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.

Coming Soon…

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.

VeLo Pharma has just opened up a community investment round with no investor accreditation required: https://netcapital.com/companies/vertical-longevity

Vertical Longevity Pharma investment QR code

Transcript of Episode 69: David Scieszka

Disclaimer: Transcripts may contain errors.

Coming Soon…

The Bioinformatics CRO Podcast

Episode 68 with Caspar Barnes

Caspar Barnes, founder and CEO of AminoChain, tell us about his mission to make biospecimen sourcing transparent, ethical, and efficient.

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.

Caspar Barnes

Caspar Barnes is founder and CEO of AminoChain, a decentralized biobanking protocol with a mission to make biospecimen sourcing more transparent, ethical, and efficient.

Transcript of Episode 68: Caspar Barnes

Disclaimer: Transcripts may contain errors.

Coming Soon…

The Bioinformatics CRO Podcast

Episode 67 with Manos Metzakopian

Manos Metzakopian, co-founder and CEO of CellCodex, joins us to discuss CellCodex’s mission to provide high-quality, scalable cellular perturbation data, ready to train advanced AI models for biology.

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.

Manos Metzakopian

CellCodex is a CRO that generates AI-ready perturbation data at scale. Our founder and podcast host, Grant Belgard, is also a co-founder and the CTO of CellCodex.

Transcript of Episode 67: Manos Metzakopian

Disclaimer: Transcripts may contain errors.

Coming Soon…

The Bioinformatics CRO Podcast

Episode 66 with Eva-Maria Hempe

Dr. Eva-Maria Hempe, who leads NVIDIA’s healthcare and life sciences business across Europe, the Middle East, and Africa, joins us to discuss her work at NVIDIA, the gaps that AI can fill in healthcare research, and the future of drug discovery.

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.

Eva-Maria Hempe

Eva-Maria Hempe leads NVIDIA’s healthcare and life sciences business across Europe, the Middle East, and North Africa. 

Transcript of Episode 66: Eva-Maria Hempe

Disclaimer: Transcripts may contain errors.

Grant Belgard: Welcome to The Bioinformatics CRO podcast. I’m your host, Grant Belgard. Today, we’re joined by Dr. Eva-Maria Hempe, who leads NVIDIA’s healthcare and life sciences business across Europe, the Middle East, and Africa. Eva-Maria, trained as a physicist, earned a Bill and Melinda Gates funded PhD in healthcare service design at Cambridge, and has since moved through roles at the NHS, Bain & Company, VMware, and the World Economic Forum before joining NVIDIA. She now guides strategy for applying accelerated computing and generative AI, think BioNeMo, Parabricks, and DGX Cloud, to genomics, drug discovery, medical imaging, and more. Eva-Maria, welcome to the show.

Eva-Maria Hempe: Hey, great to be here.

Grant Belgard: So what do you do day-to-day at NVIDIA?

Eva-Maria Hempe: I think in general, my day-to-day oscillates between two major poles, like working in the business and working on the business, or playing the short game and the long game. So on the one hand side, I am responsible for the business. And so that means we have to deliver revenue because if you don’t deliver revenue, you’re not a business, you’re a hobby. And when, on the one hand side, I have to hit a revenue number because if you don’t have a revenue, then you’re not a business. But on the other hand, NVIDIA is all about the long game. Like we are creating markets. We are building things that haven’t been built before. And so it’s really about striking this balance. And what it means, very practical, is on the one hand side, as I said, working in the business. So I have customer meetings.

Eva-Maria Hempe: I work with my team. We’re discussing strategies and tactics, like what should be our sales place? How are we going to work with startups? How are we going to work with this customer? I check out KPI if I see like, are we on track to delivering the revenues that is expected of us? I do a lot of talks and evangelizing to spread the message that NVIDIA is so much more than just GPUs that we have all this great software out there as well, which is super helpful and super valuable to our ecosystem that people can save a lot of time by building on top of what we put out there. So that’s the operational part. And then there is the working on the business. So really the more strategizing, making decisions on, should we focus on enterprises or startups? Where within healthcare should we focus?

Eva-Maria Hempe: To whom do we talk about which kind of topics? To which degree are we focusing on the sale? But where do we see new areas emerging which maybe aren’t driving a sale or even a lot of compute initially, but where we really believe that there are, A, making an impact. And then if they make an impact, eventually it will turn into revenue, which is one of the real beauties about working at NVIDIA that the company is set up in this way to build, to disrupt, to change and to, yeah, you have this luxury almost like it’s a bit crazy to call it luxury, but in a lot of businesses, it’s a luxury you don’t have to really work on your business than just working in the business.

Grant Belgard: So BioNeMo just went open source. Can you tell us about that and what pain point it solves?

Eva-Maria Hempe: Yeah, so in general, as I said before, we’re trying to do at NVIDIA, we’re trying to lift up the field. So we’re not looking for the quick buck. So that’s why we’re not looking to, we’re not gonna change the field by collecting licensed revenues on BioNeMo, but we think BioNeMo is a super interesting, super valuable tool for the community. And by putting it out there as open source, we can just make it much more available to a lot more people. And also we can increase the number of people who are contributing to it with their ideas and making it into something that is a lot more valuable to the community and more powerful and much more in line with the community. I think around the same time that we made it open source, we actually also, we changed it.

Eva-Maria Hempe: Like we turned it into, it has two pieces these days, the one is BioNeMo Framework and the other one is NIMs. So Framework is really, it’s also a collection of microservices, but it’s a collection of microservices, which you need to train and deploy models. So it has a curator and an evaluator and a guard railing part to it. And you can use all of these, you can use any of these, whatever helps you to put out models in a better way. And then we have NIMs and so NVIDIA Inference Microservices and some of them are biology specific. So we have some on folding, we’ve got some on generation, we’ve got some on docking, and you can put this together into reference workflows, which we call blueprints.

Eva-Maria Hempe: I often say it’s a bit like, if you think of a big box of Legos, it’s like the building plan, how you build the most basic thing out of them and then you can play with it and turn it into all sorts of other things. But in general, what we’re trying to do with BioNeMo is really solving the main pain points of drug discovery. So drug discovery is slow, it’s expensive and then also quite technically challenging if you want to use computer aided drug discovery. And so here we’re giving researchers tools to handle complex data, to collaborate and just in general, we wanna have an advanced biomolecular research framework out there that people can use and that they can do their best work with.

Grant Belgard: And for our listeners who aren’t already familiar with BioNeMo, can you give a quick primer on what they can do with it?

Eva-Maria Hempe: So, as I said, it is mostly about computer aided drug discovery. So one way I usually explain it, we have another framework called NeMo and that’s not by coincidence. So NeMo is all about training, deploying models that have to do with language, but by now it’s actually also multimodal and BioNeMo is that for the language of biology. So if you think about a sentence has like words and observes grammar and the same way like a molecule has atoms and observes the laws of physics and chemistry. And so that’s a bit the analogy there. And so the same way that with our language model, you might have proprietary data and you might wanna train a model on this or you might wanna fine tune a model with new data, you can do the same thing with biological data.

Eva-Maria Hempe: If you have data coming in, you can curate it and then you can also make sure, so that’s the curator part, then you can also evaluate it against certain benchmarks. So how good is my model? And then finally you can also make sure it has certain guardrails, so it doesn’t do certain things that you don’t want it to do. And so that’s, yeah, that’s in a nutshell about it. It’s about training, deploying and serving biological models for drug discovery.

Grant Belgard: So AlphaFold has made a huge splash in the structural biology world. What do you think is the next big thing that would be GPU enabled in biology?

Eva-Maria Hempe: For me, AlphaFold is really like, I’m a physicist. So I know when I did my PhD, which in my mind hasn’t been that long ago, we locked up PhD students for three years in a basement to find out the 3D structure of a protein. And now you can just do it on a computer. You can go to build.nvidia.com where we host the NIMs, I said before, and we have a model there and you could fold a protein in like a second live on your computer. And it’s just mind blowing. It even works on my phone. I’ve done it during presentations on my phone. So I’ve folded a protein on my phone within less than a second. In general, there are certain things around AlphaFold. There are certain gaps. So it has problems with dynamics. It has problems with multiple conformations. It can’t do disordered proteins.

Eva-Maria Hempe: And 60% of human proteins have at least one intrinsically disordered region. It’s also not great with protein ligand and nucleic acid interaction. So there are a whole lot of things which it cannot do. And so these are actually also the things we see in the field where a lot of work is going on. And as NVIDIA, we’re doing some research ourselves in the spirit I said before, in trying to lift up the field and trying to show what’s possible and trying to also inspire other people to go further down that path. And so we’re doing some research ourselves. We’re doing a lot of research in collaboration with all sorts of other people. Sometimes we’re open about this. Sometimes it’s not disclosed, but yeah, we’re seeing a lot of things that are going on.

Eva-Maria Hempe: And what we’re seeing in particular in terms of frontiers, I would say, are four things. So we see how do you deal with larger complexes and assemblies? How do you deal with post-translational modifications? How do you deal with dynamics, molecular dynamics? And then also how do you deal with protein design? Like how can you turn AlphaFold around? Like with AlphaFold, you have the sequence and you want to know the 3D structure. Can you have a 3D structure and figure out what is the sequence behind it? So there’s a bunch of work going on in the space and I think it’s going to be super exciting to see what will come out of that.

Grant Belgard: How do you see DGX Cloud changing the barrier to entry for academic labs?

Eva-Maria Hempe: DGX Cloud is like an interesting way, which is part of what we offer. And maybe it’s easier to understand in the greater context of what we offer. So in general, we are very much agnostic of what GPU you’re running your workloads on or what NVIDIA GPU you’re running your workloads on. And that is a huge advantage for people who are working with our software because we don’t want to lock anybody in. The only commitment you’re making is you’re going to work on GPUs, which I think is not a bad lock-in. You’re not locked in any other way, but that you’re going to be using GPUs. And those GPUs, the answer what GPUs are the right ones for you will again very much depend on your situation. Like, do you have a data center? Is your data center big enough? Has it liquid cooling?

Eva-Maria Hempe: Does it have enough electricity? Do you even want to run a data center? Or do you have big spikes where you need really high performance computing capacity in a short amount of time? And DGX Cloud is following our reference architecture. So it’s really all the different components, the GPU, CPU, networking perfectly aligned with each other. And it’s in the cloud, it’s on demand. So what we see it used quite often for is spike. And if an academic lab has that, if a lab is trying to train a huge model, it can be the right thing for the lab. And it could be a great way as well to showcase the power of it, but it’s not always the right solution. Sometimes it’s also worthwhile to build your own on-prem capacity or to go with more conventional cloud capacity.

Eva-Maria Hempe: So I think it’s an element of a larger compute discussion, but it definitely allows academic labs if they have the funding, if it’s basically baked into the grants to really get top-notch performant GPU computing on really short timescales.

Grant Belgard: And at what stages in the process does AI assist drug discovery today?

Eva-Maria Hempe: Pretty much along all of them, I think we see different levels of activity. So we see a lot of really early discoveries. So it starts with things like finding new targets, which I think is an interesting one. I think it’s one where we don’t see, I think you could see even, I would hope for even more activity. Somebody told me the other day how many people are working, how big the overlap is between working on the same targets. It’s mind blowing. And for example, what we talked before, intrinsically disordered proteins is a super interesting area to really find new targets, to be able to address parts or proteins, which so far have been undruggable.

Eva-Maria Hempe: And we’re working with a company there, they’re called Peptone, and they actually, AI supported, have found a method to figure out the structures of disordered proteins. So I think this was super exciting. So we’re starting there. And then of course, we have all the virtual screening workflows in terms of, okay, you have a target, you fold the target. Then you have something like MolMIM or like a generative model, which starting from a particular small molecule creates all sorts of variations of that small molecule. And then you take your protein and your multiple variations of small molecules you generated, and then you use another AI model, which can calculate how well they fit together. And as I said, that’s an area of active research as well.

Eva-Maria Hempe: How well can you really calculate those bindings? And again, another company we’ve worked with, they’re called Inoform. They can actually also do a, they can create models that fit into a particular, or molecules that fit into a particular cavity. So there’s a lot of interesting things around there on the real fundamental level. But then there’s even more to it. There’s, we’re trying to figure out how can we also, or companies are figuring out how can you apply AI to pre-clinic?

Eva-Maria Hempe: And then even in clinical research, or the clinical stages of drug discovery and drug development, there is still so much that can be done because so many drugs don’t necessarily fail because the biological mechanism isn’t there, but often also because you can’t recruit patients, you can’t recruit the right patient. And again, AI can actually have a huge contribution to solving these kinds of problems. And then you can go into manufacturing and selling drugs. So I always tell my clients that AI is a topic along the entire value chain. And we are seeing applications today along the entire value chain. Like every single step, there is somebody working on something and a lot of progress is being made.

Eva-Maria Hempe: You still have the whole issue that just things take a very long time because like clinical studies just take the amount of time they take. You can have a bit of time out there by doing optimized recruiting of trial participants, which is usually a pretty of a delaying factor, or you can use AI also to speed up the data analysis and regulatory writing, clinical writing, submissions processes. So there is some speed up you can do there. But I think in terms of the speed up is more happening in the earlier phases of drug discovery. And then in development, we really have more of a trying to figure out where do they work. So a lot of work I see in that area as well is around biomarkers.

Eva-Maria Hempe: Again, figuring out what works for which patients so that it feeds back into the early stages, but then also once you’re in trials, you have the right patients in your trials and you have a better chance of actually making it through phase three, doing efficacy. I said about all those different ways, how AI can help with the preclinical part. And there is actually real good data on that by now. So, and SILCO is really famous about this and they were smashing it. They had 22 developmental candidates between 2021 and 2024. And actually they were able to get on average to a developmental candidate within 13 months. So around 70 molecules synthesized per program. And the fastest was like nine months and the longest was 18 months.

Eva-Maria Hempe: And this is just like a huge, huge speed up to what you usually see, but these kinds of processes take years. Interesting, so that’s the preclinical phase where it’s really about the speed up and you can also go from target and lead identification over lead optimization in 46 days these days. So all of this is amazing. And I said before in the clinical studies, it’s then really about being better. And there was a paper which came out last year where they looked at AI discovered drugs. And for phase one, the success, probability of success was twice as high as for regular drugs. And it was still pretty bad, but it was twice as high. And then for phase two, it was in line with the averages, but for phase two, the numbers started to become quite small.

Eva-Maria Hempe: And for phase three, there wasn’t enough data. But if we assume this holds, if you assume you’re twice as successful in phase one, which is not unrealistic because phase one is all about safety and with better models, we get better idea of target effect, and then phase two and three about efficacy and a dosing on part, then this actually means we’re going from one in 10 drugs, making it to markets to two in 10 drugs. It’s still a lot, but it’s basically, it’s halving our cost per drug. And if a drug costs these days, on average $2 billion to make it to market, saving a billion dollars per drug. So this is huge. Your potential is huge, which I think is why we’re all still working on this despite all the problems we talked about of long timelines and difficulties to get funding.

Grant Belgard: Where are the biggest talent gaps in bio AI today?

Eva-Maria Hempe: I think it’s really about speaking multiple languages. And the question is also talent where? So we have and– and what keeps things from reaching or from reaching impact. So I think if you look at a lot of the biotech, tech bio, we still have the issue that the entire pharma ecosystem is set up in a particular way. Somebody said it the way, like it’s a coin flip. And we know that the coin is unfair. We know that heads gonna come with a 10% probability. Now what these companies are doing, they’re actually trying to improve the coin minting process. So by using AI, we’re trying to mint better coins. We’re trying to mint a coin, which has a 20% chance of heading up, landing heads up. But this is really hard to prove.

Eva-Maria Hempe: And the entire system, the people in the VCs, all their mindset is like a biotech investor mindset. And they’re looking for the things around a 10% coin flip probability. And it’s really hard to evaluate this. Is this really going to get us this lift up or not? And different to other areas of AI like quant trading where you have immediate feedback, you change something, okay, you’re gonna make more money. Great, let’s do more of this. Here, it’s almost the complete opposite of quant trading. You have like 10 years until you see whether it works or not. And I think that’s actually one of the biggest gaps.

Grant Belgard: Even with the 10 years, it’s small in, right? So it trickles through after 10 years.

Eva-Maria Hempe: And so, yes, I think we need to have more people who speak multiple languages of AI and of data science and of biology. But I think we’re starting to see some of that. But I think it’s really more the system as a whole and the incentives and the structures and just the fact that we’re dealing with biology, which takes 10 years to come. But I’m still optimistic.

Grant Belgard: What are your thoughts on community standards such as OpenFold and so on? Are there areas where there are glaringly obvious missing standards or areas that you think are still being held back by a lack of standards?

Eva-Maria Hempe: At NVIDIA, we are big believers in open source. So we think it’s the one way to really harness the power of community. And we are big believers in the community. NVIDIA is all about communities, about ecosystems and us doing our part to help the ecosystem develop, which is why so much of our software is actually open source because we believe in the power of this approach. And we really wanna support it to come to full fruition.

Grant Belgard: Well, it’s essential to save biotech and pharma, right? The internal rate of return on R&D has been abysmal below the cost of capital for many years now. And at last that turns up.

Eva-Maria Hempe: It’s actually interesting because of those $2 billion per drug or one and a half billion dollars per drug, only I think it’s around 300 or so are the actual cost. All the rest is the cost of the failed drugs and the cost of capital because the capital is just locked up for such a long time and you have so many failures all around. And the other thing I think, I don’t know, you’ve probably seen it, it’s called Eroom’s Law. If you take how many drugs $1 billion in research spending buys you, it’s a logarithmic downward over the last 70 years. This is not recent. This has been going on forever, but it’s just starting to get into areas where it’s just really, you just can’t continue this way. We just need a different way of doing things.

Eva-Maria Hempe: We just can’t continue spending more and more and more and getting less and less and less.

Grant Belgard: So shifting gears, let’s talk about your own journey. What pulled you from physics to health?

Eva-Maria Hempe: It was the impact. So I was sitting there in my lab. So I was doing quantum optic, which means I’m sitting in a dark lab because I was dealing with optics and lasers. So you don’t want daylight messing up your experiments. So you go in in the morning, it’s dark. You leave in the evening, it’s dark. And during the day, it’s dark. And I was just thinking to myself, what is this going to do for the world? And back then we kept saying, oh yeah, this could be used for quantum computing. But back then I was like, well, but this is going to be at least 15 years until anything useful. And I have to say, this has been more than 15 years ago by now. So I was just like, okay, is this really it? But then as with those decisions, usually two things have to come together.

Eva-Maria Hempe: And the other part, which was for the ignition to really change tack was just meeting the right person at the right time. So I met this girl and she was an electrical engineer by training. And she studied how procurement processes at the hospital affect patient safety from with this very scientific engineering frame of mind. And I just thought that it was fascinating. Like all the way I’ve been trained to think, which like I really liked the scientific method. I really liked this way of thinking, but applying it to real world problems. And that’s how I got to study healthcare service design.

Grant Belgard: Are there any insights from your PhD that you still use?

Eva-Maria Hempe: Yeah, I think it’s really that organizations are an interplay between structure and people. And that sounds very simple and very obvious, but if you’re designing an organization, you’re not actually designing an organization. You’re designing almost a scaffolding for the organization to grow around. You’re giving some structure, but an organization isn’t the org chart. It isn’t the policies. It isn’t the trainings. It’s the people which are populating those structures, which are interacting, which are meeting each other or not meeting each other. And I think that was a really important insight which has like, it pops up everywhere. Now, one of my big challenges at work is like how do I get enterprises to adopt AI?

Eva-Maria Hempe: That’s again, an organizational question. As much as a technological question, actually technological question is like, maybe not even half of it. A lot is really about how do you get people to adopt it? How I get people to use it? What are the incentives they’re listening to? Who has power in this organization? How is this organization really structured? So yeah, I still use some of the things I learned, I studied.

Grant Belgard: And what did you learn in your time with the NHS that you think tech sector often misses?

Eva-Maria Hempe: I think in the tech sector, it’s easy to look at everything through a technological lens that, oh yeah, we can improve this, we can do this. But a lot of my research and my work was about design thinking, which is very much empathy. You start with the end user, you immerse yourself into the end user. Ideally you get to observe, you get to shadow, but you get a real idea of what are people doing and what’s the real problems and how can technology help that? I think this empathy, this user-centric view is sometimes a little bit missing in tech. I think what we also discussed before, you’re creating a great tool and maybe the people you tested it with like it, but it has to fit into the workflow. It has to fit into the real life. It’s all about minimizing friction.

Eva-Maria Hempe: I was saying the other day, just like if you wanna drive real value in organization, it’s about having something that has as little a friction as possible and as much immediate value as possible. And then you’re gonna see adoption. If it’s high friction, it has to have even higher value. If it’s low value, it has to have even lower friction, but ideally it has both.

Grant Belgard: Can you tell us about your time at the World Economic Forum and how that impacted the work you do today?

Eva-Maria Hempe: Yeah, the forum really is about multi-stakeholder and what role policy plays. And again, about what are the right incentives and how can you align the incentives of multiple different parties towards a common goal. So what I did there, it was about the future of healthy. So how do you make staying healthy a business versus having people get sick first and then making them healthy again? I mean, that’s an established business model, but why are we there? Why can’t we just keep people healthy in the first place? And there it’s really about thinking through the food industry. How can we make it a better business for the food industry to sell healthy food? How can we make it better for the doctors to be paid to keep the patients healthy?

Eva-Maria Hempe: There’s models for that where they get basically paid per patient in their catchment area, but they don’t get paid for the procedures they do, but they get like a fixed fee. It has all its pros and cons, but really think through things from a joint value and joint incentive point of view. And like I said, again, when you’re trying to change big systems, whether it is an organization or whether here it is like a multi-organizational system, it’s really important. And this is something I think I couldn’t imagine a better place to learn how you navigate these things, how you deal with politicians, how you deal with all the different lobbyists and all the different interest groups and really try to drive towards a common goal. And I think there’s no better place than the forum to learn that.

Grant Belgard: Can you tell us about your time rowing in Cambridge and did that develop you in any way that’s useful today?

Eva-Maria Hempe: Yeah, I got to Cambridge twice. The first time I went to Cambridge, it was for a summer research as part of my master’s thesis. And I knew people and they made some connections for me. And so I was at Cambridge during the summer before the freshers arrived. And then the freshers, so the first year students all came in and all the clubs started recruiting and the rowing club started recruiting and they tried to recruit me. And I was like, yeah, no, I’m only here for a few more months it doesn’t make sense, I should still do it. And I didn’t do it. And then I came back to Germany where I was finishing my studies and everybody was like, oh, you were in Cambridge, did you row? I’m like, no. And then I really regretted it. I was like, well, I really should have.

Eva-Maria Hempe: So I promised myself if I make it back in for my PhD I’ll give rowing a go. And so I did, and initially I wasn’t that good. So I was in the second novice boat. I didn’t even make the first novice boat. I was in the second boat, but then I just kept at it. And I barely made the first boat in the next term. There’s three terms in Cambridge. And then in the third term, I was still in the first boat of my college, of my part of the university I was at. And then I was around for the summer. So I thought, okay, the university team is doing a summer program. I might as well try that. So I did that. And then they try to funnel you into joining the team full time. And I was like, well, Cambridge rowing.

Eva-Maria Hempe: The year, my first year I watched the Cambridge boat races and I was like, wow, it must be so nerve wracking and whatever. And then they were like, yeah, you did the summer program. Don’t you want to trial, like just try for the university? And I was like, okay, well, what’s the worst that could happen? I’d taken that lesson of where I hadn’t rowed and regretted it. I’m like, okay, I don’t want to regret. So I just went for it. And then I found myself on the starting line of that boat race, which I just watched a year before. So I went within 18 months from never having rowed in my life to rowing and winning a boat race. And I think the lesson here, as I said, there’s the one about no regrets.

Eva-Maria Hempe: I think the second one about that you’re just capable of a lot more than you give yourself credit for. And I think the third one also just about the power of habits and the power of persistence and the power of community. So there’s nicer things than getting up every single morning at five o’clock, going to the train station, going rowing, barely making it back for nine o’clock to go and to your lab and do your work. And then at five o’clock going back to row. But it’s incredibly disciplining because you only have from nine to five. There is just no, oh, I’ll do this later. You have to be done at five because then you have to leave and go train and you have to be there for training. You can’t skip training.

Eva-Maria Hempe: And so I thought that was actually really useful to fall into this rhythm and go along with it and also shape your environment in a way that helps you do the things you want to do. Because like I said, it’s just not like, I don’t want to get up at five, but I just have to. And then once you’re back from training, you actually feel pretty good. And of course winning the race, nothing feels as good as that. But even if I would have lost the race, I still like, yeah, it was interesting because just before the race, it was about an hour or two before the start. And I remember we were in the boat bay and did like a little circle of the whole crew. And until then I had a bit of nerves, but from that moment on, I was just calm. All the nervousness, all the nerves were just gone.

Eva-Maria Hempe: And I was just like, well, I put everything into this I could, I have no regrets. So whatever happens now on the water, I can look back at this day and I’m proud because I did whatever I could to get to this point. And I think that was interesting because the year before I thought those people must be so nervous when they sit on the start line. But actually when I sat on the start line, I was just calm, I was just ready to do this. And basically put in the work.

Grant Belgard: Why NVIDIA, what sealed the decision for you to join?

Eva-Maria Hempe: It’s because we are a $4 trillion company. No, of course not. Actually, when we joined, I wasn’t. When I joined NVIDIA, it wasn’t a $4 trillion company. No, it’s just, I couldn’t imagine another place right now where you’ll have this impact on the entire ecosystem of healthcare. We work with everybody. We’re the one AI company which works with everybody else. So I get to work with startups. I get to work with established companies. I’m on the forefront of what’s possible. And at the same time on the forefront of what’s possible to do an organization like the thing we thought before. I mean, on the one hand side, we’re looking at models which can design proteins based on 3D structures.

Eva-Maria Hempe: But on the other hand, we’re also looking at rolling out procurement agents because that solves a real problem in the organization today. So it’s just a really exciting place to be at the center of the action around AI and healthcare. And so in general, it just felt like a place where a lot of the things I’ve been doing in the past sort of all came together. Like the multi-stakeholder management of the forum, the strategizing of almost 10 years in consulting, the operationally leading a team and helping people and creating strategies and tactics to make your number, which I did at VMware. And yeah, it just wrapped into sort of this one package of doing something really exciting and really exciting in a field I’m super passionate about.

Grant Belgard: For early career computational biologists who were looking at entering industry, what three skills should they cultivate now?

Eva-Maria Hempe: It’s a bit difficult to say because I’m not a computational biologist, but I think it’s also maybe not so much about the computational and the biologist. I just assume people are well-trained in those fields. I think what’s really important is for them to listen, to sort of to listen where the problems are, what’s being done, where people struggle with. I think the other thing is to really understand value. So I think there’s a lot of interesting work. If you want to do really cool and interesting work, and maybe it’s a bit controversial, but then academia is the place to be. Like if you just are in for the cool, by all means, that’s what academia is supposed to be. If you’re going into industry, then you need to have a nose for value. You need to start to understand like what’s value.

Eva-Maria Hempe: And value can be very different things. Value doesn’t necessarily mean the biggest grossing drug. It can also just be in line with the research portfolio of the organization. It can be in line with individual values of particular managers, but you need to understand value. I think the last thing it’s about teamwork, because so many of these things by now become so difficult that you just can’t solve them alone. You’re dependent on working with others who are bringing complementary skills and complementary experiences. So I would say three things are listening, understanding value, and working well in a team.

Grant Belgard: For life science founders, when is it worth building their own models versus taking existing models or platforms?

Eva-Maria Hempe: So I think you have to be smart. So do you really have an edge? And AI, in my mind, I always think about in three elements. The one is data, compute, and algorithms. So compute, there are some people who have an edge because they can just buy compute for billions of dollars, but that might not be your edge as a founder. So then it probably leaves either algorithms or data. And if you have something there, yeah, you might want to go for it. But very often, actually, you don’t necessarily need to build a model from scratch. You might not even have enough data to build a good model from scratch. And it might be much more worthwhile for what you’re trying to do and you’re coming back to the point of value. What is the value you’re creating?

Eva-Maria Hempe: It might actually be better to stand on the shoulders of giants and just taking a foundation model and retraining it. And in general, I would always advocate for using frameworks out there because they make your work easier. So BioNeMo is not a model per se, but it’s also a framework which helps you do your models better. And I think you shouldn’t write your own data loader and you shouldn’t have tried to configure guardrails from scratch. Like you have, as a founder, you’re massively resource constrained. So try to think about what are the things where you can really differentiate and focus on those and then try to use platforms, existing tools for all the rest.

Eva-Maria Hempe: And I hope that people are taking something from this podcast is we have so much things out there which we’re putting out there, usually often as open source. We have frameworks and libraries and NIMs and all of this is intended to help you and avoid reinventing the wheel. Like if you’re doing medical imaging, you don’t need to write your own segmentation tool. Like this is all out there. Take it and then build a killer application on top of it. But be smart, look at what’s out there and NVIDIA can offer so much and your favorite AI engine, if you ask it, I have this particular problem, what are the latest NVIDIA frameworks? It should give you a whole list of libraries and frameworks you can use, whether it’s for data science or data frames, et cetera. There’s just so much out there.

Eva-Maria Hempe: I think the last thing for life science founders is as well look into Inception. So Inception is NVIDIA’s free virtual accelerator. So it gets you access to NVIDIA experts, which help you even better find the right tools and right frameworks, which make your money last longer. It gets you into a community of like-minded people and there’s also some programs about cloud credits and or discounts for hardware. So join Inception, look at what NVIDIA has and other people have put out there before you build it yourself and just be really smart about what really drives value.

Grant Belgard: What’s your boldest prediction for AI and drug discovery over the next five years?

Eva-Maria Hempe: I don’t know if it’s five years. I would hope it’s five years, but I think at some point we will look back at the way we do drug discovery today and it will seem as archaic and plainly said stupid as the alchemists trying to turn lead into gold. Like today, if you tell kids, oh, back in the middle ages, you had all those alchemists and they were cooking and the idea was lead is this less noble material and you can turn it into more noble material as gold. People are like, why? And I think we look at the same way a lot of things we do today in drug discovery and we’re just like, why did everyone ever think this is going to work?

Eva-Maria Hempe: And there are like on a more practical level, there’s really smart and really interesting things going on about virtual cells and like better predicting like the link between the genome and actually how cells behave. And then also not just cells because we’re not just cells, we’re whole tissues. So I think we’ll see a lot more understanding and understanding biology, at least to some extent. And I think that will get us to this point of alchemy and how could we have been so stupid.

Grant Belgard: What’s a learning resource you would recommend for every trainee?

Eva-Maria Hempe: I think it’s not a learning resource in the conventional way, but I would really encourage to go on build.nvidia.com because it just shows you what’s possible and you have all those different models and you can play with them, you can get an idea what they can do. And then you can also go to the blueprints and basically see how these are put together. So I think that’s a great resource. And then I would maybe pair that with like, I’m a big fan of perplexity, but also any other LLM agent of choice. I think they are great teachers. They can teach you anything. And the other day I used perplexity in voice mode. And so I was like making dinner and just having this really natural conversation. And there is no stupid question. There is no judging.

Eva-Maria Hempe: You can like ask it anything like just, can you please explain to me again how this works? And I sometimes also use it for some of the NVIDIA stuff. I’m like, okay, can we go deeper on RAPIDS? Can you explain the different libraries? Like how does this work? Why does this work? So I think it’s a great tool to learn about AI, but also just anything else you wanna learn. And it can also challenge you. You can actually also ask it to quiz you and to make sure you really understand things and you explain it back to the machine. The machine actually gives you feedback whether you got it right or you need to brush up a bit more.

Grant Belgard: Yeah, I was actually doing the same thing with a bit of yard work yesterday. Also highly recommend that, voice mode is great. Eva-Maria, thank you so much for joining us. It was great.

Eva-Maria Hempe: Thank you, I really enjoyed it.

The Bioinformatics CRO Podcast

Episode 65 with Jeff Bizzaro

Jeff Bizzaro, founder and long-time president of bioinformatics.org, discusses the importance of open source tools and open access 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.

Jeff Bizzaro

Jeff Bizzaro is the founder of bioinformatics.org, which is committed to hosting resources for open science, bioinformatics webtools and data, and open source software development.

Transcript of Episode 65: Jeff Bizzaro

Disclaimer: Transcripts may contain errors.

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