core values that you want, and what that really looks and feels like, and how to create this culture that you’re after, then you can build it into how you recruit, how you perform your interviews, how you onboard and introduce people into your organization so that they’re trained into your culture from the beginning. You can build it into your leadership development programs. You can build it into your executive development. You can build it into your performance management systems. You can build it into your succession management. You can build it into the language that you use in your organization and how you talk and speak and interact with each other.

Phillip Meade: And then, as I was talking earlier, you can build it into the experiences that you intentionally design into your organization that are part of the way that you do things as a company. And so, you know, as you’re doing that throughout the course of the year and the course of the life of the organization, you know these are the different experiences we have and why we’re doing it. And you can change those out and tweak those over time. But as you’re doing that, you know what you’re doing and why you’re doing it. And then, as you update it, you know how you’re updating it and why you’re doing that.

Grant Belgard: So, shifting gears to talk about your own career trajectory, what early experiences pointed you towards organizational performance and culture as your focus?

Phillip Meade: Well, you touched on it in the introduction. It was an abrupt change for me. It wasn’t a subtle shift. In 2003, the space shuttle Columbia disintegrated on re-entry, killing all seven astronauts on board. And in the wake of that accident, the Columbia Accident Investigation Board found that NASA’s culture had as much to do with the accident as the piece of foam that hit the wing. And I was asked to lead all of the cultural and organizational changes for return to flight because they grounded the entire space shuttle fleet until we could fix the culture. And so, that really set me off on sort of a life-altering path where I began looking into organizational culture and really how that impacts organizations and how important that is to how they perform.

Grant Belgard: When did you realize engineering, as of course you originally came up as an engineer, right?

Phillip Meade: Yeah.

Grant Belgard: Systems thinking could be applied to human systems.

Phillip Meade: Well, I mean, I will say it was a lifeline to some extent. I was trying to grasp for something to make sense of how do I figure this out? How do I solve for this problem of organizational culture? And I realized that an organization is a system. But the thing that I realized is that it’s not just any kind of system. It’s a complex adaptive system. And so, that’s where systems thinking came in. Because if you try to treat an organization like, you know, a car engine, you’re not going to get the right results. You have to treat it like the complex adaptive system it is. And so, when you shift your thinking and begin, you know, analyzing it and diagnosing it and working with it in that way, you get different results. So, a couple of pivotal mentors that I had, I worked with a couple of consultants very early on, Paul Gustafson and Shane Cragun.

Phillip Meade: They were very instrumental in helping me to learn a lot about organizational behavior. And, of course, I read a ton of books that helped me come up to speed on all of this. And I’ll say that one of the moments that helped shape my approach was really the fact that, you know, I thought that NASA had a great culture. And that’s really part of what freaked me out when I was asked to lead this culture change. Because I would have felt better if there were tons and tons of problems for me to solve. And I didn’t think that there were any. So, one of the moments that shaped my approach was that the results of a study was released right after I was asked to lead this. And it named NASA as the best place in the federal government to work. And it was like, okay, this just confirmed what I thought.

Phillip Meade: And so, it really shaped my approach because it confirmed that the way that we’re looking at culture might not be perfectly correct here. If culture caused this accident, and yet we’re the best place in the federal government to work, then what does culture really mean? And, you know, that’s where I came up with the fact that, you know, culture means more than just people are happy at work, right? It has to mean something more. And so, that really influenced my philosophy on organizational culture.

Grant Belgard: So, this might feed into the next question. What’s a belief you held earlier in your career that you’ve since updated?

Phillip Meade: So, beliefs that I held earlier in my career that I would have updated, I think I’ll go in a different direction on that one. I mean, I was very much an engineer in my early career. I was an electrical engineer. You know, they say you can’t spell geek without double E. And I had, I think one of the ones that is my favorite one to reminisce on is, I used to say, I can explain it to you, but I can’t understand it for you. And, you know, I had philosophies on communications that, you know, if I explained it, and I was technically accurate, and you didn’t get it, then that was your problem. And, you know, I grew a lot, you know, over my early career, realizing that being effective was more important than being right. And being effective meant learning how to work well with other people. And organizational culture, oddly enough, really is a lot about that.

Phillip Meade: Organizational culture is about how do you help human beings to work together effectively as a group. A lot of the psychology underpinnings that we use in the work that we do actually comes from work that was done with the Navy, because they were having challenges, trying to figure out how to put the most effective teams together in the control center of their ships. And their theory was, if we take the smartest, you know, best performer at each position and put them together on these teams, we should get the best performance. And they weren’t getting that. And they were confused. And you would think that that’s what you would get. But in reality, the best performance on a team comes from the teams that work best together, not from putting the best performers together. And so that’s what culture is all about.

Phillip Meade: Culture is about how do you get people and put them together that actually work well together. And in an organization, that’s what you need. You need people who feel good about themselves and have the ability that when you put them together with other people in that environment with other people, they all feel good working together. They feel good about themselves. They have the ability to adapt and interact with each other in ways that it makes the whole team perform better. Not just about each one of them trying to maximize how they work best individually, but the team suffers as a result of it. That’s not what you want as an organization. And so, you know, it’s ironic, but I was a part of that personally when I think back to how I performed individually as a young engineer.

Grant Belgard: So, diving a bit more into your learnings from your time at NASA, when people hear culture, they often picture perks, right? The ping pong table in the break room, as you mentioned. In mission-critical contexts, what does culture actually do?

Phillip Meade: Yeah, so this takes me back to the previous question where I said that, you know, being named as the best place to work in the federal government showed me that it has to mean more than, culture has to mean more than that, right? And so, I define culture as, you know, being three things. I think it has to drive employee engagement because you get so many benefits from that. I mean, when a culture drives employee engagement, I mean, there was a 2020 Gallup poll that said that disengaged employees have 37% higher absenteeism, 15% lower profitability. I mean, that drops down to the bottom line and translates into a cost of 34% of their salary. I mean, you know, engagement is huge. You know, it’s a big deal. And so, having highly engaged employees is a big part of what culture does for you. And then, it also improves people’s lives.

Phillip Meade: And that’s a big part of what having an effective culture does. But the third thing that culture does is that it drives organizational performance and market success. And, you know, for a mission-critical organization like NASA, this means that it had to support mission success, which meant taking astronauts up to space and returning them back to Earth safely. I mean, safety was a huge part of that. And so, if it doesn’t do all three, it’s like, you know, three legs of a stool. If it doesn’t do all three, you don’t truly have an effective culture. I mean, I can think of examples of companies that have any two of those three, and I would argue it doesn’t have what I would call a truly effective culture. In some ways, it’s not doing good things. And so, when it has all three of those, and that’s what it takes to truly have an effective culture, and that’s what you want to be shooting for.

Grant Belgard: What did you learn about surfacing dissent in bad news in environments where schedule pressure and hero narratives play a big role?

Phillip Meade: Yeah. You know, I learned that human psychology is complex. You know, even though we’re an organization full of, NASA was an organization full of engineers, and, you know, we like to joke that they’re not really human beings. They are human beings. And when you talk about organizational culture and what happens there, it all starts inside of the human being, and it really is driven by that human psychology. And we don’t think about this. We don’t talk about it very often in our daily lives, but we’re all actively self-deceiving ourselves, you know, on a daily basis. It’s just, it’s part of what our human psychology does to protect us.

Phillip Meade: And so, you know, when we are afraid of something, when we’re afraid that something’s going to make us feel uncomfortable, when we’re afraid that we’re going to be unpopular, when we’re afraid that this isn’t going to align with the identity that I’ve created for myself, all kinds of funny things happen in our psyche, and we get behavior that you wouldn’t expect. And so, when you’ve got engineers that live in an environment where failure is not an option, and they don’t want to be the one that says that something’s impossible or something that can’t be done, and they’re tremendously committed to mission success, and they love their jobs, and they love doing what they do, and they’re working really, really hard and long hours to try and make something be successful.

Phillip Meade: They don’t want to be the one that holds their hand up and say, hey, I don’t think we can do this, or this isn’t possible, or we can’t get this done. There’s a lot of silent peer pressure to be successful, and to save the day, and to make things work, and to not do that. And it’s not overt, and nobody’s saying anything, and nobody would call them a bad name if they did that, but it’s all below the surface, and it’s all in the subconscious. And so, it makes it very, very hard to identify and see, which is why it’s so deadly. So, many organizations talk about psychological safety and practice what behaviors from senior leaders create or destroy it. It’s really about truly encouraging and rewarding the feedback and dissenting opinions, normalizing dissent and healthy conflict, and helping individuals to increase self-awareness.

Phillip Meade: You know, that self-deception that I was talking about that’s happening on a daily basis, educating people that that’s going on, helping people to know that that’s a piece of what’s happening, and helping us all to know and be aware of what we’re doing and what’s going on so that we can recognize it and combat it. Because noticing is the first step. Until we notice, there’s nothing we can do.

Grant Belgard: Could you share an example of aligning structure, for example, reporting lines or decision rights with the desired cultural behaviors?

Phillip Meade: Yeah. So, there’s two I’d like to talk about. One is sort of a large-scale one, and then there’s another one that I like to use, which is a sneakier one. And so, I like to use it as an example. The larger one was with the Columbia accident. One of the challenges that was identified after the accident was that the way we were structured, the engineering, the technical, as well as the budget and schedule and safety, they all rolled up to the program manager. And so, it was a single point of accountability was managing all of that. And so, there was a feeling like from the engineers that they didn’t have their own voice. And so, you had one human being who was having to try to juggle responsibility for budget pressure and schedule pressure, as well as technical decisions and safety.

Phillip Meade: And so, afterwards, we split that out into separate technical authority and safety authority so that we did have the, again, we called it the three legs of the stool, but we had the three legs there where we had a program manager that was responsible for budget and schedule. And then we had a safety organization that was responsible for safety and a technical organization that was responsible for the engineering. And so, engineering, if they had a technical concern, they felt like they had a route that they could advocate all the way up and didn’t feel like they were having to go up to their boss who was more concerned about budget impacts than the technical concerns. And then the sneaky one that I want to talk about is an organization where they had quality assurance technicians that were responsible for safety and speaking up about safety concerns.

Phillip Meade: And they had to punch a time clock on a daily basis coming in to work. And the engineers that were working in this area didn’t have to punch a time clock. Nobody else had to punch a time clock. And for whatever reason, the quality assurance technicians, the story in their head as a result of punching the time clock was that management didn’t trust them to keep their time, that they distrusted them. And so, that’s the reason they had to punch a time clock. And so, they felt like because they weren’t trusted by management, then they created a similar distrust towards management, because trust is a reciprocal entity. So, if you don’t trust me, I’m naturally not going to trust you. That’s just the way that it works. And so, speaking up and raising safety concerns becomes harder. If I don’t trust management, it’s going to be harder for me to raise a safety concern.

Phillip Meade: And so, it was creating a challenge with raising safety concerns because there was a trust issue. And one of the root causes of this trust issue was this silly time clock that they were having to punch in and out of work. So, it’s just weird structural stuff. It’s all about the beliefs that are created in people through the environment that they live in and through the things that happen. And so, we create those unintentionally many times in ways that we never intended to do.

Grant Belgard: That’s interesting. Yeah. Because in the clinical trial arena, you do have this structural separation of the safety monitoring for the patients, but there’s typically not something like that in the earlier stages of drug development before patients get involved. So, for leaders inheriting legacy systems in history, where do you begin?

Phillip Meade: I always like to begin by trying to learn as much as I can about why things are the way that they are. I don’t like to change things until I understand the reasoning behind why they are and how they got there. Usually, there’s people and there’s inertia around the existing systems and processes and everything. And so, providing honor to why it’s there and being able to respect that and take the good for what it is and then only change the things that need to be changed or build upon what it is. That usually helps at least minimize some of the resistance from the people who are involved in what’s there already. And you can save time and energy too because there’s probably are reasons why things are the way they are. And so, you’re not, you know, breaking things that don’t need to be broken or, you know, doing something that won’t work.

Grant Belgard: If you had a week inside a life sciences organization, how would you diagnose the culture quickly?

Phillip Meade: I would try to be as much of a fly on the wall as I could. I would just try to hang out, visit meetings and listen, see how the meetings go, you know, see how much actual discussion happens in meetings. Are people speaking up? Is there meaningful dialogue and is there healthy conflict happening in those meetings? You know, follow people out into the hallway. Are there, is there more conversation after the meeting than there was in the meeting? You know, listen to what’s happening, the conversations that are happening in the executive meetings and what they’re, they’re asking to have happen. And then, you know, see what the managers at the middle level, what are they telling their people? Are they telling their people the same things that the managers at the upper level are telling? Or is the, does the message get distorted by the time it reaches that level?

Phillip Meade: And do the employees, or do they understand the things that the leaders want them to know? Do they even know why they’re doing what they’re doing? Just that, that kind of a thing. You know, what is, what is the, what is the general vibe around the office feel like, you know, or do employees seem like they’re happy and enjoy being there? Or does it, does it feel like it’s a, it’s a drag hanging out at the office? You can learn a lot just by hanging around.

Grant Belgard: What questions would you ask at the bench level versus the executive level?

Phillip Meade: I probably would ask a lot of the same questions. Honestly, I’d want to know, like, if they understood what their, what their strategy was, it might come out in different language, but I’d want to know, you know, do you understand how you’re going to be successful as a company? What are the values here? Or what, how would you describe the culture? Do you know, do you know what that means to be an employee here? I’d probably ask them questions about how they liked working here.

Grant Belgard: How do you tease apart performance issues that stem from process, structure or relationships?

Phillip Meade: You really just have to dive in and start asking questions and, and, and figure it out. You know, a lot of it is, is trying to figure out, you know, if the person that’s doing it, is it, are they, if there’s a challenge, is it because they, they can’t do it? Or is it because they won’t do it? Do they not have the, the ability to do it because they don’t know how to do it, or they don’t have the ability to do it because there’s something that’s missing? You know, you just have to, there’s just so many different ways it can go. You have to, just have to dig in and, and start asking questions and, and figure things out.

Grant Belgard: For, for regulated environments, of course, drug development is fairly regulated. What cultural strengths and blind spots tend to show up?

Phillip Meade: Well, I mean, sometimes you’ll have a strength from a feeling of, of sameness. You know, there can be like a, a, a sense of community or camaraderie that can come with being a part of a committee or a particular community there. But similarly, a blind spot can come along with that, that maybe there’s an over-reliance on standards or regulations to protect you from things. And, you know, that can be dangerous because many times, well, in all cases, those are only as effective as, as the people who are following them. And so, you know, you, you really have to depend on people to do what those regulations say. So.

Grant Belgard: When, when publication pressure or go, no, go, gates, loom, how do you maintain integrity of decision-making?

Phillip Meade: So first and foremost, I want to be honest, I haven’t dealt with this too much personally, but if I’m reading into the question correctly, I would say that as an organization, you would want to make sure that you are structuring your incentives correctly. You don’t want to create situation where you’re, you’re putting your, your employees into a no-win situation and, you know, putting them under undue, undue pressure to, to do things in order to save their job or, you know, or whatever. So, uh, I think that’s what I would say there.

Grant Belgard: What are the telltale signs that a strong culture has drifted into groupthink?

Phillip Meade: Uh, I think similar to, to what I said about being a fly on the wall in a, in a meeting earlier, you know, groupthink is obvious when everybody basically agrees to everything all the time. So, you know, I, I look for healthy conflict, uh, as a sign of a strong culture in, in many cases. And so I would be looking for, you know, that type of healthy dissent, not arguing or fighting, but, you know, questioning and challenging and, and people with different ideas or different positions on things. And so that’s where you get the, the best decisions and the best ideas and the best innovation. And so, um, that’s what you want to see.

Grant Belgard: What’s your approach to decision rights clarity? Who decides who’s consulted, who’s informed?

Phillip Meade: I don’t think that there’s a single answer to this one because, you know, there’s lots of different types of decisions. The idealistic answer to this is that you want the people who are affected to be involved in the decision. That’s not realistic in a lot of cases. I would say that I would lean as far towards that as is practical because the more that you can involve the people that are impacted in the decision, the more buy-in you’re going to get. And so one of the things that people don’t think about oftentimes is they, they misinterpret what it means to make a decision quickly. And they think of the time to make a decision as the time it takes to actually like decide. And I would argue that the time that you want to look at is the total time from when you start to the time to finish implementation.

Phillip Meade: And so you may get from the beginning to making the decision quickly, but then your implementation may take three times as long if you don’t involve the right people. And sometimes it may take a little longer to get to the actual decision point, but then your implementation is, is a third of the time to actually implement it. So the total time is actually shorter when you involve more people. And, you know, you got to think through that. Obviously you can’t always involve all the people and you can’t, and sometimes it is too long. And the way I just described, it doesn’t work out. And that’s the reason I said, it depends and it’s not really super clear, but, you know, I would lean towards involving more people and trying to get, you know, implementation to go more smoothly and getting greater buy-in when, when you can’t, because it really does, it really does help.

Phillip Meade: And I think that right now, in many cases, people lean too far on trying to decrease the amount of individuals involved because it makes the deciding part go faster. But then I think they’re under, underweighting how much it increases the implementation portion of it.

Grant Belgard: That’s a good point. How do you cultivate leader self-awareness?

Phillip Meade: I mean, coaching is a great way to do that. We have some workshops that, uh, that help to increase leader self-awareness, you know, reading helps, you know, as if once a leader decides that they want to start improving their self-awareness and then there’s, then just starting to pay attention and notice things can, can begin to, to be that part of that process. But as with all self-improvement, it has to start with the desire from the individual themselves to, to improve.

Grant Belgard: So how do you adapt culture work as a company scales from 20 to 200 to 2000, uh, even 20,000, right? Life science organizations come in all shapes and sizes.

Phillip Meade: Yeah. I mean, you’re doing the same basic things. It’s just a matter of how do you roll it out in tiers? So, you know, we, we always like to start at the top and then roll it down. And so you want to start with the executive team and then you want to move down to the layer below that. And then the layer below that. And so you, you just, you have more tiers. It takes a little bit more time. You know, when you start to get up to like 2000 and above, now you’ve got more mature, more well-developed HR departments. So you’re, you begin to work with, you know, more well-developed HR systems and processes. So you’ve got LMSs that you’re, you’re now integrating with and you’re, you’ve got really well-developed performance management systems and tools that you’re integrating into. And you’ve got internal HR teams that you begin to integrate into and work with.

Phillip Meade: And so, you know, you’re, the work that we do begins to integrate with the people that they have and the work that they’re already doing. And so we begin to, to weave in, into that.

Grant Belgard: What’s the best small concrete habit a leader can start tomorrow?

Phillip Meade: You know, for me, it’s, it’s just, I would say it’s, it’s learned something new every day. You know, one of the commitments that I made a long time ago was that I was, I was going to read every day. And so I try to, I try to read something new every day, but I think more generically, I would say just to, to learn something new every day. I think that’s a great habit.

Grant Belgard: What are the top three mistakes leaders make that quietly erode culture over six to 18 months?

Phillip Meade: I think the top three are not communicating, not admitting mistakes and tolerating bad behavior.

Grant Belgard: Where have you seen well-intentioned values backfire?

Phillip Meade: I think there’s two ways that well-intentioned values backfire. The first one is anytime the company or the leaders of the company don’t actually live the values or, you know, do something counter to the values that kills it right there. People see that it’s basically a lie or that it’s not true, then it becomes immediately ignored or, or worthless to them. The other one is when the values as well intentioned as they may be are over general. And Patrick Lencioni refers to these as permission to play values. And I mean, I’m not opposed to them existing as permission to play values, but I would call them that and differentiate those from your true core values. But, and these are things that almost every organization could claim that they have like integrity and respect and safety.

Phillip Meade: You know, it’s, it just feels so vanilla that a lot of times employees will look at those and they’re like, yeah, yeah. Okay. I don’t get it. You know, like it just, it just feels like it’s a platitude or, or something that is just being hung on the wall just to, just to do it because it doesn’t seem like there’s anything particularly special to it. Like, yeah, of course, you know, we don’t want employees to steal from us and, you know, everybody should have some basic respect from each other and you should expect not to die when you come to work. So that, you know, those things make sense. And so people just sort of blow it off that, you know, and they don’t pay attention to it. And so I think that those things are, are very well intentioned and there’s nothing bad to them, but it’s also very difficult to really get a lot of traction with them because they are so in most cases, vanilla.

Phillip Meade: And you know what, what Patrick Lencioni says is that, and unless you can truly argue that you have more integrity than 99% of the other companies in your industry, like it’s not really your core value, like it’s not what defines you. And so it’s, it’s hard to like, say this sets us apart. This is something that we’re going to hang our hat on and your employees see that. And it’s like, okay, like, yeah, we have integrity, but you know, it doesn’t really, it doesn’t really mean, you know, mean something special. And so it sort of just becomes this thing that we hang on the wall.

Grant Belgard: When culture change fails, what was the root cause of that failure most of the time?

Phillip Meade: Most of the time it comes down to a failure of leadership. Usually the leaders, the most senior leaders haven’t really truly bought into it and committed to it.

Grant Belgard: How do you prevent hero culture from undermining redundancy and documentation?

Phillip Meade: This goes back to what we were talking about a little earlier. I mean, this is a self-awareness issue. When hero culture is about me not truly having the self-awareness to realize that I am trying to make myself feel better by becoming the hero. And, you know, it’s that lack of self-awareness. It’s that self, it’s where I, it’s a defensive mechanism where kicking in, where, where I’m just trying to, to prevent myself from, from feeling bad. And so it’s, it’s part of my identity and I’m trying to protect. And so we want to try and raise that and prevent that from happening and increase, increase that, uh, self-awareness so that it, it doesn’t happen.

Grant Belgard: What’s the smallest viable step an individual contributor can take to strengthen culture?

Phillip Meade: The smallest viable step I would say is to increase your courage by 1%. If you increase your courage by 1%, then you’re going to increase your openness by 1%, which means that you’re going to increase the feedback that you give to others by 1%. And you’re going to increase the self-accountability that you have by 1%. And you’re going to increase the initiative that you take by 1%. You’re going to increase the contributions that you make by 1%. You’re going to increase your performance by 1%. I think if, if everybody in the organization were to do that, I think that you’d start to see visible changes in the culture.

Grant Belgard: What book, practice, or question has stayed useful across contexts?

Phillip Meade: I think the thing that has stayed useful across contexts, the practice, I’m going to go with the practice is getting curious. And it’s, it’s something that it’s something that I’ve, I’ve had to learn. And, you know, it’s, I’m not necessarily proud of it, but, you know, one of the things that is my tendency is, you know, and probably a reason why I’m sitting here answering all these questions really quickly for you on a podcast is I like being an answer guy. And so, you know, people come to me and, and ask me a question and I’m, I’m really quick to have an answer. And a practice that I started developing as a leader was to not answer the question immediately and to get curious and to ask more questions and try and learn more and say, okay, well, what’s going on here?

Phillip Meade: Or when someone would say something and I thought that they were wrong or I didn’t, you know, I thought that I had the answer and they didn’t, they were, they didn’t understand, get curious and figure out, well, why do I think that they’re wrong and I’m right? That’s been very, very useful to me across a lot of contexts to just try to get more curious instead of assuming that I always know the answer, that I always had the, you know, the right answer and that everybody else is wrong is very, very useful.

Grant Belgard: So what, what options do our listeners have to get more engaged with you through your work at Gallaher Edge? And, uh, you know, I know you have a book, you offer courses, you have, uh, consulting and so on.

Phillip Meade: Yeah, absolutely. You pretty much summarized it. We have a, we have a book that they can get on Amazon. It’s, it’s called The Missing Links: launching a high-performing company culture. They can get that on Amazon. You can go to our website. It’s Gallaheredge.com and, uh, check us out. Uh, we offer individual workshops as well as, uh, consulting engagements. We have a on-demand leadership development course that we offer. That’s, uh, it’s a micro learning format and, uh, it’s, uh, it’s a great way to get introduced to us and, and see what we did. We’re all about. So a lot of different ways. We, we also do, uh, speaking. So if you’re looking for a speaker for, uh, for an event, it’s another way that we can come and help you all out. So.

Grant Belgard: Great. Dr. Meade, thank you so much for joining us.

Phillip Meade: Thank you, Grant. I really appreciate it.

The Bioinformatics CRO Podcast

Episode 73 with Nataraj Pagadala

Nataraj Pagadala, founder, president, and CEO of LigronBio, discusses his company’s goal of using molecular glues to target traditionally undruggable proteins as a route to new therapies for neurodegenerative diseases.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Amazon, YouTube, Pandora, and wherever you get your podcasts.

Nataraj Pagadala

Dr. Nataraj Pagadala is the founder, president, and CEO of LigronBio, which develops molecular glues to target traditionally undruggable proteins.

Transcript of Episode 73: Nataraj Pagadala

Disclaimer: Transcripts are automated and may contain errors.

Grant Belgard: Welcome to The Bioinformatics CRO Podcast, where we talk to scientists, founders, and leaders at the intersection of computation and biology. I’m your host, Grant Belgard. I’m joined today by Dr. Nataraj Pagadala, founder, president, and CEO of LigronBio. LigronBio is a biotech company focused on molecular glue therapeutics, small molecules that co-opt the cell’s own protein degradation machinery to go after proteins that have traditionally been considered undruggable. The company is applying computational chemistry, bioinformatics, and AI-driven platforms like its tri-matrix analyzer to design these glues and target neurodegenerative diseases and other serious conditions where new therapies are badly needed.

Grant Belgard: Nataraj has more than two decades of experience in computational drug discovery, spanning academia and industry from early work in biochemistry and bioinformatics through postdoctoral and research roles, modeling protein structures and aggregates, to senior positions in biotech and now founding his own company. Today, we’ll talk about what he’s working on now at LigronBio, how his career path led him into molecular glues and company building, and the advice he has for students, trainees, and scientists who are now thinking about careers in computational drug discovery, or even starting their own companies. Nataraj, thanks for joining us. Great to have you on the show.

Nataraj Pagadala: Thank you very much, Grant. Thanks a lot for, you know, giving me the great opportunity for the molecular glue audience and also for the targeted protein degradation companies. This is Nataraj Pagadala, founder and CEO of LigronBio, and LigronBio is incorporated in 2023, working on targeted protein degradation space, developing molecular glues for all undruggable targets in oncology side and also in neurodegenerative diseases, mainly focused on Alzheimer’s, and later on it will be extended to Parkinson’s and also ALS therapeutics. So, primarily, we are developing the platform called as the AI TriMatrix Analyzer Platform to rationalize and discover molecular glues for the specific undruggable targets in Alzheimer’s space, and also this is linked with the diagnostic kit, which is called as an L-tag assay.

Nataraj Pagadala: This particular L-tag assay will help in the functional studies of these molecular glues to take it further for preclinical studies and also for clinical trials. So, this is a powerful engine linked with generative AI that will help in discovery of these molecular glues within 36 months.

Grant Belgard: So, for members of the audience who have never heard of molecular glues, what are they?

Nataraj Pagadala: Molecular glues are the small molecules, which is very, all the medicinal chemistry properties are similar to traditional drug molecules, except that the difference between general traditional molecules and molecular glues are these molecules, they do the protein degradation compared to the traditional drug molecules where they inhibit the proteins in the biological system. So, for the undruggable targets, basically, there is no binding pockets where actually these undruggable targets help in the progression of the disease, even though there are the proteins which can be inhibited by the traditional drug molecules. So, that is the reason why these molecular glues are designed especially for the undruggable targets for protein degradation.

Grant Belgard: When you explain your company’s mission to someone with biology background, what do you emphasize first, the disease areas, the modality, or the technology platform?

Nataraj Pagadala: So, basically, our mission is basically to design the molecular glues for any of the disease-specific proteins, which is undruggable mainly. So, at the same time, our mission is to do the targeted protein degradation for the diseases and also help in reduction of the proteins in the biological system and also the disease progression. So, our vision is very broad to develop a molecular glues for all the undruggable targets, you know, and to save the future generations from Alzheimer’s is our very big mission.

Grant Belgard: Are there any currently approved molecular glues?

Nataraj Pagadala: Yes, yeah. So, there is a couple of approved molecular glues. The two are, one is palmolidamide and also one is lenidamide, which is in the market as a revlimid for multiple myeloma. So, and also, it is a very big market for this particular molecular glues for multiple myeloma disease.

Grant Belgard: So, what convinced you that there is space for a new company in this area?

Nataraj Pagadala: So, basically, if you see from the last 10 to 15 years, many companies are developing molecular glues in the targeted protein degradation, but unfortunately, all these companies, they are literally, were not completely successful in developing molecular glues for any disease-specific or also the target-specific because of a lack of a serendipity. So, this is the reason why LigronBio came into picture. We are developing because of, you know, serendipity reasons, you know, to rationalizing the molecular glues and discovery of molecular glues is a very difficult task. So, we are developing right from the scratch. This is the primary reason why we are developing a TriMatrix Analyzer platform where actually this particular platform rationalizes the molecular glues and, you know, for a specific target using a generative AI that will help in discovery one thing.

Nataraj Pagadala: And also, at the same time, this particular platform also, you know, finds out all the off-target interactions, you know, that way we can eliminate all the serendipity problems within the biological system to develop a molecular glue for a specific target without any off-target interactions. That is the reason why LigronBio is a novel compared to all the existing platforms worldwide in terms of, you know, data integration with the AI and also high selectivity and specificity.

Grant Belgard: Neurodegeneration is notoriously difficult. What aspects of those diseases make them feel particularly well-suited for a molecular glue approach?

Nataraj Pagadala: Basically, if you see in the biological system with the neurodegenerative diseases like Alzheimer’s, right? So, that’s what I’m saying that, you know, there are many undruggable targets in the biological system that will help in the progression of the disease, not only in oncology side, but in also the neurodegen, neurological space in the neurodegeneration. So, these, as long as these undruggable targets exist in the biological system, it is very difficult for, you know, to inhibit the progression of Alzheimer’s or Parkinson’s and ALS. So, this is where actually, unfortunately, the targeted protein degradation space is not introduced into this neurological space and people are not successful as of now. So, this is where actually we need to develop these molecular glues and, you know, eliminate these toxic proteins which are undruggable from the biological system.

Nataraj Pagadala: That way, we can slow down the disease progression and, you know, restore the memory function and then also reduce the cognitive decline. So, this is where importance of molecular glues comes into picture with respect to neurodegenerative diseases.

Grant Belgard: How do you balance going deep on a few carefully chosen targets versus exploring widely across many possible targets with your platform?

Nataraj Pagadala: So, basically, this particular platform designs the molecular glues for any specific target. So, even though there is no three-dimensional structures of the protein done by crystallography or by any other method. So, this particular platform designs the molecular glue just by the amino acid. So, basically, if you see the undruggable targets, then there is a motif called, let’s say, degron. So, this degron is a six to seven amino acids or maximum 10 amino acids. So, based on that, this particular platform designs the molecular glue based on the amino acid. So, it is even the layman who doesn’t know how to design the molecular glues, this particular platform gives an opportunity just by typing, inputting the amino acid, amino, just an amino acid or a peptide sequence, it will develop a molecular glue.

Nataraj Pagadala: That’s where this particular platform is completely different from all the existing platforms worldwide.

Grant Belgard: What kinds of collaborations or partnerships are most important for a company like yours at this stage?

Nataraj Pagadala: So, at this stage, particularly because, you know, the experiments of targeted protein degradation is different than the traditional way. So, that is the reason why we need partnerships, you know, who are well-versed with the targeted protein degradation space. So, this is where, actually, we need the partners like BMS who is working on targeted protein degradation or also C4 Therapeutics or Chimera Therapeutics. You know, these companies are developing or working on a protein degradation, but unfortunately, they are not working especially on molecular glues, but they are working on other modality called as a protag. But, you know, there are some companies who are working on especially on molecular glues, but, you know, they were not successful as of now.

Nataraj Pagadala: So, we can help those kind of companies, you know, we can help, we can also partner with those companies to design the molecular glues with this particular platform and also help them to, you know, for the targeted protein degradation with the molecular glues with our platform. That’s where, you know, we can partner with those companies and we also, we can help those companies for developing a molecular glues.

Grant Belgard: When you think a few years ahead, what would success look like for LigronBio?

Nataraj Pagadala: Earlier, a few years ahead, right? You know, that time, actually, to be honest, funding is much flexible compared to this particular time where, you know, funding is a very bit hard. So, because of not successful by many of the companies. So, otherwise, you know, by today, LigronBio might have developed the molecular glues for the Alzheimer’s therapy. And by today, we might have at least reached the patients, you know, clinical trials for Alzheimer’s therapy and also might reach the patients.

Grant Belgard: And is the vision to accomplish that through partnerships or are you planning on sponsoring trials as Ligron?

Nataraj Pagadala: Yeah, actually, we are also trying to do from our side, our own clinical trials. At the same time, we are also looking for the big partners. You know, once we complete the initial phase of studies, once we file the IND, then we are also looking for the big partners to step in and also do the clinical trials, you know, as a joint collaboration with LigronBio.

Grant Belgard: What do you see as the main advantages and disadvantages of molecular glues compared to more traditional small molecule approaches?

Nataraj Pagadala: The most important advantage of molecular glues is, you know, because this is an event-driven mechanism, the effectivity and also the degradation therapy is more effective for any disease compared to the inhibition. That is a major difference between the molecular glues and also the traditional inhibitors because the traditional inhibitors are an occupancy-driven mechanism. So, as long as you take the drug molecule, then the effect will be more on the disease state. But when in the molecular glues, even though the molecular, the drug will be eliminated from the biological system, then still the effect will be more. So, that is the reason why, if you see the efficacy is also very high when compared to traditional molecules, and the effect will be 100 times more than the traditional drug molecule.

Nataraj Pagadala: So, that is the reason why, and not only that, basically, the molecular glues are treat undruggable targets, which is notoriously undruggable in the biological system and helps the disease progression. As long as these, as I said, you know, earlier that these proteins are not eliminated from the biological system, the disease progression will still be there. That is the reason why we cannot stop oncology, we cannot, cancer progression, and also neurodegeneration. So, there actually, traditional methods cannot deal with those undruggable targets. Only molecular glues can help in that particular situation and, you know, help in the inhibition of disease progression.

Grant Belgard: What makes designing molecular glues hard, scientifically or computationally?

Nataraj Pagadala: Basically, I see, basically, as I said, you know, the molecular glues, they influence the target protein based on a simple motif, which is called as a degron. So, degron is always, you know, as I said, you know, maximum of 10 amino acids, right? So, this is not a catalytic site. This is a catalytic site for our traditional drug molecules is different than, you know, influencing the drug molecule based on this particular glue, which is a solvent exposed. So, you know, to formation of ternary complex is very, very difficult with respect to molecular glues. So, this is where the difficulty comes in, one thing, because as I said, you know, the degron is only 10 amino acids or maximum of 6 amino acids. So, there will be serendipity of the molecular glues because, you know, most of the kinases, you know, most of the kinases contains this kind of a degron where, you know, 6 to 7 amino acids.

Nataraj Pagadala: That is the reason why there is a high chances of off-target interactions with the molecular glues. That’s where we need to eliminate those molecular glues. And the AA TriMatrix Analyzer platform is the one that, you know, eliminates all these off-target interactions and gives them highly specific molecules for the time, you know, that shows a target protein degradation.

Grant Belgard: How do you think about modeling ternary complexes and cooperativity when you’re working with molecular glues?

Nataraj Pagadala: So, modeling, basically, as I said, you know, we are training a very big database of ternary complexes right from the literature and also from our own in-house experimental studies. And we are also, you know, mapping the proteome in the biological system for all the undruggable targets, you know. So, that will help us in, you know, to see that using a generate AI, artificial intelligence, you know, large language models, that will help us, you know, to see that, you know, how the molecular glues is especially, you know, seeing the off-target interactions. Once we eliminate that off-target interactions, it is easy for designing of molecular glues for a specific target. So, this is where actually that we are building the TriMatrix Analyzer platform.

Nataraj Pagadala: And also, because, you know, most of the targets doesn’t have a three-dimensional structure, this is where another advantage of this platform is that even though there is no three-dimensional structure, still we can develop a molecular glue for the particular target, you know, just based on amino acid as an input. So, this is where the advantage of this one, and also the difficulty that I said, you know, in most of the companies, they don’t have a three-dimensional structure, you know, for most of the targets, you know, unless there is no three-dimensional structure, there is no molecular glue. But a TriMatrix Analyzer platform can do this. And at the same time, most of the companies, to find out a ternary complex formation, they are using a diagnostic kits. Those diagnostic kits is based on the fluorescence.

Nataraj Pagadala: They only give indication about, you know, whether the ternary complex is formed or not. But when that is taken into experimental site, then it is not replicated. The diagnostic kit is not replicated. The results of the diagnostic kit is not replicated in the biological system in most of the cases. But we are developing a diagnostic kit in, which is called as an LTG assay, which gives information about, you know, how the ternary complex is formed, which is like an alternative to x-ray crystallography. That’s where we can clearly see that how the ternary complex is formed. So, this is where the difficulty from all the big companies are facing as of now. And that’s what we want to make it easier for all these companies, with our TriMatrix Analyzer platform, or also the diagnostic kit.

Grant Belgard: How do you decide which parts of the problem to treat with more traditional physics-based structural biology approaches versus more data-driven AI-ML approaches?

Nataraj Pagadala: So, basically, in the physics-based approaches, you know, most of these approaches are for traditional therapy for all the proteins which have a three-dimensional structure of the protein, right? You know, on the catalytic side, you know, there it is easy for the physics-based approaches, you know, for designing of the drug molecules. But data-driven approaches, this where actually, where we don’t have a proper [trim?] structures of the protein, this is where actually the data-driven approaches comes into picture. Now, just like, as I said, you know, for all the undruggable targets where we need lots of data, and lots of data to develop one molecular glue for a specific target.

Nataraj Pagadala: This is where AI and also machine learning and artificial intelligence comes into picture compared to, even though, basically, artificial intelligence and machine learning is also useful for traditional therapy, but especially because that even though artificial intelligence and machine learning is not needed, still we can develop a drug molecule for the proteins which have three-dimensional structures of the protein and also the catalytic pockets. But without the data-driven approaches and without AI and ML, it is very, very difficult to design molecular glue for undruggable targets.

Grant Belgard: How important is experimental feedback for your models and what does that loop look like in practice?

Nataraj Pagadala: Basically, the experimental studies is very important because, you know, the important thing is, you know, very, very rare that we see the targeted protein degradation effectively by molecular glue in the beginning. So, the experimental side is very, very important. I know because, you know, there are many factors that we need to find out in the area of targeted protein degradation, especially with the molecular glues, because, you know, the protag development is completely different. So, it is easy to find out the targeted protein degradation with the protags. But molecular glues is a small molecule and they influence the target protein through small motif. Sometimes, you know, we don’t know how the degradation is happening, you know, how the degradation is happening, whether the territory complex is formed. You know, this is a very complex system through molecular glues.

Nataraj Pagadala: That is the reason why the experimental data, not only that, you know, it’s like, you know, if you check, you know, thousands of, hundreds of molecular glues, sometimes, you know, we end up with no molecular glue showing a targeted protein degradation. So, that is where experimental data, one experimental data, and one targeted protein degradation will give a clue for many, many stages of a molecular glue development in the biological system.

Grant Belgard: Where do you see the biggest gaps right now in this space? If you could choose one particular type of data to just have a lot more of, or better data of, what would that look like?

Nataraj Pagadala: So, basically, I see the main gap here is, especially in the molecular glue is, you know, we don’t have a ternary complexes. So, that is where actually we cannot design a molecular glues, the ternary complexes, not only, and also from x-ray crystallography, especially from the x-ray crystallography, actually, how the ternary complexes formed, except, you know, five or six cases. Not only that, you know, because when these undruggable targets, you know, the ternary complexes formed, it’s a larger, you know, it’s a very big complex. It’s very difficult sometimes to create a three-dimensional structures of the proteins through the x-ray crystallography because of its complexity in nature. So, this is where actually the difficulty is coming from in the area of molecular glues.

Nataraj Pagadala: That’s where we need to do some computational studies in the beginning with enormous, generate enormous amount of data, what the ternary complexes, you know, mapping of all the ternary complexes. That’s where we get some clues to do the experimental studies. If it is replicated, then we can say that, you know, yeah, this is what is happening from my computational studies, and this is also replicated in experimental studies. Then from that, you know, generate more, you know, molecular glues for other targets, you know, more data-driven through AI and ML.

Grant Belgard: So, to talk about your career, looking back, what were the big inflection points that shaped your career in computational drug discovery?

Nataraj Pagadala: Basically, I did my PhD in computational chemistry in 2007. And after that, you know, I did four years of postdoc in the University of Alberta and one year of postdoc in Belgium in KU Leuven University. So, I have lots of my career, you know, 25 years of experience. But, you know, all my career, I worked on a traditional way, you know, developing a drug molecules for all the proteins, for all the proteins which has the binding pockets, you know, have a very great traction record of computational drug discovery from the last 25 years, you know, published for international publications. And also, I was also rated as one of the eminent scientists in computational chemistry by Carnegie Mellon University. So, you know, but unfortunately, I never worked on this targeted protein degradation earlier, before I started my career in [biotherics], you know.

Nataraj Pagadala: There, my journey of a targeted protein degradation has changed, actually. Yeah. So, from there, you know, after going in-depth analysis, you know, then I realized that, you know, this is a, it’s not a simple thing, you know. I need to, I need to show to the world that, you know, with all my experience that, you know, how can we design the molecular glues? How can we not only molecular glues, you know, how can, I know, targeted protein degradation can be done easily? That is the reason why I started this particular career. That’s where the, I know, the inflection point has come in my career to show to the world that, you know, how can we do this? Not only that, with the doing of this, now, how can we, you know, reduce the progression of the Alzheimer’s or Parkinson’s and also ALS and also major this, this devastating diseases, you know.

Nataraj Pagadala: With this technology, we can definitely protect the future generations because we know that COVID-19 has, you know, pandemic has created, you know, havoc in entire world, right? You know, half of the world was got wiped off. So, that is the reason why I changed my career that I want to do something to this, you know, in the disease therapy and I want to show something to this, you know, how can we, you know, stop the diseases or also we can, we can inhibit the disease progression and, you know, protect the future generations for, for these devastating diseases.

Grant Belgard: What gave you the confidence to start your own company doing this?

Nataraj Pagadala: So, basically, my experience, you know, from the last 25 years, as I said, you know, I have a great track record of, you know, computational drug discovery and also because, you know, as I said, you know, I, I did a full five years of postdoc in a PhD and publications, you know, my, as from Carnegie Mellon University, I was also rated as an eminent scientist. So, based on my career, my track record and my way of doing a drug discovery, so it’s completely, a little bit different, you know, compared to other people in terms of thinking, in terms of implementation. That gives me confidence that, you know, definitely my approach will help definitely for these diseases to, for the disease progression, inhibit the disease progression.

Nataraj Pagadala: So, that is the reason why with all my computational chemistry, because not only that, you know, my other confidence is because I’m a, I’m a biochemistry background. Mainly, my, my background is biochemistry with a genetics, you know, with a PhD genetics department. And also, I’m well-versed with molecular biology and all the biology aspects. So, that’s where actually, I can easily connect my biochemistry experience with a computational chemistry experience, with a drug discovery experience, and also experience in biophysics. So, with all these subjects, you know, great expertise, it is easy for me to design the molecular glues. Think about how the drug molecule works in the biological system. That’s where I can easily connect. That’s where my confidence has come that, you know, I can achieve, not only that, you know, I don’t need big laboratories to develop these drug molecules.

Nataraj Pagadala: You know, I can sit at home and design the molecular glues in on the computer with all my expertise. So, that’s where, you know, I started, I started this company because of all my expertise and also discovery of these drug molecules without having a laboratory spaces.

Grant Belgard: Have there been any particularly helpful pieces of advice from other founders or mentors that have changed the way you run the company?

Nataraj Pagadala: Actually, because, you know, there are very less people, you know, who are working on molecular glues. So, and as of now, apart from the very big companies, like [?], and also C4 Therapeutics, and also Chimera Therapeutics, and BMS, apart from this, I personally feel that, you know, I’m the only one who started as a startup with developing a molecular glues and developing a platform. Other than this, you know, till now, I did not see any kind of other founder developing a molecular glues till today.

Grant Belgard: What’s something about the founder-CEO role that you didn’t appreciate until you were actually doing it?

Nataraj Pagadala: Yeah, actually, as I’ve basically, you know, earlier, when I was doing, working in different companies, you know, at that time, I was, you know, my ideas was not taken into consideration. But as a CEO of the company, when I was developing this TriMatrix Analyzer platform, when I was developing this, you know, designing the molecular glues, you know, with a diagnostic kit, you know, that’s where actually people completely, you know, seeing me as a different person in terms of, because, you know, there are people who are well worth the experience from the last 10 to 15 years of experience. Even though they have so much of experience, they were unable to figure out how the ternary complexes, how the targeted protein degradation is happening in the biological system, you know.

Nataraj Pagadala: But as a CEO of the CEO of LigronBio, as within a short period of time, you know, when I was doing this, you know, then people, you know, are seeing me as a different exceptional person and then who can definitely deal these particular problems, you know, help the community and help the society for and also for future generations with Alzheimer’s and also other domestic diseases.

Grant Belgard: From your perspective, what are the most underrated skills for computational scientists who want to work closely with wet lab teams?

Nataraj Pagadala: With the wet lab teams, actually, we, this is basically a different complex, you know, biology. So I need, you know, I want to work with the people who are well-versed with, especially with the neuroscience one, especially with targeted protein degradation, who has experienced targeted protein degradation in terms of molecular glues, without that, it’s very difficult, you know, to understand, to understand and do the experiments in the, you know, in the laboratory without having a knowledge about the molecular glues are targeted protein degradation. So I prefer the people from this particular background, you know, if you want to work with, yeah.

Grant Belgard: Where do you think molecular glues will realistically be in 10 years? A niche modality or something more mainstream?

Nataraj Pagadala: Yeah, actually molecular glues, as of now, molecular glues are, are in the, in the high priority for different companies and also bigger companies like J&J. So because they are small molecules, as I said, you know, they are brain penetrant, gut penetrant, and also membrane permeable. So molecular glues are the first priority as of now, and also, till now, 24 billions of money was deployed in molecular glue development by different companies and also by different VCs. So molecular glues are the highest priority in, in under the next 10 years, molecular glues is going to occupy number one place compared to traditional drug molecules. Because, you know, as I said, you know, the effect of the molecular glue will be high, very high, 100 times more than a traditional drug molecule. So it is going to, it is the first number one priority in the next 10 years.

Nataraj Pagadala: And also, not only that, in the molecular glues are going to, you know, affect on the disease therapy, especially for the Alzheimer’s in the next 10 years, there is a high chances that a molecular glue therapy will come into existence for Alzheimer’s, for Alzheimer’s, and also help the progression of, you know, and also inhibit the progression of Alzheimer’s. That way, it is a stepping stone for, you know, reversing the Alzheimer’s. If that happens in the next 10 years, trust me that, you know, molecular glue therapy will also reverse the Parkinson’s and also will reverse the ALS and also all the devastating diseases, even the cancer progression. We definitely, we can reverse the cancer progression, and also we can inhibit the cancer progression, you know, 30 to 40 percent. That increases the lifespan of the patient and also the families who are affected with these devastating diseases.

Grant Belgard: Is there a misconception about molecular glues that you wish you could correct for everyone listening?

Nataraj Pagadala: Actually, yes. You know, basically, people think that, you know, molecular glues are very difficult to design. And also, molecular glues have a high serendipity and also off-target toxicity. This is what the people think about molecular glues. But, you know, if you design properly from right from the scratch, you know, and also, we can design a molecular glue with a high target. Because last 10 years, this is what is happening with the molecular glues. Whatever the target is, basically, they are designing, but ending up at the same targets repeatedly every time and showing a degradation. So, because there is some problem in designing the molecular glues. That is the reason why we can design the molecular glues without off-target toxicity, very easily, if you do right from the scratch in a proper way.

Nataraj Pagadala: So, this is the misconception that, you know, molecular glues cannot be designed so easily. That is, that is a misconception there for the different companies all over the world.

Grant Belgard: Finally, if listeners remember just one thing from this conversation, what would you want it to be?

Nataraj Pagadala: Yeah. LigronBio, we are unlocking the undruggable targets for Alzheimer’s and other neurodegenerative diseases with the molecular glues. So, this is where actually we are the pointers in the molecular glue discovery.

Grant Belgard: And how can listeners or potential investors connect with you to learn more?

Nataraj Pagadala: So, basically, through email and also with my website, you know, all the information is given in the website. And, you know, please contact me. If you want any kind of a collaboration, if you want any kind of a help in designing the molecular glues with our TriMatrix Analyzer platform, I’m here to help you in a very effective way. And also, we can reduce the time of research and the cost of your research. And we can design the molecular glue for sure within less than 36 months. So, all the details were given in the website. Please contact me. Or else, you know, my email is npagadala@ligronbio.com. And my cell number is 412-863-3812. Please contact with any of this, you know, medium. You know, I’ll be here to help you as much as I can. Thank you.

Grant Belgard: Nataraj, thank you for joining us.

The Bioinformatics CRO Podcast

Episode 72 with Sophia George

Sophia George, professor in the Division of Gynecological Oncology at the University of Miami Miller School of Medicine, discusses her research at the Sylvester Comprehensive Cancer Center investigating the genetics and biology of hereditary breast and ovarian cancer and working at the intersection of genomics, health equity, and cancer.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Amazon, YouTube, Pandora, and wherever you get your podcasts.

Sophia George

Sophia George is a professor in the Division of Gynecological Oncology at the University of Miami Miller School of Medicine and the principal investigator of the George Lab at the university’s Sylvester Comprehensive Cancer Center.

Transcript of Episode 72: Sophia George

Disclaimer: Transcripts are automated and may contain errors.

Grant Belgard: Welcome to the Bioinformatics CRO Podcast. I’m your host, Grant Belgard. Today we’re joined by Dr. Sophia George, a full professor in the Division of Gynecologic Oncology at the University of Miami’s Miller School of Medicine and a member of the Sylvester Comprehensive Cancer Center. Her lab investigates the genetics and biology of hereditary breast and ovarian cancer and works to close gaps in cancer outcomes across the Caribbean, Africa, and the wider African diaspora. We’ll talk about what her team is doing now, how she got here, and what advice she has for scientists and clinicians working at the intersection of genomics, health equity, and cancer. Dr. George, welcome.

Sophia George: Good morning, hi.

Grant Belgard: Morning. So if you were explaining your lab’s mission to a first-year undergrad, how would you describe the problem you’re trying to solve right now?

Sophia George: Yes, right now is a great question because it has changed a little bit. So what we are ultimately interested in is understanding drivers of cancer and those drivers that lead to more aggressive disease and poor outcomes. And then we take into context what’s surrounding those drivers. So as a molecular geneticist, it’s the only thing about the DNA and sometimes RNA. But now we know that the DNA is not in isolation. Also the RNA is not in isolation and it’s in people. I mean, within cells, within people that are also exposed to factors beyond the genome. And so that’s what we do.

Grant Belgard: What questions are at the top of your list this year and why those?

Sophia George: So questions like, how can we distill spatial and temporal influences on the genome? Meaning spatial, where people are, so geography. And then temporal, how long have they been there? And I’m not thinking thousands of years, but more like tens of years. And how those exposures kind of lead to the signatures that we see, transcriptional signatures that we see in the tissues we’re studying.

Grant Belgard: And what kinds of data are most central for you at the moment? Do you now make transcriptomic, do you now make imaging, clinical, something else?

Sophia George: Yes, everything, everything, which is like, makes us work, makes work very interesting and long, long, long days. So we are looking at epigenetic data using DNA methylation assays, or assays that can tell us about DNA methylation. We’re using epigenomic assays like cut and run and cut and tag. We’re using single cell sequencing assays, transcriptomics specifically, and then spatial assays like COSMX and the APOIA system and a CODEX. And at some point even, I mean, I’m calling names of companies, but that’s how we kind of situate the type of assay and the technology and of course, 10X. So that’s what we use day-to-day in the lab. And then outside the lab in the community, we are also capturing epidemiologic data, survey data, the metadata that’s linked to the individuals that we’re studying the tissues of.

Grant Belgard: What’s a recent result or a signal that genuinely surprised you?

Sophia George: So the more you do, so one of the limitations of the stuff that I do is that one, you have to access the tissue. And of course, clinical data. So part of the metadata is the clinical data. And you’re asking recent, but I would say a while ago, it’s recent in the context of it’s just been put in guidelines. But one of the things that we discovered a while ago is that different populations in the Caribbean have different prevalence of the germline genetic mutations in BRCA1 and BRCA2. And in particular, the Bahamian population have these founder mutations that are really common. So one in four women who have breast cancer or ovarian cancer will have this BRCA1 or BRCA2 mutation specific to that population. The other well-known group are the Ashkenazi Jewish populations or groups. And they have one in 40 people in general, but 10% to 12% who have breast cancer have a mutation in the gene.

Sophia George: So you can hear the differences in these populations. That’s a surprise. So going beyond DNA that you inherit, another thing that we notice is that, at least from the perspective of the work that we’re doing, black women in the Caribbean or people of Caribbean ancestry, and we’ve also noticed there’s, of course, people of West African specifically ancestry. I can’t speak for the entire continent, but I’m speaking from the spaces that I work, have really diagnosed these cancers at a younger age and other populations. Even people with the same BRCA1, not the same identical mutation, but a mutation in BRCA1 and BRCA2. So now it’s collecting samples from all over the world.

Sophia George: We’re seeing that these ancestries with the mutation are a little bit surprising, but it’s good to see it because then we can actually attribute some at least biology, transcriptional biology, tissue biology to the prevalence and the incidence of early age at onset in these populations. So we’re seeing differences in transcriptional profiles that we’ve not yet published, but we’re doing single cell sequencing on hundreds and thousands of tissues from these populations. And so we are starting to see these signals come up, and I’m excited about what the data is going to tell us about the biology.

Grant Belgard: So in ancestry diverse cohorts, what strategies help you separate biology from environment, care access, and other social determinants?

Sophia George: Data, data, data. Really, it’s knowing what you have in the tube and who the people are, where the people are. So it’s putting things in context and why we have to capture that epidemiologic data, the clinical data to discern are we just looking at. I mean, everybody. So for example, I’m studying hereditary breast and ovarian cancer. A lot of my work is focused on the fallopian tubes of people with these BRCA mutations. They have an increased risk of 40% from 27% to like 40% to develop ovarian cancer if you have a BRCA mutation and higher up to 80% you have and for breast cancer. Maybe I’m like skewing the percentages. I think it’s 27% to 60% for ovarian, depending on the gene. OK. So there are other factors that we know are linked to cancer beyond the BRCA. They have an [imputations?] by how many ovulatory cycles or how long women have been ovulating. And that’s the same for breast.

Sophia George: If you have breastfed, if you BMI, increased risk smoking increased risk alcohol consumption. The data keeps telling us how many glasses or no glasses. But nonetheless, alcohol consumption increases your risk. And then a bunch of other things. So when you look at tissue and you isolate the DNA, isolate the RNA, and you’re looking at that signal, then you’re asking, well, for women in West Africa, what age on average do they start having kids? How many kids do they have? The fertility rates are different in the US as even compared to the Caribbean, compared to Africa. So that’s really important to be able to actually see people who are multi-parous. How does a transcriptional profile look compared to people who have one child or no child and no pregnancy or one pregnancy each time that goes to term?

Sophia George: So that is giving us ideas about one just normal physiology of the tissue and then seeing like, well, how can now? So that’s just like normal biology, right? And then we now have the complexity of genomic ancestry, which we know of people in the continent of Africa are the most genetically diverse folks. So we’re not even going down to the single nucleotide polymorphism yet, because we will need tens of thousands. But what we are doing is looking at essentially breaking it down by ethnic groups, self-identified, and also in [?] through the 1000 Genomes Project and others to be able to say, OK, well, people of West Africa, and I’m doing quotation marks, have this signature versus those who are European, or those who are admixed, like in the Caribbean, where we have a little bit of everything.

Sophia George: And one of my PhD students had come up with this logistic regression algorithm and approach to be able to kind of quantify proportion on the amount of African and European ancestry and essentially like a sliding scale and the signature that we see. And so that’s given us an opportunity to be able to disentangle both normal healthy, normal biology of the tissues that we study in the organs and then overlaying that with genomic ancestry. And of course, in the background, I’m determining whether these people have a mutation or not, because that’s also a driver of transcriptional difference.

Grant Belgard: So above and beyond all the biological and social sources of variability, what about the technical sources of variability? Do you think there are issues of collection, fixation, transport, storage, things that you think are currently underappreciated by many people for the impact they have on the downstream analysis?

Sophia George: 1,000 and 20, or maybe 1,200%. That is such a driver. So I should describe a project that we’re doing actively now. We have funding from the Chan Zuckerberg Initiative, where we were funding initially in 2021 to establish the African-Caribbean Single Cell Network. As a proof of concept, can we collect tissues, of course, at the time, snap frozen tissues, single cell tissues that we digest and get single cell suspensions from, I think at the time I started, it was like five or six countries in Africa and the Caribbean and, of course, in Miami. Just the idea of doing that and the premise and collaborating with my peers in those countries and say, do not put things in formalin. And then learning about the process of when tissue gets collected from the OR and taken to pathology and how it gets transported. How long does it sit on the bench? Do we have dry ice? Do we have liquid nitrogen?

Sophia George: That in itself, creating SOPs and changing practice to adapt to collecting tissues that are to be fresh and not just stuck in formalin in writing the OR has been a process on its own that deserves its own one to two, maybe three hour conversation. And you have to do that in each country. And so there is a saturation of the number of samples, right? So instead of saying, well, initially, we’ll digest to 10 and 20. Now we are doing hundreds each country, 400, so that there will be some that fall, right? So you have the outliers. And this is the outlier due to somebody forgot [?] and picked it out. That happens. We can see those added marks. So it takes on training, continuous training of the teams and continuous conversation and monitoring both for tissues and also PBMCs, peripheral blood monocytes, where we started and then we were like, oh, everything is failing.

Sophia George: And it’s because of how long they get kept in the minus 80 or even on the bench, right? So we’ve had to do all of that. And those technical, you can imagine, then over time and in different spaces, you will see these batch effects. So to prevent that from happening and say, we’re sequencing all serially on their own, we have to kind of wait and include samples from different countries in a batch so that when it gets to the lab, whichever lab, they’re trying to decrease the scale of variability.

Grant Belgard: This all sounds very familiar. In my PhD postdoc, we did a lot of postmortem brain work. And yeah, very, very similar challenges. You often don’t have a lot of information on how things were really processed brain bank to brain bank. And in some cases, even within the same brain bank, it will have been processed in very different ways.

Sophia George: Exactly. At the University of Miami, we have several hospitals and clinics where people undergo have to have surgery. So even within our institution, we had to optimize a protocol of transporting samples from the OR to the pathology to the lab. So that would decrease variability within our own health system, because some of them you literally have to drive, like go in a car. Because it’s so far away from the lab, right? It’s not walking distance. So we’ve had to do a lot of optimization.

Grant Belgard: And so if you had unlimited compute, but limited biospecimens, how would you allocate resources across discovery, validation, and mechanistic follow-up?

Sophia George: You’re asking really hard questions. Things that we think about. Okay, so unlimited compute, but limited resources, the tissues. Which is true, which is true, which is a reality. We can’t collect forever. I mean, it would be great to have a saturation of samples and genetic variability. So we would have to do like a test and a validation, right? One of the things that when we decided to scale this project from 15, 15, 15, so 15 fallopian tubes, 15 breasts, 15 prostate samples initially, to now 400, 400, 400, this was to give us room for the technical error, but also hopefully to get to somewhat of a saturation point with the genetic variability. Okay, I know Africa is like completely huge and so much genetic variability.

Sophia George: To test whether if we see something happening in the Ghanaian population and we see differences or similarities in Sierra Leone and in Nigeria because of the geographic proximity. So it would be testing us up, validating another, and then to use, which is something I’m actively thinking about now, use some CRISPR in vitro approach to try to mimic what we’re seeing in the transcriptomics, at least from the single cell perspective. That we still have to go back to modeling. I mean, of course, and I know there’s not like a rambling, but there’s a lot of now in silico things that you can do to mimic like the perturb-seq and all this data, this rich data that’s being generated that we might not need to go into in vitro, but it is always going to be able to say like, these either genetic alterations with this condition is likely increasing risk to develop disease. Can we model this?

Sophia George: And then eventually intercept it somehow, right? Because we know what we think is causing the change. So I would use a lot of tools, artificial intelligence, and generating so much data. Yesterday we saw we had like 1.6 million fallopian tube cells from cells from fallopian tubes just, and that’s only like 85 sample, no, a hundred and something samples, right? So it’s not, and we’re planning on doing this for like 300 to 400 samples per tissue type. And so it’s, we’re going to have a lot of data to inform on what it is that’s happening.

Grant Belgard: Are there computational approaches that you’re excited to scale up or to apply on this really large data set, right? Because oftentimes there are things that in principle people would like to do, but when, you know, you’re looking at data sets that were typical five years ago they just didn’t have the sample size to do it. But with the sample sizes you’re now working or that you’ll be getting, it might open the doors.

Sophia George: Yeah, so I really am excited about working with informaticians who want to use or who are using, I mean, we can’t really avoid it now at different neural networks, LLMs to be able to give us more information and the information, like I already know that my ability to ask questions about the data to look in front of me is limited because I cannot infer the relationships by just looking at it of cells amongst themselves and how the genome is interacting with the transcriptome beyond like the exons, like beyond the exons, right? So how, like, I am excited and I want the data to talk to me and to tell me what is happening. And so I look forward every day. I’m like, okay, what new packages out there?

Sophia George: What new algorithm did somebody come up with to the data that already exists, like in Cell by Gene and Human Cell Atlas, for example, talk to us, like, what is it telling us that I have the limitations of not even being able to ask? So I’m excited about that.

Grant Belgard: When you look at the literature on aggressive breast and gynecologic cancers, where do you see the biggest gaps that bioinformatics could realistically fill in the next five years?

Sophia George: I want more integration of the data. I want more what is happening, which these samples are hard to find, right? But they’re not, they exist. And what is the least amongst, as you asked me before, what is the least amount of data we can put in to be able to infer causality or even a relationship to disease progression? And then of course, on the other side is, well, how do we learn about all the data that we have? What do we learn from it in response to treatments? Knowing that, okay, this genetic signature from this genomic background will likely not respond to like pharmacogenomics and with the transcriptomics will likely not respond to drug X because we have modeled this a thousand times. This we know for sure. These are the questions that I would like answered and with what we already have and all the data that’s been generated like exponentially every day.

Grant Belgard: So when thinking about prevention in hereditary cancers, what does precision prevention look like in practice?

Sophia George: It’s just the old fashioned identify people at risk and then intervene with screening. And of course then there are cooler ways where, so how do you identify? So you could ask how do you identify the person in the first place, right? So how do we identify people who don’t even know that they are at risk or not aware? Yes, mom had breast cancer or ovarian cancer or pancreatic cancer and you think, oh, you know, grandma had that cancer and then you just kind of like, yeah, all people as we age, we get cancer because this cancer is the disease of the aging. Oh, it used to be so. So what tools again, computational tools, can we use to identify these individuals based on the data that they’re putting out there who would benefit from screening, genetic screening? And so that’s the population side.

Sophia George: And then of course the molecular side is of all the data that I’m generating, what are the ways that we can use small molecules to prevent disease?

Grant Belgard: If you had to bet, what’s the most likely near-term translational payoff from your current line of work? You know, is it more risk stratification, earlier detection, therapy selection, something else?

Sophia George: Risk stratification, I’m excited about some things that I brought some folks together to think about in terms of how do we use the data that I’m generating in the real world because they’re real people with real data. And so risk stratification is, you said one, but that’s one on that side. And then there’s a clinical trial that I’m co-principal investigator of where we’re looking at targeted therapy in these populations in three countries, the United States, Nigeria, and the Bahamas to be able to better identify individuals who will respond to these already FDA-approved drugs versus those who would not. I’m excited about that. That’s like long-term because the clinical trial just begun this year, but that’s something that I’m excited about learning.

Grant Belgard: Do you know when that’ll be finished?

Sophia George: Well, it’s a five-year clinical trial. So it just started today, not today, this year, so in five years, but we will be obviously getting data as soon as we see recurrence or response. And of course, you can’t make a conclusion from one person, but it is the fact that we get to do this study and all the components of it, of course, multi-omics and all the fancy things, all the tools, we’re doing all the tools, using all the assays that are available to us now and samples that we banked that we can do things in the future to be able to really go deep in understanding what’s going on. So that’s like a ways away, but in the meantime, it’s a re-stratification and again, integration of all the things that’s what we’re doing.

Grant Belgard: Something to look forward to, yeah.

Sophia George: Yeah, I’m excited. It was like we’ve done a lot of building and now we get to, again, ask really interesting questions and then hopefully have tools to help us resolve things that we don’t even, are not aware of.

Grant Belgard: Yeah, it’s kind of the, you know, biology equivalent to some of these big particle physics experiments, right? It can take a very, very, very long time to get the infrastructure in place and then you run the experiments and get the answers.

Sophia George: Yes.

Grant Belgard: So pivoting now to your own career, what first pulled you towards gynecologic oncology and hereditary cancer research?

Sophia George: Quite honestly, it was, I did a job after my PhD. I did a PhD in molecular genetics, molecular medical genetics and it was on engineering embryonic stem cells and differentiating embryonic stem cells on the cardiovascular system and looking at embryonic development and vascular genesis, angiogenesis. I wanted to do something with humans and I had considered going to medical school. I had applied to go to medical school, I got in and I had just had my son at the end of my PhD and I wanted to take a breather between all those decisions, between making all these decisions and so I applied for a job and I applied for a job to work at a biobank and the person, director of the biobank at the time, she said, but you’re too qualified, you’re overqualified. What is wrong with you? And I was like, I just want a job for a minute just to like not do anything science-y.

Sophia George: And so she offered me equivalent of a postdoc position in her lab and she helped focus, she wanted me to establish cell lines from fallopian tube and [epithelial?] cells from women who were undergoing [risk-reducing?] surgeries because at the time it had just been published and not yet published that the fallopian tubes were a likely site of origin for high rates of ovarian cancer because she’s a pathologist and her scholarship was in hereditary ovarian cancer before it was even a thing like in the context of fallopian tubes. So that’s how I got started. And then the following year, I was always interested, I’m from the Caribbean, I should state, all those listening, wondering where is that accent from. I’m from a tiny island called Dominica in the Caribbean, not Dominican Republic.

Grant Belgard: Dominica is always advertising the citizenship by investment on the planes, right?

Sophia George: Oh my goodness.

Grant Belgard: Every time you fly British Airways or something.

Sophia George: Okay, fine. So I’m from that island. We only have 70,000 people so we can afford to have visitors come. Okay. And so I’ve always been interested in health of the population, mine, I guess, and looking back. And so I got a scholarship to go to school in Canada, did my undergrad, did my PhD at U of T and during my PhD, I got to go to Venezuela with the UN and at the time, the Centre for Bioethics at the University of Toronto. And so I got exposed to thinking about doing genomics in the Caribbean and Latin America. And I had the opportunity to meet people from the Caribbean at the time, got invited to go to the Bahamas and say, oh, by the way, let’s think about genomics in the Caribbean. And I’m working for hereditary- I’m working on a project on hereditary ovarian cancer. And they said, oh, we also have this in Bahamas. And I was like, what do you mean you have BRC in Bahamas?

Sophia George: Like, it’s not a Bahamian thing. It’s a Jewish thing because I was in Toronto and that’s who had the BRCs. And that is how I got really fascinated about our population many years ago.

Grant Belgard: Just geographically, it seems being based in Miami makes a lot of sense. You’re, you know, a short flight or ferry right away.

Sophia George: Exactly. And that is my mentor. So I was in Toronto at the time and my mentor, the person who became my mentor, who was leading the study. So I said to the people when I was in Canada and I’m in the Bahamas. They’re giving a talk. Who is leading this research? And they’re like, oh, someone at the University of Miami and someone in Toronto, Steven Narod and Judith Hurley was at the University of Miami as a medical oncologist. And I got introduced to them. And she is a phenomenal woman who allowed me to ask questions and introduced me to everyone. And now I lead this work, right? But that’s how I got in to studying hereditary ovarian cancer.

Grant Belgard: So speaking of mentors, how did you find mentors and what made those relationships work?

Sophia George: Oh, wow. So Judith was serendipitous, I guess, because as I said, I was in the Bahamas and they said who might I reached out, not necessarily for her to be my mentor, but to see if I could learn more about this study. And she was magnanimous and generous. And I learned so much from her about how to engage with, who do you need to engage with to have impact. There’s always more people, but for sure, the people treating, the doctors treating the patients, you cannot, they’re not, or not to be a bystander in the work, right? Because they’re the ones that are going to see the patients to implement the things that we will eventually find and discover. So she, her personality allowed her to develop into my mentor, to learn and navigate the space.

Sophia George: Pat Shaw, who was my postdoc mentor and lead of the biobank and a pathologist, she ended up being a mentor because she knew so much about the system that we were in and what I was trying to do, quite frankly, as a woman. And it happened to me that I’m a woman of color and not that she was a woman of color, but being a woman in the space, in academia and allowing me to meet her networks and be introduced to them. So I’ve since then identified people that helped me in specific needs, areas of growth. So I tell my folks all the time that I mentor that you can have multiple, and peer mentors are really important. How we can help each other, drive each other, but also again, identifying folks for me who fill a gap and also have some redundancy.

Sophia George: So cheerleaders, supporters, folks who can help me plan and navigate, those have been factors in how I identify folks who might wanna spend time with and learn from.

Grant Belgard: What skills have you found hardest to learn on the job that you wish training programs taught more explicitly? People management, we don’t train the trainees.

Grant Belgard: I think that that is the most common answer I get when asking academics this question, right? Cause you’re promoted cause you’re good at doing science, right? And I guess the assumption is just, you pick up people management on the way.

Sophia George: Yeah, like somehow, right? We know about the DNA, RNA, protein, whatever molecule that we’re studying or trends that if you’re a population scientist, but how do you manage people? I mean, I guess people who do business and other things, they get to learn that.

Grant Belgard: Oh yeah, there are explicit training programs, coaching programs, absolutely, yeah.

Sophia George: Really? No, you learn that, you get to learn that like when you have a lab with people in it already and you’re like, wait a minute, I think I need to learn how to do this. So that, and then budgeting, finance. Although we have people that help us with the finance, but it’s not the same way of conceptualizing how much this project is really going to cost. What are all the factors involved that would cost money? And how do we identify sources of flows beyond and actually being creative about whom you collaborate with and how you do the collaborations. Again, institutions have some of those things, but we don’t get to think about that pre you come into it and then you hope that you find mentors or honest brokers that can let you know that this is happening and that’s an option beyond like thorough funding and how you partner with industry, different types of industry, all those things.

Grant Belgard: Yeah, the budgeting and project management’s a good point. I recall my postdoc advisor had spent some time in management consulting before his MD PhD and he really would use that pretty regularly and it really gave him a leg up in thinking about exactly what you said, what’s the true all in cost of a project, right? Because it’s a lot more than just what you have in the grant and then the time and thinking about recruitment and all that.

Sophia George: I mean, the time, the time, the time, the time. We are on 40 hour week of 60 hour week, whatever the week is, it’s never enough. And especially when you’re doing projects at scale where you are enabling people to lead, you have course when you are at different sites, you have site PIs and they have expectations and so on. But if you’re driving some parts of the science, it takes a lot of time to get everybody on board and a continuous training, all those things are not budgeted for. You know, there’s no line. I’m really, is there a line? Some people are like, yes, I put a line, but that line is never the true line, right? But it’s well worth the efforts of all the things. But yeah, it’s the budgeting, the project management.

Grant Belgard: How have collaborations across institutions or across countries changed the way you do science?

Sophia George: It has changed it significantly. So how? One, different systems, different cultures and practices and how to engage and expectations. Expectations vary independent of the cost. So even if you have a budget, some people want you to be fully involved. Some people want you to be not fully involved. Expectations, not talking about publications, but relationships, like what, how are these relationships built and sustained? They vary by country and they vary by partner, collaborating partner. And so for me, I have projects in, where we, in one region, three different languages. So projects in, oh four actually, Dominican Republic, in Haiti, in Benin Burkina Faso and English. So that’s four languages. And so, and each system, each country is different. And even within country, the institutions are different, different infrastructure.

Sophia George: So, and different questions that they want to ask, different priorities and how they want to ask the questions. So one disease might be more important than another, even within the same organ. And so making sure that, I call them informed believers on board, you have to also acquiesce, which is why collaborations work like the give and the take, or the give and the give, right? What it is that are you fundamentally interested in? Because even if I’m interested in like ovarian cancer, a lot of my collaborators, ovarian cancer is relatively rare compared to other diseases in some parts of the world. So they want to focus on prostate. They want to focus on cervical cancer. They want to focus on some rare disease that only is impacting their population, where I’m interested in the other part of the tissue.

Sophia George: And so how do we ask a robust question scientifically and have everybody, according to COVID, win-win, right? Like always it’s a win-win. So it’s a lot of interplay. And so the science that you see and the science that I’m thinking about is not like linear.

Grant Belgard: What non-technical skills do you find most accelerate progress in community-engaged genomics and in navigating multinational consortia?

Sophia George: One non-technical skills, communication. Communication has been a big, has been an important factor. Humility, I guess, is a behavior. I don’t know if it’s a skill, but it’s necessary. So communicating, being transparent, which facilitates the communication and humility, those things have allowed me to be with my partners on the ground forever, have allowed me to be able to do what I’m doing.

Grant Belgard: If you were advising a PI on setting up a multi-site cohort from scratch, what would you emphasize in governance and quality control?

Sophia George: So governance, setting up a team of folks at individual sites who have been trained and understand the biology enough that the representatives of you versus just managing. And then having harmonized system to collect and track whatever is going on. Like if you’re collecting blood, whatever you’re doing. And of course, optimizing protocols locally. So what protocol you write here or wherever you are is not going to necessarily translate to the T in a different setting that you do not want people to fill in gaps without your knowledge. So it’s like shopping the protocol, workshopping the protocol in each site versus disseminating one protocol and assuming that everybody’s doing the same thing.

Grant Belgard: I feel like that’s pretty universally applicable advice when you try to do anything across different sites in science, outside of science. How do you personally protect time to do deep work?

Sophia George: I block my calendar. So this year I’m interim associate director for the Center for Black Studies at the University of Miami. An interesting year to take up that role, but this is the year. And the center is on another campus. And when I go there, I can be quiet because sometimes nobody knows that I’m there, which is like the best thing. I have to be away from my home often and or my lab office and the lab in a very quiet space. My best work is in the middle of the night, but it’s not sustainable because then I wake up late and I don’t get enough sleep, et cetera, et cetera. Or I wake up early and I don’t get enough sleep when it’s super quiet. So for me, it’s just blocking my calendar and finding peace, like somewhere quiet so that I can think. I can read a paper from beginning to end and think.

Grant Belgard: And what advice would you give your first year PI self?

Sophia George: Oh, Lord, don’t be afraid to pursue the thing that you think is hard. Don’t be afraid. And be bold. Don’t be afraid. Because at once I was considered very timid and shy and sit in the back of the room. And I know that affected my ability to do more sooner.

Grant Belgard: So speaking of being bold, if you could place one big bet in your field and you had to wait 10 years for the readout, what would you fund?

Sophia George: In my field. My field is like, I mean, I’ve developed a few fields. We still, surprisingly, we still don’t have enough people sequenced. Surprisingly, we still don’t know enough between the transcriptome and the DNA, the genome. So, you know, these projects that I’m doing, we need more. We need to get to saturation. So 3,500 single-cell samples from different bodies is not enough. Even if that leads to, I don’t know, 35 trillion cells, I don’t know how many, 10,000, let’s say 10,000 cells, times 3,500, whatever that math is, 35 million. It’s not enough. No, so it’s gonna be three billion cells. It’s not enough. It’s not enough. It’s not even reflective of the number of people in the world. Right. So it’s not enough. It’s not enough. So I would do that. I’ll do more of that. And I would do like deep work, deep.

Sophia George: So the whole human kind of work where you’re not just capturing the single-cell, the RNA, but you’re capturing the epidemiologic data. You’re capturing that metadata that puts context with that piece of tissue or RNA, protein, metabolome, like the molecule, you know, that you, you know, whatever your measure is, that there is significant metadata to make it make sense, to contextualize it. So I would be doing that.

Grant Belgard: I don’t think any of our bioinformatics-interested listeners would disagree with, you know, more data and better metadata, right? Two things people always want.

Sophia George: I mean, it opens the doors to so many, you know, new additional methods and so on that can be used. King and queen. I said king. Metadata is king, but it’s also queen. Like it’s, it’s non-gender. It’s important.

Grant Belgard: So where can our listeners follow your work and your lab’s updates?

Sophia George: Oh boy. So I’m supposed to be updating my website. I post sometimes on Instagram. Sophia HLG and publications. I, yeah, kind of, I know it does, it sounds anticlimactic, right? But yeah, when we travel, we post and of course publications here and seeing the work that we’re doing. Some of it, they all look now and be like, well, this is like all epi stuff, but while we’re building the, and Grant knows and sees the different types of assays that are coming through, it takes time to get these types of rich data and to make, I’m not a, I don’t want to make fast and dirty conclusions. So the metadata and the clinical data is really important to put context with these populations and samples that we’re studying.

Grant Belgard: Thank you so much for joining us today. Really appreciate it.

Sophia George: It’s been fun.

Grant Belgard: Thank you.

Sophia George: Thank you for having me. Thank you.

The Bioinformatics CRO Podcast

Episode 71 with Christiaan Engstrom

Christiaan Engstrom, founder and CEO of BLPN, discusses his experience building a space for authentic, non-transactional business networking in the life sciences.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Amazon, YouTube, Pandora, and wherever you get your podcasts.

Christiaan Engstrom

Christiaan Engstrom is founder and CEO of BLPN, an invite-only community for life science investors and senior executives to connect.

Transcript of Episode 71: Christiaan Engstrom

Disclaimer: Transcripts may contain errors.

Grant Belgard: Welcome to the Bioinformatics CRO podcast. I’m your host, Grant Belgard. And joining me today is Christiaan Engstrom, founder and CEO of BLPN, an invite only community where life science investors and senior execs connect to help each other and make better deals with a heavy focus on authentic non-salesy conversations. We’ll talk about what he’s building now, the path that led him here through leadership roles on the tools and services side of biotech, and his best advice for founders and operators navigating today’s market. Christiaan, welcome.

Christiaan Engstrom: Thanks so much for having me. Good to be here. Excited to meet you and your audience.

Grant Belgard: So when someone new asks what you do, how do you describe BLPN in one sentence?

Christiaan Engstrom: I often say that BLPN is better experienced than explained. And in fact, when I try to explain it, people think it’s something it isn’t. So I always just say, we’re doing so many things in the marketplace, come check it out. And that energy is actually at the center of what we do. We have a mantra. So it’s best explained by our mantra, find someone to help, repeat. We are a member-led, invite only club. We don’t spend any money on marketing. We don’t have a sales team. People opt into what we’re doing. And we’ve been lucky enough to have some of the best people opt into what we’re doing.

Grant Belgard: What problem in life science deal-making or executive networking are you trying to solve?

Christiaan Engstrom: I come from non-life science background. And that means I was trained in automotive. I went through Ford Motor Company’s leadership development program. And what I learned in working within dealer channels and working regionally and nationally and internationally is there’s great cooperation amongst the manufacturers. They all share the same vendors. They need the markets to behave a certain way. Technologies that come to market get moved or aggregated quickly to the other manufacturers. They are playing, although there’s great competition within automotive, they are playing a game that preserves the industry. And when I came to life sciences, I found it to be very lonely in my vertical as a CEO, trying to understand how to navigate and bring resources to my team.

Christiaan Engstrom: So over time, I reached out and I did more and more of the partnering systems that are out there for leaders to meet other leaders. And it quite often was transactional. And finally, I just reached out to my banker, JP Morgan, and asked them if they would try to build something with me that is less transactional and more relationship-focused. And that leads to trust, which leads to business. And we’ve been doing that. So that’s the problem we’re solving. Trying to create more of a community within life sciences.

Grant Belgard: So what guardrails keep interactions constructive and non-transactional?

Christiaan Engstrom: We never sell or we try to never sell in that I have something that I need to be helped, but more importantly, I need to be helpful. And there are people that will self-select into that mode and it’s not for everyone. We know that there are certain people who we respect in life that are very focused on their ask in life. And a lot of times they’ll get it through that approach that they’re using. And then I’m not critiquing that. This group is for people who say, I’m gonna go further together. Might go faster alone, but I’m gonna go further if I partner within a community. So I think that’s, I’m not sure if that answers your question, Grant. I told you, this is tough to explain. You gotta check it out and be a part of a community where people are genuinely focused on what you’re into and how I can help you.

Grant Belgard: How do you decide which conversations or connections are worth amplifying?

Christiaan Engstrom: Our members become founding members. Through that process, we commit to supporting them and amplifying their missions. So the members are challenged as founding members to take a hold of the organization and do something really positive. And what’s blossomed from that is one of our members has a family office he invests for and started a fund within BLPN. So in the last six months, we put five investments into companies, the first checks going into amazing technologies. And then our members get the opportunity then to become coaches for those teams and bring them resources and sometimes take advisor roles. So it creates this ecosystem where company is opting in to getting this help and everybody around them has committed to helping. So it goes fast from that perspective. I think that’s one example for you. Also our members more recently have taken more and more control of our events.

Christiaan Engstrom: As we mature and we know how to do events, we can put members in charge of certain breakout rooms. So I’ll take Bio International Convention where we met for three days during Bio and many of our members are going back and forth between the convention center and where we were. So we were at Portal Innovations, Smart Labs and EPAM Continuum, which is doing a lot of bioinformatics stuff at EPAM. And Portal and Smart Labs are also very involved in this space. They opened up their facilities to our community. Our members volunteered to run forums. So for example, we had a Saudi investor forum, Israeli investor forum, South Korea, Japan, Australia, and these folks came in to not sell anything, but to get to know each other and say, I wanna be involved in the South Korean ecosystem.

Christiaan Engstrom: So it creates leadership opportunities, volunteer opportunities for people within the group to help people that are, I think that they wanna help. We get asked to help a lot in life. We create a vehicle for you to be impactful in the areas of interest that you have.

Grant Belgard: If someone joins and engages well, what behaviors do you notice from them early on?

Christiaan Engstrom: Oh, I’m gonna give a shout out to one of our sponsor founding members, Terry Stelter, who’s at Mazzetti. And what they do is a lot of like infrastructure for biotech companies, buildings and HVACs and that sort of thing. It was really important part of building out your organization. Terry continually supports our events financially, but he volunteers every event. We have member volunteers that are ambassadors and they make sure that some of the VIPs that are coming meet our members and that the members interact with each other. They just make the connections happen. It’s really into the, they’re like bees pollinating flowers within a garden. And so Terry is the best at that. We watch him make connections between investors and startups and advisors and nonprofits. So he takes great pride in that and is very good at that.

Christiaan Engstrom: And I think that at some level, most of our members have that inside of them.

Grant Belgard: What are examples of small interactions that ended up mattering a lot?

Christiaan Engstrom: Oh, I’ll tell you today, this morning, I got a call from Linda Templeman. She is CEO at PersistaBio and they are a cell therapy delivery system. And they basically are a subcutaneous implant and they deliver STEM cells to fight diabetes. And so go check it out, PersistaBio. But she spun this out from university and was very green, I think, in her expectations for how her fundraising process might go. Although she’s super intelligent woman, very knowledgeable just when you’re doing this for your first or even your second time, expectations don’t always align. Through a series of little connections, she has moved her technology forward. She entered our Moneyball program. She met one person at a time that led her to take a small investment from the fund we established. She met her head coach, Stella Vnook, who has exited five times in the therapy space, believed in her.

Christiaan Engstrom: So I think it’s a series of small connections and earning the faith of these other amazing people that they are now gonna invest in you. That really matters. You can’t be a Johnny come lately. You don’t just get to hop into the life science game and pretend. You get sniffed out pretty quick, right? And so I would say, Linda, she called me this morning and told me that she was just got some good news on some grants that are gonna fuel her business for the next couple of years. And she has some matching investor opportunities that are gonna follow those grants. And today it’s happening, right? And it was a series of small steps for Linda and Persista. And she has a lot more small steps in front of her.

Grant Belgard: Yeah, that’s really crucial right now with the private funding drying up. So if you could redesign how people prepare for major weeks, like JP Morgan or Bio, what would you suggest?

Christiaan Engstrom: If I was going to give some advice for let’s say JP Morgan, because we’re in planning process right now for our events at JP Morgan, and this year it’s never been bigger. We’re doing a investor summit where we’re taking aspiring life science investors, folks that are accredited investors, but it’s pretty intimidating to put your money into early stage life science companies. It’s a very risky space and we’re going to educate them. We’re partnering with, I’m not gonna put the names out there with several excellent life science entities to bring in venture capitalists and talk to this group of new money. We see it as the current structure is broken. So why do we keep fishing in that pond, right? We need to go fish in different ponds and pull people in and tell our story effectively.

Christiaan Engstrom: So this is also in a way some advice and maybe speaking to the startups that are preparing for JPM, we need to do different things. You can’t go to the same well. It’s not the same as it was two years ago or five or 10 or 15. Some of the folks that are still trying to raise money are operating like they needed to a decade ago and it’s different. And we have to open our minds up, have different conversations. So we’re doing this investors summit in Napa Valley for three days where we’re going to be educating potential life science investors through venture capitalists that have been doing it and bridging that gap, I would say. So preparing for that means I need to go reach out and introduce myself to the investors that I would like to have involved with what I’m doing. And this should sound familiar to startups and I’m doing it now in September.

Christiaan Engstrom: Because if I ask them in November, they’re gonna be booked in January at JPM. So I’m asking them and they say, well, tell me about it. And we set up a meeting and I’m not asking them to sponsor or get on the docket as the keynote yet. I’m just saying, do you think this is cool? And if people do, they get involved. So as a startup, you need to socialize your technology in front of JPM. And then you need to have a meeting where you present your deck or your idea or whatever it is you’re doing, whatever vertical you’re representing within life sciences, socialize your idea. And then before JPM, you need to have a business meeting with them and say, here’s the nuts and bolts of what I was talking about. And when you’re in that meeting, you’ll secure your JP Morgan meeting with the investor or the partner, whoever it is you’re looking for, if you’re both into each other at that point.

Christiaan Engstrom: If they like you and it’s very much about, and I’m speaking directly to the founders and this goes back to a question I always ask our community is should you be the CEO? Because the investor is investing in you, not your CFO, not your board of advisors, not even your technology in some cases. They don’t need to go as if they’re investing in you. So you have to build that relationship and then you need to show up and be credible. And in the end, quarter one, I hope it opens up. After JPM, we were hoping that what happened this year didn’t, there were things, macroeconomics and play that has kept the money in dry powder stage, but it should be opening up in quarter one, quarter two, and you’ll be there, you’ll be ready to take that check.

Christiaan Engstrom: If you wait, if you say, boy, I’d really like to get my 2026 planning in place, so I’m gonna start talking to people at JPM so I can tell them what my plan is, you’re too late and you’re gonna miss out on the next round of funding. So quarter two, quarter three, 2026 funding prep starts now.

Grant Belgard: How do you decide which thematic panels or formats are most useful to your community?

Christiaan Engstrom: Continuously evolving. We did yesterday a partnering panel, which we know is a great community builder for us. So we had 25 and tremendous leaders, including Mayo Clinics, Director of Partnerships, and we were just there, so I mentioned them. Thank you again, Mayo Clinic, for hosting us. And several other entities that were just able to say hello and then move into a breakout room for second hour for introductions, and we went two and a half hours. It’s amazing to see 150 people stick around for two and a half hours. And so there’s something of value going on. That’s a great meeting. We’ll keep doing that every quarter. We’re doing military medicine next. So that is in place of, we normally do non-dilutive funding, but that non-dilutive space is still, we’re waiting for the other shoe to drop, so to speak. But military funding is active.

Christiaan Engstrom: And so we can say, here’s a space that you need to know more about, how to participate. And we have top programs coming in from leaders in Department of Defense to M-TEC, which is the Medical Technology Enterprise Consortium. They’ve deployed a couple billion dollars in the last few years, and you get to come in and hear from the top leaders of these organizations how to get involved and why you should get involved. And then you get to meet them in the second hour. So we’re doing that. The next one is women’s health. We’re starting an investor club, which is focused on these emerging investors, pairing them with current venture capitalists. Venture capitalists like this because they meet potential LPs, folks that they can bring into their fund. So we’re doing that pairing going on. And I think it also depends on people raising their hands and saying, I want to lead this.

Christiaan Engstrom: So the more volunteers we get internally who are excited about a subject, we’ll make room for it.

Grant Belgard: What’s something you’ve changed your mind about in the last year regarding how the community should operate?

Christiaan Engstrom: BLPN needs to start generating some revenue some way. We won’t go into it, but we had a good year last year, slightly profitable. We have no employees. Everybody is a life science leader in some other area. And if they’re volunteering at a certain level, they take a stipend. I think it’s mostly to tell their spouse that I know I was spending all my time over here, but I’m getting something. It’s a humble pittance of what they probably deserve for taking on the role. So me, I’ve been thinking about how do I make this sustainable right now? It’s very much, I need to be involved at some level. How do I get another CEO, like the next CEO ready and have them lead this organization and have a budget for them that allows them to have a staff? So I’m thinking about that. And I haven’t ever really thought about that.

Christiaan Engstrom: And we’ve had some sustaining sponsors step up, including Collaborative Drug Discovery, which is such a perfect match for them. And Prendio, Prendio is a platform for procurement for biotech. And they’re coming in as sustaining sponsors. But that creates obligations for us, right? And I’m trying to be Switzerland neutral within the industry. So that’s a big challenge for me. And I’m not gonna keep doing this forever as CEO. Somebody else, somebody better needs to come in.

Grant Belgard: So looking back, which early experiences most shaped how you operate today?

Christiaan Engstrom: Oh man, you’ll get me going. My father, for sure. And he’s 79 and my dad has been a fixture from time to time as a host or volunteer at our events, especially the Golden Gate Yacht Club. Many of our members have met my father. My father was the son of an immigrant who didn’t graduate from college and had tragedies. I would just say early on in his life that he endured and he built a family and he built a window and door company along with his family. And we lived in Southern Wisconsin and he sold windows and doors all over the Midwest to his little window and door factory. And he hired, I think, just about everybody in the community once or twice when people needed work or they, and they returned the favor to us and to our business. And as a farming community, so people were out at the farms helping each other, doing whatever we needed to do.

Christiaan Engstrom: And for me, that was always just how you did it. The other ways don’t feel comfortable to me. I didn’t grow up in a big city where it’s every man for themselves. It’s like, I’m not sure how I operate outside of that ecosystem. And so my dad, as he watched me grow up as probably a very cocky young man who didn’t understand like this is the way that I’m built to do it. I’m maybe overconfident in my early years, excited, too excited about what I was doing. My dad always would say, I never had to sell anything in my life. I just listened to what the other guy needed and I brought it to him. And so continuously coaching me, my dad, over the years and smoothing me out and just being like, son, I see this. And so I think BLPN manifested from that relationship.

Grant Belgard: Which mistakes have been most instructive for you?

Christiaan Engstrom: Oh gosh, constructive mistakes. Like, I’m not sure if you can look at a mistake and say that that’s a learning. I was just talking with, I’m gonna name drop here, but Stella Vnook, whose daughter recently went to school and my son has transferred schools. And we were talking about trying to influence our child and what they’re gonna choose to do. And her take was, my daughter, I’m not sure. I’m gonna stay out of it. And I think that if she makes the wrong decision, I will be better on the other side. I was like, wow, my son, I stayed out of it. I really wish I would have spoken up. He had a really bad experience and now he’s transferred back home and we’re all happy. So I think we both kind of laughed about you’re damned if you do, you’re damned if you don’t. I think what I think being present in life means is that you just accept what happened, happened and what did I learn from it.

Christiaan Engstrom: So constructive learnings for me has been, you need to be, for me, take risk and get comfortable with it. And what that means for competitors is you’re gonna fail and that is so hard and everybody’s gonna be mad at you. And everybody’s gonna say, you should have done it a different way. You’re gonna have to sit back and kind of live with the decisions you make and you can choose to position them a certain way. You can explain them away. In the end, the result is what the result is and as a leader, you take that on. So the best thing that I’ve learned is learning how to gracefully accept loss and make others continue to stay with me and trust me as I get to go try again. And at some point, they may remove me from a leadership position, but I think get used to loss and don’t let it stop you. You’ve got to keep going.

Grand Belgard: Who are a few people who have changed the trajectory of your career?

Christiaan Engstrom: Oh, Santosh Patel. This is, I’m gonna tell the story of Santosh Patel. He was my boss at Toro company. And so my career was kind of went through this leadership development program at Toro within their dealer network and was at, excuse me, at Ford and was at Ford’s headquarters. And then I got recruited away to do similar things for Toro within their golf business. And what I knew is I didn’t care about cars and I didn’t care about golf, especially like lawnmowers from like a deeply intrigued perspective, I wasn’t. I was learning how to do business. And I told my boss at the time, Santosh Patel, who was a director of customer care at Toro, that I was gonna be leaving the company to go to school and go back and get my MBA. And he said, well, what if I was able to put you into full-time MBA program and you can keep your job? And I said, yes, I would stay. And by the way, we’ll pay for it.

Christiaan Engstrom: And so he went and advocated for me. Now it just happened. He had the time and talked to our CEO, very nice guy, Mike Hoffman. And Mike Hoffman had a tie to the University of Minnesota program. And those two decided to put me into the program. And I was like a rookie. It was my first year coming to Toro from Ford. And it turns out there was a list of dozens of people at Toro with vice president titles and director titles that were way above me that were scheduled to go. And Santosh just kind of stomped his foot and Mike pushed me through. And I went into this full-time MBA program at 30 years old or 29 years old. And it totally changed my life. Like I can’t say how grateful, lucky, privileged, whatever you may call it, it brought me perspectives in seeing, first of all, most of the folks in the program, older than me, 10 years plus, who had had better careers than I could ever hope for.

Christiaan Engstrom: And I said, that’s how I need to, I took notes from how I saw them operating. So they were all very wonderful. And then just the X’s and O’s, thinking about how the bioinformatic person thinks in the business or the stats person. Let’s get down to it. Learning some of those fundamentals and what clinical statistics mean. And I kind of diving into it, it wasn’t my area. I was an operations guy and I was a marketing guy and a little bit of sales. So learning what the CFO was thinking about and being a better teammate for the CFO, getting your MBA from me, didn’t give me that next job right away. Just let me talk to the people that would hire me a little bit more effectively down the road. So thank you, Santosh. Thank you, Toro.

Grant Belgard: Nice, nice shout out. So how do you think about personal brand versus organizational brand in this industry?

Christiaan Engstrom: I don’t know. Try not to think about personal brand too much because every time I do, I get in trouble. It’s just not like for me, it’s been easy for me to celebrate and hide behind our awesome members and rah-rah them and cheerlead them. So I think if there’s any brand I’m trying to get as a cheerleader for these athletes that are out on the field and making it happen. And I don’t know, that’s not a great way to position myself. But I’ve been in their seats too, I’ve been the athlete. And for me, it’s facilitator on the team, maybe the point guard on the basketball team, which I always dreamed of being a pretty big guy who’s slow. So right now on this team, I get to be the point guard. And my advice for others is you have to embrace your authentic brand. And I think for me, there is a little bit of that farm boy community, a little bit shy away from that sort of thing.

Christiaan Engstrom: Now there’s other folks that are beams of light and they got to let it shine. So, and I mean, if you’re on the introverted side and you’re really into the numbers, so might be talking to some of our bioinformaticists that listen to this, it’s okay to dive into the numbers and even share that publicly with the other folks that are into it. But I would say to those introverted folks that say, maybe it’s easy for Christiaan because he’s extroverted. So how do I do it? I think I’ll go back to our mantra. Find someone to help, repeat. You can be so effective at any room if you change your focus to what can I do for that other person? Because I believe that you do have some energy coming out of you at that point.

Christiaan Engstrom: If you’re bought into that and that’s your intention in that room and you make it be about the other person and your pheromones change, your vibe changes and you’ll find the right people in the room.

Grant Belgard: What have you learned about navigating downturns and winter cycles in biotech?

Christiaan Engstrom: That’s all I’ve been through, it seems. When I joined this group called Medical Advanced Pain Specialists, they were at the time, the largest pain specialty group in the country. And they did implants for pain management, surgeries and also they had a CRO attached to it as well. And I was a COO there. So I left from Toro to go do this. And they were on the verge of bankruptcy. So we were in a filing process at the time and I had the luxury of seeing it totally new. And I’d brought my Lean Six Sigma discipline from Ford Motor Company in Toro, which I did a lot of value stream mapping of cash flows and looking at where we can be more profitable. We went and looked at the entire patient experience from booking appointment to getting paid by the payer. And we found so many holes that money was just falling out of that organization.

Christiaan Engstrom: So over the course of the next two years, we were able to double revenue and we were actually able to cut expenses by 20% through logic. If you know Lean, it’s nothing but teamwork and logic. And I would say when you’re going into this downturn, go to your tools, they make sense. It’s like family budget. What do I need? What don’t I need? And your investors want one thing from you. They want you to stay alive and they want you to thrive. But in this economy right now, they want you to stay alive. So other things, that was a downturn. We sold that to a PE group. The owner of that group did very well. And we took it out of this bankruptcy position. The second deal was a phase three immunotherapy program. If anybody’s interested in immunotherapies, this was a autologous antibody-based personalized vaccine.

Christiaan Engstrom: So we would take the patient sample and manipulate it and send it back to the patient. And so we’re doing these milligram batches of antibody work. And we failed. I joined while it was failing, let’s put it that way. Wasn’t because of the people, wasn’t because of the tech, the market changed. We couldn’t get it funded. We had to move on. But what can we do from there? So we spun out a contract manufacturer of antibodies, a GMP manufacturer. We got very mean. We moved from 150 people to 15 people, but grew up to 10 million in revenue over four years. And that is something I say that in this industry, especially you need to be resilient. So I’ve been in, seems like the last few years, everybody who’s still around, they are resilient.

Grant Belgard: Yeah, unfortunately a lot of people have been washed out and I expect that’ll continue as the drought continues. If you could loan one habit to every rising leader in biotech, what would it be?

Christiaan Engstrom: One habit, work on your network. You’re great. I will say this, like, and I get to say this because I see so many of you when I’m speaking specifically to emerging biotech leaders. And from, and I’ll give you the perspectives in just the last week of folks that are trying to figure it out at different levels in different places and are humble enough to understand that they can’t do it alone. So we were just at Mayo Clinic working with their corporate development team and their doctors. Mayo Clinic in my opinion is the pinnacle of healthcare innovation. And if you dive into what they’re doing, they are out there failing and succeeding at the same time. And they are looking, they called for the BLPN to come into their campus and they wanted to learn more. So they’re building their network. They’re trying to grow.

Christiaan Engstrom: If you are so sure that you don’t need that help, good luck with having that strategy. I would say make authentic connections. I have made quite a habit over the last 10 years of using LinkedIn really well and reaching out to people and basically saying, I’m into what you’re into. Nice to meet you. And maxing out at some weeks when I was really committed to it, my connections with people that I just found were fascinating. And I wasn’t asking for a thing. And if they did connect, my response was along the lines of, thanks for connecting. I’m excited to root for you. And good luck with what you’re doing because I was genuinely. Now there’s some work that’s probably, if you’re gonna do, if you’re gonna max out, let’s say 100 invites a week, put aside five hours to do that, right? You’re gonna need a lot of time to be able to send those. But don’t complain.

Christiaan Engstrom: If you’re not doing it, don’t complain when you’re not getting funded, when you’re not getting the next job, because there are fundamentals there and that is a habit as a leader that you’re bored or your teammates or whoever, they’re counting on you to have the answers.

Grant Belgard: And where can our listeners go to learn more about BLPN?

Christiaan Engstrom: Go to BLPN.club. You know, I think that’s a great spot. Our LinkedIn page, BLPN, is full of just photos from Mayo right now, I guess, a lot of posts. It was a wonderful experience, but you can get a sense of what our community is. If you wanna come to a meeting, what we do is we’re looking for decision makers within life science companies, primarily operators and investors, but we also, there’s a whole list of it on a membership, associations, nonprofits, government organizations, all kinds of different groups that participate, but they need to be a decision maker. So it’s not necessarily an education forum. It’s, hey, here’s what I’m working on. You wanna collaborate for them, is what it is. You can fill out a contact form at BLPN.club and then we have a short form that you fill out and we invite you to the meetings. We get you going and that’s it. And then you kind of opt in.

Christiaan Engstrom: You either come to the meeting and if you like it, come back, I hope, but most importantly, we are all there to be helped, but primarily to be helpful.

Grant Belgard: Great, Christiaan, thank you so much for joining us today.

Christiaan Engstrom: Thank you so much.

The Bioinformatics CRO Podcast

Episode 70 with Joanne Hackett

Dr. Joanne Hackett, VP of Health Systems Services at IQVIA and Chair of the Board at eLife, discusses her hopes for the future of healthcare.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Amazon, YouTube, Pandora, and wherever you get your podcasts.

Dr. Joanne Hackett

Dr. Joanne Hackett is VP of Health Systems Services at IQVIA and Chair of the Board at eLife.

Transcript of Episode 70: Joanne Hackett

Disclaimer: Transcripts may contain errors.

Grant Belgard: Welcome to the Bioinformatics CRO podcast. I’m your host Grant Belgard. And today we’re joined by Dr. Joanne Hackett, vice president of health system services at IQVIA and chair of the board at eLife. We’ll explore what she’s building now at IQVIA, her career path across science and industry, and her most practical advice for people working at the intersection of genomics, data, and healthcare. Dr. Hackett, welcome.

Joanne Hackett: Great. Thank you for having me. And I was just saying before we started the podcast, we have known each other for a very long time and haven’t seen each other in a very long time. So it’s very nice to be with you again.

Grant Belgard: Great to see you again as well. So how do you describe your role today to someone outside of healthcare?

Joanne Hackett: Yes. So this is always the interesting thing when you’re sitting around a dinner table and somebody says, what do you do? And the people who do things with their hands are usually the ones who get the greatest following because you can explain exactly what it is that you do. But the rest of us who do more with our brains have a little bit of a harder time, either convincing ourselves that we do something useful or those around us that we’re doing something interesting. But I personally think that I have a very interesting role at IQVIA and IQVIA is one of these companies that brings together a lot of different components. There’s advances in data science, technology, and healthcare expertise. And the ultimate aim is to help customers make better decisions and ultimately improve patient outcomes.

Joanne Hackett: And the nice thing about working in a company whose mission is to help create a healthier world is that we actually do get to do that, whether or not we’re the individuals who are accelerating the innovations or making those intelligent connections across the healthcare ecosystem, we really are making progress in changing the way healthcare is being delivered. And I’m very fortunate that my role, I spend most of my time across Europe, Middle East, Africa, and South Asia, but we are a global company. And because of that, we can take best practice from one part of the world and see whether or not it can be useful in other parts of the world as well. And what I find probably the most satisfying is that we get to work across health systems.

Joanne Hackett: And for me, that’s so important because all of us are patients, whether we’re talking to someone at our pharmacist to either pick up plasters because we scraped our knee or because we’re picking up a prescription for something that’s a long term condition. We are in many aspects of the health system and we rely so much on needing that connectivity and that data. And my role is to staple that together in various different ways. A big part is understanding where governments are finding issues with regards to healthcare spend, trying to understand how they can maybe attract more clinical trials and also to make sure that the institutions that sit within these countries are fit for purpose. And that doesn’t always mean complicated systems. It means trying to map out that patient journey. Again, maybe it starts in the pharmacy or it ends up in a very sophisticated tertiary hospital.

Joanne Hackett: But what is it that we actually need that pulls these different components together? So that’s really my role is to try to help pull that together with both the local focus as well as the global experience. And because I am a geneticist, of course, I always bring a genomic and precision perspective into the way that we solve problems.

Grant Belgard: So when you look across your region, what outcomes are health systems asking for most urgently?

Joanne Hackett: Very often, most health systems are trying to find ways to be more efficient. And it’s not that it’s trying to cut costs, to try to do something cheaper because they’re trying to cut corners. There is a lot of waste in the system because it just quite literally has not been mapped out. And by the time it does get mapped out, the world has changed. So very often it’s trying to understand more about efficiencies, trying to understand what data actually needs to be collected. We spend an awful lot of time thinking more is better. It’s not necessarily always the case. So the questions are around, should I be trying to attract clinical trials to this country? Do I even have the right patient population that have certain mutations that industry is looking for?

Joanne Hackett: Are there particular types of software that will work better in my country because it’s got a particular module that’s necessary for pharmacy integration into a hospital? It’s all of the stuff that’s both local, but at the same time thinking about the efficiencies that you can pull from various different parts of the world that would make it make sense as well.

Grant Belgard: Where do you see the biggest near-term opportunities to improve patient journeys end to end?

Joanne Hackett: A lot of people think that it has to be this one-size-fits-all approach and you have to build a platform that takes in 15 different data questionnaires or whatever. It’s really not that complicated. For me, at the end of the day, I went into genetics, as you probably remember when we first met. I went into genetics because I was a child with a rare disease. And to this day, it still fascinates me that individuals can’t put themselves in the shoes of someone who’s been ill or has had an experience in a hospital. It’s not complicated. When people say that they’ve got a rash because they’ve taken particular medication, if you were to ask, did that happen two weeks ago or when it happened with the rash, if you don’t, we are alive, they’re busy. But the last thing we remember is the absolute detail. Empower the individual to take a bit more responsibility about their health as well.

Joanne Hackett: So the near-term opportunities in my mind are treating individuals, because we are all patients, like sophisticated individuals and giving them a bit more responsibility about their health care. Give them the tools that actually connect to something useful. If you want to know that I had a rash and it happened four hours after I took the medication, if I’ve logged that and you have access to it, surely if that’s integrated into my record and you can see that, it’s going to make that next step a lot easier. In addition to that, there are so many people doing research now and real-world studies, real-world evidence. Why can’t that be much more accessible so that you start to realize that the individuals who have early onset Parkinson’s were also in the majority, the individuals who had miscarriages, they also had teenage acne.

Joanne Hackett: They also were the kids who was a bit clumsy and fell off their bike. If we can start to find those patterns, we can start to treat earlier and allow people to have longer and healthier lives. So for me, it’s not necessarily about big policies and changes and sophisticated technology. A lot of that stuff will help, but I really think that we still, many, this is not just one country, this is several countries, we still just haven’t taken it back to the basic steps of I am a human being, I am feeling ill, I am ill, I need to be, I need someone to see me and I need the information to be collected and I’m going to need treatment. Really not that complicated.

Grant Belgard: How do you envision that being implemented in practice? What’s the, if you’re looking on the timescale of a couple of years, what do you think is the most feasible path towards collecting that?

Joanne Hackett: I’m really delighted to see, especially in Europe, there has been a lot of funding made available through the COVID Recovery and Resilience Fund, and a lot of countries are applying for quite creative solutions. Again, it doesn’t have to be complicated. I say creative, which is a slightly different word. And what they’re trying to understand is if we were to tackle a certain type of cancer, for example, or cardiovascular disease, they’re not trying to do everything. They’re trying to do a smaller population or a particular niche area that they’re trying to work on and solving that and then thinking about funding it further. So for me, it’s not, it’s not the point solutions that are the shiny thing that people were thinking more of the solution of five, 10 years ago.

Joanne Hackett: It’s thinking more about that, the broader aspect of what’s needed to pull all this together, but instead of waiting for that perfect ecosystem, it’s starting to carve out parts of it and think about a particular therapeutic area, for example, and then start to solve that. And what I’m also delighted to see, it’s not just in isolation. Oh, let’s build a registry because it’s helpful. The thought process is that registry is going to be extremely valuable if we’re collecting real-world data, if we have consent for research and recontact, if perhaps we have a sample that’s linked to a biobank, it’s not just solving problem that the individual is facing today. It’s again, being able to do research into what would be a perspective and retrospective data, which is also fantastic because scientific endeavors are changing on a daily basis.

Grant Belgard: What does a credible digital thread between lab clinic and home look like?

Joanne Hackett: I think they really and truly are becoming closer, which is fantastic. I love this whole concept of virtual hospitals. There’s very little that needs to be done in an actual physical institution today. We learned this through the pandemic, not that long ago, that a lot of stuff can be done remotely. I love the fact that there’s also been a lot more research going into sensors. I think it’s fantastic that you can look at people’s temperature and you can understand infection is happening way before it’s actually happening, especially from vulnerable or older individuals who don’t necessarily understand what certain symptoms are telling them. And also for many of us, we wake up in the morning feeling a little bit unwell. And probably there’s been something that’s been happening that we didn’t necessarily know about.

Joanne Hackett: So if, genuinely speaking, healthy people are catching the symptoms early, we’re no wonder we’re waiting for people to get sick before they get better. That connection between allowing the patient and the individual to be more empowered about their own health. We’re seeing more people take advantage of wearable technology that they themselves are purchasing, not just for their own health, but also to monitor their fitness levels and things like that, that connectivity with a virtual hospital and that connectivity with the physical hospital, I do hope at some point in the future, there’s no such thing as accident and emergency for people with [?]. I just, why is this happening? How can we not triage this better? So I do see that connectivity happening mostly based on the fact that we have smarter ways to collect data.

Joanne Hackett: And also individuals are a bit more curious about their own health now, because we’ve demystified the fact that something weird is lurking and you’re going to find out something very strange. If you do a genetic test, for example, you’re not going to find that someone has had an affair or they drink too much wine, you’re not going to find that you’re going to find a better, more personalized way to treat them. So I do think that connection between the precision approach and also the generalized precision public approach is starting to get closer and closer together.

Grant Belgard: How do you think about the balance between privacy and utility, especially when working with data across multiple countries and jurisdictions and regulatory requirements?

Joanne Hackett: That’s definitely one of the things that I think we got wrong about 15 or 20 years ago. And I say we as in the general, we in healthcare, we scared a lot of people into letting them think that if somebody found out something about them, it would be frowned upon or it would be a bad thing, or they would be marginalized in some way. It doesn’t really happen like that. And in fact, you start to see very creative ways that insurance companies are trying to understand protecting people from getting sick before they get sick, because it’s much more beneficial for them to do that. You’re starting to see some companies, employers saying, oh, if we could offer you some health testing and making your best version of yourself, would you like that? Of course they’re being nice, but ultimately you’re a much better employee if you’re alive and healthy.

Joanne Hackett: So we’re starting to see that the responsibility for the health of the individual is not just of the interests of the individual. It’s by other factors that are sitting around it as well. So we’re starting to see that migration a little bit differently, which I think personally is quite exciting. And that’s making people feel a little bit more comfortable about the data sharing aspect of it and that brokerage of data. The other thing that we’re starting to realize, and again, I do think that people were scared for a long time thinking that every single thing would be attributed back to them. You can do a lot of research on data that doesn’t have to be identifiable. We do not need to know my postcode to know that I’m 172 centimeters. You probably need the 172 centimeters. You don’t need the postcode.

Joanne Hackett: So what does that core data that we actually need to be able to do the research and innovation? And I just think that as an industry, we’ve gotten smarter about what that core data looks like as opposed to it’s not always more is better. And the more that individuals realize that they can be part of a study by not even ever having to give their name or the characteristics that they’re describing is actually much more interesting than the fact that they go on holiday in Spain, for example. You don’t need to share all that. And I do think that there are much better regulations now about how the data is collected and who the data processor is. And also I think in the very near future, especially in Europe with the advent of the European health data space, this will change the way you use data for primary and secondary purposes.

Joanne Hackett: And I think that will give confidence to individuals to allow that data to be collected and used for the appropriaries.

Grant Belgard: What use cases for genomics do you see are crossing from boutique to routine the fastest?

Joanne Hackett: So many people got very excited about doing consumer genetics, which is great. I think that’s a good way to get exposed to it. And the nice thing about that is it demystified for a lot of people that genomics was some weird, scary, invasive thing. So we’re starting to see that now translate much more into even some of the health testing that’s routinely rolled out is looking at some of the celiac disease, for example, things that don’t seem as scary as some very advanced, rare disease that somebody doesn’t know anything about, and we’re making it a little bit more mainstream, which I think is also helping people. And the thing that to me is going to change the way that people view genomics and healthcare is pharmacogenomics. It’s such an easy thing to implement.

Joanne Hackett: And the minute somebody realizes you shouldn’t take this medication because you can’t process it, or you have to take half a dose or double a dose, people listen to it because it’s science backed and that you get a very different outcome when people are told, everybody knows they should probably in some way exercise more or not drink as much done so. Hearing that thing over and over again is following, but being faced with the reality that you actually cannot process certain medications and they will hurt you or they won’t work for you at all and don’t bother taking them. It’s a very different response and it’s so cheap. So to me, the pharmacogenomics era is just taking off now. I can definitely see that almost being a screening mechanism for most individuals.

Joanne Hackett: You get something back about your own health very quickly, even if the answer is you don’t have anything and there’s none of these genes and drugs that you need to worry about, even if it’s only that you still have some information back. So there’s that trade off. So I think the pharmacogenomics space is the one that’s segwaying into routine healthcare very quickly.

Grant Belgard: What evidence do payers still want to see before they embrace broader precision medicine approaches?

Joanne Hackett: It’s very strange in my mind when you have to think about changing the landscape so fundamentally just for the sake of a couple of dollars. I think that’s so sad, but that is the world that we’re living in. So let’s park that to one side. I do think it has to be more about the fundamental difference that it can make in outcomes. And to me, I’ve always had the belief that earlier is better. And this is where I’m starting to be quite delighted by seeing much more interest in some of the real world studies, starting to understand these retrospective data sets. What are they actually telling us? And trying to using artificial intelligence and different technologies to find those patterns, to try to then map that back. I do think that’s where payers don’t want to be paying for something that’s not going to work. I wouldn’t either.

Joanne Hackett: Many of us are going to want to go out and buy something that’s not going to work. It just, that doesn’t work like that. So how can they get the best value? And a lot of people we need to remember think that they are amateur healthcare professionals because of this wonderful thing called the internet. And it’s actually quite, I think it is extremely frightening for healthcare professionals to be told full stop that they don’t know what they’re doing because someone has run a search and he’ll come up with something completely different and their friend’s grandmother’s sister’s brother is on this particular medication and they should be on it as well. We’ve almost got to that tipping point where the face in the healthcare practitioner has been taken away because the patient, if you will, the human being is trying to make those decisions on behalf of themselves.

Joanne Hackett: I do think that balance between taking responsibility for your own health, doing better research is useful, but ultimately the experience of a healthcare professional has to be married up at the same time. So for me, trying to understand how decisions get made, the science that sits behind it, and then most importantly, if it’s not going to work, don’t prescribe it, don’t do anything like that. That to me is the evidence piece. And we’re getting much better about looking at different types of evidence in order to be able to prove that. But genomics is a key thing to making that work.

Joanne Hackett: There’s just a lot of stuff that it’ll never, you don’t take certain medications if they’re just not going to work and why would you possibly do a cell and gene therapy on something that’s not going to have any risk and any outcome, you just wouldn’t do that and finding the right patient population, stratifying by genotype, we’re getting a lot smarter now, which to me is going to help get some of these orphan designations across and approved and actually have a much better outcome for individuals as well.

Grant Belgard: What do you consider fit for purpose, real world evidence? What makes it cross the line from merely interesting to really decision grade?

Joanne Hackett: For me, it has to do more about the quality of the data, or again, those back to the simple principles of if not necessarily more is better. I would much rather look at, I don’t know, a hundred data points that are very deep, especially if that’s what I’m looking for as opposed to 10,000, a data point, which tell me hardly anything. The other thing that is getting a lot more traction than even probably five years ago is companies spending more time looking at the diversity of data. And it’s not just a throwaway term anymore. I think for a while, people thought it was the right thing to say or do the same way, you know, getting the patient voice was something that was just thrown into an application several years ago. It’s very different now.

Joanne Hackett: And with diversity and data, the reason why is actually mainstream today is because we have it, we didn’t have it five years ago, people didn’t build the registries, they didn’t have the data. So we’re, we’re seeing, it’s just a very different, it’s a very different time now. And to me, that’s a very positive thing because it’s a very rapidly evolving area and the data is coming thick and south, which is great, which then just allows better decisions to be made. So having more data, deeper data and more diverse data is allowing the real world evidence studies to have, to be a cot above than what they, where they were even two or three years ago.

Grant Belgard: Where are decentralized or hybrid trials generally improving access to the trials or speed?

Joanne Hackett: It’s a combination between getting individuals who wouldn’t necessarily, sometimes you have to travel to a site and you have to travel because there was no other option previously. The healthcare landscape has changed tremendously. So that, that has changed in the sense that a lot of the different things that were rather being collected, whether it was just a blunt sample or monitoring something, a lot of that stuff can either be sent to a patient’s home or it can be done in a community center or a pharmacy. If you look at the physical aspect of getting people to a particular site, that has changed tremendously. In addition to that, many of the things that were being collected, you would have to come in to have a little chat with someone to go over your symptoms.

Joanne Hackett: Electronic, that was allowing people who didn’t, again, a big part was getting individuals to a physical site more than anything, and now there’s more people who are going door to door, doing things in a very different way. So the physical side of it has changed tremendously.

Joanne Hackett: In addition to that, the way that you’re able to find, especially for rare diseases, individuals across many countries, because very rarely are you going to find enough individuals from one particular country to be able to do the study, the fact that there are easier ways to share that data today, to be able to recruit across many different countries is also allowing the right individuals to be recruited into the trial, to basically run the study from many different countries, which again, even five, 10 years ago, the sheer cost in bed alone, because the infrastructure wasn’t there, was the main reason why it just didn’t happen.

Grant Belgard: Which countries are really bright spots for digital maturity and why?

Joanne Hackett: So I’m slightly biased clearly towards anything that sits in Europe, Middle East Africa, and South Asia, because I spent almost all of my time supporting and growing business in those countries. But I do probably have a very special thought in my heart for the Middle East. I’ve spent a lot of my time there. There’s a huge amount of investment in healthcare as a whole, digital maturity is just, it’s growing so quickly. Every time I, from one month to the next, something different has changed. So the sheer growth and expansion in the Middle East is just absolutely fascinating and I’m very pleased to see that happening. Where I’m equally delighted to see a lot of progress is in Africa.

Joanne Hackett: And I know it’s a struggle way to discuss a lot of different countries, but there are several countries that have been working very closely together to share against practice, to think about doing clinical studies and to even share data in a different way. And just the amount of frugal innovation that you’re able to see in Africa is again, just very fantastic because it’s changing that landscape in a way that a very small incremental change is making a massive impact. And so I think the two areas that are probably, so digital maturity of 100% for the Middle East, the access and the change in the way that healthcare is being delivered, perhaps not necessarily digital maturity, but for Africa it is happening in a very fast way as well.

Joanne Hackett: Now, I would also highlight, going back to the comment about the COVID Recovery Resilience Fund, Europe, and again, that’s a fairly broad statement covering many different countries, has tapped into some very creative ways to change the way healthcare is being delivered. And a lot of that is about investing in the infrastructure that’s needed for digital transformation. So those are the three hotspots, if I will. And I’m sure if I had to, if they think hard, I could pull out a couple of named countries, but I wouldn’t want, I wouldn’t want to do that on the spot.

Grant Belgard: That’s interesting. Thanks. Now pivoting to our second major topic, which is you.

Joanne Hackett: Yes.

Grant Belgard: What drew you into working at the Interface of Science Data and Health Systems in the first place?

Joanne Hackett: So I was one of these people who definitely wanted to be an academic. I was 100% sure that’s what I wanted to do. And starting my PhD, I was 100% sure that was exactly what I wanted to do. And during my first postdoctoral fellowship, I was introduced to the commercial world and I could see them as a way to make data or assets accessible. It wasn’t about money. It was, had nothing to do with that at all, to be with the access side of things. And that was new and exciting that as an academic, the only thing that you have is your brain, and you can only think about, you know, your next grant for your next publication, it’s not necessarily as collaborative. And I’m a trained geneticist and a tissue engineer. So this commercial world helping me to make discoveries more accessible was quite interesting.

Joanne Hackett: So then I ended up thinking about a way that I could collaborate, do things differently, which again, is not necessarily a typical academic mindset per se, and then I ended up working where I say that the triple helix, if you will, which is the intersection between academia, business, and the clinical communities, and I did love the academic world. And then when I worked at Pfizer and combined that with a very fast-paced industry job, I thought this is really quite exciting and I could see the parallels in both. And I loved that section of my career as well. Worked for the UK government, which was a very strange but interesting place as well. No one grows up as a geneticist expecting to work for the government. Actually, you’ve just did a professor of regenerative medicine, all very strange, but it was a really interesting way to see how decisions were made.

Joanne Hackett: And healthcare decisions, strangely enough, that are being made for a government or for a hospital, but of course that’s directly related to how research gets done and how industry works with governments as well. So seeing that all come together was extremely interesting. And then for me, working at IQVIA, effectively, I was, I actually elaborated with IQVIA during two of those stages of my career. And I realized that, you know, if you were to think about a global genomics dream, it can really only be achieved if you actually combine all of those different forces together. So for me, it was, it was a no, if I had to, somebody that, Oh, you have to pick one of these three sections and go back and only work there. I would go back to all of them very happily. And each of one of them was extremely fulfilling in different ways.

Joanne Hackett: But the fact that I can weave between them now is just, it’s delightful.

Grant Belgard: Looking back, what were the two or three inflection points that most shaped your path?

Joanne Hackett: The, the biggest thing that happened to me was getting access to the commercial world and that happened not because I was someone who knew what I was doing and was very progressive about that way of thinking. As I said, I was a hard and fast academic. The fact that I had a postdoc supervisor who encouraged me to think differently, who allowed me to think outside the box and expose me to that. If I didn’t have someone basically pushing me for that opportunity, I never would have been able to see that. And that kind of ended up then allowing me to be exposed to slightly different individuals. It was the job at Pfizer that got me those to the UK government. So it were these things that kind of, it was the overlap in the intersection as opposed to the hard and fast decisions in one particular role.

Joanne Hackett: But to be honest with you, I’m also that annoying person that always asks questions, wants to know what comes before, what comes after, why is this fitting together and you just, I think maybe people just get tired of dealing with people like me and say, gosh, we just got to give this person something different to do that keeps out their energy contained because otherwise they’re going to end up driving us crazy. But being curious and asking the questions gets you noticed and people start to realize that you may think of it differently, which is sometimes not a bet.

Grant Belgard: How do you decide when it’s time to take on a new remit versus deepening where you are?

Joanne Hackett: I have had to become much more selective as time goes on, mostly based on the fact that they said yes to everything, which I definitely said yes to a lot of things. When I was younger, again, so the exposure for the experience, and it was absolutely fantastic. I wouldn’t do it any differently. The thing is with certain responsibilities now, it’s not just, I have to get something back from it as well. It’s not just, I can constantly give, I want to learn. I’m not too old to learn. I’m not, you know, I’m to pasture yet. I want it to be a transaction more so than me just being able to help someone else and there’s so much to learn. And for me, understanding how I can, I sometimes can learn more from a 30 minute reverse venturing experience with a young, you know, second year economics student who’s doing an internship, for example, then I can be sitting on a board.

Joanne Hackett: So it’s all about how I think that I can both help the individual, but how the individual can help me as well.

Grant Belgard: What have you changed your mind about the last five years?

Joanne Hackett: What have I changed my mind about, gosh, so many things. I think, yeah, for me, health has always been the thing that is zero compromised. If I was told I wasn’t able to go to the gym or if I wasn’t able to exercise when I was traveling or something like that, it would just, that’s not going to happen. I never compromised my fitness and my health. That’s always been something that’s been extremely important to me. I’ve probably changed my mind a bit on how much effort I need to put into that side of things as well. You can still be quite healthy and well-rounded without putting too much energy and emphasis into it. And I think because I am someone who does have a rare disease, I think I thought if I put so much energy now, I’m almost building up a little bit of collateral for later in life when I may need it and clearly that’s not the case.

Joanne Hackett: So I’m probably slightly more relaxed about that. And also I’ve probably changed my mind a bit more on, I’ve definitely, I’ve always been a very critical person, both of myself and the people who, you know, work for me, things like that, like I have very high expectations. I’ve probably learned to be a bit kinder because we’ve all, we all have something going on in our lives and you never know if the person in front of you has just received bad news and yes, they might be sitting there taking a few extra minutes, getting their bank card out, but there’s probably something you don’t necessarily know and I think that comes with either lived experience from an individual having some something happened to them or something happened to their family.

Joanne Hackett: But I’ve probably become a little bit more tolerant towards not necessarily understanding why, but just accepting the fact that what you see is not necessarily what you get.

Grant Belgard: Which early career habits aged well and which did you have to unlearn?

Joanne Hackett: I’ve always been someone who has put a hundred percent of my effort into something I do that’s a characteristic that one of the first things people will probably always say, very hard working, that’s never served me wrong. If I’m going to do something I’ve followed through with that, that’s never been a bad thing. And if I’m going to do it, it’s going to be done well. I’m not just going to slap it together just to say that it’s done. So the hard work, dedication and doing it well has worked extremely well in my favor. Probably trying to get people to like me in something that hasn’t aged so well. We have to realize that not everybody is going to like everyone. It sometimes has nothing to do with the person. It sometimes has everything to do with the person. It’s just not worth it.

Joanne Hackett: It’s not, you have to learn very quickly how to work more professionally sometimes, as opposed to try to be the buddy of an individual. So that, that’s not something I spend a lot of time thinking about anymore. People can respect you and not like you, and I would much rather than respect me than like me. There’s that point in trying to win that fight a bit over. And the things that also probably have, has been extremely useful for me, which I’ve perhaps adapted, is how to be a lead. So some of the ways that I, and I think anybody can be a leader, you don’t have to be senior in your career. You can be quite junior and still lead people. And I think the characteristics of leadership have changed for me, but that’s probably more based on the roles that I’ve had as time has gone on, as opposed to the actual characteristics of how to lead.

Joanne Hackett: And can you share a specific failure that ended up redirecting your trajectory? People who say that failure is the best thing that’s happened to them are telling the truth. There are so many things that we fail at that we never want to talk about. And sometimes maybe as it’s happening, it’s maybe not the right time to talk about it for a variety of different reasons, and we only wait until a certain time in our lives to be able to share that, which again, maybe there’s particular reasons for that, but for many years, I didn’t tell people that I had a rare disease and I’ve suffered through some of the different consequences that were happening because of that, I didn’t want them to think I couldn’t do the job or I wasn’t good enough. So I feel personally as though I failed at being authentic very early in some of my roles.

Joanne Hackett: And it wasn’t great to feel that I was scrambling to try to make it up or to try to be a different sort of person than I was, I think that was terrible that I did that and I don’t think it would have changed anything had I just been honest and had an open conversation. I didn’t have to do anything any different. I don’t know why I just felt embarrassed about the whole entire thing, so that wasn’t great. And I felt certain companies that were hauling, they were terrible companies and it was so great that we realized it and we wrapped them up and moved on with it. And when I, the first company that I started myself, which I knew I didn’t want to leave this company, I didn’t want to be the person responsible for it, and I sold it as quickly as possible. And there’s so many people to this day that think, oh, that’s too bad.

Joanne Hackett: No, no, that to me, that wasn’t a failure to me, that was a massive success because I didn’t want to do it. So it’s strange how certain people’s failures are considered to be other people’s successes, but it’s also what you take away from it. And for me, to learn how to be my authentic self or to make the decisions that were going to be the best for me were way more important than what somebody was saying. Oh, gosh, what’s been so sad to sell your company? No, that was actually fantastic. Thank you very much.

Grant Belgard: On the topic of advice, what skill investments today will compound in the coming years?

Joanne Hackett: There’s enough, there’s, you can never take away the traits of hardware dedication, people being able to rely on you. Those are characteristics that take you an awful long way. And also being curious. It’s there’s, I can’t understand these people that we have the whole world in front of us. Ask questions like why, if you don’t know something, why just accept it in isolation? It’s find out why, what happens before and after, doesn’t this help you understand things a lot more? So I really think it’s important to be curious and dig in. And honestly, people are mean, bad things are going to happen. Cold life’s just, you can be upset about something, but honestly, you’ll only be able to be a better version of yourself. It’s grit, it’s determination. It’s just cracking on with it. We all have a huge amount to give.

Joanne Hackett: So why not put your best foot forward and take that the best possible opportunity, not just for yourself, but for others.

Grant Belgard: What would you deprioritize that’s often overrated on a CV?

Joanne Hackett: I don’t know. I don’t do all of these extra courses and brag about them and stuff like that. And these people, I think it’s hilarious when they talk about all these fancy numbers and try to, efficiency is at 4% and this and that, you’re a person. I just don’t understand these sorts of things. I don’t buy into any of that stuff. I know that a lot of people are very, I don’t know if they’re necessarily competitive with themselves or for other people, but just do the best version of you. It’s not that complicated. And I never, I get very, when I see these TVs and people are trying to take credits or turned around a complex organization in 60 days or whatever, there’s no way. You didn’t do it. And if you did do it, you had a team. And it’s the fact that you won’t take that step back and reflect on the fact that the team helped you support this.

Joanne Hackett: You’re probably somebody who I wouldn’t want to work with anyway. It’s not that hard to share the credit. There’s always enough to go around. I don’t like that thing very much.

Grant Belgard: And for startup founders, how should a new product team validate real buyer demand inside a health system?

Joanne Hackett: Yes, this is something that I think we could do a whole podcast on its own because it’s quite shocking how I will occasionally see this pitch deck come across my desk and you think, well, then it’s scary that someone has put this together and has worked on it for several months when no one will buy it. And the biggest thing that, there’s loads of things out there that could be created. There’s a lot of different things that will help. Going back to the question earlier about what evidence to payers need for things, ask your thought who’s going to pay. And it’s not all about money, but if you’re planning on selling a product, someone’s going to have to buy it. So why would they buy it? How is it going to be rolled out? There’s different regulations in different countries. Do you want to be across several different countries, different types of institutions, who is going to pay for this?

Joanne Hackett: And whether you’re a biotech, a med tech, a digital health company, you have to have a value proposition that’s going to add value as opposed to just, oh, it’s great that we’ve decided to round the edges of the door knob, great, but no one’s going to go out and commission 5,000 more of them. I know it looks better and it’s nicer, but you need to find out, find a thing that’s going to make the difference, change it, and even if it is expensive, if it’s worth it, people buy it. Look at the cell and gene therapies that are out there today. There are millions of dollars. They’re bought for the obvious reason that they were. So it’s not a cost issue. It’s a more about is it, is there actually a need for this and will someone pay for it?

Grant Belgard: When you hear a pitch about AI and healthcare, what signals seriousness to you?

Joanne Hackett: I don’t think I’ve seen one yet. I’m sorry. That’s probably not the appropriate answer. But the thing is, I guess I’m a geneticist. We’ve been using AI, quote unquote, for years now. There’s no one who can look at the human genome and understand the many different, you just, you can’t. So there’s always been tools to make our lives easier and faster. And being able to have tools that are going to do that, enhance it in a, in a way that you’ve got the right information. There’s very few algorithms that have been trained with the right type of data or the right amount of data. I think it’s fantastic that there are going to be things that will be rolled out in hopefully due course, but if we’re not there yet, why, I just understand why people get so hopped up about this.

Joanne Hackett: AI and healthcare to me would be that one of the best use cases will be for us to be able to use our phones to triage healthcare and whether it’s an emergency or whether it’s just basic healthcare needs, why can’t we think about the practical aspects of healthcare, the AI and pulling together data for research, predictive mechanisms and things like that, that is exactly where I’d love to be able to see it to go. But so many people are obsessed about the device or the whizzy thing that they can talk about that’s going to happen today when I just don’t know if the data is in the right format, in the right place, diverse enough and being pulled together by the right type of an agent to be able to make that make sense. So I personally haven’t seen it yet and therefore I’ll remain skeptical until the right thing lands on my desk, let’s do it that way.

Grant Belgard: And for health system leaders, where can modest investments in data infrastructure yield outsized returns within a year?

Joanne Hackett: A lot, a very simple thing is curating data. And it’s so boring to even say that. I’ve fallen asleep just saying that line, but it really is structuring data. If you had these people who brag about the databases they have and, oh, but we see 10,000 cardiac patients a year. And what information do you have about that? Can I collect that and cross-reference it with people with metabolic disorders? Can I then cross-reference it and look at something else? You don’t have [bone-lock sterilization?] or something like that. What use is it? So the modest thing for data as a whole is making sure that it’s actually collected in a consistent way, it’s structured in the right way, and it’s accessible. And those are very small investments. And that data is then actually worth something as opposed to these people, oh, you know, data’s like the new oil. No, it’s not. It’s completely different.

Joanne Hackett: You cannot compare that because with oil you use it immediately, with data you can’t. So it’s not new oil. You have to refine it first before it’s actually useful. So we’re not at that stage where we’re actually capitalizing on the right type of data because we haven’t invested in it. And to be very honest with you, I have never seen the front cover of a magazine or a newspaper with anyone with a big pair of scissors cutting a data infrastructure for a change. You want to be standing in front of a ribbon in front of an Eberron machine.

Joanne Hackett: So until we get fast enough for the shiny tool is the thing that we want to invest in, and investment is a real piece of something, you actually have to invest a huge amount of time, effort, and energy into what happened behind the door, as opposed to the shiny machine that’s sitting inside the room and building in the business case for interoperability, data standards, and things like that. It’s still thought of as the fluffy thing that goes alongside of the MRI machine, and until we change that mentality, we’re still going to be struggling with the physical versus the thing that you just can’t see and touch, which scares a lot of people.

Grant Belgard: I think our bioinformatics listeners will agree enthusiastically with that, right? 80, 90% of your time is spent data cleaning, data munging, right?

Joanne Hackett: Completely.

Grant Belgard: So for our early career listeners, what questions should candidates ask during interviews, but rarely do?

Joanne Hackett: I very rarely find someone who’s read enough about a complicated question to answer it themselves. And they’ll usually turn and say, it’d be interesting to know how you would approach this, or what are you looking for? And that’s the line, but how would you answer it? Very rarely do they come with this solution themselves. And I think it’s because they want it to be a dialogue issue that they’ve come up with the creative question, but answer it for me, I’d be much more impressed with you answering your question, as opposed to flailing my take on it. And they probably have a better answer to be honest, because they’ll have different ways of thinking than I will have.

Grant Belgard: This has been fascinating. And for listeners who want to follow your work and your thoughts, what’s the best way for them to follow you?

Joanne Hackett: Most of the work that I do is on LinkedIn. It’s the only social media that I really engage with. So find me on LinkedIn.

Grant Belgard: Great. Thank you so much for joining us.

Joanne Hackett: Thank you for having me. It was a pleasure and really lovely to see you again.

Grant Belgard: Thank you.

Bioinformatics Done Right, Now

Academics, Don’t Wait on the Queue: A Faster Path from Data to Publication


The email arrives: “Your sequencing data are ready.”

It’s the kind of sentence that makes a lab buzz. But after the first rush comes a familiar pause: Who will analyze this, and how long will it take? If you recognize yourself in that moment, The Bioinformatics CRO is for you.

The Shortest Distance Between Data and Figure

Our promise is simple: fast, publication-grade bioinformatics for academics at core‑competitive pricing—without the long waitlist or the learning curve.

  • Speed without shortcuts. In-house cores do important work, but they’re often backed up. We keep our queue short and our response times tight. Projects start quickly once scope is set.
  • Expert time, not training time. Our team is staffed by senior scientists who have shipped many analyses. That experience compresses timelines and reduces rework.
  • Pricing in the same neighborhood as cores. Hourly rates are similar, but our model is built to reduce idle time and cut the “waiting cost.”

In other words: you move faster, often with lower all‑in cost once you account for delays, rework, and the hours you spend shepherding a novice through their first pipeline.

Why Not Just Use a Trainee?

Postdocs and graduate students are talented. They are also busy. Courses, journal clubs, teaching, competing projects, and grant work carve away their hours. If your timeline is tight—or the analysis is non‑standard—asking a trainee to learn on the fly can turn weeks into months. By the time they’ve written code, defended choices, and redone figures for reviewers, the “cheap” path has quietly become expensive.

Working with us is different. We’ve already navigated the edge cases, the batch effects, the parameter cliffs, and the “looks great, but reviewers won’t accept it” traps. We deliver defensible results, clean methods text, and reproducible code.

A Diplomatic Word About Cores and Collaborators

Cores are steady partners, but queues are real, and revisions can be slow. Collaborator labs can be great, yet authorship and priorities get complicated. We’re designed to be your surge capacity and your clean handoff: fast starts, clear deliverables, and no unnecessary authorship entanglements.

How to Work With Us (and Save Money Doing It)

Two engagement styles both work well. Choose the one that matches your project and bandwidth.

1) Clear Scope → Accurate Estimate & Fast Delivery

If you know what you need—say, bulk RNA‑seq differential expression with pathway analysis and four figure-ready plots—tell us up front.

What you get: a tight statement of work, a realistic budget window, and a start date you can put on your lab calendar.
Best for: projects with defined questions, revision letters, or datasets similar to your previous work.

2) Engage With Us as You Go → Exploration & Iteration 

Not every dataset announces its secrets on day one. If the plan is exploratory, we’ll move in measured steps—share early readouts, discuss directions, and refine.

What we need from you: real engagement. Quick feedback keeps momentum high and scope aligned.
Best for: new modalities, mixed cohorts, or “we’ll know it when we see it” figure discovery.

A Cost‑Effective Division of Labor

To keep your budget focused on analysis (not cosmetics or copy), split the work like this:

  • Your lab handles:
    • Data and metadata hygiene (sample sheets, consistent IDs, clear conditions).
    • Figure polish for final submission (fonts, colors, journal‑specific formatting).
    • Manuscript prose (introduction, discussion, and related literature).
  • We handle:
    • QC and rigorous analysis (e.g., DE, clustering/annotation, integration, modeling).
    • Reviewer‑proof choices and statistics.
    • Figure‑ready plots and tables.
    • Methods text and code so everything is reproducible.

This division keeps costs lean and lets trainees contribute meaningfully without spending their semester learning an entire toolchain from scratch.

What to Expect From Us

  • Fast kickoff once scope is set. We schedule starts promptly and keep you posted.
  • PhD‑level analysis you don’t have to babysit. We make choices transparent and document them.
  • Figure‑ready outputs and clean methods. Drop them into your manuscript with minimal edits.
  • Reproducible artifacts. Notebooks, parameter files, and pipeline manifests live with your results.
  • Plain-language updates. Short check‑ins, clear next steps, and no jargon walls.

When We’re the Obvious Choice

  • Data in hand; publication clock ticking. You need figures in weeks, not semesters. 
  • Major revision lands. A reviewer asks for extra analyses or different thresholds. We execute fast and clean. 
  • Grant support. You want a credible analysis plan and methods you can defend. We can provide a letter of support too.

Common Questions

  • “Isn’t a student cheaper?”
    On paper, yes. In practice, hidden costs pile up: learning time, your guidance time, reruns after critiques, and the risk of delays. Our rate is core‑range, but our experienced team and shorter queue often make the real cost—and the stress—lower.
  • “Will you take authorship?”
    Only if you want us to and if our intellectual contribution merits it. Otherwise, we provide clean acknowledgments and thorough methods so credit remains where you intend.
  • “What about compliance and reproducibility?”
    We assume de‑identified data by default and return a reproducible package: QC summaries, parameter files, methods text, and code that stands up to reviewer scrutiny. 

A Short Field Guide to Faster Projects

Before kickoff

  • Write one paragraph that states your central claim or question.
  • List your cohorts/conditions, sample counts, and any known pitfalls.
  • Clean your metadata: consistent sample names, tidy spreadsheets, no mystery columns.

During analysis

  • Respond quickly to interim results; momentum matters.
  • If the path forks, choose one clearly—or approve a bounded exploration.

Before submission

  • Have your trainee apply journal style to figures (fonts, colors, panel letters).
  • Paste our methods text and citations; adjust voice as needed.
  • Use our code and QC notes to pre‑empt reviewer concerns.

The Quiet Luxury: Time

The most expensive thing in your lab is not the hourly rate of an analyst. It’s time—time before a scoop, before a grant deadline, before a trainee defends, before the field moves on. The Bioinformatics CRO trades in time saved without rigor lost. That is the value we offer: publishable certainty, delivered quickly, at a price you already recognize.

If the next dataset is knocking, let’s make the waiting the shortest part of your story.

Ready to move? Send us a brief description of your data and desired figures, or tell us you’d like to work iteratively. We’ll match the approach to the moment—and get you from data to done.

The Bioinformatics CRO Podcast

Episode 69 with David Scieszka

David Scieszka, founder and CEO of Vertical Longevity Pharmaceuticals, tells us about VeLo’s pioneering senolytic vaccine approach to clearing senescent cells and his quest for longer, healthier lives for everyone.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Amazon, YouTube, Pandora, and wherever you get your podcasts.

David Scieszka

David Scieszka is founder and CEO of Vertical Longevity Pharma, which is currently pioneering a senolytic vaccine approach to targeting atherosclerosis and aging.

Transcript of Episode 69: David Scieszka

Disclaimer: Transcripts may contain errors.

Grant Belgard: Welcome to the Bioinformatics CRO podcast. I’m your host, Grant Belgard. Today we’re speaking with Dr. David Scieszka, founder and CEO of Vertical Longevity Pharmaceuticals, AKA VeLo Pharma. David’s team is pioneering a first-in-class senolytic vaccine that teaches the immune system to clear senescent cells, those dysfunctional zombie cells that accumulate with age. With a PhD in biomedical sciences, an MBA, and even a stint as a U.S. Army PSYOP specialist, David brings a uniquely interdisciplinary lens to the quest for longer, healthier lives. We’ll dive into how VeLo’s platform could reverse atherosclerosis, where the company sits in the fast-moving longevity landscape, David’s winding path from scientist to biotech CEO, and the advice he wishes he’d had earlier. David, welcome to the show.

David Scieszka: Thanks, thanks for having me. It’s great to be here.

Grant Belgard: So in 60 seconds, what problem is VeLo solving and how?

David Scieszka: That’s a good question. To be more specific than I usually am, we are initially targeting the disease of atherosclerosis, and we’re doing so by targeting a fundamental driver of aging. And so we can potentially unclog the arteries that have already been clogged, which is something that people are trying to do right now, but to this day, no one has been able to do yet. And so one of the things on our platform, we are targeting those zombie cells like you’re talking about. And from that, we can have multi-disease capabilities where we can intervene not only in atherosclerosis, that’s just the first step. Our larger goal is to impact healthspan, the number of healthy years that you’re alive. If we can extend that for every human on the planet, we’re in a really good spot, but we have to initially focus on atherosclerosis. So that’s the key that we’re targeting first.

Grant Belgard: The term senolytic vaccine is unfamiliar to many. Can you break down the mechanism in lay terms?

David Scieszka: Yes, absolutely. So like you said, a lot of people like to attribute senescent cells to zombie cells. They are pro-inflammatory, they can cause localized tissue dysfunction, but they also feed forward the senescence phenotype. So they excrete pro-inflammatory molecules, both locally, and then those pro-inflammatory molecules enter your circulation, those go systemic. And so they transform other senescent cells all across the body. There’s a low level of these in every single cell type that we can study, including neurons. But the senescent cells themselves, being hallmark of aging, causing that tissue dysfunction, there’s of course going to be a therapeutic push to clear them out. And that approach is called a senolytic approach. It’s a little bit of a misnomer. It’s actually the apoptosis or apoptosis approach rather than an actual lysis, because that would cause even more inflammation.

David Scieszka: So it really is activating that mechanism of self-death. That natural process is a senolytic approach. So people have tried with dasatinib, quercetin. Many different senolytic approaches are being investigated currently, but they suffer from dose dependent toxicity, off target effects, and sometimes limited efficacy. And so finding the right antigen to target or the right marker to be able to intervene at is critically important so that you’re not harming healthy cells as well as senescent cells. And that’s been a real push in the senescence field recently. The vaccine approach is to basically engage your immune system to clear out these aberrant cells, these pathologic cells, allowing your immune system to do the work for you, which is hopefully seen as a positive approach as opposed to a potentially hazardous one, because you have to select the right antigen, absolutely.

Grant Belgard: Could you tell us about your preclinical mouse data?

David Scieszka: Yeah, absolutely. So pre-clinically, we have investigated the vaccine on a standard black six model. So this is a mouse model, an aged model. So it was aged 18 months naturally, and then we vaccinated. We did that, we selected that age point, because if it works at that age, we know it’ll work at every age before that as well. Things like thymic and dilution, where your thymus is degrading with age so that your immune system is responding less robustly. So we selected the 18-month-old time point, and then we vaccinated, monitored, monitored lung function, so heart and lung function, and then visually as well, multiple different metrics. It was really surprising the responses that we found. Not only did we see healthspan and lifespan extension, that was pretty expected. We also saw hair loss recovery, which is expected. That’s known in the senescence field.

David Scieszka: Qualitatively arthritic reductions, that was not expected for me. We also saw cardiovascular rejuvenation. That was really unexpected. We, as a scientist, I always am pleasantly surprised when experiments go well, and incredibly excited when things go better than planned. And so we expected the heart function to decline at the same rate as normal aging. We didn’t expect that the senescent cells would be having that drastic of effect, but we rejuvenated the heart to a younger time point based off the parameters, and incredible results. So now we’re focused on atherosclerosis because of the potential impact on humans as well. But yeah, I could go on about the beta too, but happy to talk to anybody who’s interested in reaching out to me as well.

Grant Belgard: And for listeners who are interested in reaching out to you and following VeLo Pharma, how should they do that?

David Scieszka: I’m on LinkedIn all the time. I try to connect with as many people as possible there. My inbox on email is always inundated, so it’s much easier to connect with me via LinkedIn. And I’ve got a unique last name, so I’m gonna be, if not the only David Scieszka, one of the only David Scieszkas on LinkedIn. So it’ll be pretty easy to find me, I think.

Grant Belgard: And where does VeLo sit relative to other longevity players? What differentiates a vaccine approach from small molecule senolytics?

David Scieszka: Yeah, the small molecule senolytics, right? So specifically talking about like, Dasatinib, dasatinib is a different mechanism. And also don’t mean to inundate people with weird terms, but like cyclin-dependent kinase inhibitors, cell cycle processes, P53, if you’re familiar with cancer. And so those are messing around with the internal metrics of a cell. We’re trying to go after a surface protein after it has transformed senescence. So whereas a lot of people are concerned with the senolytic approaches like Novidoclax, like Unity Biotechnology’s previous approaches, because there is an inherent cancer risk. If you’re messing around with the nucleus, if you’re messing around with the internal processes, we might be stopping the senescence transformation process.

David Scieszka: We’re going downstream of that post-senescence transformation, only killing the senescence cells after they have since transformed. And so I would argue that this is a much safer approach than all previous approaches, and especially by selecting an antigen that is all eyes to a few specific processes on the surface of proteins, as opposed to a much broader antigen. We have selected a very safe way to move forward in the senolytic space. Again, that’s just my argument.

Grant Belgard: At what point did you select atherosclerosis as your first indication? Was that after you got back the mouse data?

David Scieszka: It was, so that’s an interesting one. If you look at the data, I have a background as a computational biologist as well. If you follow the data, it’s much more cash efficient to go chronic kidney disease. Senescent cells have been implicated in chronic kidney disease. There’s even clinical trials right now against senescent cells using piscidibic trisetin. That’s a cash efficient way to get to market, but we went through what’s called the I-Corps program, which is in three months you interview 100 people, including KOLs, doctors, people on the street. From that, I actually had that as part of my AD testing. Are you more excited about a vaccine that targets chronic kidney disease, or are you more excited about a vaccine that can unclot arteries, and resounding response from doctors and people alike was atherosclerosis. It wasn’t even a comparison.

David Scieszka: And so the data, we have to follow the data absolutely. We could have generated primary data on chronic kidney disease. Instead, we reallocated those dollars to echocardiograms and the cardiovascular measures because we anticipated the product market fit really. So it was truly an internal strategy from the get-go. How do we find the best niche to fill?

Grant Belgard: Can you outline the next 18 months for us on your roadmap?

David Scieszka: Yes, for the next 18 months, it’s all fundraising. No, just kidding, but it’s definitely a big part of it. We are fundraising currently. As soon as we receive sufficient funding, we can engage in primate study, which is going to be incredibly translational. I would say we’re partnering with academics right now and also trying to open up conversations with the NIH. Part of what’s akin to their tech transfer office, so that we’re trying to get our vaccine in the hands of investigators who have the animal models to be able to test this out in their different indications and their different ideas. So we have on the horizon a vaccine study in primates that can measure whether or not it works. And of course it will, because we know the protein exists in monkeys and humans. That’s been known for a long time. We have to validate it, show that proof of concept.

David Scieszka: In 18 months, depending on funding, we can initiate manufacturing and we can do both of our toxicology. So taking a step back, there’s steps that you have to go through before you get your drug approved. And part of that is doing toxicology. Part of that is doing manufacturing. Those are the less exciting, a little bit boring aspects of it, but that’s part of the process. So we can definitely do that in the next 18 months. And as well, we can get translational primate study done. So basically we, in the next 18 months, we could have everything ready for our submission to the FDA.

Grant Belgard: Best case scenario, how do you envision VeLo contributing to a reduction of morbidity?

David Scieszka: Like what are the next steps beyond that? Yes, if we were on the market today, best case scenario, we would be able to reverse atherosclerosis to the point where another organ system would fail first. And that would be the extension of health span. So we would be pushing out [?], longevity, escape velocity. We’d be pushing out that lifespan and health span a couple of years, and then a new organ would fail. And hopefully our vaccine is also able to positively impact that organ, say the kidneys, or say metabolic dysfunction associated with a different type of disorder. If we have the capabilities to impact that, then we’re doing multiple interventions simultaneously. We just have to make sure that we’re showing that through testing.

David Scieszka: And then afterwards, in the sense of where we find ourselves in the landscape, we’re going to transition this from an injectable into an oral formulation, because our vaccine platform has to produce this in a pill form that is shelf stable, greater than six months. So we can go into the driest deserts, the wettest jungles. We can get this in the hands of everybody. As soon as we show it’s safe, we can get this in the hands of everybody and at extremely reduced cost. And that’s part of the strategy that we have too. We intentionally chose our platform because it is safe and cheap to produce. We want this therapy to go completely different way than say CAR T-cell therapy, where it costs you hundreds of thousands of dollars. That is insufficient in my future. I will not be a part of it. It’s a great approach, don’t get me wrong.

David Scieszka: It needs to happen so that we can find better alternatives though. We chose our vaccine platform for the people. We want this to be in the hands of all, democratizing the longevity process. And that’s the longer vision of VeLo Pharma.

Grant Belgard: So you were a US Army PSYOP specialist before grad school. How did that shape your worldview and how you approached VeLo Pharma?

David Scieszka: It was an incredible opportunity, an incredibly formative process. The resilience that I gained to team management, leadership that I know many people don’t go that route and they’re afraid of what the US military does and can do. And I want to say that it’s not necessarily all that way. There are great guys there. They are doing their jobs and what you learn along the way is so beneficial. I learned philosophy before I joined the army. And then I kept reading philosophy up until this day. It reinforced many philosophical principles like you work faster and better in a team. Things like if you have the right tools at your disposal, you can be a force multiplier instead of an individual. So team leadership, facilitating, giving people the right tools in order to empower them to be better on your team. All of these different things.

David Scieszka: It was an opportunity just as a young man to solidify about a foundation of hard work, resilience, stick-to-it-iveness, and an ability to think on your feet. But outside of that, it actually, it inspired me to join biotech. We were on a deployment in the Philippines, which doesn’t get better than that, right? But we were interviewing local populations. We were census takers, basically, and [?] specialists in that deployment. We would ask, where are your hospital schools and supplies? And do you need more of them? And so then we were finding from our census, if I investigated this rural area and found a guy who was, say, 40 years old, he would look like he was 50. And then if we go into the city where hospitals are everywhere, I would ask, are you, okay, what age are you? He would say 30, and he would look like he was in his 20s.

David Scieszka: So he would look younger than he appeared, and it was access to healthcare, access to simple things like toothbrush, toothpaste, good food. And so that was my inspiration into aging, really, was through the army and as well into biotech, because I thought, wow, there’s a real-world example where we can intervene in biology and see this effect. It was, yeah. I know not everyone who’s in the military has just any experience, but for me, it was absolutely incredible, one of the more formative ones in my life.

Grant Belgard: And can you tell us about your decision to do both a PhD and an MBA, why you did that and at what point? Decided you wanted to be an entrepreneur?

David Scieszka: Yeah, it was during grad school. Wanted to do an MD/PhD route as part of my undergrad. I was in biotech, and I saw that the movers and shakers, a lot of them had a lot of letters after the name, and I came into contact with an MD PhD, my first mentor, actually, Dr. Marcelo Freire, if he’s listening, shout out. Amazing man and a brilliant investigator. But he had an MD PhD, and I wanted to emulate that because of his understanding of physiology and also basic science, spanning the gamut. Got into grad school, got to talking to as many people as I could, and it turns out that I was not looking for an MD, and that was only by the advice of somebody who I deeply respected, a chair of a department, and he said, do you really want to be working on your MD PhD for the next 10, 15 years? You’re gonna have to go into residency after this. You’re gonna have to be patient’s side for several years.

David Scieszka: Are you sure that’s what you’re looking for? And thankfully, he was able to steer me in the right direction. I said, no, I wanna translate science into therapies. That’s what I wanna do. And he said, you’re looking for an MBA. If you can stomach it, you want a PhD MBA. You don’t want an MD PhD. So I took his advice. I’m very coachable in that way that people who have been there, done that, you gotta listen to them. You gotta, as long as they are an expert in their field, I gotta qualify that statement. But yeah, with the respect that I have for him, I listened to his advice, and then I was able to take on the MBA at the same time. It’s always been about saving as many people as possible, intervening in lifespan in as many patients as possible. Yeah, during grad school, I would say is the shorter answer.

Grant Belgard: So we’ve discussed what got you interested in aging and longevity. What specifically convinced you that senescence was the way to go right there?

David Scieszka: There are many approaches within the longevity space, senescence, big one. Yes, it’s as we do when we’re going through advanced degrees, we look very deeply at particular mechanisms, pathways, in this case, hallmarks of aging. And so I did a deep dive. Identity dive into every hallmark that was available at the time that we had because of [Lopez-Otin?] paper. And so I was finding that, of course, they’re all interconnected, mitochondria, nutrient dysregulation, but I found a through-line reactive oxygen species that impacted more of the hallmarks than others. And then there was an obvious phenotype associated with it called senescence. So when we think about [?], aberrant [?] causing DNA damage, aberrant [?] causing misfolding, things of that nature, nutrient dysregulation, surface receptor dysregulation, a lot of it stems from inflammation.

David Scieszka: And so then taking a look at what senescent cells do, inside the cells, there are these lysosomes that are filled with acid. And when they permeable out, that acid leaks out and it affects, well, cytosolic pH, of course, but as well, it hits the DNA. It goes and hits every organelle and it starts having proteins misfolded. And so the senescent cells seem to be a more fundamental, and I still haven’t found out whether or not there is a more fundamental layer than senescence, but it appears to me that because of the obvious ability to track and target a phenotypic expression of one of these hallmarks of aging, it’s a much simpler intervention to be able to find out is fundamentally driving the others. It’s a much more difficult thing to say, to target a tRNA synthetase inhibitor, although people who are listening should check out Mark McCormick on this.

David Scieszka: He’s doing some great work in that avenue. But if you’re trying to focus on a target, you have to be able to intervene. And for me, the senescence field was a fundamental lifting of other hallmarks of aging and as well.

Grant Belgard: Can you tell us about an early failed experiment or startup lesson that still guides you?

David Scieszka: It’s hard to pick which one because there have been so many. The entrepreneurial process is always iterative. And I think that a lot of our PhD and master’s projects are that way too. And so we learned from an early age in our budding scientific careers, how to bounce back, I would say, from a failed experiment, but learn from it at the same time. From a failed experimental point of view, for this vaccine, surprisingly, we haven’t had any. That I got to knock on wood. But from an entrepreneurial standpoint, there has been numerous. I was initially completely misaligned with investor expectations. I went out too fast. Before I understood the landscape, I would say jumping the gun is something that I try not to do anymore. As an example of what happened, I went out trying to raise $5 million initially.

David Scieszka: I talked to some guy who finally sent me straight and he said, your company right now is not even worth that. You understand that. You would be selling 100% of your company. You would have no ownership of it. And that got me thinking, oh my God, I gotta figure out what this investor landscape looks like. And so then I joined the Life Science Angels, which is an incredible group as well, taught me a lot of both sides of the founder’s side of the table and the founder’s side of the table. And so that was a really great learning experience for that. And then you take that learning and you go out and then you learn where you were wrong again. The second time I went out, I was raising too little money. There were people saying that, oh, you haven’t thought about the long-term trajectory of your company. And it wasn’t that. We were raising in small tranches being say 100K here.

David Scieszka: The next round is gonna be 250K. The next round is gonna be 500K. But that’s completely misaligned with what investors do because it’s just as easy for a small stage investor to write a 250K check as it is for them to write a million dollar check. That’s for them, it requires as much legwork. And so for me to be going out raising 100K, they’re not gonna do it. You need to find a specific localized angel that’s gonna be willing to cut such small of a check. And so it’s a constant iterative improvement but I would say strategize first and then go out as opposed to just going out because you have the action. So try not to jump the gun. That’s gonna be something that sticks with me for a long time.

Grant Belgard: And speaking of fundraising war stories, what’s the hardest lesson you took from the first iteration of your pitch deck?

David Scieszka: I would say [?] is key. It’s the word of the century, especially for entrepreneurs. As a data scientist, I love hearing yes. I love hearing no is fine as long as there’s a reason. And as an experimental scientist too, if we have data to support why yes, and if we have data to support why no, there’s a way forward. But no data is awful. If there’s an experiment that goes haywire and can’t track down why, it’s a wasted effort and a waste of time. And so from our first pitch deck, no’s and then requesting feedback and having radio silence on the other side, that was, it required a different level of self-examination than I’ve had to do up to that point. It had a lot to do with probably what people feel during their master’s and PhDs a lot too, like what’s wrong with me? Why can’t I get this done? It’s great science. What am I doing wrong?

David Scieszka: And without the data to support it, it was incredibly difficult, but it took a supportive woman, my wife, she was able to set me straight. And she said, it’s a numbers game. It’s gonna be fine. You gotta stick to your guns. You know this better than anybody. And you know you. If you start trying to change who you are in order to placate to every single person that you meet, and if you just beat yourself over the head over every single set of non-existent data that exists, you’re not gonna get out of this alive. You have to be able to stick to your guns and stick to yourself. So out of the war story, I think actually came some positive growth, but it was, yeah, it was difficult at first.

Grant Belgard: And what role does bioinformatics play in your R&D?

David Scieszka: So far, I haven’t been able to touch R or Python in about a year. And it’s, I wouldn’t say killing me, but I wanna get back to it because of the AI revolution, because of everything that’s on plate right now in silico medicine, everything that’s coming down. It will play a role in the future. I know that to be true because there’s going to be other hallmarks of aging that we can potentially target after this one is commercialized. Right now, we could optimize potentially greater optimization of antigen selection, greater optimization of peptide sequence targeting. That could be a role in the bioinformatics pipeline. That has a lot more to do with the computational modeling, docking, as opposed to what I’m more familiar with, which is omics, multi-omic analysis and integration. And as well, I’m sure that’s something that you do all the time.

David Scieszka: But yeah, I would say less now, even with this agentic tidal flow that we impending see on the horizon, but that’s a misnomer in itself. And I don’t need to get off on a tangent there, but as far as I can tell, and as far as all the companies that I’ve seen, this agentic revolution is not as close as it may appear. So far as I could tell anyway, we are several years out. And even for simpler tasks, it’s been interesting to see some of the companies that made waves earlier in the year by laying off relatively low-skill staff to replace them with agentic AI has been quietly rolling that back as it hasn’t panned out as well as they had hoped.

David Scieszka: Yeah, and I feel for those employees, to be honest with you, I know that’s going down a completely different direction but I feel the employees right now who are being subject to this unreasonable layoff system, yeah, okay, you’re on unemployment now, that we should really think about who we’re allowing to have power over these people and how we think about when it’s time to hire an AI, when it’s time to hire a person so that we don’t keep messing with these people. Yeah, hopefully it gets better. Hopefully we come to our senses and not fire people as fast. The hiring system is broken but that’s a different conversation altogether.

Grant Belgard: So if you could replay one career decision, what might you do differently?

David Scieszka: I don’t think I would. And I know that’s an unsatisfying answer for some but we were in the same unit together and I actually asked him that if you could do anything over again, what would you do? And he said, if I do anything different then I’m opening up my future to the unknown. I’m here because of everything I’ve ever done before. And even if I don’t like it, if I don’t like today, there’s still tomorrow. If I don’t like the next week, look at all the good that we’ve done before this. If I say it’s also removing all of the positive momentum that we’ve gained up until this point. And so he said that he wouldn’t change anything. And it took me a while to come around to the idea but I don’t think I would anymore either. I used to think about it. I used to think, oh yeah, I wouldn’t stand up in the middle of class or I wouldn’t forget this at that time, but it forms us.

David Scieszka: It really is who we are.

Grant Belgard: That’s interesting. I don’t think we’ve ever gotten that answer before. So for PhD trainees eyeing entrepreneurship, what hard skills should they cultivate now?

David Scieszka: I would say self-examination, a thorough ability to understand the self. If, oh, that reminds me of a quote. I don’t remember him, but he’s a prominent entrepreneur turned venture capitalist. And he said, entrepreneurship is the worst thing that I’ve ever done. I don’t know why people do it. I will never do it again. And so that kind of implies the difficulties that are facing a lot of people who are getting into this. The intrinsic motivation needs to be high, the compulsion or the specific focus or whatever it is that gets you up in the morning and keeps you up at night. If that’s a driving force behind entrepreneurship or your specific focus or your specific task, then it’s definitely worth it. And you have to know yourself to be able to know if that’s true, because a lot of the times external influences can be confused with internal motivations.

David Scieszka: So a lot of people are fronted with, I’m a broke grad student. If only I had an additional 40K a year, I’d be doing better. It’s not necessarily going to help to have more money either. So if we can separate this internal motivation from external inputs as data guy, if we could do that, then we can have a greater understanding of what really needs to happen before we even jump into the entrepreneurship idea to strategize initially. Don’t jump the gun to be able to say, okay, this is a good fit for me. And that’s true of most things. I don’t know how you feel about that, but I would think that understanding what you’re good at, what you want to be good at, where the market’s going, because we’ve seen things like, oh, autophagy wasn’t a big thing until there was a Nobel Prize for it. And so people who were studying autophagy back in what, the 60s, they had no grant funding whatsoever.

David Scieszka: So if you’re studying a process that isn’t hot and it won’t be hot, you’re facing an uphill battle, are you willing to fight that hill? If you aren’t, if it’s not those things, if it doesn’t check those boxes, then it might not be worth it. But it all comes from Socrates’ self-examination. It all comes from self-examination and it’s a real understanding of soul.

Grant Belgard: So regarding building an interdisciplinary team, what do you look for in the first 10 hires?

David Scieszka: I’m gonna quote another person that I wish I was as smart enough as him to actually make this quote myself. He said, hire slow fire fast. And that’s not necessarily true, the firing part, but the hiring slow is very true. If you’re looking for a job or a task that needs to be repeated and internal functionality as opposed to external functionality, that could be worth a hire. If you’re looking for something as a one-off or if the tasks aren’t solidified in your mind, then I would not hire yet because it is so critically important. Once you hire somebody, it’s a relationship. I don’t wanna fire somebody. I’ve had to do it before. I still, it keeps me up at night. So hiring slow, find a job that you need that can’t be done externally and then outline those responsibilities and tasks in order to make sure that they’re functional to the organization.

David Scieszka: In the first 10 hires, as critically important as they are, alignment, culture, fit. So the culture is gonna be important for the rest of your organization. And if your first 10 hires aren’t culturally aligned, you’re setting yourself up for a bad culture. I know it’s a business word that a lot of people think, oh, that’s hokey, it’s culture. If you want it as an altruist, I want to set up a culture of people who are morally aligned with what I want my organization to do. If I hire somebody who’s going to a bottom line, dollars are all that matter, it’s going to be culturally misaligned, morally misaligned and ethically misaligned. And so the first 10 people set the culture. And if you want those 10 people to meaningfully follow you through, they have to be aligned with that for sure. So I hope I explained culture at least.

David Scieszka: And then the mission as well and the vision and the functionality. So I hired our CSO because she did her PhD on our specific vaccine platform. But that was after I had gone through another 10 people who I could tell were just not there. They wanted to commercialize this and then exit immediately. For people who don’t know, that means get out and take basically your bankroll. So get paid as fast as possible. That’s not who I want. I want somebody who’s in this for the long haul. I want somebody who’s in this because they care about humanity. And so that’s when I found our CSO and that’s the kind of all things fell into place. So it takes a long time. It takes a lot of legwork. I don’t know if you probably have some insights on this too because you’ve actually hired probably quite a few people and maybe some of them better than others. I don’t need to call it out like that, but it’s hard.

David Scieszka: It’s definitely hard and it takes a lot of time but a good hire is definitely worth it I would say. What do you, I don’t know, what do you think?

Grant Belgard: Yeah, I totally agree. And culture I would say would be up there for me as well because as the organization grows larger the culture starts to get out of your hands and becomes in the hand, it gets in the hands of your early hires who are more directly interacting on a day-to-day basis with your next 10, 20, 50 employees. So yeah, couldn’t agree more. So longevity is hot but crowded. How should founders pick a viable niche?

David Scieszka: I’m gonna have to quote Matt Kaeberlein on this one. That’s somebody who actually remember Matt Kaeberlein he says that longevity is like, or the hallmarks of aging is like longevity under a lamppost where we focus on what we can see, what’s outlined for us. And so if you, well the analogy is to take a step back. If you drop your keys in the dark and then all of a sudden you start looking for your keys where there’s light shining, it makes absolutely no sense. And so it’s probably based off of an old cartoon like, oh, I can see over here, but my keys are over there. It doesn’t make sense. And so in the longevity space, if you have a hot new thing that is specifically targeting a hallmark of aging and it’s defensible, I’d say go for it.

David Scieszka: Even as crowded as it is, if you can find a niche within that, that you are either better for some reason, more defensible than somebody for some reason, or you’re potentially your AI modeling who’s going to outpace the next AI modeler, I’d say go for it. The fear is that we get outpaced by somebody else and what a terrible life it would be if we had never tried. So I think that going for it, even in a crowded space, if that’s what you found you’re the best at, I would still do it. So senolytic spaces, the crowded, it doesn’t matter. It doesn’t matter because we found a better antigen on a different realm. If you find something that’s not in the hallmarks of aging, but you think that it is, by God, go for it. There’s two organelles. There’s an organelle that we’ve never really talked about called The Vault. It’s got an HDAC.

David Scieszka: It’s got a DNA repair system in it and it’s got a telomere extension system in it. The Vault, look it up. We don’t talk about it in longevity. Nobody does. We didn’t even talk about it in Bio 101 because we don’t really know what it does. If you’re studying something out there that nobody really talks about and you think it has a relevance to longevity, go for it. Same thing with that other organelle that I can’t even remember now. There’s two of them that recently came up that we never learned of in Biology 101 and now people are hopefully studying it, but same thing. If you find yourself in a position where tangentially you’re related to longevity and you can see yourself impacting a disease as opposed to aging, that still helps span too. Absolutely go for it. I would recommend everybody pursue their dreams regardless of whether or not it’s a crowded space, as long as it’s defensible.

David Scieszka: I don’t want to tell anybody to say, go waste 10 years of their lives.

Grant Belgard: What’s one bold prediction you have for the longevity sector over the next 10 years?

David Scieszka: I am one of those crazy guys that thinks longevity, escape velocity is nigh. I really believe it. I think that our senolytic vaccine is going to be able to push the boundary of lifespan and healthspan by at least five years. I do believe that. There are directives right now. The ARPA-8 is one of them. They want you to be able to, and also Peter Diamandis’ directive as well, they’re trying to help you reverse age by 20 years. Jeez, if you’re talking about sarcopenia, God, there are so many companies right now and there are shots on goal, to quote Mitch from Ora Biomedical, there are shots on goal for the longevity field that are getting FDA approval right now. And so we’re going to be able to extend healthspan by a couple of years. And then the next therapy is going to extend by a couple of years.

David Scieszka: And then we might be the last generation to have to choose whether or not we expire naturally. That is an incredible thing. I know it’s bold. And I know that I don’t have the data to back it up, but it’s fun. It’s fun to think about the possibility that what if our parents can live forever? What if they get to choose? What if we get to live forever? We could choose. So hopefully my prediction holds true, but that’s a longevity escape velocity in the next.

Grant Belgard: I did ask for a bold prediction. What closing advice would you have? Write this on a sticky note above your desk. What would you suggest?

David Scieszka: Either “you’re worth it” or “you’ve got this” because it’s hard. It’s a hard world out there and I hope it gets better. But right now, going through master’s, PhD, undergrad, it’s easy to view the world as sharp. And it’s easier still to look at a crutch, say a bottle or something like that. And maybe it’ll soften the world for a duration of time. But as soon as you let go of that crutch, you can face it. You’ve got this.

Grant Belgard: Where can our listeners follow your work and keep up with VeLo Pharma?

David Scieszka: Yeah, I gotta do a better job than this. And actually we should probably connect after this and maybe [?] or if you have any recommendations. So I’m trying to build up the LinkedIn. You can definitely follow us on there. We have at least a landing page. We’ve got a website, vertical-longevity-pharma.com with dashes in between the letters. We’re going to be starting up revamping our media presence with updates, especially fundraising updates, progress points, things of that nature. Yeah, if you wanted to reach out to me personally, I’m just a guy just like everybody else. Happy to talk to you. It doesn’t matter if you’re looking for advice or if you feel like you can help me. I’m a big believer of the mantra, find somebody to help and repeat. And so if you need help and I can help you, reach out. If you think you can help, I’m happy to reciprocate.

David Scieszka: So yeah, find us on LinkedIn, find VeLo Pharma on LinkedIn, Vertical Longevity Pharma. You can find us on now, hopefully Twitter in the future. Yeah, that’s probably the best.

Grant Belgard: Well, David, thank you so much for joining us.

David Scieszka: Thanks for having me. This has been a load of fun.

Phil Ewels

The Bioinformatics CRO Webinar Series

February 18, 2026: Phil Ewels – Reproducible Bioinformatics at Scale: nf-core + Nextflow

Phil Ewels

​Phil Ewels is Product Manager for Open Source at Seqera. He holds a PhD in Molecular Biology from the University of Cambridge, UK. Phil joined Seqera in 2022, previously working at the National Genomics Infrastructure (NGI) at SciLifeLab in Stockholm, Sweden, where he became involved in the Nextflow project and co-founded the nf-core community. Phil’s career has spanned many disciplines from lab work and bioinformatics research in epigenetics, through to software development and community engagement. He is passionate about open-source software and has a soft spot for tools with a focus on user-friendliness. He is the author and maintainer of tools like MultiQC and SRA-Explorer, and helps lead the nf-core and Nextflow development teams.

In this live webinar, he gives an overview of Nextflow and an introduction to some of its new and exciting features for bioinformaticians looking to scale up their pipelines.

Transcript of The Bioinformatics CRO Webinar Series – Reproducible Bioinformatics at Scale: nf-core + Nextflow

Disclaimer: Transcripts may contain errors.

 

Grant Belgard: Welcome to the final talk in The Bioinformatics CRO webinar mini-series. At The Bioinformatics CRO, we help life science teams turn complex data into clear decision ready insights, providing flexible expert bioinformatic support from study design through to analysis and reporting. As part of that mission, this webinar series features practitioner focused talks with concrete takeaways you can put to work right away. Today’s talk is by Phil Ewels. Phil is a senior product manager for open source software at Seqera where he helps lead the nf-core and Nextflow development teams. Today Phil will be presenting on reproducible bioinformatics at scale: nf-core and Nextflow. After the talk, we’ll host a live Q&A session. This is streaming both to YouTube and LinkedIn and on either platform, you can put your questions in the chat or the comments at any point during the talk and we’ll bring them into our discussion afterwards. Phil, over to you.

Phil Ewels: Thanks very much for the introduction and thanks Grant for the invite to come and speak today. It’s a pleasure to be as part of this webinar series and it’s always nice to have the opportunity to talk a little bit about Nextflow, a topic close to my heart. Um I don’t know if my slides are ready to come up but I yeah so basically my talk today is in two parts. I’m going to give a bit of an introduction to what Nextflow is and what nf-core is and why I think they’re good and useful for you and why I think you should care and then I’ll talk a little bit about some of the new features which have come out especially for Nextflow in the past kind of year or so, year or six months and this is particularly good for anyone in the audience who maybe has dabbled in Nextflow especially a little while ago because things are changing quite a lot and for the better. So I hope I convince you to really pick up Nextflow and see if it could help you in your work. So yeah, so my name is is Phil and I’ve been working originally in the lab and then kind of became a self-taught bioinformatician and went slowly moved from research into kind of core labs. So I worked at the National Genomics Infrastructure in Sweden developing new lab techniques and analysis and then started kind of accidentally getting into software design. Started writing pipelines, had my own pipeline tool. It was all the rage 10 years ago. And wrote software like MultiQC which I imagine many people will be familiar with. And got into Nextflow probably about eight years ago or so while I was in Sweden at the NGI. And we were running huge numbers of samples. It was a real step up from my previous work in Cambridge where now we were running hundreds of samples, hundreds of projects, sorry, thousands of samples. And we needed to the software I’d used previously wasn’t really up to the task. So I looked around and found Nextflow and we started building lots of different pipelines and because we’re a team of about eight people building pipelines and kind of we started to standardize and nf-core was born out of that standardization of our pipelines. I’ll talk a little bit about what made that possible.

Phil Ewels: So the background to the whole picture of why Nextflow exists is these classic statistics from this Nature paper quite old now 10 years ago. Where a simple study I think it’s the statistics resonate with many of us working in bioinformatics about this reproducibility crisis where it’s famously difficult to reproduce experiments that you find in the published materials and even reproducing your own experiments kind of what I refer to as your- one of your most important colleagues which is future you is notoriously difficult to do and reproducibility is the foundation of the scientific method. And so we were kind of in a bit of a bad place 10 years ago where data was really starting to scale. NGS was taking off. We had more data than we knew what to do with and we couldn’t really reproduce the analyses that we were doing and certainly we couldn’t transfer those analyses to other people. And it’s not surprising because it’s a really difficult problem. We’re running many different tools, each one of which might have a numerous different complex dependencies. Everyone’s running on a different system. And often, you know, even if it works on your machine, it might not work at a collaborator. Everyone is doing things in their own way and there was very little in the way of provenance, of knowing where data came from when your supervisor sent you an Excel spreadsheet with some results in. So, Nextflow set out to basically try and provide an answer for this. And it’s a workflow orchestration tool. So, it takes your analysis pipeline of multiple different steps and puts it together into a language. And it’s quite a unique syntax. It’s flow-based programming which kind of makes sense for what it’s doing. It’s got some real key features which make it very very popular. Something that’s really important is it’s got built-in support for software packaging. Docker was very new about when Nextflow was first launched and Nextflow supported it almost right away. And so you can package individual tools at the level of single processes within your pipeline. So the software effectively comes built in with the pipeline. So end users don’t have to worry about installing 20 or 50 different tools every time they run a new pipeline. And all those versions are pinned so you know you’re always running the same version of the software when you run that version of the pipeline. It’s multiplatform so Nextflow supports lots of different what it calls compute environments. It can submit jobs to all kinds basically anywhere you can run computing Nextflow will support. It has one of the most popular features is the ability to resume. So it’s got it’s quite clever with a cache of completed tasks. So if you’re, if you lose power halfway through your run and it’s been running for like three or four days, you haven’t lost everything. Nextflow was able to look back and understand which tasks already completed successfully and pick up where it left off. With this kind of dash regime, it’s massively scalable. Really, you know, I’ve put thousands here, but up to millions of jobs. We’ve seen truly enormous workloads passed through Nexftlow and it’s able to scale to really massive volumes of data and in the last 10 years it’s really grown an extremely active ecosystem and community which is one of the most attractive things to the system really is that there are lots of other people building with it. Okay, so for those unfamiliar with Nextflow how does it work? What does it do? There’s basically a few different steps to building a pipeline and running it. Firstly, you define kind of processes within your Nextflow code which are the building blocks. So, a single process usually corresponds to a single tool. You say what the data inputs are, what the expected outputs are, and then you have a script which could be a bash command. It could be a Python script, an R script, can be anything really, but that’s able to be resolved on the fly and that’s then submitted to your compute environment to be run as a single task. So you describe all the different processes in your pipeline and then you link them all together with what Nextflow calls channels, which is the data flow aspect of Nextflow. And you can have one but Nextflow handles all the data flow automatically when you run for the pipeline and it handles all the dependency and all the parallelization so that when you describe this flow then Nextflow automatically figures out basically how your pipeline should be run. Then once you have the pipeline logic and the code written you have a separate step which is to write configuration and then the configuration is importantly separate to the pipeline code and this is where you describe your specific setup. So your HPC, your cloud compute credentials, your laptop, whatever, and when once it’s configured you’re ready to go and you can execute it wherever you want to, basically.

Phil Ewels: The really key points if you remember nothing else is that Nextflow is not just one thing it’s several things. It’s a language. So it’s a language and a code syntax which is designed for describing workflows, the steps and also the data flow within workflows. It has separate configuration from that syntax so that you can separate the logic of the pipeline from how the pipeline should run and it’s also an orchestrator. So it’s the actual job that you run which actually passes that code and understands it and runs the pipeline for you. So it’s both a language and also an executor. The two things that Nextflow brings are reproducibility that you can run the same workflow and it can be years apart and as long as you run the same git versioned pipeline code which has pinned the exact same software for every step and you’re using the same version of Nextflow you’re almost guaranteed to get exactly the same results out which is really fantastic. And the other thing is this idea of it being portable. I can write one pipeline code and share it with different people in different places running on different systems and they can write their own config files but the pipeline code stays unmodified. And so for the first time really when Nextflow came out, it was possible to write one pipeline and run it anywhere, which now seems kind of obvious 10 years in, but at the time these two facets in Nextflow were really revolutionary and and groundbreaking.

Phil Ewels: And so what this means, this touches on this concept of scalability which was in my talk title. So you can write a single Nextflow pipeline and you can test it out on your laptop with one small test sample and and once you’re happy that it’s working properly, you can scale that same pipeline up without touching the pipeline code to tens or thousands or millions of samples. And you can also scale up the compute that it’s running on from just your laptop to maybe a slurm cluster somewhere or cloud computing basically any kind of cloud computing AWS, Azure, Google, um, Oracle. And because of the way that Nextflow is structured and architectured, it’s able to handle that scaling and basically grow grow with your needs.

Phil Ewels: Nextflow has become massively popular because of this. The figure on the left is from a recent paper that we did for nf-core community and shows just the number of citations for different workflow managers is a bit of a lagging metric but you can see that Nextflow has become more and more popular over recent years. And then on the right we just have the number of runs and you can see there’s there’s hundreds of thousands of runs of Nextflow pipelines every day. And this is probably undercounting it quite a lot as well. So Nextflow is arguably one of the most run workflow managers certainly in life sciences.

Phil Ewels: So that’s Nextflow. Quick introduction there for those who are unfamiliar. So that was how it works and why it was built the way it is. Because for the first time Nextflow was able to give us workflows which were portable between different systems. Back in 2017-18 we had this kind of light bulb moment where up until then everyone wrote their own RNA pipeline wherever you were in your core facilities, in your labs. You had to because other people’s pipelines didn’t work on your system. And they had hard-coded paths or maybe the environment module system with the software used different names. All these different things made it very difficult to collaborate. But Nextflow suddenly removed those blocks that we could now share code for running pipelines and we didn’t all need to write our own pipeline. And so back in around 2017-18 I started nf-core with some collaborators and friends and we started taking the standards that we we built in Stockholm and kind of opening it up to the wider world and based on those principles we founded this Nextflow community called nf-core. nf-core has exploded in popularity alongside Nextflow. The two have kind of formed a very symbiotic relationship and now we have over 140 different pipelines which is astonishing when you bear in mind that one of our key guidelines is we only have one pipeline per data type or analysis type. So we have only one RNA pipeline. So that’s 140 different types of data analysis that we have pipelines for. In the recent years we’ve also grown to be more than just pipelines. I’ll touch on this in a second, but we also now have shared modules, which are basically individual processes within the pipeline. And so these themselves are shared and can be reused across different pipelines and across pipelines outside of nf-core. And so every one of those is is a different tool and it comes bundled with its commands, its usage, and its software containers and everything. And there’s now over 1,700 of those, and that number is growing really, really fast. And then we have a community Slack where we have channels for every different pipeline for discussions. We have kind of a core team and a maintainers team and kind of some level of governance within that. And we have going on 14,000 community members in Slack now. So it’s an extremely active community and of course really kind of it’s built on this concept of best practice where we you can write Nextflow is a programming language and you can write your Nextflow pipeline in basically any way; there’s huge variability in how you do that and nf-core takes a very very opinionated stance and says if your pipeline is going to be part of nf-core it has to be written exactly this way we you have to use our template you have to do things our way. And the reason we do that is that then makes it possible for components to be interchangeable and for folks to be able to collaborate. So standardized tooling, best practices and a lot of documentation.

Phil Ewels: One of the things that’s quite unique about nf-core versus other pipeline registries and software registries is that one of the requirements of adding a pipeline to nf-core is acknowledgement that it’s not owned by you anymore. It’s community owned. This is another figure from that recent paper. But I really love these plots. This is for the small RNAseq pipeline which we actually started in Sweden before the origin of NF core. And you can see that top green bar is SciLife. And you can see that we were sole owners, maintainers, contributors to start with in 2017-18 and then more and more different organizations have joined in with maintaining and contributing to the pipeline and actually SciLifeLab stopped contributing really to it around 2022. But the pipeline lives on because the pipelines are community owned. They don’t suffer from this problem of a PhD student finishing a PhD and moving on to a different position and abandoned the software getting abandoned because it’s community owned. We can build updates in based on community consensus and bring in volunteer works from from groups across the world. And that’s a real kind of superpower for nf-core.

Phil Ewels: I also want to touch on the fact that nf-core is not just pipelines anymore. This modules library and the tooling that we build for nf-core is deliberately done in such a way you can use it for any pipeline, any Nextflow pipeline. And so this is the nf-core CLI I’m showing on the right and it has a TUI, a terminal interface which you can use to create new pipelines and that very rapid example there is creating a new pipeline which is not using nf-core template and you can choose which of the features from the template you want. So you can make it very very minimal or you can have everything that NF core comes with. And it’s up to you. And once you’ve got your pipeline, you can then go into that pipeline and use the tooling again to pull in these shared modules from a community repository. So here I’ve pulled in SAMtools sort and BWM and it fetches that those modules. It fetches that code with everything that comes with it and pulls it into my pipeline. And really then all that’s left is to connect those channels I mentioned. I’ve got the building blocks of my pipeline there provided for me from a community and I just need to put them together. So, nf-core tooling really provides a fantastic starting place for for anyone building their own Nextflow pipelines that you can just mix and match and you’re building on on community best practices. You’ve got all the the learnings of thousands of scientists using Nextflow over many years. And your, the modules you’re sure are well tested and being used by many other people. So you’re benefiting from a a huge pool of community knowledge.

Phil Ewels: Okay, that’s it for my introduction. So next I’m going to touch on some of the new developments in Nextflow.

Phil Ewels: Nextflow itself is is developed at Seqera and we have a team of engineers working on Nextflow and basically the last year or so we’ve had some pretty major projects based on the community survey that we do. We try and do one of these almost every year. And for as long as I can remember, people would always say that they love Nextflow, but they find it really difficult to work with. The error messages are unhelpful. The syntax is confusing. And there’s none of the kind of nice stuff that people are used to working with when they use other programming languages. I myself write a lot of Python. Um, multiqc’s written in Python, for example. So, I absolutely sympathize with these these requests. And so we really went back to the drawing board with Nextflow about a year and a half ago and said okay how do we solve these problems and basically we took on a really massive project which is we completely rewrote how Nextflow understands Nextflow code. In the past Nextflow was what’s called like a Groovy DSL like a domain specific language. So the way it worked was you wrote your Nextflow script and that was basically cross-compiled into Groovy code at runtime and then the Nextflow engine would run that Groovy code. That’s still kind of the case but now we have a new language parser which takes your language which takes your Nextflow code and is able to natively understand the syntax that you’ve written. This is really changes the game for us in terms of what we’re able to provide for developer tooling, for error messages and things like this and means we’re kind of moving away from the days of Nextflow being a Nextflow – sorry a Groovy DSL really Nextflow starts to become its own native language.

Phil Ewels: One of the first things that was possible with this was that we launched a language server, an LSP, which um, and we incorporated that into the VS Code extension, which is probably the best way to write Nextflow code. And so suddenly we were able to bring up this developer experience for writing Nextflow code to be in line with other languages that you might be used for. The simplest thing is error reporting. Just being able to see in real time as you write your code that something’s wrong rather than having to hit save, run the pipeline, and then try and figure out where the bug is when you’ve been writing code for half an hour. There’s things like quick navigation and auto formatting of your code. So, you don’t have to argue about whitespace and things like this. But, picture’s worth a thousand words. So, let’s have a quick couple of kind of examples of what I mean. This is one of the simplest things but probably the most impactful is just the little wiggly lines that you can now get when you’re writing Nextflow code. Here you can see that the red line is telling us that that variable is unknown and it’s not defined and the clue is just above it where we have defined a variable called locations with an s and we’re also getting a warning there that we’ve defined a variable and it’s not being used anywhere and these hints are being shown as you write your Nextflow code. So it’s a huge productivity boost to writing Nextflow. There’s features like this where we have tool tips over every Nextflow language item. So when you hover over in this case a channel factory, but it can be any part of the Nextflow syntax really, you get a short description about what that is and what it’s doing. And then there’s also a link underneath to read more and that takes you straight to the Nextflow docs. There’s things like this where there’s special little buttons that pop up in certain places in your workflow. So if you have valid syntax, your top level workflow now has this button saying preview DAG or D-A-G. And you click that and it will show you a mermaid diagram of your whole workflow in the sidebar right there in VS Code. And so this is a great way to get to grips with a new workflow which maybe you haven’t worked on before and you’re inheriting from someone else. I had about six of these slides in, but I thought they were a bit too much, so I pared it back down. But this is just a taste. There’s many different things like this now built into VS Code. So if you’re writing Nextflow Code now, it’s just vastly vastly better than it was a year ago or more. So if you’ve ever tried in the past, I recommend having another go now and seeing if the experience is better. Along with the language parser, other things that allows us to do is actually change and develop the syntax of Nextflow itself. Before we were kind of limited by what the Groovy Nextflow language parser could handle, but now because we have a totally separate step, we can develop the language however we want. And so we’re bringing out several improvements as a result of that. And something that’s been asked for for a long time is static types. So here we have some input parameters for a pipeline. On the left is the traditional way to do it. You save a name and you say a value, default value, then that’s that. But Nextflow just on the fly tries to typecast stuff based on the values it’s been given which sometimes leads to problems. For example, if you have a sample name as a string, but the sample name is given as something with leading zeros and it gets converted to a number. Now on the right hand side you can see we’re defining the types of each parameter whether it’s a path a boolean an integer a string so on and Nextflow will then strictly typecast those things on input and also validate that the values that’s been given are correct. So you’ll get immediate validation and errors if you try and launch a Nextflow pipeline with the wrong kind of input. So these kinds of things are small changes to a syntax but really make a huge difference to re-usability of Nextflow pipelines.

Phil Ewels: Another thing I mentioned was error messages. So here you can see one of the old style error messages where because it was compiled to Groovy before it ran, Groovy threw an error and it was really unhelpful. it was just like top level pointing at a squiggly bracket and then you had to go through hundreds of lines of code to try and work out where the error actually was. Whereas now we throw the error at the parsing, language parsing level. And here you can see it even indicates the exact character which is wrong and and you can go straight there. It’s just again way better. And we have a lint command you can run as part of your continuous integration test for example linting just to to find those validation errors before you even run the pipeline.

Phil Ewels: Okay, I need to speed up a bit. Other features that we’ve been working on, these are kind of low-level features which you might not notice right away but are really kind of foundational blocks for us being able to build a lot of cool stuff. Workflow outputs is a new way of defining where, how files are basically published at the end of of pipelines. And data lineage gives a way of saving and storing all the information that next flow has about the provenance of all of your data. And so when lineage is enabled, you can kind of find out from any given file the entire analysis path that it took through a pipeline where it came from. And and we can do start to do some really nice things such as passing inputs between pipelines and things like this.

Phil Ewels: Before I wrap up, I want to just touch on a few things that we do at Seqera, which is the company which was formed around Nextflow. So, Nextflow is all open source and of course, and that’s kind of been my focus professionally, but if you’re running Nextflow, then Seqera has a lot of extra tooling that you can build on top of Nextflow. Tthe key thing we have is something called Seqera Platform which is basically a way to manage running Nextflow. So Nextflow is a command line tool. But when you’re running a lot of Nextflow pipelines, it can be difficult to keep track of all those different runs and which ones through errors and where they are and where the data is. And so Seqera Platform kind of provides an interface to launch and to monitor different workflows. Importantly it works with your compute. So you connect it to your AWS account or your slurm cluster and all your pipelines are still running in the same places that they were before. It’s just that they’re being exposed through the Seqera Platform interface. This is kind of an example of the kinds of things you can do once Seqera Platform is aware of the great- basically the Nextflow pipeline and everything around it. So the encapsulation of configuration and execution environment and data. So you can use it then as a control plane which you basically can build on top of. And so one of my extra little projects in the past year is a plug-in for an open source tool called Node-RED. There’s a link here, but basically you can use this as a low-code platform for setting up automation. So here it might be that when a file is added to an S3 bucket, it automatically triggers a workflow and when that workflow finishes, it triggers a second workflow and when that one is finished, it triggers the creation of an analysis studio which you can then go in and do your downstream analysis in things like that. You can basically create any kind of automation and this is all done via the APIs of Seqera Platform. And so when you abstract away all the complexity of actually configuring and launching and maintaining all the infrastructure, you can start to build some really cool solutions.

Phil Ewels: We have a lot of tooling to make basically running your pipelines faster and cheaper and better. A big one is is fusion which handles all the file operations. Nextflow is traditionally very well targeted towards working with huge data files. You know, your BAM files and your fastq files and everything and fusion basically is optimized specifically for Nextflow. It knows how Nextflow works and it’s really you know it can really fine-tune it for that use case and one of the latest things that fusion can do is snapshots. So if you’re running on cloud with spot instances for example AWS might tell you that this you’ve got one minute before your instance is being reclaimed and snapshots will now freeze that, freeze that image, that running task and you can restart it. And don’t lose all the progress you’d made in that long running task. And then just this is like an everything else slide because there’s I could give another two hour long talk about all these features. There’s so much more. But if any of that sounds interesting, I’m happy to ask answer kind of questions or yeah, come back and talk about more.

Phil Ewels: So to wrap up if you’re interested in becoming more involved with Nextflow, writing your own pipelines or getting involved in the community, we’ve got kind of a smattering of links here. The top one with the Nextflow website of course has all the documentation. We have a website called training.nextflow.io which is all basically walk through tutorials and training which you can do yourself. We’ve just had a training week last week where we had over a thousand people registered for it just in that one week. And the there’s multiple different courses. The beginner one is called Hello Nextflow. I’ve done a set of video tutorials for each of those chapters. And so you can kind of follow through with me step by step as we work through all the worked examples. Which is basically the best way to to learn Nextflow. And that’s all up to date now with all the latest Nextflow syntax. We have a very active community forum. So if you ever need any help, you can drop in there and ask you a question and you can usually get a response very quickly. Another plug, I run a Nextflow podcast. So at the moment, I’m trying to do one every two weeks. We talk to all kinds of different people using Nextflow for different things or other kind of tangentially related technical topics. It tends to be very technical deep dives. So that’s kind of fun. We have a really good blog and I’ve written a community forum twice. Didn’t mean to do that. And then finally we have a bunch of events coming up. So in a few weeks time we’ve got the nf-core hackathon which is both online and then people self-organize different local sites all the way around the world. I think we’ve something like 20 or 30 local sites already from Argentina to the UK to the US to Germany all over the place. So very welcome to join. It’s a great way to get involved. And there’s all different projects so you can kind of dive in and help people with their pipelines. And then we’ve got the the two flagship summit events. One in Boston at the end of April and then we’ll have the main online one with some in person in Barcelona in October. And there’s loads of Seqera sessions and all kinds of other events if you click that link where it might well be something near you. I think there’s Seqera sessions coming up in London and a few other places soon.

Phil Ewels: With that hopefully I’m about on time and happy to sort of take any questions. I hope that was all clear and and made sense and was useful.

Grant Belgard: Thanks, Phil. Um, so what’s the easiest way for someone to get started with Nextflow?

Phil Ewels: So the training website I think is the best way to get started really it the the examples that we use with the Hello Nextflow training are kind of domain agnostic. We we use cowpy to print a little cow to the terminal saying different messages and things. So you don’t need to really know anything specifically about RNAseq or anything else. And you can do most of that course probably in an afternoon or a couple of afternoons. And that takes you from almost nothing all the way through to building your own pipeline complete with containers, Docker containers and everything. And it’s all set up to work on GitHub code spaces. So it doesn’t, yeah.

Grant Belgard: And for people who currently use Snakemake, how hard is it to migrate to Nextflow?

Phil Ewels: So yeah, so I mean I didn’t really talk about any of the other workflow managers, but Nextflow is not alone in this field. And what I generally say to anyone is that just using any workflow manager is better than not using any. And so Snakemake especially and Nextflow um and WDL and others they share many of the concepts about kind of splitting up different tools and running them sequentially and working out risk DAG. Because of that it’s not usually not too bad to convert from one to the other. Especially with AI these days like we have our own Seqera AI which is particularly good and well versed in the latest syntax of Nextflow. And so honestly with many pipelines these days you can just dump your Snakemake syntax in and say convert this to Nextflow for me and it will do a pretty good job almost from the first go. So I would that I’m kind of lazy and a bit of an AI advocate. So that’s what I would definitely do in that situation.

Grant Belgard: If someone has a pipeline that could be useful for nf-core, how do they go about adding it?

Phil Ewels: Yeah. So, nf-core is this kind of like I say, it’s kind of a unique community because we don’t just kind of list any pipeline. We, it’s specifically kind of community owned and and only one pipeline per data type. So, because of this, it’s not just a question of kind of clicking a couple of buttons. You have you have to come and forward and put in a proposal and basically then we say yes or no and then there’s a kind of a system for going through and building your pipeline and adding it to nf-core. The short answer is go to nf-core website and click on the docs and there’s a guide saying how to add your pipeline and then there’s an nf-core proposals website where you go and basically describe what it is you want to do and get a thumbs up.

Grant Belgard: Where do you see AI fitting into pipeline development in the next couple years?

Phil Ewels: A couple of years is difficult to say. I’m struggling to predict anything more than a month ahead at the time at the moment because things are changing so fast. But I mean nothing in tech is going to be the same and I’m sure that pipelines will be included in that. We’re starting to see it already like I say converting between languages. We have our Seqera AI tool and we’re trying to kind of take the rough edges off these tools and it certainly lowers the boundary. Nextflow is known for not being the easiest in terms of learning curve and AI makes it possible to get started so much easier. So right now I think the benefits are kind of a low hanging fruit is it’s just much easier to write to debug your Nextflow pipelines using AI. And as we go forward I’m expecting kind of more foundational changes with how how we just approach the whole concept of building up scientific analysis to be honest.

Grant Belgard: Is Nextflow overkill if someone’s just running a few samples on their laptop?

Phil Ewels: It depends a bit. So I mean it depends a bit on your background and how much Nextflow you’ve written. If you’ve never written Nextflow before then is it worth you learning the whole syntax and going through the whole process just so you can run a couple of samples? Maybe not. But once you have kind of got familiar with Nextflow I kind of think it’s a bit like wearing gloves when you’re pipetting in the lab. It’s difficult. You want to, you end up wanting to write Nextflow pipelines for everything because it is self-documenting. It’s automatically versioned. You can rerun it any time in the future. And you know when you try and remember what it was you did six months ago, you can just see the next pipeline and it’s there. So it ends up being quite a low lift. So then of course I’m a bit biased in this question, but I would say yes to everything in Nextflow pipelines. That’s what I find myself doing.

Grant Belgard: Mhm. Is Seqera containers free and how does it compare to biocontainers or Docker hub?

Phil Ewels: Yeah, so I didn’t touch on this so much but Seqera containers is something we do on the Seqera side. So what one of the tools we have containers are are key and fundamentals in Nextflow and the success of bioinformatics workflows that you can encapsulate the software in this kind of clean environment on a per process basis. So your versions of Python don’t conflict and this and that. And so you almost every Nextflow pipeline you will see now have these container declarations and you have you might have 50 different or 60 different steps in your pipeline and you need to come up with a Docker container for every single one and so the bioinformatics community has kind of responded to this usage of containers in a few different ways. The biocontainers project has been wildly successful and basically every conda package gets a Docker image for free and so we’ve been using biocontainers in nf-core for a long time. The limitations we found are when you want to have a process in your pipeline that has more than one tool then you have to – the whole process for generating one of those containers is quite convoluted. And so we have we built a tool at Seqera called wave which is also open source which basically builds Docker containers on the fly. So you build, you add this into your Nextflow pipeline and you say I want to run tool A and tool B in this process and it will go off and it will request it and if Wave has seen it before it will just give you the container straight away. And if not it will sit there and it will build it on the fly and then give it to you. Which is really cool because it means you basically don’t have to think about containers anymore. They just magically happen. So Seqera containers is based on this technology and it’s exactly the same thing but it’s just a public repository. So when you build your, you request your image you build it it then gets stored there for we say a minimum of 5 years and then anyone can just fetch it and download it. So we for example are now going to be using this in nf-core where every single one of those 1700 modules will have their own custom built, you have and docker and singularity you’ll have x86 you’ll have ARM CPU processing will all be built automatically on the fly and then pinned for a long time for perfect reproducibility and it’s just free and yeah it works really well.

Grant Belgard: When can one start using static types in Nextflow?

So the syntax example I showed with those params you can do that now. So that’s out as of, we do two Nextflow releases every year one major release in April and one in October. And so the 25/10 release came out with that syntax. So you can use it for parameters today. Basically, we are working on developing more syntax which will come out in the next major release, so 26/04, which will have basically strong typing through all of your pipeline code pretty much. And so that will really take that concept and kind of bring it through and then you’ll have a lot more validation because if you try and as you’re building as you’re connecting all your processes with all these different channels, excuse me, if you say that this you know this process has an output which goes into this it will tell you immediately like well you can’t do that because those are different types. So we’re going to have that very soon in a few months but already today you can do typing for just the input parameters for the pipeline.

Grant Belgard: And lastly how do you go about deciding if it’s worth updating an ancient DSL1 pipeline?

Phil Ewels: Yeah. So, so for for those who don’t know where DSL1, DSL2, this is like back when Nextflow started, it was this Groovy DSL and this term got bandied around a lot. And then around 2020, I think there was a major language update. We used to have these huge monolithic scripts of like thousands of lines of code and DSL2 changed a bunch of the syntax and one of the things it allowed us to do is break out different files, have these modules which we now, you know, like I say, rely on for this level of granularity and testing and and community. So, but that change from DSL1 to DSL 2 was was quite painful. It was quite hard work doing a lot of the rewrites which I should say we’re taking great pains to avoid with the new syntax updates we’re doing. We’re doing it much more gently and there’s also a lot of tooling to automatically update code. But so if you have an ancient pipeline in DSL1 and you want to sort of leap frog all this and bring it forward what like six years in terms of syntax it’s surprisingly common to have this question but like basically you have a couple of options probably the easiest is the same as converting from Snakemake you chuck it into an AI tool and say rewrite this pipeline for me or you start from scratch and you just kind of copy over the logic into the new syntax and you take the nf-core template or something. Or if you really want to and you’re a bit of a sadist, you can go through and try and update all the syntax line by line, which is doable. But, you’ll probably have to go DSL, you know, it’s like a software migration. You have to go DSL1 to DSL 2 and then DSL 2 to a new syntax. It’s doable.

Grant Belgard: Well, Phil, thank you so much for joining us. Thanks to all our listeners.

Phil Ewels: It’s a pleasure. Thanks very much for inviting me.

The BCRO Webinar

The Bioinformatics CRO Webinar Series

January 21, 2026: James Opzoomer – Biophysics-Informed Spatial Transcriptomics Approaches to Identify Cytokines Causally Driving Downstream Gene Programs

The BCRO Webinar

James Opzoomer is a Senior Scientist in the Innovation Lab at Relation, where he develops single-cell and spatial genomics platforms to accelerate drug discovery. His projects span high-throughput multimodal single-cell sequencing and spatial transcriptomics technology development, generating ML-ready datasets that power novel therapeutic insights.

In this live webinar, he discussed BISTR (biophysics-informed spatial transcriptomics regression) as a computational toolbox for building biologically plausible predictive models from spatial transcriptomics by combining RNA dynamics as a readout of changing gene programs, and paracrine cytokine diffusion as a physically constrained model of cell–cell communication. By linking inferred cytokine secretion, a spatial propagation diffusion model, and receptor-associated changes in mRNA maturation, BISTR aims to suggest cell-type-specific, testable causal relationships between extracellular signals and downstream transcriptional responses.

Transcript of The Bioinformatics CRO Webinar Series – Biophysics-Informed Spatial Transcriptomics Approaches to Identify Cytokines Causally Driving Downstream Gene Programs

Disclaimer: Transcripts may contain errors.

Grant Belgard: Welcome to the next talk in The Bioinformatics CRO webinar miniseries. At The Bioinformatics CRO, we help life science teams turn complex data into clear decision ready insights, providing flexible expert bioinformatics support from study design through analysis and reporting. As part of that mission, our webinar series features practitioner focused talks with concrete takeaways you can put to work right away. Today’s talk is by James Opzoomer. James is a senior scientist in the innovation Lab at Relation where he developed single cell and spatial genomics platforms to accelerate drug discovery. His projects span high throughput multimodal single cell sequencing and spatial transcriptomics technology development generating ML ready data sets that power novel therapeutic insights. Today James will be presenting on biophysics informed spatial transcriptomics approaches to identify cytokines causally driving downstream gene programs. After the talk, we’ll host a live Q&A session. This is streaming both to YouTube and LinkedIn and on either platform you can put your questions in the chat or the comments at any point during the talk and we’ll bring them into our discussion afterwards. James, over to you.

James Opzoomer: Thank you and hello. So I’m delighted to be speaking today at this uh BCRO webinar and I’d like to thank Grant and the BCRO team for inviting me to speak with you today about relation and some of our spatial transcriptomics work within the Innovation team. So I’m going to start today by giving you an overview of Relation and our approach to data generation and then I’ll dive into a novel spatial transcriptomics an analysis method that we’re developing called BISTR and provide a worked example at the end. So, first I’d like to start with a question. What are some of the main challenges with the current model of drug development? And why is now a uniquely good moment to deploy large-scale patient genomics to solve this problem?

James Opzoomer: So shown here are four major trends that define the future of drug development and healthcare. And on the left we have sort of two negative trends. First that the cost of drugs is ever increasing. We spend more money on health care but we don’t see commensurate increases in in life expectancy. And this is also demonstrated by the the ratio of health care spend to life expectancy on the left. Now the two good trends on the right are that the cost of sequencing is is drastically decreasing. You can now do a whole genome sequencing for about $100 and the cost of compute that’s driven by titans like Nvidia has made it more accessible than than ever before. So really the problem that Relation is trying to deal with is the first one decreasing the cost of drugs. And what we want to ask is can we use these two trends on the right to solve those on the left.

James Opzoomer: Now this slide really represents a simplified overview of the drug development funnel which I’m sure you’re all well aware of. On the left we start with maybe 20 programs, 20 ideas for new medicines and we invest on the order of 1 to 3 billion across this funnel and after all that work we typically end up with just one marketed drug. So most of the attrition here is because we were wrong about the biology. So although every stage in the funnel is important, the decisions we make right at the beginning in target discovery echo all the way through this funnel to the clinic where failure is acutely expensive. And so that’s why we believe that that target discovery is really the most important problem in in drug discovery.

James Opzoomer: So at Relation our ambition is to transform target discovery into an engineering discipline. And now this means building systematic repeatable processes powered by large-scale patient data and ML models.

James Opzoomer: So the funnel that I previously showed you is another representation of this statistic on the top left that over 90% of drugs that enter clinical trials ultimately fail. So how do we transform R&D so that this number looks very different in the future? Now over the last few years several large analyses have given us an important clue. So on the right there are two examples of these. The first is is a recent Nature paper where it looked across many clinical programs from and the papers from Matt Nelson’s group. They show that when a drug target is supported by human genetic evidence the probability of success in the clinic is increased compared to targets without that evidence. In other words, genetics gives us causal anchors in human biology. The second work shows that single cell RNA sequencing of human tissue sharpens that picture. So by knowing which cells in which tissues express a genetically supported target, we can better predict efficacy.

James Opzoomer: So how do these these approaches fall into historical data collection strategies? So on the left we have large end low value highdimensional observational data. These are things like the human cell atlas um large bio bank cohorts. There’s a lot of it but it’s noisy, confounded and often only weakly connected to clear interventions that we want to make in drug discovery. And on the right we have small and high value but lowdimensional uh interventional data mechanistic experiments in model systems but in small numbers and with low dimensional readouts a few readouts and few perturbations. Now what we actually need for AI driven target discovery is bespoke multimodal perturbation data that links interventions to rich molecular and cellular readouts across diverse biological systems that are related to patient primary patient material. Now that missing data layer is what enables us to train models that actually learn the consequences of perturbing a target in a specific cell type and tissue.

James Opzoomer: And you know overall we believe that current models and data in the public domain are nowhere near sufficient to deliver meaningful impact in target discovery. So we therefore have to build the right data and the right models applied to where they most make sense.

James Opzoomer: So now that I’ve talked about why we care so much about genetics single and single cell data, I wanted to give a quick overview of how Relation is actually set up to do this in practice. And this slide represents a highlevel map of our platform. On the left you see human tissue profiling. This is where we generate deep multimodal data directly from patient samples. whole genome sequencing um single cell spatial transcriptomics single cell transcriptomics and proteomics. Now all of this is connected to the cellular modeling teams who run perturbation experiments on patient derived primary cell systems to generate bespoke data for the models and this connects to translation pharmacology who take the prioritized drug targets and turn them into to drug discovery programs. Now this is all connected to to both data science and our three main machine learning platforms. ROSALIND which identifies genetically validated drug targets, ADA which focuses on reversibility and TURING which provides drug discovery context of our targets. And I’m not going to go into these platforms in detail today because I really want to focus on the spatial genomics data that we generate in human tissue profiling and some of the new analysis methods that we’re developing to better use our spatial transcriptomics data in in drug discovery. So as an example of the type of primary patient data that we collect, I just wanted to show a case study of osteomics. This is our flagship observational clinical study focused on osteoporosis and bone disease. So in this study we partner with orthopedic surgeons across London to collect human bone waste from key surgeries. So these are total joint replacements elective surgeries associated with osteoarthritis and um hemiarthroplasty. So these are non-elective surgeries resulting from osteoporotic fracture really the end stage of osteoporosis.

James Opzoomer: So from each patient we build a genuinely multimodal data set. So that’s whole genome sequencing to identify variants and genes that causally in influence bone density, fracture risk and response to therapy. And this feeds into our genetic discovery platform at ROSALIND. We also generate single nucleus RNAseq of bone and joint tissue to map those genetically supported targets into specific bone stromal and immune cell types and states within the tissue and this sharpens our view of where these targets are expressed within the tissue. We also collect blood-based proteomics to find circulating biomarkers that report on pathway activity can be later used for for patient stratification. And in addition to this also rich clinical metadata including bone BMD or bone mineral density to anchor everything back to quantitative phenotypes. And now this lets our models learn how genetics and cell state translate into real clinical outcomes.

James Opzoomer: So in addition to the the single cell RNAseq we generate we generate spatial transcriptomics data with Xenium and the VisiumHD platforms on human bone and other tissues in associated with the other disease programs we’re working on. And this is really important because single cell data tells us what cell types and states are present within the tissue, but really we lose where they sit in the tissue and how they interact and communicate with other cells within this spatial context.

James Opzoomer: So together these genomics and single cell data sets give us a dense patient centric view of disease biology and in particular we in the Innovation Lab are interested how we can utilize this spatial transcriptomics data to disentangle the causal microenvironmental signals. So the cell communication pathways that drive cell state and cellular response to micro environment. And this has led us to develop a new analysis method called BISTR um or bioysics informed spatial transcriptomics regression that I’d like to share with you today.

James Opzoomer: So spatial technologies are key for preserving the in situ cellular context present in tissues providing a contextual perturbation system of sorts to understand some of the micro environmental signaling factors that may be driving a particular cell state within a tissue or within a particular disease. So we’re often attempting to model our disease states in less complex in vitro systems like some of the ones shown here 2D cell models and 3D organoids or organ-on-chip models. And the kind of the motivating feature of this BISTR package is to answer some of these questions. It’s can we identify cytokines responsible for cell identity and behavior in primary patient tissue and could we then stimulate cell models to mimic some of these these disease relevant or patient relevant micro environmental niches. And we hope that this can add value to the drug discovery process and to kind of our efforts in in vitro cellular modeling by using this knowledge to build experimental systems with greater disease relevance in vitro.

James Opzoomer: So a lot of this work is enabled by the advancements in the resolution of of spatial genomics technologies which is is really rapidly changing. And we recently published a review in Cell Genomics tracking these technology trends called SC trends. And this kind of summarizes the historical development in spatial omics technologies as well as some of the analysis packages available. And we also comment on these these kind of developing spatial technologies in real time since it’s such a such a fast moving field at our blog sctrends.org. So I encourage you to check it out if you’re able to.

James Opzoomer: So the work that I’m going to show you today is really focused on uh 10x Genomics VisiumHD platform and this is one of these spatial sequencing based spatial transcriptomics technologies where the increased resolution in this generation of platform now two micrometers has really enabled subcellular resolution allowing us to track several biophysical processes that are shown out here on the right. So RNA abundance, RNA localization and also RNA splicing at the subscellular level. And we can use these two micron pixels to approximately reassemble single cell data based on image segmentation tools in the imaging modality to create approximately single cell data.

James Opzoomer: So this slide sort of positions BISTR among other spatial modeling approaches. On the left are are sort of simple heuristic based approaches like using a radius around a specific cell or a k-nearest neighborhood and computing sort of some summary statistics. They’re fast. But the spatial scale is often somewhat arbitrary and the tissue is treated more like a discrete bin than a sort of a continuous space that it is. On the right, we’ve got deep learning based approaches. Now, these can be powerful, especially when they leverage analysis pipelines from the image space or are often paired with single cell data, but they’re typically more data hungry and and less sometimes less mechanistically interpretable. So, BISTR sits in the the biophysical model space in between. So we encode this process of of um intracellular signaling via ligand diffusion as a diffusion decay problem with boundary exchange to generate interpretable per cell exposure features without choosing an ad hoc neighborhood. It runs with more modest compute and also sets up a clean entry point for ML once the inverse problem is well posed.

James Opzoomer: So this is sort of a schematic representation of the BISTR computational pipeline. You have your underlying biological system and you generate subcellular spatial transcriptomics data say 10x VisiumHD data. We then use an image segmentation, vision transformer for instance, to identify nuclei and cell boundaries and infer subcellular compartments. You then quantify the transcripts on the nuclear and cellular level and then we construct the extracellular domains the space between the cells as a finite element triangulation mesh and we model paracrine signaling fields per ligand across this mesh using a finite element methods. This allows us to extract the per cell signaling features which we identify with receptor gating. So understanding the concentration of the ligand at a cell boundary and whether the cell expresses the cognate receptor to this ligand and from that we can characterize which ligands predict certain gene expression via a GLM based model.

James Opzoomer: So this is another schematic that that represents the data flow within the the BISTR Python package. You have your VisiumHD data. You identify nuclei with a vision transformer and you perform a morphological expansion of cells to create a like a cell cytoplasm boundary giving you approximately single cell data. You then build the FEM triangulation network. You use public databases to look up ligand receptor, ligand and receptor genes that are expressed within your cell types of interest and you solve the FEM network across all of your ligands within the intracellular space. Now this gives you the FE solution at the cell boundary. And we also look at ligand flux which is the relationship between the expression of a ligand within the cell and the FE solution at the cell boundary effectively identifying whether a cell is a source of a particular intercellular communication ligand or a sink, is it just receiving this signal and then we use a GLM to identify which ligands are most predictive of certain gene expression programs downstream.

James Opzoomer: So now I want to show you a kind of a worked example on a publicly available uh VisiumHD data set. So this is the BISTR package applied to this uh 10X Fenomics colorectal cancer data set. This is a a 10x VisiumHD FFPE data set that was published as part of the preprint that was released along with the VisiumHD product launch in in 2024. So here you can see a a highlevel view of the image of the tissue that has been assayed and zooming in onto a smaller subsection of the tissue. So you can see the individual cells. We use a vision transformer model to perform nuclei segmentation and then morphological nuclei expansion. So we follow this expansion to assemble the two micron spots into approximately single cell data which we annotate with its various cell types giving us a tissue representation of single cell data that looks like this. Here they’re colored by their cell type annotation.

James Opzoomer: So on the left here you can see we construct the extracellular domain and mesh. So we triangulate the extracellular space between the cells whilst using a tissue mask to limit the extracellular triangulation to the space that’s only underneath tissue. And starting with a ligand expression per cell, we formulate an FEM problem with diffusion and and decay parameters plus [] membrane coupling that allows us to solve a sparse linear system per ligand and get the FE solution across the tissue space. And here you can see the cells themselves are colored by the expression of ligand vgf-a. And you can see the FE solution in the intracellular space colored in this sort of white to red heat showing that cells express- expressing high vgf-a secrete, are predicted to secrete vgf-a into the intracellular tissue space. And we model this diffusion with decay throughout the tissue. And this ultimately gives us a FE solution across each of the communicating cells within the tissue which we gate basically binarizing them based on whether they express the receptor to a particular ligand or not. If they do express to the ligand then we calculate the FE solution across the cell membrane of each cell and also the flux. So this is the average membrane exchange signal. So effectively this is the proportion of the ligand expression within the cell and at the boundary of the cell from the extracellular space. Is this cell a source or a sink of this intercellular communication signal?

James Opzoomer: So in order to understand what ligands might be affecting certain cell types, we found that the coefficient of variation and also in a related sense looking at the mean ligand flux versus the standard deviation of the ligand flux is informative to understand the kind of most variable intercellular communication ligands across a tissue and cell type. So in this respect, in this particular example we’re looking at vgf-a here in tumor cells which is, which has a relatively high mean flux across this tissue section.

James Opzoomer: So we use then a negative binomial GLM fit to the per cell gene counts which has predictors such as receptor gated ligand exposure. So the coefficients of this model quantify how exposure shifts expected expression and we can see which exposure to which ligand are related to specific genes and then gene programs. Here we can see that our model captures the directionality of many genes known to be associated with a vgf-a exposure in tumor cells. And this indicates that we’re capturing known biological processes associated with this ligand inter- ligand receptor interaction in this tissue.

James Opzoomer: So in closing remarks I think we often find that sequencing based spatial transcriptomics technologies um have a lower UMI coverage um that’s somewhat sparer than single cell RNAseq. This has motivated us to develop novel tools to understand the relationship between intracellular ligand receptor signaling and downstream gene expression. So this tool that we developed BISTR converts spatial transcriptomic counts and in coordination with segmentation into physically constrained extracellular ligand fields and then into per cell exposure for downstream modeling of the effect of ligand exposure on gene expression. And we believe that modeling um ligand receptor interactions like this with a biophysics constraints gives more interpretability into the intercellular signaling process. And we’ve designed this BISTR method as a flexible toolbox that is deployed as a Python package which we hope to make publicly available sometime soon. The goal of this approach really is to generate more tissue contextual experimentally testable hypotheses especially where simple in vitro systems miss micro environmental signaling contexts so we can better understand the intercellular signaling processes that drive cell states in patient tissue and in particular to better understand disease. So we we will be publishing this approach hopefully as a pre-print soon and so I encourage you to to keep your eyes out for it at that time. So yeah, thank you for listening today.

Grant Belgard: James, thank you very much. Um so does the BISTR package work with spatial transcript domain technologies other than VisiumHD?

James Opzoomer: Yeah. So it’s designed from a like the core methods within designs within a spatial sort of transcriptomics method agnostic approach. I really hope that I kind of highlighted that what you need is subcellular resolution spatial transcriptomics data and from there you can reassemble sort of approximately single cell and whatever compartment you can segment with your sort of image layer into that form of data. So, VisiumHD is great for that, but we’re excited to get our hands on um hopefully the new Illumina spatial transcriptomics technology that’s coming out that appears to be sort of in this one micron resolution. But yeah, it should work across different spatial transcriptomics technologies although we have only tested it with VisiumHD but we hope to expand that outwards soon. Thanks.

Grant Belgard: Now what makes the BISTR package biopysics informed rather than just a spatial regression?

James Opzoomer: So that’s a good question. So the the kind of BISTR approach explicitly models paracrine signaling as a spatial field within the extracellular space using this diffusion with decay FEM approach solved over the effectively the finite element mesh that we build from the native tissue geometry from the spatial transcriptomics you know the sort of the imaging data and the spatial transcriptomics data itself. So this kind of we believe this builds a more representative intracellular communication space than just representing cells as nodes on a graph without understanding you know the distance but also some of the spec- tissue specific features that might exist within it. For instance, you know, a future direction that we hope to go is to to use image segmentation within tissues to create different tissue zones, right, which you can identify from H&E and other types of immunofluorescent staining where ligands might have difficulty passing through and thinking in particular, we work a lot on bone as I touched on, but you know, using that to to create more representative data.

Grant Belgard: Great. Well, thank you, James, and thanks to everyone for joining us. Join us for our next webinar on February 18th at 11:00 a.m. Eastern. Uh, Phil Ewels from Sequera will discuss reproducible bioinformatics at scale, nf-core, and Nextflow. Thanks.

James Opzoomer: Thank you.

Ania Wilczynska - The Bioinformatics CRO Webinar

The Bioinformatics CRO Webinar Series

November 11, 2025: Ania Wilczynska – Thinking beyond the single dataset: pragmatic solutions for scalable, AI-ready bioinformatics frameworks  

Ania Wilczynska

Dr. Ania Wilczynska is Director of Bioinformatics and AI at bit.bio. Her team is focused on understanding the gene regulatory code that defines their ioCell products and employing cutting-edge AI and machine learning solutions to data analysis. She has over a decade of experience in Bioinformatics and Data Science and over two decades in molecular, developmental and cancer biology.  Prior to joining bit.bio in 2020, she held positions at the University of Cambridge, MRC Toxicology Unit and the CRUK Beatson Institute (now CRUK Scotland Institute).

In this live webinar, she explores how modern bioinformatics must evolve from one-off analyses toward robust, interoperable platforms capable of integrating multi-study, multi-modal data at scale. Drawing on best-practices in data architecture, metadata design, workflow automation, and AI-ready infrastructure, she discusses evolving omics pipelines into a discovery engine.

Transcript of The Bioinformatics CRO Webinar Series – Thinking beyond the single dataset: pragmatic solutions for scalable, AI-ready bioinformatics frameworks

Disclaimer: Transcripts may contain errors.

Grant Belgard: At the Bioinformatics CRO we help life science teams turn complex omics data into decision-ready insights providing flexible expert bioinformatics support from study design through analysis. As part of that mission our webinar series features practitioner focused talks with concrete takeaways you can put to work right away. Today’s session features Dr. Ania Wilczynska presenting “Thinking beyond the single data set: pragmatic solutions for scalable, AI-ready bioinformatics frameworks”. Ania is the Senior Director of Bioinformatics and AI at bit.bio. Her team is focused on understanding the gene regulatory code that defines their IO cell products and employing cutting edge AI and machine learning solutions to data analysis. She has over a decade of experience in bioinformatics and data science and over two decades in molecular, developmental, and cancer biology. Prior to joining bit.bio in 2020, she held positions at the University of Cambridge MRC toxicology unit and the CRUK Beatson Institute. In this live webinar, she will explore how modern bioinformatics must evolve from one-off analyses towards robust interoperable platforms capable of integrating multi-study, multimodal data at scale, drawing on best practices in data architecture, metadata design, workflow automation, and AI ready infrastructure. She will discuss evolving omics pipelines into a discovery engine. We’re live streaming this on YouTube and LinkedIn. Please drop your questions in either chat or email them to [] and we’ll bring them into the discussion. Ania, over to you.

Ania Wilczynska: Thanks very much, Grant. It’s great to be here. Are we sharing? Oh, here we go. Okay. Right. So, welcome everybody. today I’ll be talking to you about how teams can move beyond single bioinformatics data sets toward scalable AI ready bioinformatics frameworks. the talk is grounded in our experience building such systems at bit.bio but really should be equally applicable across academia and industry. We’ll talk about principles that have guided our thinking over the years and highlight cultural changes in how we need to think about data and workflows. So everyone in bioinformatics faces scaling challenges. And this is going to be about practical ways to solve them. So just as an overview of our talk we’ll first start with stating the problem of data growth. We’ll discuss some principles of scalable design. We’ll talk about infrastructure so automation and building a platform integration of data focusing a lot on metadata and using SOMA objects as an example of data integration and then we’ll go into discussing AI workflows human in the loop and how we integrate bioinformatics data in the new AI world and really how we move into creating AI native data sets.

Ania Wilczynska: So a lot of bioinformatics still operates on single studies and ad hoc analyses and of course modern AI and ML requires scale, structure, and reproducibility. So the question is really how do we evolve bioinformatics platforms to address these questions. Data generation currently outpaces analysis and every omic imaging and metadata stream grows exponentially. So classical ML and now large language models give us tools that turn data into insight but only if our systems are consistent and reproducible. Thus, we can’t treat these data sets as isolated projects anymore. And we need to think about platforms that integrate data, automate quality control, which is obviously the first and very important step. And enable us to use all of the information throughout our organization, be it again industry or academic. And this will be the thing that will enhance discovery, precision and scalability. And this is the area where reproducibility, standardization and machine intelligence intersect. So we need to treat data systems as long lived infrastructure not one-off workflows. And by building structured and automated systems, first of all, this is very simple but very important to everybody. We reduce costs. we do accelerate discovery and then create data that as a consequence AI can actually learn from. So this is building into the future. and if analyses can’t be repeated, they can’t be automated. So platform thinking means designing for reuse. Every data set, every model, every workflow should be modular and interoperable because reproducibility means scalability.

Ania Wilczynska: So now moving on into what scalability can mean. So the first principle that we use is this simplicity scales. And we build modular pipelines where the complexity is contained within the module, but the interfaces between the modules are really clean and clear, which then as a consequence means that the complexity is localized to a module that can be easily interchanged. new platforms can be plugged in easily. Now what’s really key to highlight and we’ll be going into more detail on this shortly is consistent language and naming and shared hierarchies are really important in this because this is how we have clarity both across data sets and across teams.

Ania Wilczynska: And finally, by designing for API and cloud integration, we can future-proof our systems so that new technologies can be onboarded very quickly. So, the princ- the take-home from here is design for evolution, right?

Ania Wilczynska: So, this is how this can look like in practice. So automation, end-to-end automation, in our case connects experimental metadata compute analysis storage and dashboards in a continuous loop. It allows multiple analyses to run in parallel. This is instantly reproducible when new data sets arrive. And so the outcome is that scientists including bioinformaticians spend more time interpreting results and less time essentially babysitting pipelines. So this infrastructure supports hypothesis generation and iteration. And it creates a complete cycle from data to new insights. The automation of course doesn’t replace scientists, it amplifies them. And so by automating the flow of data ingestion to reporting to API access we iterate faster and keep quality consistent. So the schematic on the right shows you how we automate the full bioinformatics cycle from data to insight. We start with metadata capture in Benchling as well as our in-house built app. And we ensure that every sample and condition is traceable. This is really key. Next pipelines run automatically with the use of AWS Batch and Lambda and these are scalable as new data volumes arrive. The results are stored in AWS S3 then linked through APIs to feed dashboards and AI tools and LLM agents can summarize the results, flag patterns, QC issues and the scientists interact with the data through dashboards. And so the key idea is that the loop data in analysis inside out runs reproducibly at scale. And it frees our time to focus on interpretation rather than execution.

Ania Wilczynska: Right? So, I’ve mentioned metadata quite a lot already because in our view it really is the connective tissue behind all of the data sets and capturing rich technical biological provenance metadata early allows us to integrate across studies, perform batch correction, and reuse analyses efficiently. We employ fair principles, define once, reuse everywhere. This is really essential. And standardized metadata allows for not just traceability, but also creates a central data store that ensures findability and reuse and structured data feeds directly into AI and ML tools. So metadata can of course be vast. One thing that we found that’s been really important for automation of pipelines, tracking of samples has been a unified sample naming system. Again it sounds pretty trivial. It’s- it takes a little bit of engineering and a little bit of cultural change to to deploy, but it’s been extremely important for us. Again, as a relatively trivial example. So again, just to reiterate that metadata turns messy data into machine readable knowledge.

Ania Wilczynska: So, a slightly busy slide. But this is just an overview of what our platform looks like in terms of integrating research and again using metadata as a foundation as well as automation. So our in-house bioinformatics platform connects this metadata with the sample tracking analysis pipelines QC and reporting through a unified database. it provides live links, data sanitation, and API access, enabling researchers to explore, analyze, and develop AI workflows directly. And as a result of this, we have a self-service interactive research environment where data flows seamlessly from experiment to model. And the way we think about this is that we move from a data set to really a living research system. And we use this platform internally to handle everything from single cell to genotyping to plasma design. and we emphasize empowering users and bridging all these systems.

Ania Wilczynska: Okay. So now we’re, now we start moving into making AI — sorry — making data AI ready. So of course data alone isn’t enough. it needs to be transformed into structured knowledge and we do that by explicitly extracting relationships from our experimental data metadata as well as publications. A lot of our work relies on relies on external open source data and we codify these relationships into a knowledge graph and now we can start to infer new connections using AI. So this structured understanding supports predictive biology which is what we as a company do. And we hope that this will give us the ability to anticipate outcomes of experiments rather than just to measure them.

Ania Wilczynska: So AI now helps us to ask better questions about the data that we are actually generating, generating in house and the workflows that we’re developing using our data as well as again like I said external data from publications from open source data sets is — the loop consists of first defining a question then again aggregating lots of data moving through AI agent synthesis through human review and I cannot stress enough that at how important it is at this stage to have the human in the loop. We- we’ll talk about that again in a second. And finally updating the knowledge graph and iterating again. The human element is very important in terms of both ensuring quality scientific rigor and of course eliminating faulty or hallucinated information. So again to emphasize we’re not at the stage yet of replacing the scientist were augmenting their ability to work. And of course this reduces lag between experiments and insights. So every iteration enriches the knowledge base and therefore improves the outcomes for the next round.

Ania Wilczynska: So, the more we automate retrieval and summarization, the more time our scientists have to focus on creative reasoning and high complexity tasks. So, this is really what this simple schematic at the right is showing you. We are less a lot less focused on the medium and low complexity tasks thanks to both our automated modular pipelines as well as the plugging in agentic AI to be essentially better scientists. Moving a little bit away from the engineering into the more creative science. And this of course implies huge efficiency gains.

Ania Wilczynska: So once again emphasizing the human in the loop. but on the engineering side of things we implement all these principles through a retrieval augmented generation or RAG stack. It allows the AI models to query internal data safely without retraining or exposing to sensitive — exposing sensitive information. Again the architecture is modular. This is obviously a theme. And each of the agents, be it a specific bioinformatics agent or imaging agent or a developer agent, specializes in a particular domain. And this is all coordinated by the human in the loop scientist. So of course this dramatically accelerates tasks that used to take hours that can now be done in seconds. The structure is secure, modular, with replaceable components. So again this is how we think about scalable AI in kind of pragmatic production. All right. So circling back in a way to something a little more formally bioinformatics focused. I’m going to talk to you a little bit about how we scale to multi-data set and multimodal analysis. And this will be mainly focused around single cell data sets. Because this is really an area where the concept of scale is quite obvious and quite a pain point for a lot of researchers. So single cell data sets scale to millions of cells. And integration of these data sets becomes a core challenge for many reasons. But a lot of it is because it is a data engineering problem not just a problem of statistics. So internally at Bit.Bio, we routinely handle data sets from millions of cells across multiple studies and modalities. And to integrate these data sets successfully, we have to normalize technical variation while preserving biology of course and build models that scale efficiently. So data sets need to be aligned across batches, labs, modalities, be that RNA or ATAC-seq, spatial data, protein data, imaging, what have you. And new algorithms do scale to millions of cells and to multiple modalities. There are of course integration tools such as Seurat, Harmony. This is all available open source and scales to unprecedented levels. But the volume of the data is still a huge challenge even when it comes to just loading the data objects for analysis. So the way we’re currently addressing this question is with the use of SOMA. So that stands for stack of matrices annotated. It’s a new open standard from the Chan Zuckerberg Initiative designed exactly for this challenge. So, it provides an array based data format that supports multimodal data sets, again RNA, ATAC-seq, and so on, at a massive scale. It’s fully interoperable across R, Python, C++. And it enables out-of-core access to data aggregations much larger than single host main memory, ensuring distributed computation over data sets. So SOMA provides a building block for higher level API that may embody domain specific conventions or schema around annotated 2D matrices like cell atlases. So for us adopting SOMA has meant that we can first of all store huge amounts of data in one place, slice it quickly and any way we like and share and finally share the data reproducibly preparing it for AI training or in or more simply retrieval. So what this looks like in practice as an, as one example is we’ve integrated a number of perturbation screens where both the technology in terms of sequencing as well as conditions were very different and about 2 million single cells in total have been integrated from our own data. We’ve also been able to put this together with pseudo bulks for each of the screens as well as pseudo bulks from the open- source 44 million single CELLxGENE census data set. This is all integrated in a unified SOMA data layer. What this means is that for all of these data we have a consistent schema for metadata. One of the important things to highlight here is we have, we’re using a unified gene annotation which does require some data wrangling as especially external data sets can use very different annotations. Now that everything is put together, we can easily query slices of the data in seconds across different data sets instead of waiting for minutes or sometimes even hours for our data to load. And this is really the first step towards truly AI native data sets because it- the data is structured is standardized and is really ready for automated reasoning. And with that a kind of whistle stop tour of our thinking. I’ll end. So just three principles to summarize. Treat data as infrastructure not a byproduct. Make metadata-first design non-negotiable. And realize that AI readiness is an outcome and emerges naturally from reproducibility and structure. So the goal is not to automate everything but to build systems that let us scale and scale the scientific discovery. So we need to work towards these scalable interoperable systems instead of just thinking about individual scripts and creating silos.

Ania Wilczynska: And thank you very much.

Grant Belgard: Thank you Ania. As a remember — as a reminder, live viewers can submit questions to the live chat on YouTube or LinkedIn. To kick us off, how do you ensure reproducibility across so many heterogeneous pipelines?

Ania Wilczynska: Yeah so we have I think over 30 different sequencing technology pipelines as of my last counting. And so of course deliberate design is very important. In terms of particular pipelines I cannot emphasize the need for containerization enough. having versioned pipelines, fixed parameters and in it — just to sound like a broken record, very well structured and deliberate metadata capture.

Ania Wilczynska: Having a centralized way of sample submission is also, has also been really important for be, for us being able to very quickly version our pipelines as well. We have relatively rigorous testing approaches as well. But yeah, so containerization versioning and metadata first and foremost.

Grant Belgard: How do you align or normalize data sets across modalities and platforms?

Ania Wilczynska: Yeah. So batch correction is obviously a a big nightmare. And we’ve already spoke about spoken about tools like Harmony, Seurat. There are plenty of publications that talk about various pitfalls of the just single cell integration tools. But again metadata is extremely important. And for example, in our hands, thinking about integrating imaging with transcriptomics is a nontrivial problem especially in the absence of spatial data. So we do single cell but we don’t do spatial transcriptomics. And we’ve spent a lot of time thinking about how we can integrate some ML approaches to to image analysis with our transcriptomics data. And once again, unsurprisingly, metadata, excellent sample tracking, and integration of the, of systems, including, ELN’s, ELN systems like Benchling has been really key to this and sort of deliberate. So there’s also an element of deliberate experimental design again thinking beyond a single experiment. Because you know and I appreciate that academic labs will be less naturally used to thinking about consistent experimental designs because that’s not kind of the core way of thinking in academic labs. However, I still think that asking the question “does it scale” is extremely important also in academia. I mean in my academic career I found that the lack of the question of “does it scale” has meant that we often missed a lot of opportunities to integrate data sets because everything about them was just incompatible.

Grant Belgard: Yep. What’s the advantage of SOMA over existing HDF5 or anndata approaches?

Ania Wilczynska: Yeah, so it’s really the out-of-core scalability, the fact that there is multimodal support and the interoperability. So it’s, it is really from everything that that we’ve seen so far in actually implementing these huge SOMA objects. It’s the next step towards big data rather than single experiments. We’ve — you know I — this may sound like a plug, but we’re, yeah, we’re really excited about how this is enabling us to just to iterate through computational experiments if you like very quickly.

Grant Belgard: How is AI readiness different from just automation?

Ania Wilczynska: Well, so AI readiness is really it means a different way of thinking about structure and about semantics. You know automation, with automation reproducibility is sort of your main output and your main gain. Whereas AI readiness means that the data is discoverable and learnable. And so it does require cross experiment but also cross function thinking. I think that’s another you know thing that people often disregard is how important it is to think about data and about computational biology not just within the computational biology function. But also make sure that the wet lab scientists or indeed in industry other functions understand how everything fits together in a data stream.

Grant Belgard: So related to that what cultural or organizational changes are required for this to be successful?

Ania Wilczynska: Primarily cross functional collaboration. And I think a little bit of mix of evangelizing and education can really go a long way. So we work both on the, on embedding AI workflows into bioinformatics but we also work with other teams for example you know the commercial team in our company to create AI workflows and that of course it helps the business understandably but it also means that there is a lot more understanding across the company as to why such workflows are important, why data is important and why data structures are important. And again this is, this does require a little bit of outreach, a little bit of evangelizing. Structured metadata and deliberate experimental design can at first seem like a little bit of an overhead in the lab because oh it’s another thing I need to capture.

Grant Belgard: We’ve never seen that before, have we?

Ania Wilczynska: Indeed. But showing people the value of that rather than going, “Oh, ping, my whizzy whizzy machine gave you a new result,” but rather going, “Well, because of that overhead, we’ve now been able to bring three data sets together. One that we did three years ago and one that we did now, and now we have a better outcome.” And again, this is, you know, this is all kind of cultural shift, but I found that, you know, that a little goes a long way in that respect. So, so yeah, a bit of showing by example, a bit of evangelizing and also treating colleagues as partners.

Grant Belgard: What’s the next step beyond AI native data sets?

Ania Wilczynska: Well, of course, in the utopian brave new world, it is AI scientists. I don’t think we’re quite there yet. Or at least so we’re all telling ourselves or we’ll all be out of jobs. But really it’s the closed feedback loops, data, models, experiments, self-improving hypotheses. And I think that’s really where things are heading very quickly.

Ania Wilczynska: But there is also well I guess everyone’s talking about it now. There’s also a lot of hype about what AI can do. But I think a lot of what we’re all in a way promising ourselves is is really bottlenecked by data.

Grant Belgard: And so I’m hearing it’s really essential to train people to think in terms of cycle time, right?

Ania Wilczynska: Yeah.

Grant Belgard: Uh, so we have an emailed question. Uh, how can these platforms be translated into the clinic or are there regulatory requirements that need different setups of the platforms?

Ania Wilczynska: Yeah. So, everything I’ve talked about, just to be clear, is in a R&D preclinical setup. I think the important thing to remember about regulatory requirements is that everything is extremely slow for good reason. And there is very little at least as far as I’m aware existing regulation around AI tools. I think that will probably take quite some time. Which again brings me back to this to this idea that you know the AI tools are — I mean we’re all blown away by stuff every day but because these, the regulatory principles don’t really yet exist the experimentation is still necessary. And so again I — thinking about the fact that it’s not just the tool the data has to come before it but also will for some time come after it is very important. Of course on the other hand in terms of you know just building automated modular pipelines you know there are the a lot of the cloud platforms provide certain standards so it you know we’re sort of working towards it but I think we shouldn’t expect the really novel solutions to be adopted all that quickly.

Grant Belgard: Well, Ania, I think we’re at time, but thank you so much for joining us. Um, and the series will resume January 21st at 11:00 a.m. Eastern with Jake Taylor-King from Relation Therapeutics, followed by Phil Ewels from Seqera on February 18th at 11:00 am Eastern. Uh, mark your calendars and thank you everyone for joining us today.