Transcript of Episode 54: Evan Floden

Disclaimer: Transcripts may contain some errors.

Grant Belgard: [00:00:00] Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today is Evan Floden. Evan, would you like to introduce yourself?

Evan Floden: [00:00:07] Yeah, awesome. Thanks a lot for having me Grant. I’m Evan Floden. I’m the CEO, co-founder of Seqera Labs, previously been building the Nextflow project for the last ten years or so. So I’ve been very interested in following the developments in bioinformatics over that time. It’s great to be on the show.

Grant Belgard: [00:00:23] Thanks for joining us. And I’m sure most of our listeners have heard of Nextflow, but maybe not everyone’s heard of Seqera. Can you tell us about the company and its origins and pulling the strings behind Nextflow?

Evan Floden: [00:00:36] Yeah, absolutely. It’s an exploit was started by myself and co-founder Paolo Di Tommaso. And really the idea around Seqera was really a continuation of the project, but really bringing it to fruition in terms of a commercial sense. So whilst we focused originally on a lot of the work that Nextflow was doing on pipelines, now we’ve expanded out a fair bit from that. So Nextflow we began ten years ago. Seqera has been around for about five years now. We’re really focusing on taking some of the principles that Nextflow has. The idea of empowering scientists with modern software engineering came about from the use of things like containers, the adoption of cloud, really enabling scientists to use those tools and to focus on that. And Seqera is just a continuation of that, but now broader sense. So really making the whole bioinformatics pipelines accessible, but going beyond the pipelines as well.

Grant Belgard: [00:01:25] And what’s your business model?

Evan Floden: [00:01:27] Very much focused on bottom up adoption from the open source. So in terms of Nextflow usage, we’re looking at around 100,000 people in total. So use Nextflow and that gives us obviously a really cool base. In terms of business model, it’s mostly focused on selling to enterprises, to organizations, to folks who are scaling up from single bioinformaticians to running things in production and really providing them the infrastructure, the tools that they need to build the pipelines out. And increasingly so the aspects as well.

Grant Belgard: [00:01:58] Have you seen adoption beyond bioinformatics?

Evan Floden: [00:02:00] Interestingly, in Nextflow, yes. Nextflow doesn’t have anything too specific with regards to bioinformatics in the way that it’s written. However, obviously its application is very much being focused and being used in bioinformatics. So we’ve started to see use cases and things. For example, image analysis, you start to see it. For example, satellite image analysis, also radio astronomy. Anywhere there is scientific workloads that have particularly batch component to them. I think an element of that, the user base has developed a lot of content in Nextflow through things like nf-core, and that obviously lends itself to people picking up Nextflow itself and using it for life sciences. But it’s not to say it’s not being used in other areas and obviously we’re happy to support that and see where the community takes that.

Grant Belgard: [00:02:45] How did Nextflow in Seqera evolve? Can you take us back to the beginning and what your thoughts were then and how that’s played out over time?

Evan Floden: [00:02:53] Absolutely. So Paolo and myself were working in a lab in CRG in Barcelona, and our lab was looking at multiple sequence alignment. Folks in Bioinformatics may be familiar with some software called T-coffee. It’s a very commonly used multiple sequence alignment tool that was developed by our former supervisor, Cedric Notredame. And as part of that, the job in the lab of Paolo was to enable us to run those analysis. And it was, we were particularly interested in high throughput so tens of thousands of sequences and looking at how small variations in those sequences can have an effect on the multiple sequence alignment and the resulting outputs. That was the topic of my PhD and that was what was intended to go and study. Obviously as I got there, I started to spend more and more time on Nextflow and that evolved from there. It was a very small project to begin with. We just published it onto GitHub. It started with I remember, after a year I think we had a list of the ten people who were using it or 30 people who were using it, and it was a very a small start. Over time we were able to just continually evolve and adapt it.

[00:03:57] It’s one of the great things about open source is you’re able to get that feedback and people are able to contribute ideas back, issues back, and it allows us to really evolve from there. It’s been a fantastic journey over that time. We got to probably be about five years into the project and realized that there was first a commercial opportunity, but secondly, it’s something that we both love doing. I was getting towards the end of my PhD and I just really wanted to keep working on the technology. I saw a huge potential. Paolo and myself traveling around Europe and doing training courses and just really saw the opportunity to take that to the next level. And that’s the spark for creating Seqera and seeing all the opportunity that there was from that, I should say. So since starting Seqera, Nextflow has increased its usage at least tenfold on that. So I guess there was a slight risk at that time in doing that, but we were pretty convinced on the project and it’s really been the foundation for everything we’ve built so far.

Grant Belgard: [00:04:50] Yeah, it’s gotten very widespread adoption in biotech for sure. I think it’s one of those situations where people will want to use a tool that is nice and robust that a lot of the potential hires they would be looking at have experience with. And I think Nextflow has gotten to that critical mass where it’s not this really niche thing. It’s certainly for people who have been in biotech for a few years, a lot of bioinformaticians have experience with it.

Evan Floden: [00:05:24] Yeah, I think that’s an interesting point on how does something like Nextflow essentially become a de facto standard. It’s an interesting one in that if you look at there’s been many groups or many times that folks have tried to create standards, whether this is in academia or in industry bodies and the like. And if we look into parallels of the areas, things like the Docker container is almost is the standard for containerization. But that was started by a few folks who had an idea and created a company. And now really revolutionized the world of modern software. I think that Nextflow has similar ideas and that it was we were trying to do something a little bit against the grain, not necessarily sanctioned by anybody. And that almost spurred us on in some sense. But then once you get that critical mass has taken off, I think that there’s touching on the aspect of I agree, it’s fantastic that folks can come in, they’ve already got the skills and Nextflow and then there’s that other whole piece to it, which is what I call the content, but it’s really the pipelines and all of that material which enables folks to take take those off the shelf. There’s now things like nf-core, there’s modules. We’re getting up to over a thousand modules there which you can really mix and match the components of your pipeline and obviously use the framework and the tooling to build that there. And that’s really can save organizations so much time just even getting started with that analysis. For example, add their own module in which is specific maybe for their chemistry on some sequencing, but they can use the rest of the pipeline. Those kind of examples were prevalent and it’s something which I think is possible from having this open science approach to things.

Grant Belgard: [00:07:03] And what’s your vision for the company?

Evan Floden: [00:07:05] [] at the start we’ve really been focused on the workflow execution piece and I think this is going to continue to be our bread and butter. We still see the challenges exist with regards to scaling generally across bioinformatics, but also across life sciences as well. The volume of data is not decreasing. It’s if anything, it’s increasing the use cases for sequencing as well. And imaging analysis is increasing. The multi-modality of the work which is coming in is requiring almost different approaches. So we focused a lot on that high throughput piece. There’s an element where we have been building up a collection of open products, things like Nextflow. We have MultiQC, which is the most widely used analytic and reporting tool. We also have FusionWAVE, which are two infrastructure tools which allow folks to run these pipelines at scale. And that’s a like a core layer of infrastructure within building on top of that secure platform, which is essentially the main product which our customers purchase. And as part of that, that’s the piece that we’re scaling up beyond the pipelines into things like data management, into things like interactive environments and going from there. There’s a lot of platforms which claim to do the same thing. I think we have a slightly different approach to that and that’s I think kind of the one of the key differentiators here as well.

Grant Belgard: [00:08:19] And who are your competitors and how are you different from them?

Evan Floden: [00:08:24] There’s been genomics in the cloud. It has been around for a while and there’s obviously been some big players there for a fair amount of time. There’s obviously a whole bunch of of newer ones as well who have received funding recently. We still see the biggest competitor in at least the majority of deals is folks building it themselves. They are typically building these systems. You often have people who are, say, familiar with a certain way of doing things and they try and basically do the same thing in the cloud or they want to scale up beyond single users. And we see a lot of customers who purchased the platform. They’ve already tried to build their own thing first. So that’s the core competitor that we see in terms of building that out. The other competitors there are when I think about generic genomics in the cloud. They’re really focused primarily on a lot of simplification. And I think that there is certainly a subset of users who do need that simplification. But one of the things that we think about a lot is when we think about our value that we provide, we’re not necessarily just helping people sort of simplify.

[00:09:25] We also are making the science easier and also making it possible to do harder things in some sense. So it’s really about taking modern software engineering, providing those tools to scientists. And it’s a little bit like treating scientists like they are developers and giving them the tools to do harder things than to specifically run things in a more simple way. The other aspect of that is that whilst we have our open source roots, that really means that when customers run an exploit pipeline, they run in their environment, they run in their cloud. If you connect up our platform, you connect it up to your cluster. It could be running in Europe and you could connect it up to your Azure instance, which is running in West Coast. You are moving the workload to where the data is in this case, as opposed to the other way around. So it’s a very much more like an open framework and open platform that allows you to connect that as opposed to more of a walled garden, which you see in the other approaches.

Grant Belgard: [00:10:20] What challenges have you encountered the dramatic growth you’ve had in your user base?

Evan Floden: [00:10:25] I think the challenge is often from an organization side of things is really scaling up, how do you go from a group of people, a small group of people, really building something to be able to replicate that across an org. It’s a lot about investing in folks. Not everyone you hire is going to have a PhD in bioinformatics and being able to translate those skills and to be able to have that customer empathy and that customer understanding and almost like scientific understanding of the problem is a challenge. And I think that that’s kind of applies a lot. You see in some other organizations where bringing folks in maybe without any life sciences background or ability or willingness to learn in that doesn’t necessarily translate so well. So from an organization perspective, it’s a lot about building that context and building that organizational knowledge and memory to be able to do that. On the user base side, I think we haven’t really had too many challenges, I would say on that community growth. We’ve been very fortunate that projects like nf-core really came out of the community. They were organic in the sense there is folks who building their own training courses. There is people who have just built so much content around Nextflow, the plug in systems, the AD pipelines on nf-core. That’s really almost I would say, really happened organically and therefore it hasn’t really involved too much in terms of necessity of resources or work from our side other than really just trying to foster that community and enable those people to solve the problems for themselves.

Grant Belgard: [00:11:55] This is your first company, right? So I guess there have been a lot of new things to learn. What’s surprised you the most?

Evan Floden: [00:12:01] I previously had worked at a startup for 4 or 5 years, which was very interesting. Experience was very small at the time. The company ended up going public, so I spent some time there and doing product development. It was at the bench though, so it was very much a scientific role. I saw that there, that it was very interesting. However, it was just very slow to do things at the bench given to what you could do and my inkling for tech really got the better of me and went into the bioinformatics field. When I think about how that journey has progressed and I think particularly the last three years as you start to hire and work, I was surprised at how important the personal relationships have been. I think as a scientist you often think of the world of business or you think of the world of creating an organization. You think it’s very transactional. And when I think about the folks that we’ve partnered with on the investment side or the people that we’ve hired or the partners that we’ve brought on the customers, those relationships are now, in some cases ten years old. And I’ve just been so surprised at how important and how deep they have been just given my maybe slightly naive view coming from a purely academic perspective so I think it is the key one I always go back to when I think about that.

Grant Belgard: [00:13:17] That’s an interesting observation. I think it aligns with what I’ve maybe seen as a broader perception in academia where actually many things are more transactional in academia than they often are in biotech, although in both contexts those personal relationships are crucial and very, very long lived. Because it’s a very small world. And you often work with the same people for many, many years in many different contexts. I know we very often work with the same people, but at different companies because there’s so much churn, they’ll leave one company and go somewhere else. We work with them there and and then someone else from that new company leaves and goes to another, but it’s actually those personal relationships can play a much larger role than the formal relationships with the companies in some cases.

Evan Floden: [00:14:12] Yeah, absolutely. We’ve had one customer who’s on a company number three, and he’s a buyer number three as well in terms of spreading the word there. So that’s the relationships which you think about. They grow over time. I think the community aspect of Nextflow helps a lot with that. We really think that there’s a lot of value you can add and through that community, through knowledge sharing to solve those problems with those folks and hopefully bringing some software to them which adds that value. And then obviously as part of that, that can help them broaden and strengthen the relationship on the academia side. It’s definitely very important. I think particularly around some of the relationships you form with folks like your supervisors across that time. I think those are very special relationships. They can last a long time. I’m not going to go too controversial and try to think about the order of first author ordering as often happens in some academic papers. Thankfully, I haven’t had too many situations like that, but yeah definitely don’t envy that.

Grant Belgard: [00:15:11] For sure. On a completely different topic, how do you think about on site versus remote versus hybrid at Seqera?

Evan Floden: [00:15:19] Yeah. It’s interesting one for us. We started. We really got our Pre-seed funding in March of 2020. I quit my job at the CRG and I was like, we’re going to do this. In February, I started working at home because we didn’t have an office for about 2 or 3 weeks, and then the rest of the world joined me on that. So that was an interesting transition. It’s like we hired our first people during that. We raised our first money in March of 2020. So that was like being forced into it, particularly in Spain. It was particularly long and strict lockdown. As part of that, it forced us to be essentially distributed team from the beginning. And given the focus of the customer bases, which is primarily in life sciences hubs. So you can think of Boston, Massachusetts area, California, so US East coast and offices, some stuff in Cambridge, UK, that was going to always be the central hub for our customers. And we had to deal with that from the beginning. So that was a reality for us and saying, now we try and build ourselves out from hubs themselves.

[00:16:22] So we believe that it’s great for people to be able to get together, if anything, for the social aspect of it and to get to know each other and to build those relationships more than actual the work of. Because most folks are going to be in Zoom calls for a decent chunk of the day anyway. So that’s our take on it, believe in building those relationships. And it’s not easy, though. And I think it’s particularly not easy if you’re a young company and you start like that in a, I would say, non-intentional way. It was definitely not our intention to do things in a sense. It kind of happened and we’ve tried to do the best that we can in terms of managing that, but it’s something that we would have learned a lot from. And I guess like a lot of the tools and like a lot of folks, that’s become the new norm for many things.

Grant Belgard: [00:17:10] If you hadn’t gone down the Seqera route, what do you think you would be doing now?

Evan Floden: [00:17:15] Interesting. I don’t really think about that stuff too much. I think that I still see myself as a scientist at heart. I really do enjoy the scientific process. I enjoy discovering things and learning in this way. I could definitely see myself tinkering a lot and I would continue to do that whether that’s in more product development roles or scientific method development. So very much like what we were doing and doing a PhD. That’s the thing that I really enjoy doing. I think it’s part of this role though. I’ve been learning a whole lot of new stuff which also excites me as well. Like it’s many different things that I didn’t think that I would be doing. So it’s hard to say. I’m very glad that I’ve kind of gone down this path in terms of what I would be doing. In another sense, struggle a little bit to think about it.

Grant Belgard: [00:18:00] If you could go back in time to give yourself advice in 2018 as you started the company, what would that advice be?

Evan Floden: [00:18:09] The best piece of advice that I give myself in some sense really about the bigger picture sometimes because it’s very easy to get drawn into the day to day and the small things. And I think particularly as a company scales, you can often you find yourself thinking of those little things. And it’s really only when you step back and you see the growth or the success or the things really matter. So being able to zoom in and yes, the small things do matter, like getting those things right is important, but also being able to scale out sometimes. And I guess just getting that balance right is difficult. It’s a very intense job. It’s a lot of hours and it’s a lot of time. I think that trying to get that balance right, I wouldn’t even call it balance. There’s harmony in your life. And by having those different perspectives and also different perspectives on the different elements of your life, that’s the advice I would give myself to try and work on. And I think you can tell from my description something I’m still trying to work on now.

Grant Belgard: [00:19:06] Are there any practices you’ve adopted over time? Having a protected half day a week or something to focus on that? Or has it been a moving target?

Evan Floden: [00:19:15] Very, very much. For me, it’s about like routine. It’s the way that I’m able to structure my life. That’s typically starts with beginning in the morning, spending some an hour or so with my son before I have to go to work. And then really trying to fit in all the things that I need to do to to feel good around the work. So for me, that involves a cycle to work. I need to get to work. So I cycle there. It takes 45 minutes or so and then I do the work and then maybe at the end of the evening I’ll be able to cycle back and then try and fit in those times just to try and make it work in a way where I don’t feel like I’m going too much in one direction. So being able to pull those things together the way that it works, I do find this is very difficult with travel though. And obviously it makes it very difficult to fit in the routine in that. So I’m trying to be a little bit more structured about that. And one of the things I’m working on to improve as well, I guess a lot of folks have similar challenges there.

Grant Belgard: [00:20:10] Do you have travel system down now? Checklists and things that feel like you’ve optimized?

Evan Floden: [00:20:17] What I’ve been working on now is more around basically having Monday to Friday where I’m trying not to travel during that period. So I will travel on the weekends to different places and I’ll be in a location for a week, even things like staying in an Airbnb if possible, because then you have got a relatively normal house where you can get into those routines, just trying to do that more. That helps me a bit. I’m still not super. I wouldn’t say I wouldn’t call myself having a system down or having particularly a way of doing things. I like to have a set up, a structure. So where I’ve got my laptop with a keyboard mouse guy, so having that set up and structure just helps me a lot as well.

Grant Belgard: [00:20:51] And what things do you find yourself traveling for Seqera these days?

Evan Floden: [00:20:55] Yeah, we’ve got a lot of events that we’re running. And so given the focus in a lot of North America, we’re spending a fair bit of time there. So we have, for example, the Nextflow summit, which is going to be in Barcelona, but also in Boston this year. So we’ll be spending some time there. We also do secure sessions, which are great events for the community to get together. We’ll often have some talks on technology, things that are coming updated, product updates, roundtables, this kind of thing for 3 or 4 hours in an afternoon. Previously done those in San Francisco and in San Diego, Boston as well, the hubs and continuing to build out that. We’ve been doing a few shows around. We’re going to be at ASG this year and going to be traveling a fair bit around that. And those are the most of the areas. We also have, as I said, a distributed team. So being able to spend time with them is really important as well.

Grant Belgard: [00:21:45] Great. We’ll have to have our operations director come say hi. I won’t be going to ASG, but we will have a booth there.

Evan Floden: [00:21:52] Yeah, folks are absolutely welcome to come and say hi. We’ll get you some next swag and always happy to give folks a demo.

Grant Belgard: [00:21:58] Nice. What message would you have for our listeners about Nextflow and Seqera? As I said, I’m sure most of our listeners have heard of Nextflow and probably many our users, but for those who haven’t used it before, how would you recommend they get started?

Evan Floden: [00:22:15] Yeah, absolutely. If you’re thinking about running pipelines in a way where you want to run them in your own infrastructure, where you don’t want to deal with the complexity of setting that infrastructure up, then Seqera platforms are a great way to start out. We have a community showcase where there is collections of pipelines which are available, where you could log in, select those pipelines and run those and get a feel of how it works. We also continue to add in more options around that, which is enabling on the data management side. So by the time this podcast comes out, we’ll have a data explorer which enables you to really browse and search across different buckets, across different object storage that you may have. And we’re also looking to bring out more functionality and interactive space. So that’s a great place to get started. If you go to tower.nf or if you go to seqera.io, you’ll be able to log in there and find that out. It’s absolutely free to go, go work that and give it a go.

Grant Belgard: [00:23:07] Great. And for people who are already casual Nextflow users, how would they best further build their skills?

Evan Floden: [00:23:17] Yeah, I think there’s some interesting courses which have come out recently, which we’ve been developers with the community as well as from folks at Seqera around advanced Nextflow usage. That’s been a really useful set of resources which have been built out. I think being around the nf-core Slack and the Nextflow Slack is always a great place. There’s a lot of people doing very innovative things there, platform and being able to connect it in there. And then of course attending the events is always a great place to see that. We have 50 speakers, I believe across the events of Nextflow summit in Barcelona and Boston this year. It includes sequencing companies. Obviously the large cloud providers are all going to be there presenting the latest things. We have customers and developing kits. We have customers working in population genomics sequencing projects as well as obviously a whole bunch in biopharma. So that range of use cases can give people a really nice understanding of what other folks are doing. And I think that format as well, where you can really interact with people, can go a little bit deeper into the specifics of how they’re solving those problems is a great way to learn.

Grant Belgard: [00:24:23] So in theory, something like Nextflow would be fantastic for scientific reproducibility, right? Which is obviously been a major issue in the life sciences. But what do you think are the major barriers to adoption of Nextflow for those purposes? Because you usually hear about Nextflow in the context of people trying to do analysis on their own data for their own projects and so on. And it still seems pretty uncommon to see papers published where they have single button reproducibility anyway.

Evan Floden: [00:25:00] Yeah. And I would point folks to the Nextflow paper from 2017 that we published, which is really a little bit of inception here, but we published an excellent paper, obviously using Nextflow, which is really describes a lot of that. And from that git repository you can reproduce everything calling Nextflow from notebooks. So the idea is of open science, I think they’re worth exploring because it goes a little bit beyond just what people consider open source. And that open science is really is a key part of that. So if you think about open source, it’s almost like it’s a license. It’s like, okay, you put Nextflow software out there, people can use it. People can do what they want with it, the Apache 2.0, etcetera. Open science goes beyond that, and it goes to that point where, as you say, people are, for the most part still just publishing papers. But we start to see more and more adoption of folks who are not publishing papers, but they want to publish the paper and the analysis or even just the analysis in itself. When you want to run that analysis or even reproduce that result there, if it’s not going to run on your laptop, it’s going to be very difficult to do so you’re 100% right that Nextflow enables that piece. It does it through a couple of ways. One is obviously containerization, so that integration of containers means that the environment that the task runs in is essentially absolutely the same byte for byte. The other piece of it is that you can run those containers then in any infrastructure so you can run them in [] or you can go run them in your cluster or you can run them on your laptop.

[00:26:21] That piece then enables people to reproducibly do that and almost validate the result that then has a little bit of a flywheel effect. Because if I publish my analysis in that way or my tool in that way, you can then take it and then you can put your data into there as well. And that’s the real important piece I think that Nextflow has enabled there. If we think about that going further, one thing that we’ve really stressed is this idea of empowering scientists with modern software engineering so you can reproduce the workflow, but how are you going to reproduce the environment that you use to set that up, or how are you going to reproduce the data set that you use in this sense? And that’s really what we’ve been working with Seqera is the whole thing is defined or can be defined from API. There’s a CLI as well. So you can say import this pipeline or define this computer environment in this way, import export from that. And it’s treating the whole research environment in a reproducible sense, not just the individual component. And this is very much in the vein of infrastructure as code like setups where folks have been using things like Terraform for building those environments and just taking it to the next step specifically for bioinformatics.

Grant Belgard: [00:27:32] What do you think it will take to get that to become standard practice? I mean, there are some individuals and a few groups that routinely will do that. But majority of the time, it seems these are done by custom scripts that are available upon reasonable request and nobody ever gets them.

Evan Floden: [00:27:55] It definitely is changing as depending on where you are. So if you are developing a new tool, it’s kind of by default. It has to be there. If you consider it was going to be in a paper, the reviewers would essentially have to run the tool and try it out. I think the more you go down like two different areas, then you’ll see I agree it gets less and less in terms of that compliance. I think it’s probably very much like carrot and stick in this sense. Carrot in the way that if you consider yourself, like when you write something in Nextflow or you write a pipeline or an analysis in a reproducible way, you’re really just doing it for yourself in three months time. Because if you’re anything like me, in three months after you’ve done an analysis, you come back to it and then you have to rerun it because you’ve got a new sample or you’ve got some new parameter. It’s just absolutely impossible to remember how you did it, what you did it like, exactly that. So that reproducibility piece is almost like for yourself in a very selfish way. That implies the carrot. The stick bit is coming from this publishers. So as our former supervisors, Cedric Notredame, he has one of the journals and as part of that, it’s really about publishing pipelines, publishing things in this way. And it is using standards like nf-core to do that. So you have to publish in a completely reproducible way, you can define exactly what you are publishing, and I can really see us moving towards a situation where the paper is just one artifact of the actual output. However, it’s not the main output. The actual main output is often the case is like the actual analysis in the tool and say this is particularly relevant for tool development, which is obviously very, very widely used in bioinformatics.

Grant Belgard: [00:29:31] Nice. So maybe changing gears a bit. Can you take us back to your childhood? What got you interested in science?

Evan Floden: [00:29:40] Yeah. So I was originally born in New Zealand. I spent probably the first nine years there and then got the opportunity with my family. We lived in Malaysia and Sweden growing up for some years. I think in New Zealand it was a very kind of natural environment in some senses. It’s obviously a lot less people and a lot more nature, got me interested in bio. And I vividly remember thinking about biology in the sense during high school. I got a little bit obsessed with scientific nonfiction and saw myself really wanting to go into biotech. Bioinformatics at that time was much less prevalent. I guess it was very early for bioinformatics. So that’s what led me to study biotech and then to spend time going into molecular biology. I had a really interesting opportunity for a couple of years as an undergrad working in a yeast laboratory, and what we were doing was essentially had a knock out set of yeast. So it’s you can imagine very large agar plates. Each one of those plates has got really a couple thousand samples on it and each sample has got one different gene removed. And you can treat this with different chemicals or you can make these yeast together and you can look at chemical interactions or genetic interactions and understand what’s happening there at a genetic level and how it integrates with those pieces. There was obviously a bit of robotics, obviously a lot of yeast culturing and a touch of bioinformatics as well. And I think that’s one of the things that sparked my interest into bioinformatics later on. Although to be fair, I didn’t do that until I went to Italy to study a master’s there. Bioinformatics wasn’t available in New Zealand at the time, so it was my opportunity to jump into the field.

Grant Belgard: [00:31:20] So you finished your degree in New Zealand in 2010 and what did you do then?

Evan Floden: [00:31:27] I joined the start-up. It was a very interesting startup. It was about five people at the time were developing a medical device, which sounds nice and clean, but the medical device itself was coming from the fourth stomach of sheep, so I’m not sure if the listeners are familiar with haggis is essentially one of the stomachs of the sheep. It’s a very interesting material. We were trying out lots of different materials and the idea was to see if we could create a bio scaffold. So essentially a tissue which could be used for soft tissue repair in surgery, you would remove the different layers on the top, decellularized it, freeze dry it and essentially end up with a shelf stable product which could then be used in different applications. So the first few years there did a lot of product development. We got FDA approval for the basics of the platform and really ended up developing several other products, for example, creating multiple layers of this for breast reconstruction or hernia repair. And this was really just involved in that whole start up phase. It was really exciting. It was really interesting. I saw how I saw the determination which was required to create a startup, but I also saw how interesting it could be to work on many different topics and many different things. And that change I really liked and I just was just enthralled by that. And I got my, I guess if I [] place the seed, let’s say, for what was happening with Seqera later on.

Grant Belgard: [00:32:48] And what brought you to Italy then?

Evan Floden: [00:32:50] I really wanted to get into bioinformatics. I think it was something I’d been pushing for and that’s where I got an opportunity. I got a scholarship to do a master’s there. It was a very interesting time. I got to fully focus on that and I knew some basics of programming, but I really got to fully hunker down and spend a good 18 months or two years just purely focused on that. The bioinformatics program in Bologna is quite widely known. We got to do fantastic things. For example, we would build a Markov models from scratch, from the individual components really got exposed to how machine learning was working in sequence analysis itself. It’s quite a mathematical program, but it really gave me the basis for many of the things that came later on. It’s actually where I met my supervisor, Cedric, and that’s what started the journey into Seqera. I had a little bit of time in Cambridge in between in the UK working at RFM, but that was what got me started in that.

Grant Belgard: [00:33:47] Nice. And then after your stint at Cambridge, you went back to Italy, right?

Evan Floden: [00:33:52] It is Barcelona. Yes. Sorry, it’s Barcelona. That’s where I started my PhD, and that’s where I met Paolo. And the story kicked off.

Grant Belgard: [00:33:59] Nice. And then afterwards you stuck around in Barcelona at the CRG. Were you working at all with the CRG during your PhD?

Evan Floden: [00:34:07] Yeah, so my PhD was at the CRG. It’s a research organization, but I mean technically you’re part of a university as well. Although you spend the whole time in the research organization, it’s more of an affiliation so that they can provide you with an academic degree. Yeah, really interesting place. And there’s a lot of the leading biomedical research center in Southern Europe, fantastic location as well, very international. And it provided a fantastic opportunity to learn there and be surrounded by smart people. And obviously it’s what we’re doing.

Grant Belgard: [00:34:39] And this Seqera Avenue formal relationship with CRG or is it kind of just another institute where there are a lot of Nextflow users?

Evan Floden: [00:34:49] Obviously, CRG being home of Nextflow, let’s say the original home of Nextflow, there’s always a special relationship there. The usage of Nextflow is obviously very wide in the organization there. We consider ourselves like a spinoff of the organization. And so the relationship stays special in that way.

Grant Belgard: [00:35:09] That’s great. And do you have any advice for our listeners who might be scientists who are considering the entrepreneurship journey?

Evan Floden: [00:35:19] Yeah, it’s hard one in the sense that, like, you don’t know until you really jump off the diving board in that sense. I found it personally to be very rewarding and very fulfilling. As I said related to your question before, I can’t really imagine myself having not done this or doing something else. At the same time, I fully admit it’s not for everybody. There’s a lot of sacrifices you make in other aspects which are difficult. It’s a way that you can have a very fulfilling role, very fulfilling job. And for me, being driven by the impact of it, I think it’s just the way that I felt that I would best be able to build something that would scale and that would have the most impact on it. I think that one of the reasons behind Seqera at the beginning is really just to spread that I was one of the first couple of uses of Nextflow. It really changed how I was working and I wanted to put that into as many people as possible. I feel the same way about what we’re building in Seqera. There’s great technology which we just want to put into the hands of scientists to help them work. That entrepreneurial journey is for me, it’s really much it’s just the way to get that done. And it’s that the way that it can manifest, I would say.

Grant Belgard: [00:36:26] So if you look forward ten years from now, what would you consider a success for Seqera?

Evan Floden: [00:36:33] We really want to see ourselves as first and foremost, having helped thousand biotech biopharma organizations really reach their own goals. And for that, that’s usually outcome in patients. We want to see biotech continue to grow. We want to see the adoption of those technologies. We want to see things like personalized medicine become available to people. We want to see the promise of genomics technology become a reality in that. That’s the first, I think, that we can play a really important role in making the analysis part of this data analysis, part of this accessible, available, open and build the bioinformatics tool framework that in the world that we want to see in there. From an organization perspective, one of the things I really would love to see is that from Seqera, we almost create our own ecosystem as well. So whether that means of employees who create their own things or really new projects which sprout from the Nextflow ecosystem, really seeing that gives me a lot of satisfaction because it shows that you can start one thing and it can really flower into a whole bunch of other areas. Just myself personally, just really ten years would just love to be obviously healthy, still enjoying the job and really hopefully having made as much impact as possible on those areas.

Grant Belgard: [00:37:49] Well Evan, thank you so much for joining us today. It’s been a nice conversation.

Evan Floden: [00:37:53] Awesome. Thanks a lot, Grant. [See anytime] and folks, if you do want to join us at Nextflow summit, both Barcelona and Boston are still open. We’d love to see you there and thanks so much for the time.

Transcript of Episode 53: Linnea Fletcher

Disclaimer: Transcripts may contain errors.

Grant Belgard: [00:00:00] Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today is Linnea Fletcher. Linnea, welcome.

Linnea Fletcher: [00:00:07] Thank you. And thank you for having me on your podcast.

Grant Belgard: [00:00:11] Thanks so much. Can you tell us about what you’re doing at InnovATEBIO and at Austin Community College?

Linnea Fletcher: [00:00:18] Sure. So I’m the executive director for InnovATEBIO, the National Biotechnology Center Grant, and it’s funded by NSF, the National Science Foundation. And my job as being PI on this Grant is to support and coordinate bio technician training across the nation. And there’s over 130 programs. But this also means I have to know about the career path starting from K-12 all the way up through PhD, because most of my students and the students across the country who become technicians continue their education. To keep boots on the ground, so to speak, I still run a program at Austin Community College where I’m educating individuals to be technicians. And just to give you a snapshot of my students, I have a high school program. It’s fairly large, and these students get a certificate in bio manufacturing. Most of them go on to four year schools. Some of them come into my two year program, and my two year program has both two year students who tend to be older adults coming back for another career. And the rest of them, 50% of them already have a four year degree. And I’ve been in the area long enough that local industry tells the four year student to come and get an advanced technical certificate from that community college down the street because I’m the one who does. I emphasize hands on training. Some people have said it’s like graduate school in a regulated environment on steroids because they learn a lot in a space of a year, the four year students.

Grant Belgard: [00:02:07] How common are programs like that elsewhere in the country?

Linnea Fletcher: [00:02:11] Well, actually 130 programs is not a huge number across the nation. And I would say they cluster around the biotechnology industry clusters. For example, here’s a lot more in California and also there’s some in Florida where you are. And I work with them because Florida actually is a great job of training the workforce. And there’s more in North Carolina. And of course, there’s a large number in the Northeast. So it really depends on how much industry is present for these programs because they are workforce programs. So you have to justify your existence by having companies in the area that need your students.

Grant Belgard: [00:02:56] How do you get that feedback from local companies?

Linnea Fletcher: [00:02:59] Well, an advisory board, all these programs have an industry advisory board and I meet with mine once a year to get feedback on what I’m teaching. But then also, like many other programs, community colleges don’t have a lot of full time faculty. So all of my courses pretty much are taught by adjuncts and they’re all from industry. So they tell me what I need to put in my program constantly. And as a result, there are a lot of basic biotech people need to know that doesn’t change, like how to make a solution or the math or the regulatory affairs because we have QA QC and we do run our program like a company, so they have to do all the documentation. But all the emerging technologies, we had to modularize all of our curriculum such that when we needed to change out a module to put in a new module based on an emerging technology like we just added metagenomics and we took out another module that was probably a little out of date to add metagenomics and the ability to do next gen sequencing, which is so important.

Grant Belgard: [00:04:18] Do you find a large degree of variation metro area to metro area in terms of the skill sets that are needed? Or are they mostly shared?

Linnea Fletcher: [00:04:31] Well, it’s kind of interesting. Local area dictates needs, but usually local area mirrors what’s going on across the nation in general. Some of the bigger differences are if like North Carolina, California, where’s the large scale biomanufacturing that is going to be different than the small scale biomanufacturing or mid scale that teach because I’m not doing reactors that are doing like 200l. I’m doing much smaller reactors, so there are variations. I also have a lot of medical diagnostic companies and so I do a lot of PCR and real time qPCR because my students need to be able to do that kind of technology in the companies. We also have a lot of maybe more instrumentation, HPLC, GC, maybe some mass spec. One thing I am looking at incorporating is flow cytometry, which I haven’t done yet, and we do stem cell too. So you’re right. I can tell you all the different areas across the country, like in Wake Forest, also in North Carolina, there’s a lot more regenerative medicine. And then in the state of Washington, they have a lot of immunotherapy.

Grant Belgard: [00:05:57] What changes have you seen over time in the skill sets in short supply?

Linnea Fletcher: [00:06:03] So what I’ve seen, I’ve been in training individuals, educating them for 25 years. And I would say that it has become more complex in terms of what technicians need to know as essentially the industry is converging more with other areas. So as biotech advances into other areas overlapping, such as nanotechnology, electronics especially, you see this in medical devices, in regenerative medicine, and then the need for another one that I’m now having to anticipate is AI, artificial intelligence and robotics. So now my students are actually having to learn how to program machines to deal with cell sorting and identification and running some of the automation in terms of the equipment in laboratories.

Grant Belgard: [00:07:10] What areas would you say have the most unmet demand right now?

Linnea Fletcher: [00:07:15] Oh, unmet. Well, definitely in regenerative medicine and immunotherapy and a new set of skills standards have just been published by the Wake Forest Institute for Regenerative Medicine. It was a grant funded activity in this area of what technicians need to know be able to do their jobs. If you think about it, in many cases they actually have to take care of the cells that are removed from patients and grow them up before they’re genetically modified and put back into the patient. So that is a major area that’s not being covered. Large scale biomanufacturing, we’re not meeting the demands for the technicians in this area. And so that’s something that has to be addressed. And then another area that hasn’t even quietly shown up on our radars is Bioindustrial. And I’m already working with process technology programs. And if you process technology, is the oil and gas and chemical industry because Bioindustrial is large scale chemical engineering and the idea of having those programs actually educate students to cross over from oil and gas into Bioindustrial is something that we’re working on for the future.

Grant Belgard: [00:08:45] That’s interesting. Certainly I think at the level of PhD training, you have some quite a bit of segregation among students who would be doing immunotherapy versus those on bio applications in oil and gas. But I guess at the technician level, you have options in essentially the same program where you’d be sending students to those very diverse industries afterwards.

Linnea Fletcher: [00:09:12] Yeah. And that’s what kind of is interesting is so when someone gets a PhD, they usually it’s in one area of research and it’s very targeted and they have to do that, become experts in a narrow range where my technicians and the technicians that are being educated across the country, they learn a variety of skills from cell culture, manufacturing, bioinformatics, instrumentation, such that they can go into a variety of different jobs within the industry. It’s like very similar to what is now happened in MDs, focus on one area. But physician’s assistants, they actually get education in all different areas and they actually can move from one area to another area that’s just like my technicians. And they do it based on changing interests.

Grant Belgard: [00:10:14] And what changes do you see that might be coming down the pike for the workforce?

Linnea Fletcher: [00:10:19] Well, for one thing is we need to do a better job within the governmental sector to make sure that there is a promotion of educational institutions working together instead of working in silos. For so example is if someone is in K-12 high school programs in biotech, their courses need to transfer to a variety of institutions and get credit, two year and four year like advanced placement and then biotech programs, their courses near to transfer to four year schools. And I know it’s hard because they don’t necessarily have the same courses. But the thing is, if you don’t do that, students don’t want to be in biotech, if not all their coursework transfers. And the other trend that needs to be worked on is if someone in industry learns what’s equivalent to one of my modules, they need to be able to test out of that module or a course in my program and get credit for what they’ve learned on the job and not have to repeat any of that training or education. It’s a waste of funding and money and time. So essentially how anyone learns, whether it be on in industry or in an educational program, they need to be made equivalent and we need to work towards that if we’re going to meet the needs of the workforce. Workforce needs are going to increase, not decrease, as biotech becomes more important across the board in all these different industries. It’s now even applicable for solutions in climate change. It will make manufacturing sustainable.

Grant Belgard: [00:12:22] That’s a great point. I really like how your program has this advisory council and it sounds like you’re very nimble and adaptable to what those needs are. I can say from our perspective in terms of what we see, which we’re looking at a very different workforce. We’re typically looking at PhDs in bioinformatics and related areas. There is often a mismatch between what is needed, like the skill sets needed in the biotech industry from the skill sets that are frequently trained in PhD programs. I mean, they’re broadly congruent for sure, but there are definitely some areas where there is an excess of people with those skills that just aren’t aren’t needed that much in other areas where there’s a glaring deficit. And my sense is in PhD programs, the skill sets are driven by the research programs of the PIs in the department. There’s not this close tie or not as close to tie to industry groups as your program has. I think that’s really interesting. And I wonder have you seen much of a movement to try to take those industry needs more into account in PhD training as well?

Linnea Fletcher: [00:13:44] Well, it’s interesting you say that. I have seen that. Of course, what I’ve always seen is if you look at engineering programs, engineering programs traditionally have close ties with industry and they run their labs more like what’s going to occur in industry. In fact, if you look at engineering programs, you’ll see that a lot of their funding comes from industry and they’re educating people mainly to get into industry and not necessarily into research labs. The one thing I thought was really good, I can understand why PhD programs like in Biology or some of the more pure research labs aren’t tied to industry because you do need pre-research, that research that isn’t always necessarily applied because industry research is applied research. But I felt that after getting my PhD and getting involved with educating people for industry, the QA QC that I learned and now I’m teaching my students. The regulatory affairs information and the need for documentation was actually a great way to run a lab and ensure quality within the laboratory. So I feel that even if you’re doing pure research that isn’t applied, taking some of the quality assurance and quality control principles, total quality management and applying it to some of the PhD programs would not be a bad thing. You’d end up with students that are better able to run their own labs once they finish their PhD and do a postdoc.

Grant Belgard: [00:15:40] That’s a great point. It’s so common for people to have trouble tracing exactly what solution was used in a particular experiment and so on and so forth. These things come up in academic labs all the time where the documentation is not to the standard of industry and it causes problems. Other things that you think doctoral training programs could take from this?

Linnea Fletcher: [00:16:07] Oh, I do think one thing I think is really worthwhile is so I was an unusual PhD student in that I did two different postdocs, two different areas. I always felt that that was beneficial, because then I actually had some education in other areas that could be applicable. And I feel at this point there’s going to be more convergence of science areas. And if you’ve had experience in different areas and you encourage that, you’re going to be better able to take advantage of opportunities from other areas in science. So my first postdoc was in immunology on monoclonal antibodies and my second postdoc was on the 3D structure of messenger RNA. Totally different areas, but it has served me well by doing those totally different areas. I’ll tell you, my second postdoc, I had a luckily the other postdocs were very patient with educating me with the molecular biology techniques that I needed to know because I pretty much was a straight biochemist. And then of course, the immunology I had to learn and the monoclonal antibodies. So each time I’ve put myself in a place that was slightly very uncomfortable, but I gained a lot by doing that.

Grant Belgard: [00:17:42] What do you think are the lessons PhD programs can take from that? Would that come at the point of rotations? Not all schools have rotations before students select their labs. So I did my PhD at Oxford, for example. We didn’t do rotation. I think rotations into a lab and finish your degree in that lab.

Linnea Fletcher: [00:18:01] Yeah. I think actually rotations before you make your final choice should be obligatory.

Grant Belgard: [00:18:08] And do you think doing it over the course of a year is adequate or do you think maybe that it would be beneficial for that to be prolonged?

Linnea Fletcher: [00:18:17] Well, I think you’re only going to get everyone to agree to a year.

Grant Belgard: [00:18:21] Yeah.

Linnea Fletcher: [00:18:22] To be honest.

Grant Belgard: [00:18:23] [] what’s possible?

Linnea Fletcher: [00:18:24] No, I think a year would be enough. Right, exactly. Because I totally understand what my husband, he’s also a PhD in microbiology and he ran a lab. I totally understand why he has to have his group focused on the work that he’s doing. It’s so competitive.

Grant Belgard: [00:18:44] And changing gears a little because I guess this is starting to affect everything. How has ChatGPT and the explosion of other AI tools on the scene in a very powerful way in recent months impacted, how you’re doing things and how do you anticipate that will impact the training you provide going forward?

Linnea Fletcher: [00:19:09] So I actually was part of a project that I had to develop an AI module for biotechnology programs, and the other faculty members at the community college had to do it in their area like computer science. The main thing I saw is that just like any new technology, you need to quickly understand what are the limitations and the advantages, especially the limitations. And I think it’s really interesting. I now have students when they have to write up original work, I have them write up their original work and then I have them put it in ChatGPT to show them what it would look like. And if you ever do this with students, the other thing that’s really interesting if you have them use those, the AI write up an original article, what’s supposed to be original article based on what, they should understand how it gains the information then ask it to do references. All the references will be false. So the main thing is they have to understand the limitations. And now it’s being used in medical diagnostics. Well, it’s only as good as whoever programmed it and what’s available. And that little hint of innovation and original thought that very much is a human trait may not be there. So you have to keep that in mind when you use these. They’re only as good as who programmed it and gave it the information. It’s only as good as the databases it has access to and is researching to be able to do what it’s supposed to do.

Grant Belgard: [00:20:57] And given the rapid increase in what these tools have been able to do over even the last 18 months, what are your thoughts on on where that’s headed? I mean if you look out five years from now. I mean, this will certainly be a lot more powerful than they are now. And no one knows how much more. But how would you expect that to shape the workforce?

Linnea Fletcher: [00:21:19] Well, I don’t think it will decrease the number of jobs or anything like that. What it will do is require that my technicians or the technicians across the country being educated will actually have to know how to use it effectively and be able to troubleshoot it. And then once again, it’s adding another area for technicians, depending what they’re involved in, to have to learn. And that’s the other reason why I think we really have to tie our educational institutions together, because students can only, they’ll gain so many competence passes through high school, so many competencies through two year and then four year and on up. It scaffolds the information. And we all have to do a better job of making sure that we interface better with this instead of having no overt. We don’t need overlap, that’s for sure. We’re going to have to do a better job. And one thing I focus on that I think all educational programs is that my students know exactly what are the outcomes for their education, and they can articulate this in an interview. And when they don’t know something, they’re very honest about it and they know what they don’t know, but how to get the information. You can only teach somebody so much, but they have to know what they don’t know. So and be able to articulate it and not promise anything.

Grant Belgard: [00:22:56] Do you have any training for job interviews and.

Linnea Fletcher: [00:23:01] Oh, yes. We do from the very first course on. We have like seven courses. So the very first course is career awareness, because I can’t afford them to get to internship and not know what area of the industry they want to be in. I can’t afford to have them get into an internship and say, Oh no, I didn’t want to do this because it takes a lot of time for companies to do internships, so they have to review everything that’s local. Every semester they change their resume based on what they learn in the program. They have to verbally articulate that to the instructor every semester. And then the the Gate is an interview committee when they are doing their internship. We’ve learned the hard way. Oh, the other thing is the class size for my program is 12, not 24, not 200. So that’s why it’s like graduate school, because every student has to demonstrate in the lab they can do it by themselves and not in a team, even though we have them work in a team so they have no chance of getting someone else to do it for them. And they do a lot of presentations.

Grant Belgard: [00:24:28] That’s great. So there’s a book I always recommend to people entering this space by Toby Freedman called Career Opportunities in Biotechnology and Drug Development.

Linnea Fletcher: [00:24:37] Yes.

Grant Belgard: [00:24:38] Are there any books that you would advise for listeners who might be just getting into this space?

Linnea Fletcher: [00:24:44] Well, if anyone wants to educate people, Lisa Seidman for Madison College has like the Bible in how to educate people for this industry and also for the students. It’s by Tech Manual by Lisa Seidman and then other books by Freedman. I’ve read all his books. He has several books that I think are worthwhile.

Grant Belgard: [00:25:09] It’s fantastic. What advice would you have for the listeners of our podcast? many of them are people who are in bioinformatics or they’re interested in bioinformatics, and some may be considering going to biotech for the first time, having only worked in academia. What advice would you have for them?

Linnea Fletcher: [00:25:31] Well, one thing is everyone should keep up on emerging technologies and there are a variety of sources in addition like your podcast and then other areas. You need to always keep aware of where the industry is going and what are some of, I read science religiously nature. I have to admit, I do a lot of reading and listening to podcasts and then the other thing is you have to really consider the fact that whatever you pick for a career, most likely you won’t stay in. So always be open to opportunity. Most people nowadays don’t stay very long in one job. They’re constantly looking, not necessarily because they’re dissatisfied with the job, but their interests have changed. Or they want to try something new. And I say you should always be willing to do that. So keep an open mind. My husband as a microbiologist, he went from the university to the chemical industry and he ended up in the petroleum industry. That wasn’t planned necessarily at the start.

Grant Belgard: [00:26:46] Right. I think that’s kind of a theme from the people we’ve interviewed on this podcast. Very few people had followed a career trajectory they imagined when they were a student. There were a lot of things that were unplanned. There was a lot of serendipity. A lot of people ended up in roles that in some cases they didn’t know even existed at the time, or in some cases didn’t even exist at the time as new jobs are created and so on. How do you think academia, government and industry could work better together?

Linnea Fletcher: [00:27:24] Well, for one thing is I don’t think there’s enough funding opportunities that foster and promote collaboration among the educational entities between high school two year, two year and four year. And I think there should be more. I think if they’re not willing to do it themselves, it should be possibly forced a little bit more with opportunities that foster collaboration. I do feel we need even more input from industry. In fact, there was a paper out by ACC, the American Association for Community Colleges, that was done by Harvard, and it indicated that we need a lot more input from industry concerning these educational and training programs and for them because we need to know more what they need and be able to anticipate. I know they’re really busy, but if they want the very best employee, we need their help and their voice. I think there should be more apprenticeships and more internships and apprenticeship programs that you can get scholarships and are paid for like some of the what the other countries do because that’s the best way. At least I know for my companies with internships, they hire these people or bring them on in internships with the thought that they’re going to stay in the company. And if we could have more apprenticeships, we’d have more transition into industry and it would be more seamless. So I think funding in apprenticeships needs to increase, not decrease. It’ll be money well spent.

Grant Belgard: [00:29:11] There’s a lot to unpack there and it started out with one of the earlier things you said, I’m involved with a school that has been very active in getting articulation agreements in place with regional colleges and universities for credits to transfer. And in learning about the process, I was surprised at how everything it seems, at least where they are, is essentially bilateral. It’s a bunch of bilateral agreements. It’s not being done at the level of a system, for example.

Linnea Fletcher: [00:29:46] No, it’s not. [overlap] finding individual school. I know. Yeah, it is. You’re exactly right. It’s like I have an articulation with the University of Texas at Austin. It’s only for biochemistry and it’s not for the rest of the system. And it was really hard to get. And I understand their concerns because they’re worried about quality. How are they going to monitor the quality of the students they’re getting from my program? It’s more interesting, K-12 in a state, you can get systematic across the state if they’re called core courses and they automatically transfer to all four year schools. I think more needs to be done to figure out better ways of ensuring quality from one program to another. Now, what some states are doing is it’s called still skill standards and competency. So the students graduate from a program and it’s guaranteed what skills they can do and the state controls that. I think if we had more of this to ensure quality, maybe the four year schools would be more willing to do systemic type articulations. I get why they don’t though. We just have to work on coming up with better systems to ensure quality when a student graduates.

Grant Belgard: [00:31:12] What is your most controversial opinion on this topic? Where you’re certain that you’re correct, but a number of colleagues would disagree with you.

Linnea Fletcher: [00:31:23] You have to. Okay. So industry needs to stop requiring a four year degree in biology to be a technician. That doesn’t insure anything, to be quite honest. That does not ensure quality. It does ensure that they made it through a four year program, but it doesn’t ensure that they’re ready for the job. Otherwise I would not have 50% of my students who have a four year degree are coming to me to get an advanced technical certificate.

Grant Belgard: [00:31:55] Yeah, that was a really interesting stat you gave.

Linnea Fletcher: [00:31:59] Degrees don’t ensure quality or at least maybe they ensure quality in some areas, but they don’t ensure that they’ll be able to be what industry needs. So they need to be more focused on competency based education instead of degrees and certificates at least asked for the competencies that are associated with the degrees and say, Can you guarantee me these students can do this and this? I can guarantee industry this because we just don’t graduate them unless they have lab practicals. They test out in all of this.

Grant Belgard: [00:32:41] That makes a lot of sense. Another thing that maybe sticks out to me is not just the difference between the responsiveness of your program to what industry needs, but maybe wrongly. But at least my perception, is that there’s less of that in four year programs in the biological sciences.

Linnea Fletcher: [00:33:07] But they’re not funded to do that.

Grant Belgard: [00:33:09] Right. It seems like it’s much more about getting people ready for grad school and so on.

Linnea Fletcher: [00:33:15] So a lot of my colleagues are in four year institutions and the universities and a lot of four year, they’re not funded to educate people for technician positions, we are. They don’t even have the equipment that I have for educating students and they certainly aren’t allowed to just have 12 students in a class. And so it’s really hard to do that.

Grant Belgard: [00:33:47] You mentioned that you had a, you know, Illumina sequencer and a nanopore sequencer. And I certainly never saw these until grad school.

Linnea Fletcher: [00:33:57] Yes. The fact that we moved from the Illumina platform to nanopore and now Oxford and the fact that we put a sequencing center, it’s grant funded through the NSF at a high school, and that we actually do the same thing in our program at the two year school. So we have high school students who are doing sequencing and interpreting sequencing data as well if our students at the two year and it’s actually in the very first course of our program and the repeated again. And at this point, we’re getting the students to do sequencing projects for other departments. The biology department at Austin Community College is doing a moth barcoding project, and there’s plans to have our students do the sequencing for that project and then share the data with the biology students. So we’ll have some peer to peer sharing of information. So this way, this models what’s going on in industry.

Grant Belgard: [00:35:07] Yeah, that’s fantastic. I mean, for sure, when they get an industry, if they’re doing anything omics related, NGS is going to be a huge component of that.

Linnea Fletcher: [00:35:16] That’s how we got involved with helping start up companies, is doing industry based projects for them. And then the idea of doing undergraduate research in addition to industry based projects. I think that’s the best way to engage students because if you get them involved in real projects, especially ones that make a difference, that then they can see why they should be learning science in the first place.

Grant Belgard: [00:35:44] Great. Would you have any final words for our listeners? Maybe something that you think is an important message to get across that that hasn’t come up yet?

Linnea Fletcher: [00:35:54] I think a final word for our listeners is I’ll share with your listeners the one thing that got me involved in education when I moved from research to education was the realization that the most important resource that we have in this country in the world is our students, our children. So why aren’t we doing more to engage them in what are real projects? That’s the way it used to be. That’s the way it was when people were on the frontier. That’s what they do in apprenticeships in other countries and we are doing in the US too. But the best way to educate people is not in a textbook, or at least not completely in a textbook, but in a lab and using exactly what they use in industry and in research. That’s when students really appreciate their education and their interest in science is have them do the real thing. And we should be doing more of this, not less.

Grant Belgard: [00:37:04] I couldn’t agree more. Thank you so much for your time. It was really great chatting.

Linnea Fletcher: [00:37:09] Thank you. I enjoyed speaking with you.

Transcript of Episode 52: Yuri Deigin

Disclaimer: Transcripts may contain errors.

Grant Belgard: [00:00:00] Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today is Yuri Deigin. Yuri, can you introduce yourself please.

Yuri Deigin: [00:00:08] Sure. Thank you for having me on the podcast. I’m Yuri. I’m a drug developer. I’m leading a longevity company called YouthBio. We are developing in vivo partial reprogramming gene therapies to ultimately create rejuvenation therapies, but in the interim, create therapies against existing age related diseases like Alzheimer’s, for example. Happy to chat about any topic today.

Grant Belgard: [00:00:36] Yeah, I’d like to learn more about YouthBio and how related do you see the the underlying causes of various age associated diseases?

Yuri Deigin: [00:00:47] All right.

Grant Belgard: [00:00:47] How much convergence do you think there is?

Yuri Deigin: [00:00:50] Yeah, right into it. I guess YouthBio is really the culmination of most importantly, my understanding of how aging works and that is epigenetically driven and that obviously all age related diseases in my mind are caused by the underlying process of aging. And of course different diseases could be different manifestations of the same process or different results of this process of gradual epigenetic slowing down of the repair systems and all other systems that help maintain homeostasis. And so reprogramming is one way to actually intervene on a level of epigenetics, because while reprogramming just epigenetically brings the gene expression pattern all the way into the embryonic like gene expression pattern. And that results in all of the changes associated with cell morphology and function ultimately as it’s of course driven by gene expression. But it all starts in changing the gene expression pattern. And what was noticed that during the reprogramming process, the cell is also rejuvenated both physiologically on the level of its transcriptome basically on the level of gene expression. In the initial stages of reprogramming, it seems that the pattern of the gene expression of an old cell gets shifted closer to a pattern of gene expression of a younger cell, the cell of the same cell type. And so this is really the foundation of partial reprogramming where we try to use this process of rejuvenation during reprogramming and stop it at that precise moment where the cell still remains of the same cell type that you start with. But it gets rejuvenated to some degree.

[00:02:38] And we don’t want the reprogramming process to proceed further because that could make the cell lose its function and lead to all sorts of problems. So that’s the partial aspect of partial reprogramming. And yeah, basically the underlying assumption, underlying hypothesis that aging is epigenetically driven, really synchronizes well with the approach of reprogramming, which as I mentioned works on the level of epigenetics. And that’s why I think it’s a very powerful paradigm. If we can figure out how to do this safely in all sorts of different cell types, because I think it actually is necessary, like different cell types necessitate different approaches to using reprogramming in those cell types, then I think we can have very powerful instruments to rejuvenate those cell types. And so eventually have a systemic therapy where you can target multiple cell types, multiple organs and eventually obtain this holy grail of systemic rejuvenation and make aging either slow down or even reverse aging to some degree in healthy people. That’s the ultimate goal of longevity research to make healthy people healthier for longer and make aging slow down in healthy people and radically extend lifespan and healthspan using such approaches.

Grant Belgard: [00:03:55] How do you think about aging fundamentally and what is the relationship between that and the epigenomic changes that are associated with aging?

Yuri Deigin: [00:04:05] Yeah, it’s a very interesting question because just recently we had a discussion on what is aging for a while. I think it’s been spearheaded by Vadim Gladyshev. And just recently we had another installment, his talk on the topic. So it’s actually interesting that there isn’t a consensus or at least a complete consensus in the field on what aging is exactly, because different people call different things, aging. And obviously there’s some common things that we observe that we realize is what aging is associated with. We all know aging when we see it. We know an old person from a young person. And even non-experts in gerontology, like people obviously are very good at telling apart an older person from a younger person. So obviously we have that really actually deep down in genome recognizing aging and recognizing old individuals from young individuals, even across species. So this is obviously something very fundamental to life, to what we will at least guess mammals and higher animals recognizing old from young. But on the level of gerontology and biology, I think to me, aging was just the process that causes mortality risk to increase with time. So basically, if your mortality risk does not increase, to me that means the organism is not aging. So if the mortality risk stays constant or actually decreases with age, then that’s an example of either a non aging organism or a non aging stage in the life history of an organism.

[00:05:44] For example, I think between ages like 5 and 8 or 5 and 9 in humans, the mortality risk actually decreases and reaches a minimum between ages like 8 and 10. And then after puberty starts, then that mortality risk starts to increase and it keeps increasing exponentially until we die with potentially a plateau between like age 20 to 30, 35. Some gerontologists actually argue whether there is a plateau or there is a slow down or there isn’t, or just an aberration or statistical aberration. But basically this is just, I think, one key observation or definition even of aging. Basically, aging is the process that increases mortality risk. And in humans, of course, it doubles mortality risk every eight years. So it’s an exponential increase. And in other animals, it’s not necessarily. So in some animals, actually, the mortality risk decreases with age. There’s examples of turtles, for example, who seem to have observed, we observed, decrease in mortality risk and actually increase in their fertility in some species of turtles. But of course, there are other ways to define aging as, for example, accumulation of various damage, intracellular extracellular or other manifestations, other hallmarks that people associate with aging. And they call that the aging process rather than the downstream effect of increased mortality. So as any academic field, you can have a lot of debate about the precise details ultimately coming, circling back to what we were starting to discuss in the beginning of this question. We obviously know aging when we see it and we would know rejuvenation when we see it.

[00:07:29] If you are able to take a 40 year old person and rejuvenate him, her to look and act and physiologically be the level of a 20 year old or say any two numbers, obviously everybody would agree that that is true rejuvenation. And you can do this, of course, in other animals as well. Mice, for example, two specialists in mice would obviously see rejuvenation. If you take a two year old mouse and rejuvenate to a little one year old mouse, there won’t be any question that this is actual rejuvenation. So from that standpoint, you can have and obviously there’s other biomarkers of aging. I think right now there’s physiological biomarkers of various functions of the organism like lung function, kidney function, heart function. There’s also epigenetic biomarkers are built on the known data of health status, mortality and all sorts of different ways that you can approximate the aging process and evaluate whether you’re able to observe a slowdown in the aging process or actually a reversal of the aging process. If you see, for example, biological age being reduced after some interventions. Sorry, I think I’m starting to maybe go a little bit off.

Grant Belgard: [00:08:39] No, this is good. It kind of raises a natural question. How do you think about aging and biomarkers of aging in the context of drug development? Because certainly different stages you would need to consider different things. And how does all that tie into the question of what is aging? Is that question essentially more of an academic debate or would it really impact at a fundamental level what your primary endpoints would be in a clinical trial?

Yuri Deigin: [00:09:15] I think we have at this point, enough biomarkers to assess to a pretty good degree any interventions that claim to rejuvenate or slow down aging precisely because we have different biomarkers and we can test function and we can test epigenetic age. So from that standpoint, I don’t think there’s anything limiting drug development or therapy development for rejuvenating therapies, especially because at this point and in this regulatory framework or regulatory landscape, you really need to go after interventions after diseases. Interventions have to go after diseases. So it’s not like you can have clinical trial of a healthy taking some sort of rejuvenation therapy and then measure endpoints. You really need to have some patients that you administer the therapy to, and then you evaluate clinical endpoints and biomarkers that are improved by that therapy from that standpoint.

Grant Belgard: [00:10:16] What indications do you think would be especially promising for that as a gateway to ultimately develop drugs that more generally target aging? How would you think about starting?

Yuri Deigin: [00:10:31] Well, we have examples of senolytics and osteoarthritis of the knee. People thought that would be a good indication to try. And I think there’s different ones that you can go after. And well with the metformin trial is another methodology where you have a whole basket of age related conditions and you have people susceptible older people to those conditions. And you then evaluate whether your therapy, your drug metformin in that case is able to reduce the incidence of a number of age related conditions. That’s one way to actually measure the effectiveness of a potential therapy. And other therapies. I depending on the actual intervention, they could have different optimal targets, optimal diseases, which could be the first ones to go after in a clinical setting. Reprogramming is if we’re talking in the context of partial reprogramming, I think it has a whole host of different areas in which you could demonstrate benefits in that particular disease model or actual disease or predisposition to disease, where you can confidently say that not only is this helping this particular disease, it could also have a systemic effect if you’re able to target multiple tissues. In the context of reprogramming, skin is people think is a low hanging fruit because you can have very quick results, visually observable improvements.

[00:12:02] And that’s why I think a couple of companies are going after skin, are going after dermatology. And also other areas in terms of hematology and immunology, people are also considering. But also I think in the context of the central nervous system, brain diseases, if you’re able to demonstrate using approaches of partial reprogramming, even a slowdown in the onset of various neurodegenerative pathologies like Alzheimer’s, Parkinson’s, etcetera, then it’s a very compelling. Again, very compelling case to be made that this is truly a therapy that’s at least slowing down the onset of aging in this particular tissue, the onset of age related symptoms of this particular disease. And also even if you’re able to reverse the symptoms, that’s a much more stronger case that you’re actually rejuvenating the underlying organ. So I think, yeah, while they’re very neurodegenerative diseases, they’re obviously hard nuts to crack. I think the upside in their cases is really high because there’s huge unmet clinical need and Alzheimer’s, for example, you’re able to demonstrate clinical benefit there that could be very compelling case for people to take notice that this approach is very strong, rejuvenating approach.

Grant Belgard: [00:13:23] So I think neurodegeneration is a really good use case because we do know there is some degree of genetic overlap, several neurodegenerative diseases and also gene expression signatures associated with age and then GTLS for those and so on have been identified in the context of brain age. What concerns me a bit though about the paradigm of slowing aging overall and having a substantial impact on the rate of age associated disease is as if there were a single underlying factor of aging that affected overall physiology in a similar way, something analogous to G and intelligence or whatever, you might expect to see that in genome wide association studies of different age related diseases in different organ systems. And to have some of the usual suspects coming up time and time again and within certain classes of diseases of course. You see some of that neurodegeneration tau, for example, comes up quite often, but you see very, very frequently, which you would expect if you have age matched controls versus people with this disease or that disease. On the other hand, of course, there are many facets of age associated biomarkers that are strongly correlated with one another. So these frailty indices and so on. And of course we do have genetic associations with measures like that. So do you think about it as, okay, we will identify treatments that are effective for maybe somewhat broader classes of diseases and reducing the rate of onset? Or do you think there is a good possibility of slowing everything down together?

Yuri Deigin: [00:15:38] I think theoretically there is a good possibility of slowing things down together. I think aging is centrally regulated, to be honest. I think actually it’s a program and I know I’m a minority opinion on this because there’s so many life stages that are definitely centrally regulated. And we have to have in a very coordinated concert to be developing just at the right time and the right proportions to have an embryo develop properly and also to have puberty onset at the right time and all organs start growing at the right time. So there is obviously a central synchronization mechanism. Now we still haven’t figured it out how exactly it occurs and well, there are some hypotheses that the hypothalamus could be one such controlling master regulator of aging because it is actually controlling a lot of daily monthly processes or maybe even on a longer time scale obviously. During embryogenesis it plays a very important role. That said, I think this is a parallel search for this master synchronization system. And also, of course, I think if we were able to slow down development, we see that that obviously slows down aging or actually puts aging on pause if we’re able to delay the onset of sexual maturity. And actually some people are pursuing that as a rejuvenation strategy. There is a gerontologist in Ukraine, Valerie Golub, who’s studying this in rats and reptilian, trying to see because obviously we’ve seen in many organisms that neoteny greatly extends lifespan and also experimentally, if you’re able to pause development or slow down development, that also extends lifespan. And it seems to put aging on pause.

[00:17:35] But coming back to the partial reprogramming approach, it’s a bit of a different way of going about it. It’s just targeting key organs that are playing the biggest role in our age related pathology of course. Heart disease being one of them and other organ systems and hoping that we can target enough key organs and key systems to that result in a systemic effect where we can slow down aging of the entire organism enough to produce sizable gains in healthspan and lifespan. But also there is a hope and a hypothesis that we’re also exploring that maybe by modulating in certain areas of the brain, rejuvenating them, maybe we can then achieve systemic effect. But obviously that’s just a hypothesis that we need to explore. But to answer your question, I do think that it might be possible to have an intervention going at the entire aging process, or at least the actively driven aspect of the aging process, because obviously after some time there are also stochastic things happening that are not part of the epigenetic landscape being driven, but they’re side effects of genes allowing stochastic changes to produce enough damage that it actually starts to accumulate. But that’s a bit of another tangent that I’m happy to explore.

Grant Belgard: [00:19:15] There are maybe a couple of tangents we’ll go off on in a bit, but I think it’d be really interesting to hear about your journey, How did you end up here? What drove you in the direction of science etc?

Yuri Deigin: [00:19:28] Oh yeah. My journey to longevity started with just a regular drug development and meeting people who are also wearing the same drug development ecosystem. But there were also longevity minded people who just shared with me the idea that aging is the causal factor in age related diseases that I was working on. They were working on Alzheimer’s, for example, and basically saying that we can and should try to intervene in aging and slow aging down and ultimately, hopefully reverse it. And that was a revelation at the time to me because as most people unfortunately on the planet, I never thought that aging is something that needs to be intervened in or should be, or is it possible to be intervened in? I thought aging is inevitable, just everything ages and didn’t even think that there is something that could be done about it until my eyes were opened by my friends at the time. And obviously after they made me aware of this whole industry and research community looking into aging, fundamental mechanisms of aging and trying to intervene in aging, that very quickly I realized that it makes all the sense in the world that, yes obviously aging is the causal factor in all those diseases and there is no good reason why we should settle for the lifespan that we inherited biologically from our ancestors. And we have the intellectual capacity to analyze biological processes and finally have the tools to intervene in biological processes that it only makes sense for us then to turn our intellect and our abilities to intervene in biology to the biggest limiting factor in our lives, which is aging.

[00:21:24] And very quickly, I became a very passionate activist at first about aging research, about the necessity to research into aging and just trying to open other people’s eyes, just like my eyes were opened by very, very simple and logical information and just making people aware that there is a thing like aging research out there that it’s possible to intervene in aging. It’s possible to extend lifespan in model organisms. It has been done many times and it’s only a matter of time until we find effective interventions that can be applied to humans and produce sizable gains in both health and lifespan. And eventually I was able to then not only do the activist part, but do the drug development related or start doing drug development related to longevity. And that came in the context of partial reprogramming, because at the time when I learned about partial reprogramming, I was already of the mind that aging is driven by epigenetics. To effectively intervene in aging, we need to intervene at the level of epigenetics if we’re talking about already formed organisms. Of course, if we’re talking about new organisms, probably we can intervene at the level of the genome.

[00:22:39] But unfortunately we’re already cooked organisms using the cooking instructions stored in our DNA. So if we want to change something in our biology, we have to go after gene expression. And as I mentioned in the beginning, partial reprogramming is one way to intervene at the level of epigenetics, at the level of gene expression. And it’s a very effective mechanism because it uses an underlying process that’s already fortunately encoded in our genome to do this, to restore gene expression of the primordial embryonic stem cells. And it’s a very powerful tool that we can use to to essentially hack for our purposes, to rejuvenate adult organisms, adult cells. And yeah, I started on the path to try to translate this paradigm into therapies very early on back in 2017. The paper came out in December 2016, and then I founded the first company in Ethereum dedicated to translating this paradigm in the summer of 2017. I got really excited about it because it fell on fertile soil in my mind, that partial reprogramming paradigm, because I already thought that epigenetics is the driver of aging. So again, I took a little longer maybe than I should have to answer your question. But yeah, happy to dive into any details of the journey or any particular aspects of it that could be useful to other people.

Grant Belgard: [00:24:10] Yeah. I think it’ll be interesting to hear about the lessons you’ve learned through that. But before we get into that, to go on a bit of a tangent, you’re obviously quite early to this. And of course now partial reprogramming and aging research is much more mainstream than even five, six years ago. But you were also very early on the important aspect of coronavirus Can you tell us about that and how did you get there? I recall reading your very, very long blog post years ago, and I was really impressed, what was the impetus behind that? Because at the time that was very, again not mainstream but it’s one of those things that has become much more mainstream in the years since, in large part because of what you kicked off.

Yuri Deigin: [00:25:09] Yeah. It was just another tangent essentially that I got curious about. The origin of the coronavirus, COVID, I guess not just any coronavirus, but the coronavirus that caused the global pandemic. Basically like everybody else was curious in early 2020 where the virus came from, and initially a lot of people made the obvious connection that Wuhan has the premier lab in Wuhan Institute of Virology that has been studying coronaviruses. But in the very early stages of the pandemic, this making the hypothesis that the lab had something to do with the outbreak was considered a crazy conspiracy theory. And the powerful and very respected scientists were putting their names and their reputation behind this to dispel this conspiracy theory. And initially I just trusted them. I thought hey, if nature medicine says that the virus must be natural and there’s like 5 or 6 virologists and Anthony Fauci saying, that there ought to be listened to, then they know what they’re saying. And but at some point just decided to dive a little deeper. And instead of just outsourcing my thinking to them, decided to try to see for myself from first principles. Does it make sense this conspiracy theory makes sense or not? And so I read the Nature Medicine paper, and I was a little disappointed by the logic because once you strip out all the virology lingo, you’re left with very unconvincing logic that basically it’s the absence of evidence that the authors are trying to sell as evidence of absence, evidence of possibility that this could have come from a lab. And then I started digging deeper and I saw that the research that they were doing in Wuhan was exactly potentially similar to what could have produced this coronavirus.

Grant Belgard: [00:27:25] And of course, this was long before the diffuse proposal had come out and so on.

Yuri Deigin: [00:27:29] Oh yeah, yeah, yeah. Diffuse was a year afterwards and yeah, basically it was just circumstantial evidence of look, this is clearly a huge red flag that the lab that’s been collecting coronaviruses from all over the world and manipulating them in all sorts of ways had the outbreak of this coronavirus with the very unusual feature, a couple of unusual features, one of them being this furin cleavage site. And it was very suspicious to a lot of people. And basically, yeah, I wrote this up in the blog in like early 2020, published it, and then it took on a life of its own and got me deeper into the rabbit hole and into the community of people, like minded people who were also very suspicious that this could have come from a lab and were continuing to investigate all aspects of everything surrounding basically the outbreak, the work in the Wuhan virology and other EcoHealth Alliance as well of course, what their involvement was and how they were using grant funding and their close cooperation with the Wuhan Institute of Virology and other labs to essentially collect and manipulate viruses in the lab to make them more infectious for the purposes of assessing how dangerous this would be if it happened in nature. Basically try to second guess or try to guess what nature could do to make viruses more dangerous for humans. And they did this with SARS like viruses. They did this with MERS like viruses, MERS being the Middle Eastern respiratory virus that is very deadly. It’s like a 37% fatality rate. And actually they did a lot of research on that in Wuhan Institute of virology together with EcoHealth Alliance as well.

[00:29:19] We learned that a year or two well, not to a year and a half after, of course, the initial outbreak because of all the Freedom of Information lawsuits that were filed to NIH and other agencies to actually see what has been going on in this research between EcoHealth, NIH, who funded a lot of these studies, and the Wuhan Institute of Virology, who were doing a lot of these studies, but again diverged. But yeah, basically once I gotten interested in this origin of coronavirus, the community of other people that I got together involved with we, started calling ourselves drastic. It was a Twitter based group of activists trying to investigate the origins of the coronavirus. And we investigated some additional aspects, published a bunch of papers on very suspicious discoveries. And basically, at some point, yes, the diffuse proposal was leaked by drastic. The diffuse proposal being, of course, the joint grant proposal by EcoHealth and the Wuhan virology in which they pitched to DARPA this idea of collecting novel SARS like viruses and also engineering novel furin cleavage sites in sites like viruses, because up until now and actually never since SARS, like viruses, never had a furin cleavage site at that spot. And it seems actually to be evolutionarily discouraged in bats because in bats, these viruses are gastrointestinal viruses and furin cleavage sites actually seem to be detrimental to the viruses being tropic to the GI tract or something. Basically, furin cleavage sites for some reason make viruses more preferential to the respiratory system. And but of course, for humans, that’s the biggest way how viruses get transmitted through the once they’re airborne through the respiratory system.

[00:31:22] So for a virus to jump from bats to humans, the key skill for it to pick up is to actually become tropic due to our respiratory tissues and cell types and inventing or getting a furin cleavage site actually is a catalyst for a virus to do that. It basically turns the virus from a gastrointestinal virus into a respiratory virus, and that makes it very high threat for transmission. It makes it so much more transmissible for humans, especially. And in the context of research on coronaviruses, it’s been very well known that the furin cleavage sites expand tropism of such viruses and make them actually respiratory viruses. And it’s been experimented with other virus types, coronavirus types to engineer novel furin cleavage sites and observe how that changes the tropism or the preferences of the virus to different cell types and different systems, including the respiratory system. And so in that diffuse proposal, one of the things they described to put in their experimental roadmap was to try introducing novel furin cleavage sites, basically to assess the risk of how likely it is in nature for a furin cleavage site to arise and to also model what would happen if a particular virus got a furin cleavage site. How likely would it then be to jump to humans? And of course the SARS-CoV-2 virus has this receptor binding domain that’s very highly preferential to human ACE2 receptor, which is very, very interesting characteristic. It scores like number one on the list of all different animal ACE2 receptors to be preferential to the human ACE2 receptor, which is very odd for a bat virus. But maybe there’s a species of bats who has a very similar or an ACE2 receptor that’s also very high binding affinity to that particular conformation of the spike protein on the SARS2 virus. And of course, not only does the spike protein binds well to our ACE2 receptor, the furin cleavage site makes it so much more transmissible and so much more preferential to our respiratory system. And so once the DEFUSE proposal was publicized in the summer of 2021, to a lot of people, this was very convincing additional nugget of evidence.

Grant Belgard: [00:33:49] That was what pushed my posterior probability on this way over 50%. I recall reading through it and saying some words that would not be appropriate for a family podcast. It was just shocking. And I was certain at that point that these surely global regulations on gain of function research would be coming down swiftly. And the amazing thing is, even though later that summer, we got to the point where most Americans believe, WIV was the origin of the virus. And of course former CDC director during the outbreak said something along those lines. Obviously a lot of people within the US intelligence community were concerned that was the case and several people in Congress. So one would think something would have been done about it by now. What’s shocking is there’s probably more manipulation of coronaviruses happening now than before. It’s extraordinarily dangerous. I mean, even if you don’t buy that it came out of WIV like surely even without that, just knowing how common it is for viruses to escape from laboratories and so on, the cost benefit of this kind of work is so overwhelmingly on the cost side and not the benefit side.

Yuri Deigin: [00:35:18] Yeah, for sure. And yes, the SARS-CoV-2 escaped definitely once from the Taiwan BSL-3 laboratory and there were actually acknowledgments in internal emails that before that escaped within a Chinese laboratory. And the first SARS virus that was much less transmissible escaped four or six times from different labs. So coronaviruses obviously can escape from even high level of security biosafety labs. So and yes obviously, I’m sure there’s countless times where people were infected handling coronavirus samples these days because so many labs now work with the coronavirus. And obviously it’s so hard to track now where the infection you got as a researcher is from a lab or just somewhere outside. I mean, in Taiwan, they could track it down because essentially it was very low circulating SARS-CoV-2, if not zero at that point. And they were able to genomic tracing to confirm that this particular sample is the one that infected the researcher. But yeah, also all sorts of potential gain of function research that people have been doing or are doing now with the COVID virus, SARS-CoV-2, one of the earliest ones was these Italian researchers in Siena. I think in the beginning of 2021, they passaged SARS-CoV-2 in the presence of neutralizing antibodies from people who had COVID or were immunized to actually see how it could evolve to escape those immunity escape, that antibody immunity.

[00:37:01] And they succeeded. They created a whole new strain that was very good at infecting people who already had immunity to the original SARS-CoV-2 virus. If that escaped, we could have had a whole new episode of the pandemic with a whole new strain. And what happened with Omicron to me is highly reminiscent or highly similar to what could have happened if a gain of function research could have produced a strain, then just got out and infected people and started circulating. Because Omicron is just so different from its last known ancestor, it has like 30 spike mutations basically like that. It developed seemingly without any intermediates. And like to this day, it’s still a mystery. How did Omicron arise and where was it circulating between like June or November of 2020 when its last ancestor was seen in November or December 2021, when Omicron emerged. Where was it all this time? And the idea that it wasn’t just someone immunocompromised patient in which it could have developed those 50 mutations, including 30 spike mutations.

[00:38:17] To me, it’s a very implausible hypothesis because we have been observing quite a number of immunocompromised patients and in none of them did we observe such a huge number of mutations arise. There’s usually a couple of dominant ones arising and then maybe a few others. But I think the most we’ve seen is like ten mutations in the spike of a person who had like 18 months infection present for a duration of 18 months, they couldn’t clear. And so but now we in Omicron, seemingly between a year since seeing its last ancestor developing 50 mutations with 30 spike mutations, it’s really, really crazy. So yeah, it could have been a lab leak as well. And who knows what other gain of function research is going on because obviously it’s not a regulated space. And even if you regulate it in one country, who knows what happens in countries that are not signed on to be parts of some regulation. So it’s a huge, huge existential level problem for us, for humanity, that unregulated gain of function research of pathogens going on in all sorts of labs all over the world could lead to pandemics, maybe even worse than this one, I hope not.

Grant Belgard: [00:39:32] It’s just wild to me like when I was reading the DEFUSE proposal, one of my immediate thoughts was this is kind of the Manhattan Project moment for the life sciences, except far more people have been killed by this than have been killed by nuclear weapons. So I was expecting surely there would be.

Yuri Deigin: [00:39:51] DEFUSE proposal was actually much broader than that. They proposed actually creating vaccines for bats with which they would go out into the field and immunize bats preemptively. And who knows what wrong could have gotten from that approach. And you know what? Maybe new combinations of viruses, bats could have developed due to that thankfully DEFUSE proposal was not funded. And that particular aspect of it of trying to actually go out and change ecosystems with preemptive vaccines in wild animal populations. This was criticized by the reviewers, but of course the molecular biology experiments, even if they weren’t funded by DARPA, they could have been funded by many other agencies, including Chinese funding agencies or even like you don’t need a lot of funding to do genetic manipulation of viruses if you already have the viruses and you collected some and to insert 12 nucleotides into a coronavirus genome doesn’t really take a lot of money or a lot of time for your post-docs or PhD students.

Grant Belgard: [00:41:00] Why do you think there hasn’t been a reckoning on this? Most people believe this is what happened. Plenty of people in power have said this as well, and yet there hasn’t been an aggressive effort to shut this down.

Yuri Deigin: [00:41:16] Oh, like to shut down gain of function? I mean, yeah, it’s very hard, I think, to come up with novel global regulation of an entire field of virology. If we’re talking about pathogen research and classifying what exactly is gain of function for a pathogen and what isn’t and in what context it should be permitted or should it be like completely banned? And what about places that don’t sign on to this convention? Virology is just going to migrate or virology labs will just migrate to those geographies that don’t have that regulation. Of course, if you ban it in the United States and Europe, that’s the major sources of funding. So I think that could lower risks of various pathogen gain of function research ongoing and greatly decreased in number.

Grant Belgard: [00:42:08] But I guess the challenge is, it’s much more inexpensive and widely accessible to do this kind of work than it is to enrich uranium and build nuclear weapons.

Yuri Deigin: [00:42:19] Oh, yeah. These days, absolutely. Biology, I think from an existential risk perspective, is much higher on the list than nuclear war or nuclear terrorism, because bioterrorism by comparison is much cheaper unfortunately, Hopefully, we won’t have bioterrorists listening in and like, Oh, okay, I was enriching uranium in my backyard. I think I’ll go build a virus lab now. But I think the process is actually ongoing. It’s slow and we see some of it starting to happen with now the Republicans having these hearings and maybe they will result like there’s two two questions. One is about the origins, which is of course important. But it’s not like even if we don’t find out the exact origin.

Grant Belgard: [00:43:11] Even if we’re not certain, certainly I think it’s undeniable that it’s a very plausible source. And even in light of it being a plausible source, it very clearly is something we need to button up and make sure that that thing does not happen. Or even if you say it didn’t happen the first time, that it doesn’t happen in the future, right?

Yuri Deigin: [00:43:34] I mean, yeah, not like just the SARS leaks I mentioned happening in the past couple of years. And just before the Wuhan outbreak, there was a brucellosis outbreak in China, in Kunming, in Yunnan, in the lab, and like 100 people got infected by it’s much less transmissible, thankfully, bacterial infection, but still, obviously it’s a lab borne infection. So these things happen and these things are dangerous. It’s just a matter of the right pathogen escaping that could be so much transmissible that it could cause a pandemic versus something that isn’t. But yeah, absolutely should be taken seriously. And I think also the problem is that politicians are blissfully ignorant on where biology stands right now. And when they find out their hair stand up how easy it is to manipulate biology and how cheap it is these days. And basically what Wild West, to quote Jeremy Farrar exists in all over the world in labs. Basically what I mean, nobody is regulating what you can do in the lab. If you want to supercharge a virus, the virus police won’t come and say, Why did you do this? Didn’t you realize that this is dangerous? So, yeah, hopefully soon enough, people in power will come to understanding of what’s going on and what’s possible and what’s dangerous.

Grant Belgard: [00:44:58] If someone is just hellbent on doing it, it would be difficult to stop them. But certainly the vast majority of the work ongoing where something like this could accidentally get out and cause lots of problems. It’s funded by funding agencies. People are doing it to publish work that’s peer reviewed. If there were a common consensus even if scientists just got their act together and did this even without any kind of government intervention, that would dramatically reduce the likelihood. But that doesn’t seem to be happening.

Yuri Deigin: [00:45:38] Yeah, exactly. And yeah, I think the incentives are misaligned here because there in order to publish something we have in virology. You have to create interesting research. And right now it’s very hard to do interesting stuff without genetically manipulating viruses or modeling some different changes that could make viruses more pathogenic or more transmissible. I think so. But at the same time, from the benefit of society or humanity as a whole, this type of research supercharging viruses or creating more transmissible viruses or more dangerous viruses, I think has a very, very little benefit, if any. But at the same time, the risks are huge and the risks are obviously shared by the entire world. And so if we’re worried about just one bioterrorist, it’s one thing. But if we’re worried about thousands of virologists doing this thing and making some mistake or not even a mistake or just supercharged virus could escape, even if they follow procedures, maybe some equipment malfunction or something happens that people don’t anticipate, that could lead to a pathogen to escape then the probability of this if a thousand people are doing it versus just one crazy terrorist is doing it, I think are much higher. That’s a different conversation.

Grant Belgard: [00:47:06] So I guess coming back now after, I think it was a pretty interesting digression in your own career, kind of a side project. So if you could go back several years before you started the founder journey, what advice would you have for yourself?

Yuri Deigin: [00:47:25] Oh, that’s a tough question. Invest in Bitcoin maybe. That’d be one advice. I mean, like if we’re talking something useful for people these days who are entering longevity, like in terms of career advice, I think now is a much better time because there’s all sorts of fellowships and programs like LBF Longevity Biotech Fellowship available for people who are just getting interested in longevity and want to quickly orient themselves. I think the number of resources available is really large compared to Decade ago or more when I was entering the field. So just the advice would be to study existing description of the landscape of the field and various areas of research and just, not be afraid to reach out to people and ask for advice if you’re interested in career switching. There’s all sorts of companies open to people coming from different fields. And obviously, if you want to do this, if you’re interested in longevity, one advice I would give is not to defer, not to procrastinate, but to get into it as quickly as possible. Because I think the more you get drawn into a career, the harder it is to to switch over. And longevity, I think right now is at a good point where you can enter into a lot of new companies or even labs on the academic side and the tidal wave that’s coming, it’s going to lift your career and your progress in this field as well. So maybe just don’t delay.

Grant Belgard: [00:49:06] Yeah, it’s interesting because you had a somewhat circuitous path where you were in tech for several years before getting into pharma.

Yuri Deigin: [00:49:17] Oh, yeah. Before drug development, I was in tech. My first career was in tech and I did computer science degree in mathematics and yeah, I worked for IBM. I did a startup actually in mobile applications back in like 2002 before it was cool, but I pivoted then into drug development. I wanted to do drug development for a while. And actually the time has come back, like in 2008 for me to do this. So I decided to do an MBA to help me get into the pharmaceutical business development, and that was the key pivot. But then to get into longevity drug development, that took me also some time before an opportunity came, which I think was a perfect opportunity with partial reprogramming because it was just the right approach for my current and then understanding of aging as an epigenetic process.

Grant Belgard: [00:50:11] Well, I guess in terms of getting it early, you’re two for three. So longevity field, the COVID origins, but not Bitcoin.

Yuri Deigin: [00:50:21] Oh yeah, yeah. There’s a lot of other investments that if you had hindsight, it would be nice to know about back and a decade ago.

Grant Belgard: [00:50:31] Cool. Is there any advice you would give to people who are launching their first company network?

Yuri Deigin: [00:50:40] I think yeah, networking is really, but obviously it’s pretty obvious advice and don’t get discouraged because it’s very easy. As an entrepreneur, there’s always a roller coaster. There’s always going to be down times where you think everything’s going badly, but you just have to keep going and keep the faith that eventually there’s going to be another positive development that will, and like longevity in terms of being an entrepreneur is also very important. You have to keep doing what you’re doing and if you believe in it. Of course, if something changes and you realize that the business model you had was wrong or the therapeutic approach you were exploring doesn’t really work out, then of course you have to change. But if all the fundamentals remain the same and it’s just the environment is not really good, like right now, economy is not great. So a lot of pundits are saying there’s going to be like an extinction of company of startups coming. But do you have to close yourself to the noise and just keep doing your job and just try to achieve your milestones that you put forth for yourself and for your company. It’s probably the same advice everybody gives.

Grant Belgard: [00:52:06] No. I mean, it’s good to know what people emphasize because there aren’t enough hours in a day to do everything one is advised. And so even hearing the same thing time and time again, you get extra votes for the importance of that.

Yuri Deigin: [00:52:26] Yeah. I think everybody says resilience is a key attribute for entrepreneurs and that’s true.

Grant Belgard: [00:52:33] Get punched in the face.

Yuri Deigin: [00:52:35] Yeah, exactly. But it’s true. You have to keep going in the face of adversity. And of course, also the more you do it, thicker your skin becomes and the better you become at managing it. So also, yeah, I think for younger entrepreneurs it could be a little tougher, but it’s just something like with anything else with the time and practice, you get better at it.

Grant Belgard: [00:53:03] You develop thick skin.

Yuri Deigin: [00:53:06] Yeah. Stick to it. Don’t get discouraged. And also, yeah, I think it’s also important to have people who can help you both emotionally and with advice and like, support groups are important. And that’s why I think also within the fellowships like the ODLB Fellowship or the Longevity Biotech Fellowship, these mastermind groups as you know, they could be very helpful because there you can connect with people going through similar things, similar entrepreneurs or scientists, researchers or postdocs going through similar adversity and connecting with them and getting support, both emotional support or just some advice or some other help. I think it’s really important to have that kind of support structure, support network for everyone, entrepreneurs and academics alike. So yeah, get that support structure in place, network and try to form this group of people who can support you through the tougher roller coaster rides.

Grant Belgard: [00:54:04] Solid advice. Thank you so much for joining us, Yuri. It’s been a pleasure.

Yuri Deigin: [00:54:10] Thank you. For me as well. Hopefully it was useful for the listeners. So thank you.

Transcript of Episode 51: Adam Freund

Disclaimer: Transcripts may contain some errors.

Grant Belgard: [00:00:00] Welcome to the Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today is Adam Freund, the founder and CEO at Arda Therapeutics. Welcome, Adam.

Adam Freund: [00:00:10] Thanks, Grant. I’m happy to be here.

Grant Belgard: [00:00:12] Yeah, we’re happy to have you. So what can you tell us about Arda?

Adam Freund: [00:00:16] Arda is a seed stage biotech startup. We are based in the Bay Area, and our mission is to find and eliminate pathogenic cells. And we do this in chronic diseases and ultimately in aging itself.

Grant Belgard: [00:00:30] How do you think about pathogenic cells? What in your mind makes a cell pathogenic?

Adam Freund: [00:00:36] It’s a great question. At a high level, cells are the functional units of life. And because of that, we also think that they’re the functional units of disease, which to put it another way is that when a tissue goes wrong, it’s because cells within that tissue have gone wrong. And in some cases, the cases that are particularly relevant to Arda, that means too much of a particular cell type. So think for example, myofibroblasts in fibrosis. They’re an important wound healing cell type, but in certain diseases they become overly abundant and then they don’t resolve or go away when they’re supposed to and so you get disease. And in cancer therapy, targeting and eliminating pathogenic cells is sort of a tautology. That’s what the entire field is based on. But in other diseases, we think that it’s a missed opportunity outside of cancer and maybe a little bit autoimmune disease. There are virtually no drugs that directly eliminate cells. Instead, what most modern pharmacology does is modulate individual pathways or proteins within the cells that drive disease, the pathogenic cells. But cells are complex networks, and because of that their behavior can be difficult to change via the modulation of single targets. And so we believe that it will be more effective to eliminate entire pathogenic networks, that is entire cells.

Grant Belgard: [00:02:02] So once you’ve identified a pathogenic cell for a particular indication, how would you go about eliminating them?

Adam Freund: [00:02:10] So our approach primarily focuses on using cell surface factors to drive biologics, antibodies generally to those cells and deliver cytotoxic activity. We’re really lucky actually that the cancer immunotherapy field over the last ten years or so has done this incredible job of creating a modular, programmable, therapeutic toolbox for eliminating specific cell populations. And unlike previous generations of cancer therapy, which were chemotherapy and were based on the internal metabolism of cells, these new tools are based on cell surface markers, which means they can be used any time you have a unique or enriched cell surface marker. And so that’s what we do. We think that the potential of these immunotherapy tools, multiple different modalities within this space are applicable and have really amazing potential outside of oncology. And that’s what we’re trying to bring to the to patients.

Grant Belgard: [00:03:08] So how would the regulatory pathway look for that?

Adam Freund: [00:03:11] It should look like most other biologics. As with anything, we will be developing the antibodies. We’ll be doing a lot of preclinical testing for efficacy and safety and then we will be registering for an I&D and moving into a phase one where we’ll be looking at ascending dose safety things like that. And then in phase two and three, looking for efficacy. One of the things that we will be focused on from a mechanistic perspective is making sure that the drugs that we’re developing are, one, eliminating the cells that we want them to eliminate, and two, not eliminating a bunch of other cells that we don’t want to eliminate. And then of course, we’ll have biomarkers related to that. And then we’ll also be looking at the efficacy readouts, which is to say, do we actually make the patients better and healthier.

Grant Belgard: [00:03:58] How do you think about animal models in this kind of work?

Adam Freund: [00:04:03] I think about them a lot because I think we all know that they are highly imperfect. A lot of the failures of drugs in the clinic can be attributed to poor preclinical models. And if you think about the way that we develop and evaluate preclinical models as a field, it’s sort of easy to understand why, especially when we’re talking about rodent models, mouse or rat models. What we usually do is based on some general understanding of the human disease, guess at a set of interventions, either genetic or environmental that might cause similar problems in the rodent. And then we assess histopathologically. That is to say, we look at a slice of the tissue and in a usually pretty subjective way, ask whether the tissue now looks like, the tissue from a human disease patient. And the issue with this is that it’s quite qualitative. It’s quite subjective and it usually doesn’t involve a large sample size. And so consequently, when we assume that because an intervention works in a rodent model, it will work in the human model, we’re often disappointed, At Arda, we can’t solve the entire preclinical model problem via biotech. I think this is a real issue and it’s something that I think the field needs to grapple with. But really at the end of the day, there’s no substitute for testing in humans. And that’s not something that we can get around. However, we can I think do a little bit better than the current status quo.

Adam Freund: [00:05:29] And that’s because when we compare human disease to preclinical models, we do so at the single cell level. So I haven’t really mentioned this up to now, but we’re really focused on using single cell data to identify pathogenic cells. And what that means is that we look at human data at the single cell level, usually single cell RNA-seq data. And we also look at single cell RNA-seq data from rodent models of the disease as well as other types of preclinical models. And in that high dimensional single cell space, we can compare and contrast models to the human disease. So we can look for the overall cellular architecture of the disease, the overall transcriptomic similarity. We can look at whether our specific cell populations are present in the disease, the populations that we find in humans. We can ask whether they are also present in these preclinical models. And then lastly, when we eventually hone in on a specific target, something that’s on the cell surface of the cells that we want to eliminate, we can ask whether the expression pattern of that target is similar in preclinical models as it is in humans, which tells us whether some of the safety and efficacy data will be translatable to humans. And so combined, we actually think that this will allow us to select better models, maybe even develop better models and better models lead to better drugs.

Grant Belgard: [00:06:44] When you talk about pathogenic cells, is it usually the case that a pathogenic cell state is accompanied by an unusual accumulation or depletion in the relative abundance of that cell type or do you see those things as quite unrelated? So I’m thinking here of something like fibrosis, right? I mean, you certainly have a change in the abundance, but then also concomitantly in the state and likewise with Microglial activation proliferation, is that the rule more exceptions that come to mind.

Adam Freund: [00:07:23] So I think that if we’re talking about a state, a cellular state that only exists in the disease context, then sort of by definition that state is accumulating. Now, it might not be accumulating because cells are actively dividing. It might be accumulating because cells are shifting from one healthy phenotype to an aberrant phenotype. And that could change how much we expect cell elimination to work. So one of the things that we think about is that not every disease is a good target for Arda’s strategy. Examples would be sarcopenia or neurodegenerative disease. These are diseases mostly of cell degeneration. And that’s not a context where eliminating more cells is likely to cause benefit. However, there are many diseases, inflammatory diseases, fibrotic diseases, other kinds of local tissue remodeling diseases where we do think that eliminating cells can either help the tissue restore balance, can trigger even in some cases regeneration, or can break a positive feedback loop that is causing that tissue to remain diseased. And so one of the ways that we carefully choose our indications is by assessing based on everything we know about the disease as well as the single cell data, do we think that there’s a cell population here that is causal? And if we have a reasonably strong hypothesis that the answer is yes, then we can go and more deeply characterize and investigate the single cell data.

Grant Belgard: [00:09:03] How many pathogenic cell indication combinations would you say are pretty much off the shelf based on the existing literature, you have pretty high confidence that eliminating the cell type would causally change the course of the disease for the better? And how much do you have to put into novel target identification, which I guess in your case is curiously kind of a combination of cell types by specific surface markers, right?

Adam Freund: [00:09:39] That’s exactly right. When we think of target identification, we think of two layers. There’s the layer of, can we find and identify the cells based on mostly their transcriptional signature and their enrichment in disease. But then can we find specific cell surface targets on those cells that can be used to eliminate them and not healthy cells. So that’s exactly right. And to your question about how many indications is this sort of do we expect this to be a useful strategy? We actually think this will be quite broad. And partly this has been driven by what we’ve seen in the single cell literature over the last five or so years. So as single cell data has accumulated, we’ve seen huge numbers of papers coming out where people will examine a disease, usually from human patients for the first time using single cell data. And they almost always ask the question, do we find cell states that are enriched or unique in the disease state and that appear causal? Correlation is not causation. So just because a cell state is enriched in disease, doesn’t mean it’s bad. It could be adaptive. In the case of if you’re looking at normal wound healing, the presence of myofibroblasts is adaptive. It’s helping that wound heal. But in the case of fibrosis, it’s maladaptive. And so there’s a little bit of biological inference that has to happen here.

Adam Freund: [00:11:00] But by and large, finding a cell state or multiple cell states that are enriched in patients and not really present in controls, and that those cells express genes or factors that are thought to be causally pathogenic based on the understanding of disease, it appears to be the rule rather than the exception across tissues, across cell types, across indications, across groups as well multiple groups from all over the world who are doing this kind of work are finding the same thing again and again. And as we’ve compiled data sets that have been produced by these groups, we find the same signatures again and again. And so we really do think that this is quite a broad space. To be specific, the initial areas that we’re exploring really because we find one, a strong therapeutic hypothesis and two, quite a lot of single cell data already available. And those are fibrotic diseases and as I mentioned, inflammatory diseases. So some autoimmune diseases, other sort of local inflammatory diseases and so that’s where we’re starting. But eventually we plan to expand to multiple other indications and hopefully ultimately aging itself, which we think of as being driven in part by a chronic inflammatory process that has its roots in the accumulation of pathogenic cells.

Grant Belgard: [00:12:20] And how do you see the work that you’re doing now building up to tackling aging more broadly? Do you see those as completely distinctive programs with no overlap, or do you think a lot of what you’re doing now can feed into that?

Adam Freund: [00:12:33] I think a lot of what we’re doing now can feed into it, especially on the inflammatory disease side of things, because that’s our best hypothesis for how this kind of approach might impact aging. And that could change over time, of course, as we learn more. But one of the things I’ve often focused on when thinking about aging and just to clarify, I’ve studied aging for quite some time. I’ve been fascinated by this topic ever since I was in graduate school and I first came across the idea that aging is not just unavoidable wear and tear. It’s not just the accumulation of entropy in some physical law kind of way. It’s actually a process that can and is modulated by genes and biology across the entire animal kingdom. Once you believe that aging can be modified, it’s hard not to get excited about studying it. And so I’ve been fascinated by this for many years. And one of the things that people in the aging field think a lot about including myself is how do we develop a drug for aging. And there’s two kinds of drugs that one could imagine, a drug that slows down aging. Every year you take it, you only age half as much as somebody who’s not taking it. And then a type of drug where you reverse aspects of aging. The first one poses some serious problems from a clinical trial perspective in that if primarily what you’re doing is slowing down the aging process, the earlier that you start dosing, the better.

Adam Freund: [00:13:57] First of all, in terms of a person’s life, which means the healthier they are, which means the more liability any safety signal has, and the longer your clinical trials have to be because you’re effectively waiting for your control group to age. And then you’re hoping that your experimental group doesn’t and that can take years depending on how many people you have in your study. It could take many, many years. In contrast, the idea of reversing certain aspects of aging is a lot more tractable because in this case, you’re now looking for your experimental group to get better and your control group to basically just stay the same. And depending on the mechanism, theoretically this could happen in elderly people who already have experienced some degenerative changes as well as in younger, healthy people and really I think the only reason people aren’t more focused on finding this latter type of intervention, these age reversal processes as opposed to age delaying processes, is that I think there’s an assumption that it’s harder. It’s harder to turn something back than to stop it from happening in the first place. But I don’t actually agree with that. I think that if you imagine a biological process that’s been tuned over millions, billions of years of evolution, the idea that we’re going to be able to tweak it just enough so that it works even more efficiently than it already does and so we age just a little bit slower or maybe even a lot slower than we already do, that we’re just going to be able to tune homeostasis to be perfect such that we never decay.

Adam Freund: [00:15:25] That to me sounds quite challenging. Imagine building a car where it never needs service, right? It just runs perfectly forever as opposed to a car where you recognize it’s going to break down and you just know that every year you’re going to take it into the shop and do a full tune up. The latter approach to me actually seems quite a bit more tractable. And so one of the reasons why I’m excited about Arda’s approach is that it fits squarely into this reversal camp in that what we are trying to do is to identify cells that over time accumulate with age and that cause detrimental tissue changes. Once we find those cells, if we eliminate them, we’ve effectively set the clock back at least for that particular process. And we don’t think that this will cure every aspect of aging. There are some parts of aging that really just probably need to be addressed using joint replacement or other kinds of non-pharmacological interventions. But for many things including those caused by the accumulation of chronically inflamed cells, I think that eliminating those populations could be a really effective path to treatment.

Grant Belgard: [00:16:25] Are there companies in this space taking this approach?

Adam Freund: [00:16:28] So there are companies that overlap with Arda in specific areas, although none that we know of are taking an identical approach. Perhaps one that people in the aging space might be most familiar with are companies that target senescent cells. Essentially senescence is a cell state that different types of cells can enter in response to damage or other external stimuli. It’s traditionally been described as an irreversible cell cycle arrest where the cells can no longer divide. And we found empirically over the years that we’ve been studying this process that in addition to not dividing, they also upregulate certain inflammatory processes and pathways. And when this was discovered, it launched a series of companies that thought that if we eliminated these cells, that these might be the sort of wellspring of age related changes and that eliminating them might reverse some of these changes. And that hypothesis had a lot going for it, and it still does to some extent. However, we’re learning that the senescence state has been mostly characterized through in vitro cell culture experiments and has not translated super well to the in vivo setting. And we’ve seen some clinical trial failures in this space and I suspect we’ll see more before the day is out. Therefore with Arda, we’re trying to do is go the other direction. We’re starting with patient data and letting the data tell us which cells to eliminate and how to eliminate them, rather than starting with a predetermined disease hypothesis about what the state of those cells should be. But from maybe a 30,000 foot view of the general aging space, I think that it sometimes seems like what we’re doing and what some of these companies are doing have some similarities. And to the extent that we’re both trying to find bad cells and eliminate them, there are some similarities. But I think that there’s a really important distinction there as well.

Grant Belgard: [00:18:21] So tell us about your path to founding Arda, because you’re a solo founder, right?

Adam Freund: [00:18:28] That’s correct, yeah. I left my previous role and started Arda as a solo founder.

Grant Belgard: [00:18:34] So what? It’s obviously a pretty huge leap to take. I’m sure you had a lot of scientific confidence that had been building over over the years that led to that. So can you tell us about how that journey looked?

Adam Freund: [00:18:48] I would be happy to. I really was thinking about Arda and ideas related to cell elimination for a long time before I ended up starting the company, almost going all the way back to my graduate work. So did my graduate work with Judy Campisi, studying of all things cell senescence, and it was in that lab that I was first exposed to the idea that eliminating these cells might be a good thing. And in fact Unity, the first senolytic company was founded essentially right in Judy’s lab while I was there and grew after I left. And that was really exciting thing to see. After the cell senescence work in my graduate school, I went to Stanford where I did my postdoc studying telomerase biochemistry. So also trying to understand more about the processes related to aging. And again, I continued to think about telomere shortening is one of the major causes of cell senescence, at least in vitro.

[00:19:43] And so I continued to think about this idea of are these cells accumulating? If so, what are the processes? And then when I got to Calico, which was my role after my postdoc, there I was a principal investigator and a scientific lead on a few drug development programs and in my lab, we spent a lot of time thinking about how to represent organism state and cell state from high dimensional data. And I kept abreast of the cell senescence literature, watched how that field evolved and kept thinking about how we might be able to use high dimensional data to develop new therapies and eventually came around and saw a few other interesting proof of concept papers coming out from different academic groups where instead of targeting senescent cells, they were targeting fibroblasts in certain kinds of fibrosis or microglia in neuroinflammation. And this idea just developed that maybe we could do this in a more systematic and deliberate way and ride this wave of single cell data, because it really is. Single cell data allows this to happen in a way that we never were able to do it before. So for decades right since the advent of microarrays really, we’ve been able to measure the expression of every gene in the genome at a bulk tissue level, and that let us find which genes were most upregulated in disease. And it turned out that this was a pretty good place to start for finding targets. As mentioned earlier, correlation is not causation, but if you pick the top 15 most upregulated genes in a diseased tissue, chances are some of those are causally pathogenic.

Adam Freund: [00:21:18] But we didn’t have something like this for cells. If we wanted to understand which cell populations were enriched in disease, we had to do this very biased search using a handful of markers and then assessing via [] FACs. And consequently the resolution we had was extremely limited. We could talk about fibroblasts versus Myofibroblasts or we could talk about M1 and M2 macrophages, but that was really about it. And now we can see right with the advent of single cell data and the amount of data being generated, we have cellular catalogs of disease at unprecedented resolution. So although I had been thinking about this idea of eliminating pathogenic cells for a long time, it was only really when the technology reached the stage we’re at now that this became actionable. And so when that happened, to me it didn’t really seem like a big leap to say, well let’s combine single cell analysis with the tools of immunotherapy and let’s see what we can do. And that’s when I finally decided to take the leap and start Arda.

Grant Belgard: [00:22:21] Since Arda was your first biotech startup, what surprises did you encounter along the way?

Adam Freund: [00:22:30] With any origin story, there were some fits and starts along the way. As you mentioned when I started Arda, I was starting as a solo founder and I was leaving my current role at a biotech company and starting, and that meant that really I started with very little. And so it was just sort of me and a pitch deck really. I mean, there was no data that I owned because I couldn’t take anything from my previous role, nor was there anything really relevant from my previous role for the company. I didn’t have an academic lab where I was spinning something out of, and I don’t think I appreciated at the time how rare that was in the biotech world. It turns out that most biotechs start in one of two ways. They’re either spinning out of an academic institution. And so there’s a whole body of data and there are papers that are directly relevant, and there’s perhaps even a scientific advisory board already in place, or they come out of these venture creation firms where in a similar way they’ve been incubated with a team that has generated a bunch of data supporting a hypothesis.

Adam Freund: [00:23:36] And only once they’ve reached a certain level of empirical confidence in the overall idea have they launched. I didn’t have either of those things. And one of the pieces of pushback I got a lot when I was initially raising was just why do you think any of this is going to work? I mean, my pitch deck was full of examples from the literature, but it wasn’t my data. It was other people’s data. I had a few computational analyses I had run that sort of suggest that this could work. But by and large it was pretty bare bones and it really took a lot of talking to different investors before I found investors who were willing to take both on the idea, but also really, frankly, on me because there wasn’t much else to bet on at that point. I’m really fortunate to have the investors that I have. And so the round did eventually come together at the sort of end of 2021. But it was scary for a while there. It did not come together as fast as I had hoped it would.

Grant Belgard: [00:24:32] And you joined the On Deck Longevity Biotech Fellowship in the fall of 2021. How have you found that?

Adam Freund: [00:24:40] The On Deck Longevity Biotech Fellowship? That’s right. So how did I find that? Nathan Cheng, who runs on the On Deck Longevity Biotech Group, reached out to me. I guess he found me through, boy I don’t even know, maybe some of my papers or maybe a contact. And it seemed like a great opportunity to meet other people who were at the stage of starting companies or considering starting companies that were focused on extending healthy longevity. And I think I joined that in 2021. It’s been a great opportunity and a great way to meet other people in the field and learn from other people who are further along in their startup entrepreneurial journey than I am. And that really has been powerful. Also I should say that that’s been powerful in a lot of other contexts as well. So On Deck is one group that I’m a part of, but I’m also through my investors and through other organizations and part of other groups of scientists, mostly who have started companies or who are early at biotech companies. And it’s really helpful to be able to learn from other people’s experience. There’s so much to know about starting and developing companies that I didn’t know, and there’s so much I still don’t know. But being able to ask questions and get advice allows you to shortcut a lot of things, even something as simple as, Hey, what law firm should I use? How do I pay my employees? Or What’s a reasonable benefits package? I mean, you can go on the Internet and research this for two weeks, or you can ask the three founders you respect most and guess that if it’s good for them, it’s probably good for you too. There’s so many decisions like that. It’s incredibly helpful to be able to shortcut some of that stuff.

Grant Belgard: [00:26:20] What are other groups that you found helpful in that respect?

Adam Freund: [00:26:22] So let me look at my Slack workspace, because they’re all right there. So I found that the On Deck Longevity Biotech is one, Andreessen Horowitz, our lead seed investor, has Biohealth community. They also have email lists that are incredibly helpful that bring together, by the way, people who aren’t just in bio. There’s also a lot of people who are in tech and health tech and crypto, and it’s really interesting to see which lessons they’ve learned apply to bio and which don’t because they don’t. Bio is a different animal in a lot of ways, but there are also some consistent patterns and ideas around starting and growing companies that cross sectors. There’s also the Village global community, which is another one of our investors. They provide a space for founders and entrepreneurs to discuss ideas like this. And then there’s a few longevity groups like Longevity SF, which I’ve recently joined and again is more focused on the aging side of things, which always fascinated me. So it’s just helpful to have these communities because it can be when you’re just starting out, your company is very small. There’s just there’s not a lot of people to talk to so having these communities in place can be very, very helpful, both professionally because it allows you to sort of network, but also just psychologically. Because it can give you the feeling of a community which it’s otherwise hard to get from a company of just a couple of people.

Grant Belgard: [00:27:38] If you could go back to send a message to yourself in the summer 2021, what would that message be?

Adam Freund: [00:27:44] I think I would send a message to set my expectations appropriately. I would say it’s going to be hard to fundraise. And then when you eventually do fundraise, it’s going to be even harder to hire because once you know you’ve got the money, then you have to convince people to join you on your crazy mission. And it’s been a journey. And now we’re of course building traction and things are really moving. But it does take a certain degree of stoicism and staying the course and developing thick skin. I will say that too. I think one of the things that I have changed the most in the last year and a half, I’ve been doing this is that more people have said no to me in the last 18 months than in like the rest of my life combined. So I can’t tell you. And it’s not that anyone’s rude about it. I mean really honestly, venture capitalists sometimes get a bad rap, but found that the vast majority were professional and they were polite, but they were politely saying no. And that’s hard when you go to someone and you say, okay, so here’s the idea I have. It’s literally my best idea.

Adam Freund: [00:28:46] Like of all the things I think in this world, this is my best one and I want to spend the next X number of years of my life solely dedicated to making this work. And the person across the table from you looks at it and says, Yeah, I don’t think that’s a very good idea. That’s hard to hear, even if they’re being nice about it. And the first ten times it really hurts. And then you sort of grow calluses on your soul and it starts to hurt less and that sort of sounds like you go dead inside. And it’s not totally true, but it’s also not totally false because you have to be able to weather the nose. You have to be able to just smile, say thanks and move on and in a way where you’re not ignoring useful feedback. Because one of the most important things is you go through this journey is adapting to the feedback that you receive, but also not letting no cause you to stop moving. Because if you do that, you’ll never get to port. So you have to be able to weather the nose and I think that at this point, I’ve developed some pretty thick calluses.

Grant Belgard: [00:29:45] Yeah, well the good news I think on the hiring front is, at least from what I’ve seen as you grow and get better established and so on, that becomes easier and easier, right? It’s a lot harder to hire your first or second employee than it is to hire your 20th or especially you’re like 200th by that point. When people Google your company name, they won’t just find your company page. There will be articles about it. They can send it to their friends and family and have them not try to talk them out of joining such a young, seemingly unstable company, even though I think that’s often an unfair characterization. But it’s certainly a common perception nonetheless.

Adam Freund: [00:30:34] It’s so true. One of the things that I feel like we spend the most time doing as early founders and early employees of companies is building credibility. That’s all it is, right. Most people when you tell them about your idea for your company, they’re not judging the scientific merits of your idea. Not really. I mean, that’s part of it. But what they’re really doing is watching you and saying, does this person, are they a charlatan? Because most other people unless you’re talking to someone who’s also an expert in the exact same things that you’re claiming to be an expert in, they know that they don’t have the same expertise you have. And so they’re not going to sit there and say, well I’m going to argue with you about X, Y and Z in terms of the scientific details. What they’re trying to establish is, are the scientific details that this person is telling me reliable. Is this person trustworthy? Does this person have credibility? And there’s a million ways that we signal or don’t signal that we have that credibility. I was I think fortunate in that when I went out to do my raise. I had spent seven years at Calico, which had a great team from Genentech, Art Levinson, Hal Baron, Cynthia Kenyon on the Longevity space, and others who had created a company with a lot of credibility. And then before that was institutions that carried some credibility.

[00:31:43] So I was able to go into rooms and say, Look, I don’t have any data. I just have this pitch deck and this smile and somehow still at least give some indication that I was a real scientist who knew what I was talking about. This is one of the reasons why it’s so hard to hire early employees. I was very fortunate in that my first hire was a person who I had worked with actually. He was a postdoc in the same lab when I was a graduate student and he had gone on to actually work at Unity. He was there for eight years, was the first employee and understood the space really well, understood it probably better than me frankly and knew me. And I knew him and we knew we worked well together. So the first hire was reasonably straightforward. And that’s Remi-Martin Laberge, who is our vice president of research. But then after that, exactly as you said you’re meeting people who don’t know you and you’re trying to convince them that this is the thing they should bet their life in their career on for the next couple of years at least. And that can be hard. But as soon as you see that, oh, half a dozen people have made that same choice, it starts to seem a little bit less crazy.

Adam Freund: [00:32:48] And so then it gets easier. And by the time 20 people have made that choice, you’re like, oh, okay, this isn’t totally crazy. But what people don’t realize sometimes is that it’s sometimes useful to fight against that tendency because earlier that you get into a startup, the much more impact you can have and the much bigger upside there can be both professionally and financially if you ever look at a curve of the way equity dies off by employee number, it’s pretty substantial. And the probability of success doesn’t necessarily go up at the same rate that equity and options for career growth go down. In fact in many ways, if you’re joining a company that just raised a round of financing and they haven’t hired anybody so they haven’t spent it, that company is in better shape than a company that has just hired 15 new employees, spent the bulk of their A round and now needs to turn their attention to a B round within six months. So I understand why it happens, but I actually think there’s a lot of reasons to if being in the startup world is something that excites you, really try to pick companies not based on the credibility signals being sent by others, but based on your own assessment of whether that company is a good fit for you.

Grant Belgard: [00:33:59] And what factors would you weigh heavily in that? Because I think sometimes the kind of go to is there may not necessarily be the most predictive.

Adam Freund: [00:34:12] This is hard. This is a hard question. There are so many unknowns. Just like when you’re hiring for a role, you’re trying to learn as much as you can about this incredibly complex person who’s sitting across the table from you in 30 minutes. There’s only so much information that you can gather. And the same thing is true when it’s in the other direction. And here you are a candidate trying to decide which startup or which company you’re going to join. So if you are a scientist, of course the science. As much as you can dig into the science, pressure test it, ask the questions, and if you get any sort of defensiveness as a response or anything like that, that’s very concerning. If you get a lot of, well, we can’t talk about it because it’s super secret. Personally I think that’s also concerning. I think that generally people secret sauce is not as secret or as delicious as they think it is. It’s more about like execution ideas are cheap. So definitely on the science side. But then also I would look for how thoughtful the people at the company are, especially the leadership about building a culture and an incentive structure that are carefully designed. I think sometimes there’s this idea that startups can just be these places where it’s a little bit of the Wild West and everything just sort of works and clicks together.

Adam Freund: [00:35:31] And that might be true up to something like ten people. But once you get above that, the laws of human interaction assert themselves in ways that are difficult to predict and often not what’s best for the overall growth of the company unless there are processes and systems and a culture in place to realign people. And it’s amazing when this goes right, because startups do provide this incredible opportunity to have everyone rowing in the same direction. One of the things that that I think a lot about is for any person in any organization, there’s this question of how they should prioritize their activities. And in many cases, what’s best for that person is not what’s best for the organization. Think about middle manager at some large pharma company who’s they have stock and they have an individual performance bonus, but they don’t really have the ability to impact in a huge way the outcome of the company. They can do it in little ways. And if the company is wildly successful, it doesn’t directly impact them by a lot. What matters more to them is their individual base salary, their bonus and their title. And so you get a lot of internal politicking and people working on these sort of local optimization problems.

Adam Freund: [00:36:45] And if every person at the company is doing that, it’s only by chance that that comes up to a point where it’s actually benefiting the organization as a whole. And it’s almost certainly not the most efficient way one could do that. And the alternative is at a startup. The best way for every person at a small startup to succeed both professionally and financially is for the company to succeed. It doesn’t make any sense to work your way up the corporate ladder of a six person company to become the vice president of nothing. And then the company tanks and you have to go get another job. It makes a lot more sense is to say, I’m going to spend every waking minute trying to make this idea, this company actually work. Because if that happens by virtue of me being early, I will have so much potential. I’ll have so much experience. My equity will be worth so much. There’s all this stuff and that creates alignment. And alignment is so hard to create otherwise. And so to me, that’s really important. But you have to put these other processes into place. You think carefully about this because the bigger you get, the harder it is to maintain that alignment.

Grant Belgard: [00:37:45] And once lost, it’s hard to regain.

Adam Freund: [00:37:47] Oh, completely. Absolutely right. And I mean like one or two people can act as dominant negatives and people take their cues from others about what’s appropriate and acceptable in an organization so it’s really important. And this makes or breaks companies. And so if I were evaluating different startups or different companies, I would look really carefully at the incentive structures and how they’ve been designed and how thoughtful people are about them.

Grant Belgard: [00:38:12] It’s really insightful. Thank you so much for joining. It’s been a really great conversation.

Transcript of Episode 50: Alfredo Andere

Disclaimer: Transcripts may contain errors.

Grant Belgard: [00:00:00] Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today is Alfredo Andere. Alfredo, can you introduce yourself, please?

Alfredo Andere: [00:00:09] Totally. Thank you so much for having me, Grant. I’m Alfredo Andere. I’m the co founder and CEO at LatchBio.

Grant Belgard: [00:00:17] Welcome. So tell me about Latch.

Alfredo Andere: [00:00:20] Totally. I mean, we’re a young startup here in San Francisco creating data infrastructure for biotech companies. And specifically, we’re helping scientists analyze their data and get insights from it without having to know how to code and without having to build any infrastructure in-house. So what that means is as you know very well from your bioinformatics experience and from all your experience in bio, the data generated from a biological experiment has been annexing every two years. That means that over the last ten years it’s 100,000 X, which means that on the ground floor for biologists, we’ve gone from finishing an experiment and visualizing it with the human eye to now, ten years later, finishing an experiment and getting back a file with 100,000 lines of ACGG or 10,000 amino acid sequences or 100,000 proteomics images or microscopy images. What do you do with that? You’re not going to look at that with the human eye. You have to run it through algorithms and workflows that you’re then going to boil down into statistics, plots, graphs that you can actually look at. The problem with that is that these plots are really hard to generate. It’s not super intuitive. You need to use Python or command line or different algorithmic interfaces that biologists are not really familiar with that. So that’s problem one. And so you need a new interface to do this. So biologists sensitive to bioinformatician, to bio computational biologist, they process the data for them and then send the results back to them.

[00:01:56] Okay, that’s just a new way of doing things. It’s not, per se problematic. The real problem that we found is that these computational people are really hard to find. These bio developers, these bioinformaticians like you Grant, that have the ability to transform this data into visualizable formats. They’re really hard to find. I’m talking like 20 biologists to one Bioinformatician or one bio developer is what we usually see. And so what ends up happening in this new dynamic is biologists finishes their experiment and they send it over for analysis, and then they wait and they wait for a day. They wait for a week. They wait for a month just to get back the results of their experiment that ten years ago they got back instantly. And so I’m looking at this from a perspective coming into this from a software engineering side. I was working at Google and Facebook doing software engineering and one data engineering and the other and just getting to see the world’s best data infrastructure, the state of the art for processing terabytes of data and most of it being used for what? For optimizing advertisements, right. For getting you to click on stuff you don’t really want. And on the other hand, you have these biologists, these scientists trying to cure cancer, trying to cure heart disease, trying to cure aging, global warming. I mean, you name it.

[00:03:20] And they have some of the worst data infrastructure that we’ve seen and it’s slowing down their iteration cycles. It’s really like, how can we solve this? And we realized that initially to solve this, the best wedge into the market was to just create no code interfaces for the biologists to be able to process their data, upload it to our platform, run it through different workflows and then visualize the results. And so that’s where we started. After 3 or 4 months of market research, we raised our Series Seed, and we started working with the IGI and we started working with [AlgoBio] and we even worked a little bit with Bit Bio, which obviously you’re super familiar with accessing these no code interfaces and giving them to biologists and collecting these biologists as users of this platform. And first hundreds, then thousands. And once we created that movement, now we’re moving on more to generalizable bio development framework, and that is a type of marketplace dynamic that we want it to become the AWS plus GitHub for biotech companies. And what that means is anytime you have a marketplace dynamic, you’re going to have a chicken and egg problem. Like why are biologists going to use our platform if there’s no workflows available to them? And why are bioinformaticians or bio developers going to upload workflows to the platform if there’s no biologist to use them? And the real answer is they won’t, neither side will.

[00:04:52] So what we initially did is we uploaded all the workflows. So the no code interface I was telling you about expose them to biologists. Now that we have all these biologists using the platform, now we’re going back to the developers, to the computational biologists, to the bioinformaticians, and telling them, Hey, instead of uploading your workflows to GitHub where no one’s going to be able to replicate your academic code, to put it nicely or to react where you’re going to have to maintain a server for the next five years, just use our open-source SDK to upload it to the large platform. We automatically generate a no code interface that biologists can use. We also automatically generate an SDK and an API that other bio developers can use, and we take care of the cloud infrastructure, the scaling, the data traceability. And so we want to create the self-propagating positive feedback loop marketplace dynamic that becomes the central AWS plus GitHub for any biotech to just plug into from day one and start analyzing their data and getting insights ten times faster by not having to code and not having to build any data infrastructure. So that’s what we’re building at Latch right now and we’re well on the way there. We recently raised our series A, and we’re just building as fast as we can to be able to accelerate all these amazing companies that we’re working with.

Grant Belgard: [00:06:13] Cool. How does Latch fit into the broader ecosystem of companies in this space?

Alfredo Andere: [00:06:20] I see two main levels that we deal with. You have the all the biotech companies or really the biology companies, whether it’s Bioproduction or actual therapeutics or 100 other things. And then you have the software providers. Within the software providers, I really see it as fitting neatly into the DBTL cycle. The design build test learn like what part are you augmenting with your software that is serving then the biologists and the biology companies. I think within that DBTL there are many tools that have been growing a lot in the design and the build, so things like Benchling and other types of software that helps with the life scientists in their work. All these lymph systems that are coming along and different notebooks, they help a lot in the design and the building. Then as this data grows more and more, you have the testing phase you’re going to put into some sequencing or you’re going to create a large amount of data. And the testing phase is really interesting because there’s so large gap there from the testing that was previously biological. The microscope makers, the different wet lab makers that were doing test machinery to now it’s all computational and there’s a huge gap and there’s some companies that came along and are trying to fill that space. But it’s still a very new space, that space being computational.

[00:07:54] So that is where we fit in. Once you get your data back, you upload it to Latch and then you start processing it, visualizing it. We are not yet going into machine learning. We do a little bit of machine learning, which I see as the as the computational part of the learning phase, because that learning phase was previously human. It was you look at the results, you use your brain and you learn what experiment you’re going to do next. That part is also turning more and more computational. It’s also going more and more into machine learning, guiding the next experiment. I don’t think the space is ready for someone to build a prominent software tooling for that space because first we need to get the data right in the testing space. The data that is coming in, we need to get it organized, we need to get neatly processed. But whoever takes this testing phase is going to be very qualified to then build software for the learning phase because machine learning models are very similar to bioinformatics workflows. If you look at them from input output perspective, it’s a lot of similarities. I think whoever can build a strong foundation in the testing phase is going to be uniquely equipped for that learning part.

Grant Belgard: [00:09:01] So what’s your long term vision for Latch?

Alfredo Andere: [00:09:04] That’s a great question from that perspective, because I believe in 5 to 10 years, Latch will completely replace AWS and GitHub. And then a lot of the computational and data analysis will take place on top of Latch. We are going to be the central marketplace if you want to see it that way, where all the computational analysis are going to live, all the machine learning analysis are going to live. And so the second that an experiment finishes, all the data is automatically going to be analyzed. And then that data that is being analyzed and then the results are going to be given to a learning phase, a different machine learning algorithms that are going to be deciding the next experiment and then Latch is automatically going to send a signal to some type of cloud lab or a scientist that is manually doing these experiments to then start the next iteration cycle. And so we believe if we can build good software here, we can completely automate that whole part of experimentation and we can not just automate but also empower the people that are doing the more complex analysis. And so yeah, I mean replacing and GitHub would be the long term vision.

Grant Belgard: [00:10:22] It is ambitious, which is good for a startup. That’s where you want to be, right?

Alfredo Andere: [00:10:26] It’s hard. Yeah.

Grant Belgard: [00:10:27] So I remember the first time I spoke with you guys. It was pretty early on. I think your origin story with Latch is really interesting. Can you tell us about that?

Alfredo Andere: [00:10:39] Totally. Yeah. First time we spoke, if I remember correctly, we were inquiring about the bioinformatics CRO and the great work you’re doing there and trying to learn a bit more about the space and the market and what kind of customers you were serving. And we literally just booked a meeting with you as if we were looking for a service and had talks with you and some of the other people you work with. So the company officially and we had been working together before that Kyle, Kenny and I, but the company officially started in February 2021. And when we initially started, we had to incorporate because we went to some investors in Berkeley and we were telling them like, Hey, this space is really messed up. We are seeing just from a high level overview of Kenny working on the labs and on a couple companies. We’re seeing USB sticks being passed around. We’re seeing people running scripts on their laptop that should be running on the cloud. We’re seeing pipelines written F-sharp. We’re seeing all these kinds of messed up stuff. And then the investors were like, okay, so what are you going to do about it? And we were like, We don’t know. We’re going to go find out.

[00:11:55] And so blessed Silicon Valley, they actually gave us some money. And actually one of them invited us to Taiwan. There was no Covid there at the time. And invited us to Taiwan to just to a hacker house to go there for three months. And what we told ourselves is we’re actually not going to build anything. We’ve made this mistake in the past in other projects we’ve made. And so this time we’re not going to build anything until we know people have a problem and we understand the problem. And we have people that would be willing to pay for a solution to that problem. So for the next three months, we just talked to people and did contracting and we would do everything from booking 100 bioinformatics CRO and clicking book to emailing hundreds of people every day to LinkedIn messaging. Like we would have a rule where Kyle, Kenny and I every day would have to max out our LinkedIn requests, connections, and some people might not know this is possible, but it is. And so every day we would have to max out our connections.

Grant Belgard: [00:12:53] And if you do it too many times, they can ban you without notice.

Alfredo Andere: [00:12:57] Yeah. And we would have all these hacks around that and we got blocked from multiple email servers from companies because we would blast them with emails, not selling them anything, just asking them, Can we please have 20 minutes of your time to talk? And then when we did, we would have multiple calls a day and we would just ask them, What are your pain points like? We’re not going to tell you anything, but like you tell us what are your pain points? And then we would test hypotheses on them like, hey, we’ve been hearing people complaining about this. Is this right? Is this not right? And after three months, we had so much data on what people were struggling with. And even more valuable, we narrowed down our hypothesis of where we had six customers that were willing to pay for something, for a vision of something that sounded similar. And so only at that point, after having been contracting with some people and then having all these evidence and then having these six people that were willing to pay for something, then we went to investors for a seed round and we told them, Hey, there’s this huge problem. We want to hire two of our friends to help us solve this and we’re going to go and build it. And at that point, we raised our initial seed round. We were actually super surprised that we were able to raise a seed round and it got competitive and everything. And at that point, I mean, we had dropped out of school four months ago and at that point then we raised our seed round from Lux Capital and we started building out the platform that today is Slack.

Grant Belgard: [00:14:25] It’s fantastic. So when you were newly at Berkeley, were you planning to start a company or anticipating starting a company, or did this just naturally bubble up from the problems you were seeing?

Alfredo Andere: [00:14:40] I think it’s a combination of the two. I was planning on starting a company at some point, and it’s something I’ve always had in my plans being. I think being from Mexico, a lot of the people I know are business owners, whether medium to large business owners. And so a lot of the role models around me were always just business owners, not startups, more like commodity. Commodity sellers are different types of businesses, but always business owners. So I always had that in my plans, but it didn’t have to be out of college. I was planning on graduating. I was fully planning on graduating. I mean, I had a semester left. I was actually adding a major at the time in math on top of CS and planning on seeing if I could minor in it. And then during the summer of COVID when I went back home, I started talking initially with Kyle actually, because him and I were really interested in neuroscience. And so we started talking not about starting a company, but more so about starting a project like could we build something that people would use? Could we make something useful for people? And talking through different projects, we started working on neuroscience and I think a literally a couple of weeks after that, we all went back to Berkeley and bless COVID, which is something you rarely hear.

[00:16:00] I was supposed to be in Palo Alto at Google. Kenny was supposed to be in Boston at Asimov, and then Kyle was supposed to be back in L.A for the company he was working at. But because of COVID, we were all working remotely from Berkeley. And so as soon as we got back, we went to a party with Kenny and we started telling him about some of the ideas. We had ideas at that point and we were like, Kenny, you should consider working with us. And he decided to join. We started working on projects initially on a cognitive science, a neuroscience project to try and predict people’s emotions from an EEG headset and try and predict their focus and their emotions and just hacking on different machine learning and neuroscience stuff and then looking where to apply it. And so we looked at focus applications, we looked at marketing applications, and we started building some projects there. But really, that is the worst way to ever make a startup, like starting with a technology, well maybe in biotech. But generally when you’re making a startup, starting with a technology and then trying to fit it to a problem is what I didn’t know at the time, a terrible way to do a startup.

Grant Belgard: [00:17:18] Well, I think that’s probably one of the reasons biotech is so hard, right? Is it often is based on technologies. And some have great commercial potential and others don’t. But it’s just such an IP minefield.

Alfredo Andere: [00:17:36] Yeah. 100%. And I think so too, because in normal software and tech land, that is the worst way to do a startup. And the real way you should do it is you find a problem you’re completely passionate about and you want to be solving for the next ten years and you try out a thousand different solutions. If you start out with a solution, you’re going to be left in a very unflexible way. But we did that. We didn’t know. We were just working on a project and and working on different ideas. And at some point while working on the project, we actually started giving it to people. Nobody wanted it and we couldn’t understand why, like, this was such a good idea. Like we could predict people’s emotions with an EEG headset. But we started getting noticed. And at that point we actually had a YC interview. We applied to YC. We were like, Oh, let’s try this. We’re still in school summer and then went into the school semester and a week before YC came into my map and in our map and we started reading about the mistakes not to make. I mean, we started reading a lot about startups, but one of them, the mistakes not to make when making a startup. And for me it was more like a checklist. It was like, Oh yeah, we did this. Oh yeah, we also did this, Oh yeah, also this thing. And at some point, I think it was me, but generally agreed to by the team. We were like, Guys, we need to pivot. Like if we actually want to make anything out of this, this is not the right way to start.

[00:18:59] And so that’s around September, November. But the special thing there and going back to your question of when we started working together, the more special thing there wasn’t the idea, the more special thing was having worked with Kyle and Kenny and all of us working with each other. We knew there was something special there. We knew that the way we got along and the way we shot ideas and the way we discussed and the way we got to the truth and the way we built and the hard work we put in, we knew there was something very special there. And so we knew that we wanted to work together further and build something as big as we could. We decided to go for it and we were like, Hey, let’s trash this idea. Start from zero. The only fundamental being we’re going to work together to solve a huge problem. What do we solve? So let’s spend some time really figuring out what to solve. And so for the next month, we just read through stuff textbooks, articles, papers trying to figure out what to solve. I remember I told you that the company was founded in February. Around January, we had to make the decision by January of whether we were going to continue with school or work on this full time, and we were getting excited about biology. We knew there was so many problems to solve here. We knew we wanted to work together, so we decided to fully drop out one semester left of school, we were like, This is the team that is going to build something really big and we’re going to figure out what that is. And so we just dropped out of school.

Grant Belgard: [00:20:35] How did your parents take that?

Alfredo Andere: [00:20:37] I would say my parents were not very happy. They were actually the most unhappy out of our three parents. I know Kyle’s parents were just like his mom was just super encouraging. Like, Yeah, yeah, this is what you were meant to do. Kenny’s parents were a little put off, but he told them it was a break at first. My parents were completely pissed off, like my mom up until recently, she was still telling me, like asking me when I would go back to school. I still joke that if we IPO one day my mom will be super happy because then I’ll be able to go back to school to finish Berkeley.

Grant Belgard: [00:21:15] Go finish your last semester. Sorry. Y’all like, technically on an extended leave.

Alfredo Andere: [00:21:21] Well, that’s the beauty of Berkeley. I think they give you something like ten years to come back and finish your degree. So, I mean, you never know. I might just take some summer courses for fun because I love school. I love classes and there’s so many classes I would love to take. So it really was dropping out because the team was there and we just were all doing it. So you were not going to get left behind. So yeah, I’m glad we did. But yeah, my parents weren’t too happy.

Grant Belgard: [00:21:48] So one other thing that sticks out about Latch to me is your brilliant social media presence. Some of our listeners may have seen your coordinated pictures. You’re wearing black tops and sunglasses on a white background and things like this. Can you tell us a little bit about your marketing?

Alfredo Andere: [00:22:11] I would say there’s two branches to the marketing, the aesthetic and the different part, which is the pictures, which is a couple quirky things we do here and there. And then there’s the aesthetic parts. And I’ll start with the more quirky parts and I’ll say that actually came off. It comes off naturally from Kyle, Kenny and myself. We like to push the edge of what is allowed, where the line is at and what you can do. But those pictures actually come from a funny story. Back in the day when we were in our Berkeley office, the Berkeley office was probably not much bigger than one of our conference rooms in our current office, literally could barely fit three people. And in there, we had never raised a single dollar of venture capital. We were just three guys with a dream. And we looked at TechCrunch and you see every picture of a new financing. And I was actually making fun because it’s either in a couch or with a nature, a tree background, every single picture. And I’m seeing like, these are the people that are changing the world. These are the people who claim to be doing the most innovative, different work, the ones that are thinking outside of the box. And every picture you see is the same thing with three founders or two founders or a couple founders just on a couch or on a tree. Like, why does no one do anything different?

Grant Belgard: [00:23:40] I bet they use the same small group of photographers and probably a lot of the same backdrops.

Alfredo Andere: [00:23:47] Yeah, I would not be surprised. And so we’re looking at this and we’re like, we’re never going to be like this, we’re – if, and it was a clear if, right. If we ever raise any venture capital and if we ever go on the news and make a news announcement, we’re going to do something very different. And so we started brainstorming like, what would we do? It was either Kenny or Kyle pulled up the Lonely Island pictures. They’re really well known for taking just wild pictures. And they have one where they’re right behind each other. In that one, they’re in black turtlenecks and glasses in one of them, and in the other one they’re in blue turtlenecks and they’re like, We’re going to do something like this. And it was just like a a running joke, right? That if we ever did get funding and did big enough to get an announcement, we would do something like that. And then we got our seed round, which was actually a pretty hefty amount of money and from Lux Capital. So it was newsworthy. And the decision came and it was like, are we going to go back to the norm and do what every founder does, like we made fun of? Or are we going to stick to what we said and take those pictures and publish them? We obviously did the second and we took the pictures and we had a great time in the photo shoot. We actually took the whole founding team at the time, six people. And yeah, I think those pictures are going to go down in definitely the history of Latch and hopefully history in general.

[00:25:15] That’s the pictures and then the aesthetic of the website and the aesthetic of our Twitter page and all our whole platform, I would say that’s in big part too. I think the whole team has a lot of an eye for aesthetics and for making sure things look good from a user perspective. But really two people, Max Mullen, our founding engineer, and then even more Nathan Manske, our founding designer, he was a blessing because he was Max Mullen and Aiden. They were good friends from ours in Berkeley. But Nathan, I interviewed 100 designers and I wouldn’t like any of them. And then Nathan came along. I interviewed him. I loved his designs. He came to SF from Minnesota to interview for three days. We loved his work and we hired him. And he has been one of the biggest blessings to Latch. His ability to design and his ability to just take in your input like, Hey, I want something like this and that and then make it into the science that look beautiful and then code them up himself if need be, either in the front end of the platform or for some landing page. He actually coded our whole landing page himself. He has been a blessing to this company and we love him.

Grant Belgard: [00:26:36] You’ve brought up a couple of employees, and I’d like to talk a bit about that now. Because obviously starting a company like this in college, you’re not coming into it years of management experience. And also, how have you found the experience of, not just starting a company and having the vision and all this, but building out a team? And how many people do you have now?

Alfredo Andere: [00:27:03] I think at this point, it’s 14 people. Three of them are interns. So it’s really 11 people full time, which is wild. If you had told me a year ago that we would be here. But yeah, that’s a great question. And I think it’s one of the biggest problems that you face as a company founder is just building a team and not just leading the team, but putting the team together and finding the right people. Because if you want to build an iconic company, you have to bring in people that are very, very special and that are going to lead the company themselves and lead themselves. And this is just a constant thing that we think about every day. Thankfully, I was blessed by always being interested. And I can say the same thing about Kenny and Kyle. We are have always been interested in finding really special and talented people, especially engineers, and not because I was like, Oh, I want to recruit them in the future. It’s just the type of people I find really interesting are the scientists, the engineers, the people that are that are technical and builders and creating things. And so in Berkeley, I would say we surrounded ourselves a lot with those types of people. Machine learning at Berkeley was a club we were in at Berkeley and it was very selective. And it’s the people there are incredible. And actually I think half of our company now is from that club itself.

[00:28:23] And so we’ve always been very keen about this. And so now when we did start a company, it’s like, Hey, now we have a cost, now we have a mission. And it’s not an ad optimization mission. It’s not another SaaS startup. We’re actually building software really hard engineering problems that then help biologists and scientists tackle some of the world’s biggest challenges. Let’s recruit some more of our friends and let’s recruit some of the people we know. And so it’s something that we think about a lot in the company. We actually have meetings every week where we talk about the people that we’re talking with and the people that we want to hire in the future. And no timeline is long enough for someone that you want to hire. Like you might want to hire someone in a year or two years and they’re currently busy, but you know that they’re amazing and that at some point there will be a great fit for the company and it is part of your job for every engineer, for every person, for myself especially to continue building relationships with those people and following them and setting up the company so that in the future they’ll be uniquely positioned to drive our mission forward. And so it’s something we take very seriously and that we drive forward every day.

Grant Belgard: [00:29:38] Who and what are your near-term plans for Latch in terms of expansion? It sounds like you’re in the Bay Area. Does everyone work on site? Do you have a hybrid team? How does that look and what are your plans going forward?

Alfredo Andere: [00:29:53] So we actually work fully in person, which seems to be different these days with remote COVID return, but we hope to stick to it fully in person for the foreseeable future, at least to 20 people, hopefully to 100 people. We work from San Francisco, from the Bay Area. We’re hoping to double over the next year and we’re about to get a bigger office down in Mission Bay. And yeah, actually it’s funny enough, it’s the first question I ask now in interviews. As soon as I hop into an interview, I ask them about are you willing to move to SF and work in person six days a week? And yeah, it’s six days a week. So we actually work on Saturdays, so it’s Monday to Saturday and then NSF and that disqualifies a lot of people, which is surprising because it’s one of the top things in the job postings. But that’s currently the first question I ask.

Grant Belgard: [00:30:50] I wonder if people are really reading them thoroughly.

Alfredo Andere: [00:30:53] That’s the thing. But that’s the first question I ask and it disqualify some people. But the people that are coming into the office, they’re very special.

Grant Belgard: [00:31:05] What do you think are the influences from when you were growing up and so on that led you to do this, the semester before graduating. I think most college students wouldn’t have the confidence to do that. They would be too scared, frankly.

Alfredo Andere: [00:31:23] Yeah, I think for me definitely a combination of the entrepreneurs that have talked about their story and have inspired me since I’m young and then my parents. And in the sense of the entrepreneurs, I think my first vision of Silicon Valley was actually when I was 13 or 14. I read Steve Jobs biography by Walter Isaacson, and I remember just feeling chills through my body throughout many parts of the book where I was just so surprised and impressed that something a brand I already loved. Like at that point, I was already a huge fan of Apple and then seeing how it had been built by a team and a person and just been built out from scratch in a lifetime, less than a lifetime. And I think that put a lot of things into perspective and inspiration. And from there, reading about Elon Musk and then reading about the Twitter founder, which I mean, it’s a little more problematic. I love biographies and reading about all these people doing it and going through it, It was always a huge inspiration for me and what actually inspired me to then go study at Berkeley instead of in Mexico where I’m from and try and pursue that myself. And so when the opportunity came and it felt like the right opportunity, I knew that my parents, as much as they would love for me to finish school or they really want and have always pushed me to do, is to just follow what makes me happy and follow what I love. And so with that combination, when the right opportunity came and the right team was there which in my mind is the most important thing. I just knew I had to do it and school could wait and school is going to be there. And I think it does take a little bit of being fine with risk, but also some logic around risk and what you’re really missing out of. So yeah, that’s how we had the ability to drop out. And I think I don’t regret it at this point, but initially I definitely had my days where I look back and I was like, Wow, is this the right choice?

Grant Belgard: [00:33:38] What have been your biggest surprises?

Alfredo Andere: [00:33:41] I would say two. One of them is definitely the timelines, stuff takes time. And it’s not just hard work, but hard work over a long period of time. And just understanding how long stuff is going to take and how hard building good software is and how hard getting a single user is, and then how hard getting ten users is and 100 understanding that a lot of the superficiality that you see in TechCrunch is actually just very few data points over a lot of companies. You’re always seeing companies getting funded and so you’re always thinking like, Oh yeah, this goes really fast. But when you actually look at the background of these companies, they’ve been being built for five years, ten years. And it’s only now that they’re reaching the point of really rocket ship trajectory. And so stuff takes time and stuff takes a lot of hard work over that time. And understanding that we’re dealing with ten year timelines and 20 year timelines has been a huge surprise to someone that my biggest timelines had been four years for college. On the other hand, it also surprises me how from the inside, a lot of companies that look very prominent and like everything is going perfectly are actually not that great. Actually, if you can find a team of people that are really willing to work super hard for an extended period of time, let’s say ten years, actually that in itself is super special and super hard to find. It’s very rare. So those are two of many surprises I’ve had while doing this.

Grant Belgard: [00:35:30] What advice would you have for young entrepreneurs possibly starting a company during undergrad or grad school or whatever?

Alfredo Andere: [00:35:39] My first advice would be to not take anyone’s advice. Not anyone’s advice, but be very selective and filter out a lot of the advice you get. I like to say that all advice averages out to zero. And so I think you have to be very selective because when you’re a young startup founder, especially in biotech. Like if I had a dollar for every person who told me that I cannot start a biotech software company without a PhD, I wouldn’t have to make a startup. But it’s very discouraging to talk to people and to get everyone’s advice because most people will tell you can’t do it. And sure, the probabilities are low. But the other advice I would give is don’t do it just for the sake of doing it, because it’s really, really hard and it’s not a good life. I mean, unless this is your calling and you might not know if it is. You should probably not do this. It’s really hard to make a startup. It’s working seven days a week, 16 hours a day, every single day for the next ten years of your life. It’s really painful in many ways. You miss out on a lot unless you are convinced that this is your calling and this is what you want to do, you will not enjoy it. And so that would be my other piece of advice is don’t do it. And I like to give that advice because I think if you’re the person that’s the type of person who’s going to do it anyway, you’re going to do it anyway. But I would recommend not doing it.

Grant Belgard: [00:37:16] If you’re contrarian enough. Yeah, it’s rough. It’s really hard. If there’s one message you would have for our listeners about Latch, what would it be?

Alfredo Andere: [00:37:32] We are not a no code platform. We are a development framework for bio developers to be able to easily yes, create no code interfaces, but also to easily create other interfaces and also to easily deploy to the cloud and to easily be able to track data, traceability and all of these benefits that wrapping your code in our SDK gives you. And so we’ve been dealing a lot with that. We were initially a no code platform because we were dogfooding our own SDK and a lot of people are like, Oh, I can’t do my work in a no code platform. We know, we understand, and now we’re open sourcing our own SDK that we use to create those no code platforms to the rest of the world and hoping to get the developer community in bio to adopt it and accelerate their work and accelerate the work of their company. And so if I can say one message and you’ll hear me saying this a lot over the next coming months is Latch is not a no code platform. It is a bio development framework.

Grant Belgard: [00:38:41] Nice. Well, thank you so much for joining us today. It was great.

Alfredo Andere: [00:38:46] No, thank you Grant. Really appreciate you having me. I’m a huge fan of your work so thank you for inviting me to your podcast.