Transcript of Episode 68: Caspar Barnes

Disclaimer: Transcripts are automated and may contain errors.

Grant Belgard: Welcome to the Bioinformatics CRO Podcast. I’m your host, Grant Belgard. Today, we’re speaking with Caspar Barnes, founder and CEO of AminoChain, a startup marrying biobanking and blockchain to make biospecimen sourcing transparent, ethical, and efficient. We’ll explore what AminoChain is doing today, how Caspar’s path unfolded, and the advice he has for the next wave of biotech builders. Caspar, welcome to the show.

Caspar Barnes: Thank you so much for having me, Grant. Excited to be here.

Grant Belgard: How do you describe AminoChain to someone who you meet in an elevator?

Caspar Barnes: Yeah, absolutely. So AminoChain is a decentralized biobanking protocol. It’s an infrastructure company that connects hospitals, biobanks, pharma companies, and other users and actors in the life sciences industry. And it allows any number of decentralized healthcare applications to be built on top. The first app that we are building on this decentralized biobanking protocol is a biosample marketplace that we call the Specimen Center. And how it works is we want to turn donated specimens into non-fungible tokens. And then we let those digital assets get listed onto a marketplace. And we let life sciences companies license these biospecimens for research use. And we can encode rights into the NFTs that represent broad consent from the patients and royalty rights back to the individual donors, and perhaps even MTA and licensing conditions of the biosamples.

Grant Belgard: Which steps in today’s biospecimen procurement pipeline are most painful?

Caspar Barnes: Oh, there’s so many. Where do we begin, right? So like today, in the United States alone, there’s around 2,500 biobanks out there. And a biobank, for those that don’t know, is a really big fridge filled with donated cancer samples mostly, but all sorts of other disease tissue used for research. And across these 2,500 biobanks, there’s roughly 200 million retrospective specimens stored readily available for research use. And only around 10% of all of those samples ever see the light of day. And that is because of many different reasons. We’ve spoken with hundreds of biobanks when we started AminoChain. And the most common themes that crop up are firstly, poor financial planning. Biobanks say that they’re scientists, they’re not business people. So from their perspective, they’ll go and raise grant funding, they’ll build the infrastructure to store specimens.

Caspar Barnes: The second they start collecting samples, they’ll just go back to doing research. And they don’t think about access policies or governance on the samples or distribution policies or cost recovery models. And so effectively, poor financial planning is the main reason why these biobanks are unsustainable resource. The second thing is searching for samples is really difficult. So today, if you’re a scientist and you want to get access to a specific biospecimen from a bank, you’ll have to go individually to each bank and ask if they have what you need. They have their own bespoke access procedures where you go and try to contact the PI at the institution. They’ll go and look for the sample. And if they find it, then you’re in luck. And if they don’t, yeah, move to the next one. But there’s no real way to harmonize search across these disparate databases.

Caspar Barnes: And then the last thing, of course, is licensing the thing. Once you find what you’re looking for, you could spend on average like three months back and forth debating the conditions of a licensing agreement called material transfer agreement. And then once you’ve actually reached those types of conditions, you can sign a document and get the samples. The whole process of finding, acquiring, licensing, distributing these pre-clinical research assets from biobanks scientists is just riddled with problems. What does a three-month delay in specimen access cost to mid-sized pharma program? It can totally vary, right? But speed is everything within pharma. So your mid-sized biotech or pharma company could, especially if they raise money to build out their own library of omics data, which maybe they’re training AI on, or they’re doing target ID and validation and so on. Speed is everything.

Caspar Barnes: So three months could mean being the first person to find that insight or validate that target or not being that. And if you’re not that, then maybe that’s the entire USP of your company kind of down the drain. And so we found in many instances, people, A, want access to data straight away. And then B, if they can’t have the data, they want their retrospective specimens so that they can turn those specimens into data. And then C, if there are no specimens available, then they want actual human beings so that they can donate samples, so that they can get the specimens, so that they can turn that into data. And so it’s quite difficult to quantify, but it can quite literally be the matter of lack or death for some of these companies. And so speed is everything in the industry.

Grant Belgard: Why did you choose a permissioned blockchain rather than public chain?

Caspar Barnes: Well, we have a lot of different things to consider within the chains that we’re working with. We are actually settling on a public chain. So we most recently decided to work with a private app chain company called Syndicate, which means that we’re able to customize a little bit of the data that isn’t, isn’t visible. It could be permissioned in that aspect, but we still do settle on Arbitrum, which is an open public blockchain. And so we do leverage the security and open transparency of public chains, but we also customize to some extent the transaction data or the publicly visible metadata within a sort of private permissioned app chain infrastructure. And we straddle these two different strategies specifically so that we can be a fully decentralized protocol by settling on Arbitrum eventually. But we also tailor the app chain specific needs towards our users.

Caspar Barnes: We found before, for example, we tried to have a totally open public app on Polygon and that data being totally visible on a public blockchain made a bunch of our users nervous and the language needed to cover all of the functionalities of the blockchain and our technology and our stack in a provider agreement that we would then present towards a hospital. So a biobank was incredibly confusing to these biobankers and researchers that have never even heard of crypto before. And so for all these reasons, we ended up, you know, deciding to focus on developing our own app chain, which basically means we can customize a lot of the information that isn’t as invisible, but we still are leveraging all the benefits of being on a fully decentralized protocol, like by eventually settling on those chains as well.

Grant Belgard: Can you walk us through a typical search match compliance workflow with AminoChain?

Caspar Barnes: Yep, of course. So today, a scientist will log on to the specimen center. They can see all the different biobanks that have created the profile and they’ve listed their specimens on the search platform. We currently have a global network of over 20 biobanks, some in the European Union, some in Eastern Europe, some in Canada, and some in Africa, and some of the United States. Folks can log on and they can see the profile pages of these banks. It’s totally open and transparent. None of the specimens are blinded. None of the suppliers are blinded. Experience is meant to recreate something like Facebook for biobanks. So you can go on and connect, browse each other’s profiles and see the high level overview of the collections of each biorepository. Then you can go down to a more granular level. You can go towards a specimen level.

Caspar Barnes: And across these 20 different biorepositories, we’ve ingested all of the metadata of the collections that these biobanks have. And we’ve mapped all this metadata into a universal. So if you go into the specimen center and you’re looking for a glioblastoma from a Caucasian male, you will also get a brain cancer sample from a white man, for example. Those are the same specimen, but they just have synonyms of each other to describe it, right? So we’ve used a series of different LLMs and AI technologies to map all of these metadata against each other. And now users can come on and search for what they need and harmonize, or they can search across all these 20 different repositories. From there, they can then select the specimens and then turn inquiry. They have extra information that they want to know about the samples or about collection or about provider.

Caspar Barnes: They can add that context into a chat and send that context with the request that they get pinged directly to the biorepository. So we don’t actually get involved in licensing at the moment. We don’t involve payments. We’re just nailing the search experience for the users and for the biobanks.

Grant Belgard: How are you handling private key management for sites that aren’t crypto native?

Caspar Barnes: Right. So the specimen center in its first iteration, she doesn’t have anything on changes yet, right? So we’re slowly starting to integrate all of the app chain enabled features right now and bringing the existing transactions onto the blockchain. How we are going to do it is work closely with third party key abstraction providers, like for example, Privy.io, the company recently acquired by Stripe. And they’re fantastic. We can work with them and they can outsource all of the key management and compliance and they can provide a fantastic that abstracts away the crypto in the backend. So they make building on chain a lot easier than it used to be.

Grant Belgard: What are the best traction metrics for Amino Chain? Samples onboarded, active buyers, cycle time reduction. What do you think best encapsulates your story?

Caspar Barnes: Yeah, fantastic. It’s a good question. So the key metrics that we’re tracking is first of all, the size of the network, right? So, I mean, how many buybacks are on the platform? How many have bought into the mission of improving their visibility and improving their cost recovery? And so that the first and foremost, the main thing that we track is how many providers do we have and how many specimens do we have? And then of course, how many unique individual donors or patients do we have? That’s the main thing that we track. The next thing of course, is how many users, how many scientists are logging on, how many people are looking for biospecimens. And then the most important KPI perhaps is how many requests are actually happening on the platform. So how many channels do people log on? Do they use the full search experience? They find what they need and they send a request to the bank.

Caspar Barnes: And there’s a dual-sided approach there where you need breadth, of course, because you need to be relevant and applicable to so many different types of scientists. But often people will come and if they don’t find the specific bit of insight that they’re looking for, then they would churn and they’ll just go directly to the bank or they’ll go to another one and they’ll try to find the specimens they need elsewhere. So apart from breadth of all these different collections, you also need depth. We need highly detailed information on all of the sample donors and different collections. And at the moment, we’re only tracking perhaps, you know, 18 to 20 different fields of metadata. And some of those fields have largely unstructured data, so people can drop in clinical notes or path notes and so on.

Caspar Barnes: But the way where our search is going is into vector embeddings and into more sort of natural language processing and so on. So that means that people can come on and ask questions in natural language. And we can have, you know, agentic tools to help us find the exact specimens that they need. And we can see then if any of the insights these people are looking for lies within the data that is uploaded onto the specimen center. So all these things considered, I think the most important KPI for this would probably still be transactions or requests, because that shows that our search is providing the experience that the users want.

Grant Belgard: And how do you defend against large CROs that might try to spin up a similar platform?

Caspar Barnes: Yeah. Well, the good news is that over the last like 30 years, people have tried many times and no one has made a lasting successful platform. And the reason for this is the traditional marketplace model is to de-identify where the specimens come from and to add a markup and to force people to do payments and transactions to the platform. We are largely of the opinion that we shouldn’t be brokering retrospective biosamples. We don’t think it’s an ethical practice to add, you know, markups on top of selling diseased tissue. First thing is just the values perspective. But then the second thing as well is that if you don’t de-identify where these specimens are coming from, then there’s the risk that you have marketplace slippage. And that’s the same with any marketplace. So people would log on, they would use your platform for search.

Caspar Barnes: And then if they can see exactly where the sample is, then they’ll just go offline and buy it directly. And, you know, all CROs, all buyer sample brokers, all the big players out there, they’re forced to make money by putting the value of the transaction on the actual brokery of the tissue. So we have tried to find a way to provide value for a network without necessarily focusing on trying to extract value out of a buyer sample transaction. And I don’t think that’s actually really been done before, let alone successfully done before. So that’s our approach. If we make it totally open, and we don’t mind if you do the transaction on our platform or off platform, right? We just want to nail the search experience. Then we could end up being the platform that everybody comes back to because it is actually the thing that is more engaging for the providers.

Caspar Barnes: It does have more rare specimens on it. And there’s no reason to jump off. You actually get a better user experience finishing your transaction on the platform because there’s no, there’s no reason not to do that, right? It’s not going to be more expensive for the user. And then once we have that good retention and we have good network of both provided and procurers, we can monetize in many other ways. Firstly, with the blockchain, all the amazing things we want to do there when the specimens are protocol integrated. But secondly, even without the blockchain, just nailing the search experience is already a good, good value add for these procurers. So like, for example, when you go onto LinkedIn, you can go and scroll through everyone’s profiles in a sort of freemium way.

Caspar Barnes: But there’s these amazing, you know, added tools on top like LinkedIn Sales Navigator or LinkedIn Recruiter or whatever that people have to pay for having an extra service. But we can totally do the same thing for biobanks here, right? If you have a phenomenal search experience and you want to have automated feasibility assessments for prospective collections, you want to have a gently tools where you can drag and drop your protocol and you have an agent search the marketplace for you and so on. All of these amazing things that we want to do later, we can charge subscriptions for or other things for and we can put that onus on the actual, you know, researcher that’s looking for the specimens. We don’t have to provide any barriers towards the providers.

Caspar Barnes: And of course, the most important thing is we can take the whole emphasis on brokering tissue off of, you know, that retrospective transaction.

Grant Belgard: How do HIPAA, GDPR and other laws and regulations interact with your cross-border workflow?

Caspar Barnes: Yeah, that’s a great question. So we do have banks in the EU at the moment and what’s particularly difficult is that each country, you know, can have their own interpretation of GDPR. And so even GDPR in of itself isn’t like, you know, a standalone uniform beast that you can just address one time because each user interprets it differently. So first of all, what we do is we take in de-identified sample metadata. We take in very high level information on the collections and we make that searchable. We don’t actually get involved in the licensing and the payments of the samples as well. We highly vet all of the providers that we work with to make sure that they are GDPR compliant. It’s written into our provider agreements that they also assume the risk of being GDPR compliant and that they have the capacity to erase data if that’s what the users wish.

Caspar Barnes: And we customize the specific fields based on their interpretations of GDPR. So for example, the French banks, they think that including information on a patient that has an age above 90, for example, would be personally identifiable. So for the French banks, or under five as well, by the way. So for the French banks, we would then change the fields to say, you know, 89 plus or six under or something like that, right? So that happened, similar things happen all around the EU. And we basically meet biobank where they’re at. We customize the data fields to their stipulations and their interpretations of the regulations. And we have it baked in, in our process of vetting the providers and in the provider agreements that we assign to these different biobanks.

Grant Belgard: What mechanisms ensure donor reconsent if the intended research scope changes?

Caspar Barnes: Well, at the moment, we’re not in the process of engaging the research participants. It’s all just focusing on retrospective collection. This problem of, you know, the hundreds of millions of samples out there that are sort of languishing and they’re really expensive to keep and they’re never seen the light of day. The first thing that we can do to help the industry is just to go out to those folks and say, we’ll help you increase this sample exposure and visibility and harmonization of search. We’re going to continue to do that likely for the next six to nine months. But at the same time, what we’re currently doing right now, [?] is building out our own prospective cohorts. And that’s a really exciting pivot that AminoChain folks got, or evolution of the product. That looks slightly different.

Caspar Barnes: That basically involves working closely with clinical sites, closely with advocacy groups, designing custom interfaces and user experiences for patients connected to advocacy groups. And either ourselves sponsoring new collections at those sites or raising money on behalf of these advocacy groups to sponsor collections at those sites. And then when these patients are consented, those specimens will be banked in the specimen center. And the data, the multi- data that’s produced from these studies would be put into a database and access to the database would also be governed by smart contracts. And so if a pharma company would have paid out access to the data for discovery or any other researcher would pay to have access to it for research purposes, in that transaction, we can pay back the people that helped sponsor the collections.

Caspar Barnes: We can pay dividends and royalties to advocacy groups, to trial sites, to patients, to anybody who was involved in the curation of data set. The buyer of that data can use it exclusively for an embargo period. And after which the data would be made available within the decentralized biobank, and it can be repackaged and relicensed in another product to somebody else. And so all this considered within this new prospective collection and data management product that we are soon going to launch, the donors will always have a way of identifying how their data set is used within this decentralized biobank. We use a combination of private-public key photography. They can authenticate how their data set is being used by [?] committed on chain. And from that, they’d also be able to claim rewards if the data is used for commercial in certain ways.

Caspar Barnes: And then through this system, we hope to have a fully incentive-aligned, decentralized, community-owned biobank.

Grant Belgard: If you could only track one KPI for 12 months, what would it be and why?

Caspar Barnes: That’s a good question. So it would still be, I mean, in the context of the specimen center, the one KPI would be requests. It would be, you know, that’s our true north is how many suppliers do we have? How many users do we have? And then ultimately how many requests are we making? And then the context of this new product that we’re launching, sort of the prospective collection metric, the one KPI is licensing data for exclusive use. Like, you know, are we finding people that want to buy access to multi-omic data sets for discovery? And that probably is the most important metric, because I think if we’re able to prove out that flywheel of funneling data, aggregating data, and you know, selling it, then all the other apps on top are easy to build and they benefit from the network.

Grant Belgard: Was there a formative experience that pushed you towards decentralized solutions?

Caspar Barnes: Yes. So not so much like a decentralized solution, per se. It just turned out that crypto was a good way to fix the problem of, you know, biosample tracking and so on. But I certainly did have a formative experience, you know, biosamples in general and, you know, the bioethics of donating tissue. And so very quickly, I’ll give you an overview of that. But I grew up in South Africa, right? You might be able to hear that from my accent. And growing up in Cape Town, South Africa, my mother, she started the charity called Yabongo, which helps women with HIV and AIDS get access to antiretroviral treatments and provides homeschooling support towards kids in the townships outside of the cities. And so growing up, I would spend a lot of time in and out of these townships with my older sister.

Caspar Barnes: And so we had very regular discussions around race, equity, privilege, and especially in post-apartheid South Africa, right? There was always a big emphasis on having these conversations openly and saying, why do we live in this area of town? And why do other people live in this area of town? So trying to find ways to give back throughout your career has always been a really big familial and cultural value that’s definitely now playing into the vision of AminoChain with an aspect of health equity. And the second thing was when I was around 12 years old, I had a malignant melanoma and I was very lucky because it was caught super early. So I just needed one big operation to remove tumor. But since then I’ve been thrown into a world of healthcare, right? And I still remember listening to these doctors explain concepts of healthy cells and malignant cells and how cancer spread and so on.

Caspar Barnes: As a little kid, I just listened with wide-eyed fascination and fell in love with biology at that moment. I was like, I have to work in life sciences in some capacity. And so then since then, I, you know, at the age of 16, would already spend my summers working in [oncolytic?] biovector research, did my undergrad degree in neuroscience, did a graduate degree in biotech, another one in bioethics, all at UCL, Columbia and Harvard Medical School. And so I’ve always been in love with biology since those early days. But the key thing is I can still remember waking up from the surgery and the doctor was standing over the bed and he was holding this biopsy sample. And he was like, check it out. This is what we cut from your lower back. And I was like, dude, that’s so cool. Can I take it home? I want to show my friend. We’re not going to believe this.

Caspar Barnes: And the doctor said, no, we need to keep this biospecimen so we can research it. And I was, of course, too young to understand the connotations of what was going on. But my mom, importantly, was like, yep, sure. That’s for the benefit of science. Let’s sign this consent document and give away the sample. And we never saw it again. And then many, many years later, when I was in the lab at Columbia, I was doing research on somebody else’s donated tissue. And we’re generating all this valuable information, finding all these markers. And I went to my PI and I said, can we tell the patient about this information that we’re generating? And she said, I don’t know where that thing came from. It just came from the biobank. And that blew my mind. I was like, how is that possible? How many people around the world are doing research on biospecimens, generating so much data?

Caspar Barnes: And you’re telling me that none of them know where the samples came from. They all just came from the biobank. And the more I dug into it, it turns out consent rates are incredibly low. There’s almost 25% at some major institutions. The people that are periodically not consenting to having their samples and data used are marginalized communities and patients of color more often than not. And so I got fascinated by the problem and I just got kind of sucked down the rabbit hole where I just did everything I could to try to find an interesting new emerging technology that could fix this. Turns out crypto is great, right? It has all the benefits of immutability and prominence tracking and ownership and agency. And this happened to be around the same time as when crypto was booming in 2020. And so it just seemed like a great match. And I was just like, this is awesome.

Caspar Barnes: Let’s go and try to see what all we can build at the nexus of all these incredible fields, which is emerging tech, life sciences, health equity. And now I’ve just become so mired in this, this like interdisciplinary platform and approach. And so it’s kind of become my life’s work and I don’t think I know. So many consent needs to be disrupted. The current consent model is like as recommended by, you know, the OHRP and the HHS and so on. It’s just like informed consent. That’s it. Here’s one document, write what you want on it, get someone a sign. And then if you see a signature, great. That’s basically your waiver of liability. You know, it’s, it’s not at all a way to meaningfully engage someone in what’s happening with their, their samples or their data and so on. And it’s, that’s kind of like a problem that we find ourselves in right now.

Caspar Barnes: Since 1970s or so, we had a period of, you know, like progressive change within America, right? It was like women’s rights were coming up. There was civil rights coming up and there was all these, you know, bioethical discussions happening as well. We ended up having the Belmont report, 1978, 1979. Of the Belmont report, we came up with these principles for human subjects research, which were, you know, autonomy, justice, and beneficence, right? So of these guiding principles, how could we, you know, have a, a scaffold for involving people in research? Well, informed consent legislation seems to be the best policies. Since the late 1970s or eighties, they were like, okay, let’s try to codify this into law as much as we can or at least make it like public policy that anytime you do research, you can only do it with informed consent.

Caspar Barnes: And it was all done with the, you know, great intentions and it made a lot of sense. And so then since the 1980s, we have to ask everybody for informed consent before they’re involved in a procedure, before they donate the best ones and so on. But, you know, even though those consenting frameworks haven’t changed in the last like 45 years-ish, the world has drastically changed. You know, it doesn’t look the same as it did in the 1980s anymore. Particularly the storage of biospecimens for secondary research has become a booming practice, enormous. In the 1990s, we spent billions of dollars to sequence one human genome. Now we can do it in a matter of days or hours for a few hundred bucks. It’s an enormous progression where we now live in a world where there’s a diaspora of data. And it’s like, all these samples are stored for secondary research use.

Caspar Barnes: And things are becoming increasingly re-identifiable. Things are becoming more and more personal, especially with whole genomes even seen. And with all this considered, informed consent just doesn’t cut it anymore at all. People are asking for a one-time consent document and then they’re doing whatever they want with the tissues afterwards because they’re just getting broad clauses. So all this considered, how do we see a new world where consenting can change the biomedical research industry? Well, we’ve jumped up a new framework called Demonstrated Consent. And under Demonstrated Consent, we can basically take personalized conditions for broad use from research participants. And basically like they’re personalized terms, we take them, we put them as metadata of a specimen, as an NFT. So we have ways to automate the record keeping of the samples.

Caspar Barnes: And then we list them on a platform where anybody can acquire these specimens for research use. Their protocol upholds the consent that the patients originally gave. And if it does, then they can license it, they can use it. At all times, patients have a way to stay informed with the outcomes of research and they can stay informed with how the samples are being used. And so therefore, the blockchain would be demonstrating to you how your sample are being used, as opposed to somebody just saying that they’re using it the way that they will. And that changes the paradigm that actually makes a better experience for the research participants. And you actually have the need for flexibility research, which is like a societal benefit, right? You don’t have to compromise between asks for progressing research and the ask for promoting patient autonomy. And so that’s what we see as the future.

Caspar Barnes: And that’s what we want to embed into the AminoChain protocol. It’s actually like personalized conditions for broad use and an automated way to re-contact and re-engage participants.

Grant Belgard: What surprised you the most in your customer discovery process?

Caspar Barnes: Um, surprises? That’s a good question. Many things are surprising. I think, I think, you know, when starting this out, I thought a biobank was a biobank because, you know, it’s just like, they’re all the same. It’s like, you know, a place where you store samples and that’s it. And I didn’t really realize the complexities that go into biobanking and how many different types of users there are within biobanks and how they are all separate from each other in terms of their, their priorities and their missions and approaches and so on. And so what surprised me, you know, one of the things that surprised me was that you, you have so many different types of doing things in biobanks. Some commercial brokers go and buy remnant material from hospitals and emerging economies. And then they add enormous markups and they sell those specimens to labs in Boston and in San Diego.

Caspar Barnes: And I was like, that’s crazy. I didn’t know that was a practice. And then you try to speak with, you know, other biobanks in America and they’re the part of AMCs, academic medical centers, and they don’t really care about cost recovery at all. What they care about is publications and they care about, you know, insights and they care about all these other things that will make them more eligible for grant funding. And so they’re not brokering tissue. They’re more focused on, you know, moving knowledge forward. And so I thought that was super interesting. Others are independent and they’re part of government labs and others are part of hospital networks. And some biobanks just collect remnant materials from clinical trials, which are associated with the pharma companies. And you’ll never be able to see any of those biobanks.

Caspar Barnes: And so all these things I found really interesting, just landscaping the different customers out there, like speaking with them and hearing what their needs are. It’s been fascinating to have the same conversation with different users, but to see the differences and important factors crop up and motivations for each of them.

Grant Belgard: How did you pitch A16Z crypto differently from life science species?

Caspar Barnes: Yeah, that’s also a good question. So, um, you know, building what we’re building, you have to toe the line between the crypto language and the non crypto language quite delicately. A16Z is fantastic because they have, you know, investors across both verticals. They have a healthcare fund and they have a crypto fund. And so when we were pitching A16Z crypto, we can, you know, pitch the crypto vision and how this becomes the Ethereum of healthcare. You know, the world’s biggest composable blockchain for people to build healthcare Apps. And they get it and it makes sense. And biobanking is the wedge to get there and they love it. But then if you try to say the words that I just said to you there, it’s a, the A16Z bio and health team, they get very confused. And as a matter of fact, that’s like what happened. So we spoke with both of the funds.

Caspar Barnes: And then eventually after a few rounds, we first went through their accelerator program, and then afterwards we’re reinvested as a full portfolio company and so on. Even with an A16Z, we have the practice of pitching both the crypto side and the healthcare side. But all in all, how life sciences VCs look at this as opposed to crypto VCs is, you know, how is this an extension of what’s currently happening today? And if you don’t have to give me complex crypto jargon, but you can just explain in normal language, how already what we see in biobanking lays a precedent for distributed ledger technology to help engage, you know, and to help improve user experiences or improve outcomes or whatever, then it builds a more convincing narrative in their head. So our second biggest investor, Socano is the family office of Paul Allen. They have a lot of life sciences companies in their portfolio.

Caspar Barnes: And so when we pitched them, they were basically our life sciences investor. The language that we had to engage with them was stuff like benefit sharing, stuff like co-ownership of IP, concepts of automating provenance tracking and supply chain management and so on. And if you, if we could just, you know, convey the same technology benefits of the tech that we’re using in non crypto language, then, then eventually it made sense to them. And then it, you know, ticks across all the people that we have on the, on the cat table.

Grant Belgard: What traits do you screen for when hiring at the biology web three interface?

Caspar Barnes: Yeah. It’s a great question as well. The people that are well versed and experienced at exactly the nexus of the two are few and far in between. And so when you find them, you really got to look after them. But then all things being equal, I’d see my job as the founder of AminoChain as being the person to stimulate conversation between the either non life sciences experienced people or the non crypto experienced people, such that they learn about the industry and they become experts at both, or they become at least knowledgeable of, of both the fields in which we’re building. And so we have people just that focused on the life sciences with their PhD backgrounds. They’ve worked in bio sample procurement and, and, and in life sciences research in general.

Caspar Barnes: And then on the other side, we hire people that just have cryptography experience and they just have blockchain engineering experience and they know how to build amazing software. And across both, the main thing that I look for is proactivism. Somebody that just says, just let me take care of that. I’ll, I’ll make sure that gets done. I mean, anybody that is autodidactic, anybody that is, you know, self-starter and proactive, tries to make life easier for their teammates is just an instant green flag. We would sooner have someone, you know, that is very proactive, but maybe less experienced as opposed to someone that’s super experienced, but not very motivated. So across both of those, that’s what we look for. And then second to that, we probably do focus mostly on, you know, the experience and the network that built out within the industry.

Caspar Barnes: So we have some folks that have been doing this 30 years and they’ve got fantastic connections within the space and they can just click their fingers and make things happen. And then I think the last thing as well is people that you can just trust, right? I think that’s the most important thing. So the people that you don’t have to worry if they’re, you know, not working today or if they are working today, just trust that they’ve really bought into the mission and they think that we’re building something incredibly important. And they understand that the faster we built, the faster we could actually help human beings. And so if we have that level of trust across anybody with any level of background and experience, that’s probably the most important thing. And I’m very privileged and like grateful that we’ve managed to build that with the team we have so far.

Grant Belgard: What’s the single best piece of advice you’ve received from a board member?

Caspar Barnes: The single best piece of advice I’ve received from a board member, they give us so many pieces of advice. I think, you know, maybe they sound a little bit cliche, but I think probably the most important thing are the best advice. The only time when you are guaranteed to fail is when you give up or when you stop trying. The whole first year of AminoChain, we were picking pennies, trying to make it work. We were like five people living off of a hundred thousand dollars in New York City, like really trying to make it work. And, and we did, you know, we were super frugal, very resourceful. We incredibly proactive, went out and spoke with everybody and did everything we could to move the needle. We took 250 VC meetings before we got our first yes. And somehow that first yes happened to be Andreessen Horowitz, which was incredible, but it was a long, long, long process.

Caspar Barnes: And the one thing that particular board member I’m thinking of reminded me of the entire time was, well, the only way that it’s a hundred percent not going to work is if you stop trying right now. And that was like a real fuel of motivation that got us through the early days. And that’s, you know, kind of resonate with me for a long time.

Grant Belgard: So looking back five years, what would 2020 Caspar find most surprising about today’s AminoChain?

Caspar Barnes: He would be so mind blown that we found ourselves in the situation that we’re in right now. I think that I, old me would probably be very, I’d like to think he’d be very proud of all the things that we’ve achieved so far, but he also probably been very unsatisfied with how far we’ve come because there’s always more to do. But in 2020, we had the earliest trappings of an idea of AminoChain. And so we knew what it could be, but it was so nebulous at the time. We just knew there was potential. It was not at all clear where we should go. We’ve learned so much throughout the process. I think that old me would probably say, we would probably just be excited for the years to come because it’s like, nothing’s guaranteed. Everything’s difficult. So many people are relying on you. It’s not an easy job at all, but for some reason, you just can’t stop.

Caspar Barnes: And so I think you would be happy that we’ve gotten closer to finding something that’s worked. Honestly, I still think we have a way to go to prove the real product market fit that we need to nail the adoption. But maybe 20 year old me would already thought that we’d taken it further than it could have gone, which means now there’s only one way up to keep going and double down the direction that we’re going in. And so it would be a mix of excitement, maybe pride, but then more so above all else, like motivation to keep going. So I’d like to think that’s what 2020 capital would say where we are now.

Grant Belgard: What early mistake would you warn every tech bio entrepreneur about?

Caspar Barnes: Oh, well, don’t over dilute your capital too soon. And I think everybody says that. And I also see other people warn early stage founders about things around the capital and who to bring on and advisor shares and like over promising equity to people that don’t add any value. It’s not all these mistakes I read about, but I didn’t really know what they meant until I found myself in the situation. And so I guess now I’d pass the same advice on to other early stage founders. Be careful with your capital, do the research into what the term mean, what are drag along shares, what are rights of first refusals, what are all these things. Understand it well, model out what your capital looks like between rounds very carefully. And then if you’re going to give anybody more equity than needed, give it to your team, give it to your employees.

Caspar Barnes: Don’t give it to advisors that are just trying to shop for freebies or investors that are giving you very aggressive jabs. So I would definitely say research and be diligent and careful around how you structure your cap table. And on that note, I’ll just put a short plug for a program I did called VC University through Berkeley Law. It really teaches you the fundamentals of venture capital, which as a founder, very useful to understand the nature of the pressures that your investors are under from their own limited partners and to really have a more holistic understanding of the ecosystem.

Grant Belgard: What vanity metrics do you see startup decks overusing right now?

Caspar Barnes: Good question. Vanity metric. I think the first thing that comes to mind, I’m sure there’s many more, but the first thing that I can think of is like logos. There’s people overhype the logos, right? You know, like I think there’s a team slide and there’s like logos that pop out, but then, you know, there’s, there’s Disney, Amazon, Harvard, and MIT on there. And then you look through it and it actually turns out that, you know, I shopped at Amazon one time and I took an online course at MIT or something, like something ridiculous. And so I think people massively overinflate the use of logos, both on the team side and the customer side, that it can come across as a little bit disingenuous and maybe TAM, Sam, some metrics. I think that slide tends to be really overhyped.

Caspar Barnes: And if people say they have, you know, a trillion dollar addressable market, I always like, you know, focus more on that slide and see what they really mean and what they’re actually building.

Grant Belgard: So to wrap us up, when people talk about AminoChain in 20 years, what do you hope they say?

Caspar Barnes: I hope that they say, wow, look at this case study from Harvard business school on AminoChain. They proved that you can build an incredibly successful business by putting bioethics at the heart of your business model. And this company proved that if you really care about patient engagement, patient experience, and align incentives for human beings that make research possible, then downstream, everybody benefits. You know, it’s not like providing a better consent experience compromises pharmaceutical interests. It actually aligns with bringing drugs to market and helping people. And along the way, you know, it’s a fantastic protocol and it’s crypto enabled and it’s innovative and whatever. But like, I really would love it if people talk about AminoChain as being a company that proved you can make, you know, a lot of success by caring to people first and foremost.

Caspar Barnes: And so that’s, that’s the real mission of what we’re doing. I’ll happily hang out my hat once we, once we prove that out.

Grant Belgard: So where can our listeners go to learn more and how can they follow AminoChain’s journey?

Caspar Barnes: Amazing. Yeah. So our website is just www.aminochain.io. You can check out the specimen center. If you like, you can log on there. It’s totally open, free for anybody. Go and browse through the hundreds of thousands of biospecimens that we’ve aggregated on there. You can also find us on LinkedIn and follow us on Twitter. We’re just AminoChain. And yeah, if you’re a builder in the space on the life sciences side, or you’re a protocol crypto engineer, then please don’t hesitate to reach out to us through our website as well. We’d love to.

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

Caspar Barnes: Thank you so much for having me, Grant, it’s been a whole lot of fun.

Transcript of Episode 67: Manos Metzakopian

Disclaimer: Transcripts are automated and may contain errors.

Grant Belgard: Welcome to the Bioinformatics CRO Podcast. I’m your host Grant Belgard and today I’m joined by Manos Metzakopian. Today’s episode is special. We’re using this conversation to introduce CellCodex to the world. Full disclosure, I’m a co-founder and the CTO of CellCodex and Manos is co-founder and CEO. We’ll explore what the company is setting out to do, the scientific and engineering choices behind it, and Manos’ path to this point and practical advice for anyone building at the intersection of wet lab and AI. Let’s dive in.

Manos Metzakopian: Wow, this is amazing. Thank you for the invite.

Grant Belgard: So what would you like listeners to know about CellCodex?

Manos Metzakopian: When we started CellCodex, we imagined a world where there’s abundance of drug targets and basically that there is a cure for every disease. And a major development that happened in the recent years was artificial intelligence gaining this capability of taking large sets of data and providing such solutions. That happened with large language models, with ChatGPT, where all text has been collected and you can now interrogate all text that has been around for use and you can gain a lot of speed in your daily tasks. So imagine if you had an AI model for biology, for discovering new drugs. And that model helps you increase drug target discovery efficiency, but also efficiencies going to the clinic and increasing your chances of success once you go to the clinic. Because at the moment, most of the drugs that reach the clinic fail. And there’s a lot of iteration that goes into drug discovery.

Manos Metzakopian: So AI has the potential of solving these problems. Now, for biology, there isn’t this counterpart of data sets that was there for ChatGPT and text. And there is a big need for data so that the right AI models are trained to realize this future. And yeah, and this is why CellCodex has been brought to the forefront as it’s been created. It’s to solve biology’s biggest bottleneck, which is data. And AI, as I said, has the power to transform drug discovery, but it needs the right kind of biological data, systematic, reproducible, and at scale. And that’s what we want to deliver. Our vision is to accelerate the arrival of the world where every disease is curable. And the first step is giving model builders, AI model builders, and drug target hunters the right fuel, which is the data.

Grant Belgard: So CellCodex is a CRO that generates AI-ready perturbation data at scale.

Manos Metzakopian: That’s correct.

Grant Belgard: So what problem in biology or drug discovery feels most urgent to address right now, and why start there?

Manos Metzakopian: So at the moment, because of the arrival of AI models that can solve these big problems, the creation of superior AI models is moving at a very fast pace, almost at the pace of weeks and months. Whereas a data generation that can feed these models and allow them to be trained, it’s still very slow. And it’s moving at a speed that is not satisfactorily reaching the speed of model creation and testing. So the most urgent gap is reproducible perturbation data we have. And we have plenty of observational data at the moment. However, these are snapshots of what cells look like. So from observational data in biology, we have almost 14 times the amount of data that was there to train ChatGPT. However, it’s the quality of the data and the kind of data that is available that is important.

Manos Metzakopian: And unfortunately, we do not have that right type of data, the perturbation data, the intervening data in cell identity, cell state, and cell function. Without that, AI can’t move from correlation to causation. We started there at CellCodex to create large-scale perturbation data to solve this problem and to allow AI, artificial intelligence, to realize its promise, speed up drug discovery.

Grant Belgard: When you imagine the ideal outcome of this effort five years from now, what does success look like to the end user?

Manos Metzakopian: The success rate for the success is very simple for the end user. It looks like there’s faster drug discovery programs, fewer dead ends, more success in drug target identification, and higher success in the clinic. And for those to be powered by our AI enabling data sets. That’s how I see our success in five years from now.

Grant Belgard: What kinds of decisions do you hope our work helps people make faster or confidently?

Manos Metzakopian: There is a lot of work that goes into drug discovery that takes many, many years. And we can speed it up at the rate of weeks and months to be able to make these decisions in weeks and months versus years. And that includes which targets to pursue, which mechanisms are causal, which disease models are worth investing in. Right now, those decisions often take years and huge budgets, and we want to make them faster, cheaper, and with higher confidence.

Grant Belgard: What milestones are you most comfortable sharing at this stage, and what should listeners watch for next?

Manos Metzakopian: So a major milestone for us is that we’ve set up and are continuing to build our platform, the CellCodex platform at the Babraham Research Campus at the moment, where we are going to launch our first collaborations, partner projects, client projects. We also want to publish benchmarking data sets that show what AI-grade data really looks like. So listeners should watch for collaborations where our data sets are powering new models or enabling novel drug targets and emerging new drug targets due to our data sets and enabling of client models, our AI models.

Grant Belgard: When building virtual models of cellular behavior, what principles guide how you define the unit of prediction or simulation?

Manos Metzakopian: So we think of units not as just a number of cells that are being evaluated as it’s being done in observational data, but we are also thinking of cell states, cell states under perturbation. So a meaningful unit for us isn’t just a cell at rest or it’s just in its normal environment. It’s a cell that is responding to a defined change. This is the building block for causal AI modeling, I would say.

Grant Belgard: Where do you draw the line between a correlational model that’s useful and a model that supports causal reasoning?

Manos Metzakopian: So a correlation model is useful for pattern recognition, but causation comes when you’ve systematically perturbed the system. So our role here is to generate that causal data so customers can build models that go beyond what co-occurs and moves to what actually drives change. For example, in disease, point mutations can lead to changes in cell state, and these are not just co-occurring mutations. They are driving the change. So we are interested in data sets that empower models that can quickly identify mechanisms that actually drive change in cells.

Grant Belgard: In your view, what types of measurements provide the most leverage for learning cell state transitions under perturbation?

Manos Metzakopian: For us, at the moment, we need single-cell multi-omics, and we have two major capabilities to sequence RNA, cells messenger RNA at scale, but also to acquire epigenetic changes, the epigenetic landscape in the cell through ETAC sequencing. So that captures which areas of the genome are open, and so you know which genes are expressed, but also correlate those to which areas of the genome are open as well. So these two data sets provide, number one, which genes are expressed, how are they changed under perturbation, and very importantly, which features of the epigenome change. So when you sequence, when you have ATAC sequencing, you can also correlate the changes in many features of the genome to the gene expression changes as well. So that adds a lot more information to interpret causation versus correlation.

Grant Belgard: How do you think about biological context, cell type, state, microenvironment, when designing a modeling target?

Manos Metzakopian: That’s a very, very important question. This is essence of what we do in CellCodex. So in CellCodex, we have the functional genomics capabilities to produce these large-scale perturbation data sets through our genetic screening approaches and gene editing technologies. However, the foundation that can lead to the right type of data are the models that we would use to generate these data sets. And so we design our experiments according, of course, to what the clients would need with the cell identity and function in mind, developmental states, co-cultures, which cells need to be together in the dish, and the microenvironment that they are supposed to be growing in. So you take all of that together, and then you have your human cellular models that you would use for your perturbation experiments.

Manos Metzakopian: And if you want to think about it, a perturbation in a neuron means something very different than a perturbation in a fibroblast. So that’s cell identity. So we co-design with customers to choose the context that matters to their question and to problems that their AI models would want to tackle.

Grant Belgard: What would you count as a falsifying result that sends you back to the drawing board?

Manos Metzakopian: A very important thing is the quality of the data, and a lot of it goes into data reproducibility. So we put measures, quality control measures, in place at every step of our platform, so our data sets are reproducible across batches. It would be very challenging if we don’t have batch-to-batch reproducibility. If you think about the cellular models that we are using, so every time we perform tissue culturing and using the cellular models to produce the data, they need to be the same and reproducible. And the data sets that are coming out of these models, the perturbation data sets, need to be reproducible. So we have very strict metrics around that. I would say that would be one of the major falsifying results that can happen in the platform. And we have very stringent mitigation strategies for that.

Grant Belgard: And when you plan data for model training, what are the first three design decisions you lock in and why those?

Manos Metzakopian: Most important thing is the context of which cell models to use, because that’s, if you think about disease, they don’t happen in isolation. They happen in a specific context with specific cell types involved and cell-cell communications happening there. So the cell models to use are one of the first decisions we need to make. Of course, they need to be applicable to our screening strategies as well. And then which perturbations to apply? Is it a gain-of-function perturbation, like using CRISPR activation, or is it a knockdown perturbation, or are we looking at completely knocking out the gene? So which perturbations, and depending on the experiments, the different scales. So we might need a few million cells for an experiment, or hundreds of millions of cells for an experiment.

Manos Metzakopian: So if you’re thinking of foundation models, for example, versus very specifically trained models that would need fewer cells. And which readouts, right? If you’re thinking of omics readouts, which of those readouts, so that you can balance resolution, cost, and downstream utility.

Grant Belgard: What’s your approach to quality control from sample prep through to process matrices?

Manos Metzakopian: Our approach is to have quality control steps embedded in every part of our platform and our process. And to have the right type of standard samples or tests in every component of our measurement. So that then we can always have a good measure of the quality of the data that’s coming through. So from tissue culturing and the cells, quality of the cells that we are using for our perturbations, the quality of the material that we are extracting from the cells, and finally, the quality of data that is being extracted from our cellular models. I would say we have very clearly defined pass-fail criteria up front for customers to know what they’re going to be getting regarding quality of experiments and data.

Grant Belgared: What’s your stance on foundation-style pre-training versus task-specific architectures?

Manos Metzakopian: So I think both are going to be important. You will have customers that are looking for models that can generalize very broadly. So they would be building foundation models, and those would require vast, diverse data sets. So there’s going to be breadth and depth required for such models. And task-specific models will need more precisely curated data sets coming from very specific contexts. And in both cases, that will decide the number of cell types and complex cultures that we would be using to deliver the data sets for both types. Foundation models will have quite broad utility, but the task-oriented ones would be more specific. And we will be producing data sets for both types of model training approaches.

Grant Belgard: What does a convincing benchmark look like to you for the model understands a cell response?

Manos Metzakopian: It can be covered by just one word, I would say, replication and validation. So if, sorry, two words, replication and validation. And that is that we are able to reproduce the same perturbations providing the same data. So that would be replication. And also validation, the outputs of the models that can be validated in turn. So I think these are going to be very important benchmarking tools that we have. That’s how I would think of it in simplistic terms.

Grant Belgard: How do you separate evaluation of biological plausibility from pure predictive accuracy?

Manos Metzakopian: So I would say that it’s very important to focus on biological plausibility. Because if the data itself isn’t biologically valid, accuracy metric will matter in the sense. So it has to be applicable to the scientific challenge that the client wants to tackle. So I would put a lot of focus on biological plausibility, initially, especially in scientific design, in the experimental design.

Grant Belgard: What forms of external validation replication blinded test challenges feel most meaningful?

Manos Metzakopian: It would be great if independent labs can replicate. If you think of it from a replication point of view, if different labs can generate the same data with the same approach, that provides a lot of confidence. But in our case, I think we would think of it as customers successfully using our data to build their models and generalize to new biology. So if they are able to use our data, generalize to new biology, and identify targets, solve their biological problems, and expedite the therapeutic discovery path and increase its effectiveness, then I would say that’s the most meaningful external validation.

Grant Belgard: Who stands to benefit first from this work, and how might they plug it into existing workflows?

Manos Metzakopian: I think at the moment, there is a race happening of different entities and institutions and consortiums and consortia that are working towards delivering a model that can solve a lot of the drug discovery problems. And that includes biopharma teams, and that includes biopharma teams, and biopharma teams, and consortia, and so on. But I think what is currently being understood that it’s not going to be a one-dataset-fits-all. It’s going to be models that are going to be trained to solve specific problems, and they’re going to be requiring their bespoke data sets to be trained with. And so I think it’s going to be less of a race towards the best model, but more of a joint effort to generate the right data for the right models and solve pressing issues in the world. And I think that that day is upon us, for sure.

Grant Belgard: What kinds of collaborations or partnerships would be most impactful at this stage?

Manos Metzakopian: Companies that have bottlenecks in their pipelines where our data can actually resolve that issue.

Grant Belgard: How do you weigh openness, sharing resources, or benchmarks against the need to build a durable business?

Manos Metzakopian: I would say it’s very important to make sure that we are leading in the space of high-quality data sets, AI-grade data sets. And we should think of best ways of sharing benchmarks and best practices openly. However, the large-scale perturbation data sets are contract-delivered, and so there needs to be a balance that ensures both impact and sustainability.

Grant Belgard: What drew you personally to this specific problem space?

Manos Metzakopian: I have always been involved in projects and challenges that require large data and perturbation data. Most recently, we’ve used this know-how in the cell programming field. So to democratize cell types for drug discovery research and cell therapies, and that never required large-scale data sets and so on. And during my time solving these problems in academia and in industry, I realized that the potential for AI to solve the drug discovery bottleneck and lead to a world where there are cures for every disease requires us to rethink the way that we produce data, the quality of the data, its reproducibility and its scale, and the context at which it is delivered.

Manos Metzakopian: And so as I was progressing in my academic and industry career, I’ve realized that setting up a platform like this, which is CellCodex in this case, to generate AI-grade data is timely and very, very important to do so now, where we are at the verge of arising to artificial intelligence-enabled solutions in therapeutics.

Grant Belgard: Looking back, what set of experiences most shaped how you approach leading a science-driven company?

Manos Metzakopian: The most important experience that I had during my academic career and my industry career is managing people effectively, making sure that we are all goal-driven, we are ambitious, and we are enjoying what we’re doing. And in my academic career, I’ve mentored PhD students, master’s students, and postdocs, research assistants, and technicians. It led to amazing work where we’ve published over 30 scientific manuscripts in the fields of genetic screening, cell engineering, and drug target discovery. And similarly, in industry, leading larger teams, the most important thing that leads to success is the team, the people that are involved in driving the work and the goals that we set ahead of us. So I think goal-setting and the people that are along for the journey are the most important pieces of the puzzle.

Grant Belgard: How do you structure your day to balance science, product, people, and operations?

Manos Metzakopian: It’s not always easy to balance between everything. It depends on the stage at which the activities are. If it’s joining a mature corporation where they’ve already set off and they’re on a journey, or in this case, CellCodex, where we are just launching, everyone in CellCodex wears multiple hats, and we try to support each other and help each other so that we can deliver the needs of the company. And I structure my day where I look at the needs of the people, if there’s any way I can help in their day-to-day activities, the needs of the company, and in designing the strategy, and what type of products we’re going to have, and offerings. And of course, now, when launching, we’re thinking of operations. How are we going to operate most effectively? And I would say, at the moment, it’s split 30% equally throughout everything.

Manos Metzakopian: So I would say it’s equally divided across strategy, products, and operations.

Grant Belgard: What advice would you offer to scientists considering a leap into company building?

Manos Metzakopian: You’re not going to feel ready. So at any time point, especially when it’s your first venture. So I would say, if you have the right ideas, and you have a very strong feeling and passion about these ideas, you have people equally passionate with you, and you can work together to make them materialize, then I would jump in, and I wouldn’t wait until you feel fully ready. You probably won’t get to that type of feeling. And it’s not a bad thing. And bottlenecks are not going to fix themselves. So if you see one clearly, then that’s your opportunity to jump in with your ideas to solving a problem in the world.

Grant Belgard: What practices help a small team avoid cargo cult, ML, or overfitting ideas type cycles?

Manos Metzakopian: I wouldn’t chase hype cycles. I would ask if the method helps explain or actually lead to a solution. So I would really think and investigate very, very, very well, very deeply, if a new direction, a new tool, a new approach is really going to make a big difference. And ask yourself if it’s worth the investment. So I wouldn’t chase. I would investigate and research what new things come out.

Grant Belgard: What advances outside your control would most accelerate your roadmap?

Manos Metzakopian: So that’s a great question. So outside, so currently, as I’ve said before, throughout this conversation, this podcast, there are a lot of companies out there that are generating their own artificial intelligence models, and they are using them for predictions that can progress drug discovery. Now, there are a lot of companies that are doing that at the moment already, and there is a big need for data. However, as soon as these models start showing the power that they have in increasing drug target discovery and driving efficiencies in therapies, there’s going to be even a larger need, and there’s going to be a larger number of models that are going to be generated to be trained, and there’s going to be a lot more data that’s going to be needed to train these models.

Manos Metzakopian: So I would say that since there’s going to be such a huge need for data advances that can increase the number of cells that we can analyze in a multi-omics context and technology development that can allow us to analyze multiple modalities from similar samples, all of these will allow for better data, larger-scale data that can provide the fuel that these new models will need in the future.

Grant Belgard: Well, Manos, thank you for sharing the CellCodex vision and the thinking behind it. It was nice having you on today.

Manos Metzakopian: Thank you very much for the invitation. It was a great, great conversation. Thank you. For listeners who want to follow along, the best place is cellcodex.bio and also our LinkedIn page. If you enjoyed this, please subscribe and share with a colleague who cares about building predictive biology. Thanks.

Transcript of Episode 66: Eva-Maria Hempe

Disclaimer: Transcripts may contain errors.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Transcript of Episode 65: Jeff Bizzaro

Disclaimer: Transcripts are automated and may contain errors.

Grant Belgard: Welcome to the Bioinformatics CRO podcast. I’m your host, Grant Belgard, and today I’m joined by Jeff Bizzaro, founder and longtime president of Bioinformatics.org, and a tireless advocate for open access in the life sciences. Welcome, Jeff.

Jeff Bizzaro: Thanks, Grant. Thanks for inviting me.

Grant Belgard: So you’ve called Bioinformatics.org a Swiss army knife for biologists. How would you describe its core value proposition today?

Jeff Bizzaro: Well, we created the site with several goals in mind. First, to host collaborations. Second, to host open source software development. It later grew to include non-developmental goals. First, hosting web tools for analysis. Second, hosting data. Third, hosting education resources and for promoting open science. And hosting forums, primarily news and a jobs board. Many users express surprise at the variety of resources that we have.

Grant Belgard: Several classic web tools still get heavy traffic. How do you keep legacy software alive without stalling innovation?

Jeff Bizzaro: So there’s a sequence manipulation suite, or known as SMS, and a web tool that we have on Bioinformatics.org. It was created by Paul Stothard, who was an early contributor. And it’s one of our most popular tools, actually. The number one tool in terms of traffic, overall use. A lot of the tools that we’ve been hosting, they’re still being developed to some extent. Whether they’re not maintained or new tools are developed to replace them, that really all depends on the community.

Grant Belgard: And you have this Benjamin Franklin award, it’s a marquee event for Bioinformatics.org. How does that fit into your broader mission in 2025, and what impact stories have past winners shared?

Jeff Bizzaro: The Franklin award, it recognizes one member of the community each year, and the contributions that they’ve made to open science. It started off as open source, and as that ideology has spread to publications, it became open access. And then just open science covers both of those, or all of those ideas. We first presented the award in 2002, so going back quite a way. The award’s actually been paused since 2020 because of the COVID pandemic, which shrank the venue, and the host had cut back on the program. It was actually a very relevant award for the early 2000s, when it wasn’t a given that any particular software package that you would download would be open source, even if it was developed in academia. Actually, a few universities had policies that allowed the developed software to be licensed as open source, most of them weren’t familiar with it.

Jeff Bizzaro: At this point, now we’re considering perhaps a different award, maybe one that recognizes innovation. So we’ll see how things go.

Grant Belgard: Bioinformatics.org predates GitHub and even SourceForge. Why keep an independent platform instead of migrating everything to one of the giants?

Jeff Bizzaro: People ask that question from the very beginning, why should they choose bioinformatics.org over SourceForge for hosting. For code management, there wasn’t anything we offered that SourceForge didn’t. Our argument was that we offered more than just SourceForge management. We offered actively hosting tools through the website, for example, SMS. We could also host data, education tools, and we had some other features like news and a jobs board. You didn’t see any of those in SourceForge, you didn’t see that, you don’t really even see that in GitHub. All of these are with a focus on bioinformatics and science, while SourceForge hosted source code for generic software. The same could be said about GitHub today, but GitHub’s source code management tools, of course, far exceed those of SourceForge.

Jeff Bizzaro: Something we’re working on now is a way to integrate GitHub tools with our site using their APIs. So in terms of the availability of source code management tools, we hope to be able to be on par with GitHub just by integrating with them. And still maintain and work on these additional resources for the community.

Grant Belgard: You’ve spoken about serving the citizen scientist. What design choices make the site approachable beyond academia?

Jeff Bizzaro: Well, try to have as much as possible, be free of charge. That’s been important to us. Our basic membership is free. Hosting open projects is free. Downloading resources is free and subscribing to the jobs board is free. So it’s very easy to get on, get started, you don’t have to worry about paying for anything for any member with basic level of resources that they’re looking to make use of.

Grant Belgard: How are you financing your server costs today? Still donations, sponsorships, ads? Any experiments or freemium tiers?

Jeff Bizzaro: It’s been mostly sponsorships, which includes ads. We also have a pro membership tier for a fee, but the basic membership has always been a free tier. Going forward, closer collaboration with sponsors and doing things such as contests and bounties would provide more income.

Grant Belgard: What’s a feature on your 12-month roadmap that you’re most excited or nervous about?

Jeff Bizzaro: Integration with GitHub. As I was saying, it’ll make available the resources we currently can’t afford, such as space to store and manage many more of the bioinformatics resources. Another one is hosting Jupyter notebooks, if you’re familiar with those. Yet another one is hosting bioinformatics games, likely develops using JavaScript since that’s something that really can be done in the browser these days. It doesn’t require anything on the back end or the use of Python. There are quite a few ideas that actually volunteers are welcome to help implement them.

Grant Belgard: What are bioinformatics games? I’m not familiar with this.

Jeff Bizzaro: Well, there have been games developed in the scientific community for quite a while, even more recently. There may not necessarily be games like shooter games, but things like folding at home or whatever, where just the community can learn and participate in research at the same time. So yeah, I guess the overall goal is to help teach bioinformatics, but also related areas of biology, genomics, proteomics, and so forth.

Grant Belgard: Can you do some rapid myth-busting about bioinformatics.org for us?

Jeff Bizzaro: Sure.

Grant Belgard: What are some misconceptions people have sometimes?

Jeff Bizzaro: Yeah. Conceptions? Yeah, sure. What was it you’d like to know?

Grant Belgard: What are common misconceptions people have about the site that you’d like to clarify?

Jeff Bizzaro: Well, whether or not the site is for coders. As I mentioned, we made an effort early on to distinguish the site from SourceForge, which really isn’t a site for coders. So if you come to the site, you’ll find all sorts of other tools, analytical tools, databases, at some point games. So it’s not just for coders. Come and check it out.

Grant Belgard: So take us back to 1998. What unmet need convinced you to register bioinformatics.org? How much of that need exists today?

Jeff Bizzaro: Well, actually, the first name we used was the OpenLab. And that reflected our mission to bring the ideals of the source community to the bioinformatics one. Oddly enough, many of the founders of the open source movement have said their mission was to bring the ideals of the scientific community to software development. I like to say that it’s a double reflection that we’re kind of reflecting back the ideals that people think that we have or the general public thinks that we have with respect to scientific research and collegiality. I wouldn’t say the need for hosting is as great as it used to be, but we still offer shell accounts for selected projects, which requires a lot of trust. There’s some need there. As for the name bioinformatics.org, one of our co-founders got the name. So it started off as the OpenLab became bioinformatics.org in the year 2000.

Grant Belgard: So your background is chemistry and biochemistry. What first nudged you towards computational biology?

Jeff Bizzaro: Well, it was my undergraduate advisor and long-term collaborator, Ken Marks, who was a professor at the University of Massachusetts in Lowell. I was determined to become a biochemist at that time, but I also had an extensive background and an interest in computers. Going back to around 1981, I had always thought I had to choose one of the other science computers. I liked them both. Ken showed me that they could be combined with this field called bioinformatics. This was back in 1995 when bioinformatics was a new buzzword.

Grant Belgard: Can you tell us about the moment you realized bioinformatics.org had outgrown a hobby?

Jeff Bizzaro: It didn’t take long for the site to catch on. We were getting new hosting requests and volunteers in rapid succession in the early 2000s. We were invited by several conference organizers to participate in their events around that time, too. See, we’re on to something. Remember, this was all during a time when anyone could choose a generic site like SourceForge for hosting.

Grant Belgard: How did creating the Benjamin Franklin Award change your own thinking about open science?

Jeff Bizzaro: There were some interesting developments in open science over the years. It was nice to be at least tangentially part of it. Many of the recipients of the award went on to pioneer open access publications and standards for sharing data and results. And we saw all of that happen early in giving the awards.

Grant Belgard: You’ve been both a tool developer and a community builder. Where do those mindsets clash or reinforce each other?

Jeff Bizzaro: A lot of the ethos behind bioinformatics.org came straight from the open source software movement. And I had those luminaries as role models. People like Richard Stallman and Linus Torvalds who created the Linux operating system. Between the late 90s and the early 2000s, which were the formational years, I avidly followed posts on a site called Slashdot, which is an inventory site largely for the open source community. In reading Slashdot, I got to know the ins and outs of working with a community of developers.

Grant Belgard: How did your graduate research projects feed into features on bioinformatics.org?

Jeff Bizzaro: I’ve actually had a development project that ran in parallel to bioinformatics.org, although I haven’t really mentioned it much. It’s been going on now for over 25 years. It started off as a distributed workflow system for bioinformatics. Several points I tried integrating the system with Beltzim and Poly and with bioinformatics.org. It’s morphed a lot over the years, this parallel project. It’s not currently a distributed workflow system. What it is now is something that’s shaping up to be a framework for future development, development of the website. And I think website will at some point become built on it.

Grant Belgard: Open source culture has evolved from CVS to GitHub copilot. What cultural shifts surprised you the most?

Jeff Bizzaro: The most surprising thing to me was the development of again distributed workflow systems by other companies and groups that had no knowledge of my earlier work on the topic. Someone who volunteered back in the day later on told me that whenever he sees one of these systems appear, he thinks back on what we did. So I would say that, and of course, more recently, I would say, yeah, probably the advent of AI and LLM, but I think it’s surprising all of us.

Grant Belgard: Were there any mentor figures or seminal papers that shaped?

Jeff Bizzaro: I’d have to go back to what I said earlier that it would be Richard Stallman and Linus Torvalds, not really bioinformaticists necessarily. These ideas really influenced the founding of bioinformatics.org. Pretty persistently tried to instill those ideas into what we’re doing. And even today, I’d like to stick with that ideology.

Grant Belgard: How do you measure personal success 27 years in?

Jeff Bizzaro: Hearing from people that they personally benefited from what I’m doing really means a lot to me. I think anyone would love to hear that. I’ve met many people at conferences over the years who’ve told me that they got into the field of bioinformatics because of the website or because of the organization. Affecting the trajectory of someone else’s life is amazing to me, I think. And anyone would love to hear that. So it means a lot to me to hear that.

Grant Belgard: What still keeps you up at night?

Jeff Bizzaro: Well, keeping the servers up for the website running, keeping the site secure, paying the expenses. Those are the biggest stresses for me.

Grant Belgard: So turning now to your advice for the next generation. If a student asked, what should I learn first, R or Python or prompt engineering, what would you answer?

Jeff Bizzaro: Well, I’d give the same answer for the field of bioinformatics and now AI. I’d say definitely Python of those. I’ve worked with R in the past, great language, but I think the future really is with Python. There are just many modules that pertain to science, big data, to AI. As for prompt engineering, I think it’ll probably become unnecessary as AI models improve. But I’d also recommend learning a language that can be compiled and even run in parallel. I’ve done some work in C. MELTSIM, for example, is written in C and really needs to be because it is a very demanding application in terms of processing power.

Grant Belgard: Many listeners sit inside big pharma or hospitals. How can they champion open science without risking their jobs?

Jeff Bizzaro: I think it’s much less of a concern than it used to be. I’m not so sure people would be risking their jobs. All of the software giants have now come to embrace open source. Just look at Microsoft as an example. Many of their development tools are open source and they own GitHub. There’s no need to say more if I can say Microsoft is an example of open source advocacy and helping the open source community. Yeah, that would have really surprised the early Slashdaughters, I think. So is it still worth pursuing? I remember a early presentation given by Lakenstein for one of our Benjamin Franklin Award presentations. The ceremony. And he mentioned that the field of bioinformatics would probably disappear at some point as these tools really just become an everyday part of doing biological research.

Jeff Bizzaro: And he joked around that there would be a Microsoft blast application at some point in the future. So I think we’re almost at that point. And a lot of giants like Google are involved in bioinformatics research. Is it Blue Gene actually? IBM has the Blue Gene project and you have the folding projects by Google. So there is, yeah, the open source software is very much standard at this point.

Grant Belgard: Is it still worth pursuing a traditional PhD or are there faster routes to credibility in 2025?

Jeff Bizzaro: I think if you’re going to take the traditional routes, you’ll need the traditional degrees. In academia, you can’t go very far without a doctorate. Even with a doctorate, it’s very competitive. Many businesses also seek PhDs for their R&D departments. If you’re looking for a more technical position, you may want to get at least a master’s. I’d say if you already have a degree, but it’s not closely related to bioinformatics, that’s where certificates and individual courses could help.

Grant Belgard: What ethical frontier in bio data keeps you cautious?

Jeff Bizzaro: Well, yeah, there are a number of ethical issues in bioinformatics in the life sciences. On whole, there’s patient privacy access or open access to data and balancing that with patient privacy. Genomic engineering, everyone working the life sciences, I think, should take a course in bioethics. I think the advancements being made in bio are even more concerning than those in AI. They’re both very concerning. But there’s so many different issues and a lot of people probably aren’t aware of.

Grant Belgard: If you had $10,000 in six months to teach yourself a new skill, what would you pick and why?

Jeff Bizzaro: I think I’d probably choose robotic automation, for example, for laboratory and agricultural use. And AI. It seems those are the big tools of the future.

Grant Belgard: What role do you see for micro-credentials and certificates versus formal degrees?

Jeff Bizzaro: At bioinformatics.org, we created short online courses on various topics in bioinformatics starting around 2008. And this was years before sites like Coursera existed. It was challenging because online conferencing and video streaming were not mature technologies at the time. Our angle was that we taught practical methods or applied bioinformatics. We did it quickly, whereas university courses focused more on fundamentals. If it took a course at a university, it’s a whole semester or nothing, really. There’s pretty much no choice of taking a short course. But I think this type of education, even though it’s a niche, there’s still a need for it.

Grant Belgard: So just a few quick questions to finish this out. What’s your favorite open source tool right now?

Jeff Bizzaro: I’d say Microsoft VS Code. Come back to Microsoft. I think it’s actually probably the best piece of software they’ve ever made. I’d include Windows in that. It really is something else if anyone hasn’t tried it. I was using Eclipse as an editor. I noticed that there’s this thing called VS Code that everyone’s picking up in the community, especially in web development. So I thought I’d give it a try. It works, and it’s great. It has a lot of extensions. It’s even being used as the foundation of a number of third-party editors, a number of editors that are used for AI. For example, one called Cursor. And that shows you that it’s a very flexible environment and very well developed.

Grant Belgard: What’s your coding font or theme of choice?

Jeff Bizzaro: It’s nothing fancy. I just use the Menlo font, which is, I think, the default font for fixed-width text in the Mac environment. I also prefer light themes, as it makes switching between apps easier on the eyes. Some people get to the, these days, have gotten to the point where they try to switch everything, even word processing documents, to have a dark theme. Maybe they’re getting a bit carried away with that. But you’ll always find something that’s not dark, and when you switch from dark to that, it can be a bit annoying. So I think just sticking with light, the traditional use, which is used for traditional interfaces, is fine.

Grant Belgard: So where can people follow you, and how can they get involved?

Jeff Bizzaro: My email address is jeff@bioinformatics.org. Second off bioinformatics.org, I’m on LinkedIn, and connecting with me there is a good choice.

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

Jeff Bizzaro: Thanks, Grant. It’s been a pleasure.

Transcript of Episode 64: Afshin Beheshti

Disclaimer: Transcripts are automated and may contain errors.

Grant Belgard: Welcome to The Bioinformatics CRO Podcast, where we explore the data-driven frontiers of biology and medicine. Today, we’re talking about space biomedicine, keeping humans healthy off planet and bringing that knowledge back home. Our guest is Dr. Afshin Beheshti, a physicist turned systems biologist who has just launched the Center for Space Biomedicine at the University of Pittsburgh’s McGowan Institute for Regenerative Medicine. Welcome to the show.

Afshin Beheshti: Yeah, thanks for having me. Excited to be here.

Grant Belgard: So when someone asks, what is space biomedicine? What do you say?

Afshin Beheshti: It’s basically the exploration of how to make humans safe and travel in space, but it has a lot of clinical applications too. So because a lot of people might say, why is, why are you studying space? Because it only affects a tiny, tiny fraction of humans, right? So, but the reality is, as we probably discussed as we keep going, it’s, it’s, it has a lot of implications to everything that happens in space. So space biomedicine is to make humans travel in space, but it’s also to make humans healthier on earth.

Grant Belgard: So the, the Pitt Center for Space Biomedicine only launched last October. What, what gap did you see that wasn’t being filled by NASA centers?

Afshin Beheshti: NASA centers, they’re a government agency, right? So they have their own rules and, and bound by their own agendas, right? So it’s good that you could collaborate with them, get grants by them, because NASA always gives funding, just like NIH does to investigators like myself and other folks in the, in the US so that that’s a good role to have. And also they have their internal projects, so they have their own agendas, but they’re bound by what is set for them, right? So in an academic setting, you’re bound only by your imagination, right? So then that’s the key there. So when you come to the, let’s say Center for Space Biomedicine here at Pittsburgh, my vision is that we’re, we don’t have any limits. You could work with government agencies like NASA, get grants. So that’s great. But then you could also work with commercial agencies.

Afshin Beheshti: There’s a lot of commercial space agency, like not just Space X, but there’s lots of folks like Axiom Space, Vast Space, the Sierra Space. You go down the list, there’s a whole bunch of new players in the field and then more will pop up, I’m sure in the future. So then the goal is to get everyone excited about it and get lots of collaborative work going, just not just in the US but globally to collaborate with people at Pitt, the University of Pittsburgh Space Center, Space Biomedicine Center. And then also this will help really accelerate the advance because space is a big problem, right? You get one person can’t do it on their own and I don’t think one agency can do it on their own and you need all the space agencies out there, the government ones like NASA, European Space, but you also need all the commercial ones and you also need all the individuals to work together.

Afshin Beheshti: So there are certain rules and regulations because NASA is paid by the taxpayer. So of course they have to follow and be bound to what using taxpayer’s money correctly. When you’re in the institution, like here in the University of Pittsburgh, academic institution, you have grants, which are great, but you all could also have other options to play with, to make the advances you need rather quickly or or efficiently.

Grant Belgard: And what key research verticals are you prioritizing at the beginning?

Afshin Beheshti: I could say I could be cheeky here and say all of them, but that’s the big answer. No, but so for space biomedicine, the ultimate goal, the ultimate goal for anyone working in space biomedicine is to make it safe for humans to travel. So you want to develop countermeasures. So that’s my ultimate goal, but to develop countermeasures, you have to understand the science behind it, right? So you have to know what’s happening in space to take a little backstep is space. Obviously is we humans have not adapted to go to space. Our bodies have been evolutionary here on the earth. The gravity we have, the lack of the space radiation is there. Luckily for us, otherwise I don’t think none of us will adapt very well. But in space you got no microgravity, very minimal gravity to none. And also the space radiation that’s up there. So in space you got the heavy ions all in the background space.

Afshin Beheshti: So you got protons and majority of it, the smaller ions, which some of it’s produced by the sun and solar flares, you get high acute doses, but also the background radiation from other activity in the cosmic radiation is these protons are produced. But then you get the heavier ions, anything, some helium, but anything from oxygen to silicon to all the way to iron particles, huge ones, sometimes bigger particles. And I, and typically from what those are from like supernovas or black holes, they just invented radiation. And then that’s your background radiation. So that, that in itself causes a really harsh environment out there. And there you get this accelerated model for aging. You don’t age faster, but all the conditions with diseases would come with it. You’re aging faster that way. So it’s an accelerated model for a lot of diseases too.

Afshin Beheshti: So for space biomedicine in itself, we want to cover all these health risks that are out there, which then will turn into the countermeasure development that I mentioned in the beginning. But to understand how to, what the countermeasures are, what, what to target, you have to understand all these health risks. And this is where it comes to the fact that every health risk under the sun is, I’m not making space sound very sexy to travel, but I think eventually it will be, once we understand this space will be really important and fun to, for the humanity to actually explore and make it go on to the next phase of what’s happening in our next step, evolutionary for humans too. So yeah, so that’s why we covered a lot of health risks out there because there’s things like cardiovascular risks, brain risks, central nervous, CNS risks, liver issues, just go down the list.

Afshin Beheshti: These are the different health risks we could talk about that are out there. But the ultimate goal then is to what’s happening and then come up with the ultimate way to mitigate damage caused. You might not be able to stop the damage caused, but then you could prevent it from progressing to then make it safer for humans to be up there. And then when that happens, let’s say those disease models that are accelerated in space, those are actually, if you come up with a countermeasure to mitigate the damage that could easily be translated down to earth, the same, the same, like let’s say heart for heart disease or cancer risk, those drugs can not be novel new drugs that you could apply it to help patients in earth for the, all these other diseases that we, everyone has to deal with on earth.

Grant Belgard: So what does success look like for as little as five years? What, what, what would make you say the center’s been wildly successful?

Afshin Beheshti: Yeah, so that’s a, could be a tough question. Well, obviously if we came up with the ultimate cocktail of countermeasures to make it safe for everyone to travel. So in five years, everyone’s out of business and we’re all in space. That would be, that’d be great, but that’s an ambitious goal in five years to do. But that’s everyone’s goal is that, and I think a lot of people’s goals then in the field. But I think in five years you could, my, my goal is as successful as to, for when I can take a step back, when a lot of people in the health sciences, they, there’s a, there’s a small fraction of folks already working on space biomedicine around the US and around the world, there’s a lot of people who are not aware of it, or again, have questions like how do I do applies to space research?

Afshin Beheshti: So when I joined here in University of Pittsburgh and started the center, one of the things, a lot of people were obviously curious and interested in like, Hey, I always wanted to work with space. How do I get involved? And I say, this is colleagues I have in the pulmonary department here. I was like, well, everything you do is involved because of the health risks I just mentioned. So that was one of the goals is create awareness. Then people start realizing that what they’re doing can be applied to space and then their knowledge can be circular. It goes to space. It comes back to the earth, everything that they do. So the XLA model, and then now people are applying for grants. There’s NASA grants solicitation that no one was aware of. So then no one could apply for that. So that one metric of success would be that people start getting funding to do space research.

Afshin Beheshti: So in five years, let’s say even, even if it’s a 5% or 10% increase of people getting funds to work in space world, that’s, that’s successful because that had happened before I showed up, right? That’s one success of that. Another success is to bring awareness that to make Pittsburgh and a central hub of people coming to to say, Oh, where do we go if we want to collaborate with folks on space biomedicine? Well, they come here, they work with us. And then those other people become also knows you, you have one node and then you can start planning the nodes and the network grows now globally. You could create this whole central network of people working together in space biomedicine with, and then the Pittsburgh and the fighters might be recognized as we were the, we are the hub of it and we are creating this. So that’s another goal of it.

Afshin Beheshti: Ultimately it’d be good to, the funding is one, one metric that everyone goes by, right? So if a lot of funding comes in, then you can do a lot more research, whether it’s from government, like NASA funding or other government agencies or commercial or philanthropy, all that stuff is probably important to come in and see how that happens. And the other metrics is to start the volume of papers to come in and publishing in high impact journals, which is one thing, you know, there’s another in academics, obviously papers are your, your, your, your mark on how well you’re doing right in the higher impact journal you publish and the more attention against obviously you’ve done work that’s more impactful.

Afshin Beheshti: So that’s another goal to let more and more people within the Pittsburgh community and the University of Pittsburgh are starting to publish nice impactful papers on what they’re doing and how to apply to space research. So five years is a lot, but if the minimal is like these things that happen. And then in the process, let’s say we come up with some really cool and novel countermeasures that maybe in five years says, Oh, someone like me or the average person can go to space without having too many of the health effects. That’s that’d be, that’d be a huge success, obviously, but that might be five, 10 years or it might be the next year if we get lucky, but probably not.

Grant Belgard: So how, how are you thinking about the evolving funding landscape given, given the coming budget cuts and so on? What do you think is a likely mix of funding resources for the center and the, the, the years ahead?

Afshin Beheshti: Yeah, that’s a, that’s obviously a concerning question in everyone’s mind. Right. Not just space world, but of course, NIH world and health world and no clinical work. Yeah, there’s been, I don’t think it’s approved by the Congress yet, but maybe by the time that this, this goes on, it will be that, yeah, it’s been posed that. So in NASA, as we talked earlier, a lot of the funds, a lot of them, a lot of the funds that come to support space by administration in the US come from that. So it exists like NIH for a lot of the clinical side, which is great. But so, and NASA has a different centers and divisions money comes from so like science mission director. That’s a lot of basic research that is on animal research or using cells in a dish and things like that. So a lot of those research focuses on different types of topics that like plants and other things too.

Afshin Beheshti: That is the human research program that, as it sounds, is a concentrated human and countermeasure development. And each of them have their agendas, but of course the overall goal for a lot of the NASA solicitations to understand the basic science, but also in the meantime, come up with that countermeasure. So it’s been the budget proposal for the SMD, the science mission director, I think previous year was like the, I think about 38, 40 million for what was publicly released, but they’re trying to cut that down to 4 million for the entire thing, clean grants, people’s salaries over it. So that’s a concern. So I know for example, I have some NASA grants in that division and that’s a concern, like what happens if they do that? Can they still fund what they said? They’re going to fund future grants. Will they be getting future announcements listed, which is the key.

Afshin Beheshti: You need these solicitations to what I said earlier, to come up with the countermeasures and help humanity and the human research program. I don’t know exactly the numbers that’s been released, but I think it’s like at least half their budgets could be cut too. So then I forget the numbers there, but that’s a big concern, right? So, so that’s that if someone’s reliant only on the funding, which is essential to do scientific research and help humanity, and most people don’t understand for like every dollar, taxpayer dollar spent on grants, usually it’s been estimated there’s a two, $3 return on society based on what, what it becomes out of, not just job or job growth, because they say, if I get funded, I can employ people to work for me, right?

Afshin Beheshti: From the, if I discover a drug, that drug is going to go to another higher thing and I’m going to employ more people and then save lives and maybe also not only help space, but also when I said coming back to the clinic, lower health costs, because now people don’t get those diseases to put a strain on the community. So those are the things that people might not be aware of, that these cuts are going to have a downstream trickle effect, not just in the NIH, but that’s the world, the same thing. So then you have to start thinking of alternatives. For example, I have some funding also from industry that helps develop countermeasures, just so people, more and more people might have to think about that, which is, it’s there. So industry, for example, has to think about how does space help them.

Afshin Beheshti: And one of the things like, for example, I’m, I have funds to look at this mitochondria supplement. This one company asks, I do a lot of mitochondrial research and mitochondria are the powerhouse of your cells, basically all your energy produced, but this is a very simplified view of it. This does a lot more, but it provides a lot of energy. So you’re in space, we’re showing that mitochondrial is heavily impacted in space. You get that means your energy production is lowered and this is bad news because downstream it causes a lot of downstream effects of immune system dysregulation and so on. So they’ve provided some funds to say, we have a mitochondrial supplement that could just work in space. So they provide funds for me that now I’ve shown that potentially has a lot of promise to maybe be a part of a countermeasure cocktail or to cover some of the damage done.

Afshin Beheshti: So that’s an example of industries coming. And how they benefit from it is now they see, oh, it works for this. Well, they can market it that way, too. You know, also the applications, as I said, clinically, I’ve already said to them, like this, this supplement might also be potentially beneficial for long COVID patients, because what I see happen in space is very mere as what happens with people who had COVID and now experiencing long COVID. So now we find potential therapeutic, which there’s no therapeutic, there’s no help for unfortunate the millions and millions of people suffering with long COVID. But that’s a space application from a school funded research that not only will help mitigate space damage, but also now go back to the clinic that helps potentially help exactly do the tests and that they can potentially provide more funds or someone else. Well, then we can do that.

Afshin Beheshti: So and then, of course, philanthropy is always good because if people there’s a lot of rich people out there, right, if they’re listening. But a lot of those rich people also can be interested in space research. And again, they might be interested in potential ways to provide funding for this. That’s another resource. So those are the things that we I think as a community, especially in this center, we have to start thinking of pivoting to it is it’s a balance of things. But the unfortunate side is, as you mentioned, the government funding and it’s going to be tough, at least in the next three and a half years, things might bounce back afterwards. Things change. So, yeah, that’s that’s a concern for everyone. Not just in the space world, but of course, across the board for all.

Grant Belgard: Well, maybe following on that, get into some of some of your research. Well, tell us about what you discovered about mitochondrial stress and astronaut samples.

Afshin Beheshti: So I guess a little bit of mitochondrial 101 basics of mitochondria, because mitochondria can get very complex, too. As I said, it’s a mitochondria is used to be long, long time ago. It’s a bacteria. It’s not to be bacteria. It’s all an organ, right? And a long time ago, evolutionary cells and cells started interacting with this bacteria. They realized, wait a second, these these things are providing us a boost of energy, right? So why don’t we incorporate this into our cells? And that’s what happened to evolution. They said, oh, wow, this is great. This is actually going to be helping not just humans, but plants have it. Every every animal or invertebrate or vertebrate has mitochondria. Right. And it’s for the energy production. So that’s where bacteria don’t have it, because mitochondria was a bacteria. So that’s the only that’s one of the reasons it doesn’t happen.

Afshin Beheshti: But that’s the thing where evolutionary mitochondria got about to provide their energy. And then this downstream of that makes you also helps your immune system. That’s why there’s some antioxidants out there to reduce reactive reactive oxidant species that causes mitochondrial deficiency. That’s why there’s a lot of antioxidants out there to lower that and then improve your mitochondrial and improve your energy production. Now, your two most bioenergetic organs in your body are your heart and your brains. For example, your your brain’s only two percent of your body mass, but 20 percent of the mitochondria in your brain content.

Afshin Beheshti: So that you could imagine if you get, let’s say, damage done over time or in brain, if something’s targeting your mitochondria, that could be detrimental because things like brain fog or changes in the brain for how you really dysfunction that way, your mitochondria severely damaged can affect your whole body. Again, your heart’s another bioenergetic organ. So that’s the one. Mitochondria damage there. That could be a concern. So that’s the kind of a crash course. Of course, mitochondrial biology and metabolism has a lot more to it. But the simplest they put is, as you might hear, it’s the powerhouse of your cell. That’s a simple simplified version. So. Yeah, getting jealous about about work you’ve done with mitochondrial targeted subjects. Yeah, yeah, yeah, so exactly that. So in space, what we see is that mitochondria give you the little crash course of the mitochondria biology first.

Afshin Beheshti: But in space, so what we see in mitochondria is actually heavily suppressed. So, you know, if the radiation damage that so your energy production is heavily suppressed and then this across all tissues from experiments we’ve done from sending mice to space cells and also simulated experiments we can do on Earth from simulated radiation, space radiation and like microgravity kind of similations. So we see that across the board. That’s detrimental. Right. So how do we stop this? So one of the mitochondrial supplements that’s been funded by this company at Succo, this is a Japanese company, the Nutri-Cellulosecocide had this supplement called Chemferryl. So Chemferryl is a flavonoid. It’s found naturally. You probably had some at lunch, breakfast or when you’re eating this, whenever someone’s listening to dinner. So it’s found in leafy greens like kale, spinach.

Afshin Beheshti: Watercress actually has the highest content of Chemferryl. So out of all the plants that I know of, some fruits have it like blackberries. And I forget, there’s a whole list of them that have it. And this is the flavonoids are a bunch of different flavonoids, Chemferryl is one. And so we’re testing this because Chemferryl is an antioxidant. It actually targets mitochondrial biogenesis, meaning it boosts that signal. So as I said, space actually lowers your mitochondrial energy and your mitochondrial copy numbers and your content. So you want to have something to boost your mitochondrial signal by creating more mitochondria there. And this is one of the things that it does. So this is one thing we’re testing.

Afshin Beheshti: And so far we’re showing that, like, for example, we have these, one of my collaborators, Rob Schwartz at Well Cornell Medicine, he can do these like organoids in a chip, meaning things that are derived from stem cells that someone had determined a long time ago. You put us in factors and you could differentiate the cell into creating like a heart in a dish from cells or creating your liver in a dish. So not real heart, real liver, but it’s from a stem cell and you could create that. So and for example, the organoid, heart organoids, they beat in the dish the same kind of beating your heart does. So it’s cool. So, but for example, radiate, this is the space radiation. What we see is that the heart is actually the beating is actually reduced significantly because of the damage done by the space radiation, which could be detrimental. But we give it chem furrow.

Afshin Beheshti: And remember, I mentioned the heart is one of the most, in your brain is one of the most bioenergetic organs. So it makes sense why the reduction of the beat happens because your mitochondria is being severely damaged. We give chem furrow to that. And now it’s back to the control level. So that was like, wow, this is great. Complete mitigated damage. We’re working on the papers now to probably in the next couple of months, we’ll submit all these papers so the public can see it and then go down the list. Like the liver is heavily impacted by it starts, as I said, space and exhilarating model of diseases. So in the liver, what we see seems like cirrhosis might be being advanced in space for the liver and different factors like that.

Afshin Beheshti: So metabolism, for example, liver, there’s a lot of drug metabolism and the metabolism, a lot of things in your body, those activity gets lowered when you give it the space radiation. We give chem furrow and it starts coming back up to the depending on the radiation dose. We gave it similar space. It starts coming back up to like the normal levels, control levels, that radiation. So that’s really promising. Now, there are some factors that it doesn’t rescue because I don’t think this is one pill is going to not cure all damage done. So I think then we have to think about in mitochondrial, different organs can be targeting different types of mitochondrial factors and pathways. So this is the part now, I think it’s probably a mitochondrial cocktail that eventually will make it safe for people to travel in. So this is the part where this is promising.

Afshin Beheshti: Now we have to go on the avenue and think about what other kind of mitochondrial nutritional supplements or there’s other type of flavonoids similar to this that target different types of mitochondrial biogenesis or metabolism. So what kind of cocktails to get? So this is where more fun things would need more experiments to do. And then once we have the ultimate cocktail, this is like the magic pill in sci-fi movies. Oh, I just took this pill and I’m cured. I could just walk around and get exposed to all this radiation. But that could be reality in five, 10 years. Maybe we’ll do it. That’d be our measure of success. So that’s the key. That’s some of the exciting results that were some of it’s already available in the preprint that we’re addressing. Some of the reviewers comments, this one paper.

Afshin Beheshti: But a lot of these results are going to be public or submitting for peer review papers and publications in a couple of months. And then that would be once it’s gone through the peer review process and then hopefully be published by the end of the year or so.

Grant Belgard: Can you tell us more about the similarities and differences between long COVID and space flight, mitochondrial damage?

Afshin Beheshti: Yeah, yeah, definitely. So this is this is like a really good example of like what I said earlier, when people ask, why do you use space station? Why should we care? So the so, you know, I always tell people it’s always circular. What you do in space accelerates disease models. And then what you find there comes back to the clinic. Vice versa. What you find in the clinic can help space. So it’s a nice circular loop that helps everyone just to back up this one adult thing, like from the Apollo mission, everything like in the morning when you get up, half the stuff you use is what’s developed from the space research space mission, like your camera phones, a camera in there got miniaturized because they had to figure out in satellites how to get miniaturized cameras into all these components.

Afshin Beheshti: That’s the technology of all that, like your glasses here, the scratch was resistant that the glasses have was actually developed from the visors from helmets to prevent space debris. So your glasses are going to maybe potentially, I mean, of course, a little thicker. I would advise going into space with just your glasses. But so this is examples how technology has evolved that way. Now, medicine is the same way. So this is the long COVID example, which is really great. So in SARS, what we have shown from our research is that SARS-CoV-2, the virus of COVID, that it actually targets the mitochondria. So what we in the Q phase, meaning that first get infected with the virus through this one microRNA and microRNAs are these small RNA that target thousands of thousands, bind to thousands and thousands of genes that would inhibit genes. There’s some good microRNAs and some bad ones.

Afshin Beheshti: Sometimes what viruses do is hijack the machinery and incorporate the part of the microRNA that would bind to your genes. And in that case, then what it does is it uses that machinery to produce more of this microRNA to recreate the landscape, bind to all the genes and it needs to bind for it to thrive. What it turns out, what we found, I mean, this is the published data we have in the past few years, is that this microRNA is actually binding to all the mitochondrial genes that you need. So the virus then, if it does that, then it recreates the landscape and inhibits all the energy production your cells need, which is the oxidative phosphorylation activity to create ATP, which is your energy production. And then the virus could thrive better.

Afshin Beheshti: And this is why, let’s say, when you get COVID, you get the brain fog because the brain fog is related to the mitochondrial defense or you get cardiovascular issues or you feel tired, very tired and very, you can’t get out of bed and you get exercise intolerance. Again, your energy production is really heavily damaged. Downstream of that, then it impacts your immune system and all the other things you see that happen to many people. So similar, similar mitochondrial damage happens in space, same kind of activity. Now, people who don’t get long COVID luckily bounce back. The mitochondrial, even if you looked at the data, this is another paper we’re working on, hopefully to be submitted for publication in the next month or two.

Afshin Beheshti: But what we see is that people who recover from, don’t get long COVID, their mitochondrial is actually comes back to level, gets boosted back to normal signals, even maybe it gets a little higher than normal because it’s creating this per basically repairing all the mitochondrial damage done. Now, unfortunately, the people who have long COVID, they have their mitochondrial levels and the suppression that happened never recovers. Even a year after from the data we have in this paper we’re working on. And it’s same in the brain, from the animal models we looked at in the brain, indeed, like the certain regions that are involved with critical thinking or how you’re, how you could actually concentrate with this relates to your brain fog, that suppression like in the cerebellum, for example, that suppression happened of the mitochondrial signal.

Afshin Beheshti: Again, this is like a dissimilar profile that you see in space. Downstream of that, you get an increase of reactive oxygen species, you get more of a hypoxic or lack of oxygen in your cells produced by this, and then it causes cell death or dysfunctional immune system. So that parallel, although long COVID is caused by this virus doing it, space is caused by space radiation and then microgravity, the outcome is the same. In space, that happens a little quicker than the long COVID patients would do. So that’s an auxiliary model of diseases. So this is where now this conferral potentially could be a therapeutic for long COVID patients now, but now we have to get the funds applied to that and see, test it out. I could be wrong because as well as science, you have a good hypothesis, you test it. If it works, that’s wonderful. If it doesn’t, you admit it, you’re wrong.

Afshin Beheshti: You move on to the next step. But that’s what says this is how discovery is made, right? Not every discovery is going to be, not every hypothesis you’re going to be right. But that’s part of science. And then when you learn it’s not right, that helps the community too. So no one wastes money, wastes their effort and repeats the same mistakes or not mistakes, but the same wrong hypothesis.

Grant Belgard: Can you tell us about upcoming flight experiments you’re involved with?

Afshin Beheshti: Yeah, sure. So these are potential flight experiments, I should say. We’re applying for, there’s a company, Sierra Space, that does Dream Chaser and Dream Chaser kind of looks like the futuristic shuttle. It’s one of those, it’s going to basically not be a rocket that shoots up. It’s going to be, looks like a, and right now, currently, usually the payloads that go up is on a rocket, it goes up, right? And it comes back down as like a fireball and then it has a parachute comes up and lands either in the desert or in the water. This one actually is like the old shuttles, but now more futuristic looking. And it’s going to glide back into the atmosphere and land on the runway. So that’s the Dream Chaser, which is being built by Sierra Space. It’s supposed to launch, the first launch of it is in November or December sometime or October, some of them aren’t there.

Afshin Beheshti: So we potentially have opportunity to apply for this. And if they select the opportunity, then we could put, this is unmanned free-flyer missions that we could actually put some cells or some other types of experiments in there. So that’s one of the missions coming up that potentially we have access to. As I said, these commercial entities, NASA has their potential opportunities to get things in space, but then all these commercial companies make it, space is actually becoming more and more accessible to everyone because of all these great things companies do. I have some potential NASA grants in the, in the works or not in the works, but in the review process, which has potential flight to happen if they select it. But again, the grant process goes over peer review. If you get a good score, hopefully there’ll be funding and they select it. Then I could launch that.

Afshin Beheshti: So some of the experiments I do, would like to do with these future experiments is that one of them is can add killifish in space. So it sounds odd, you know, killifish. So this is with my collaborator, Jason Perdesky at Portland State University. So killifish are when they’re hatched normal fish, they’re, they’re just normal fish, not extremophiles. And, but when they’re embryos, they become extremophiles. And extremophiles are basically are a category of organisms that as the name sounds, it are, they’re extremely resistant to a lot of different things. And, and, and in this case could be radiation, could be heat, could be whatever else. And they’re creatures that have developed to live in some extreme environments, hence the name extremophiles.

Afshin Beheshti: So these killifish, they’re, they’re actually found in the Amazon riverbeds, African riverbeds, and nine months out of the year, approximately the riverbed dries out. So it’s basically, it’s like a mud, mud pie sitting in the rain scum that floods again. So the fish three months out of the year have hatched, they’re floating around. So now they have to survive. So they lay their eggs and now the eggs have to survive in that extreme environment without any water. There’s all that heat and everything else. So they’ve developed this evolutionary to become an extremophile. So they have the three stages in their embryos called diapod stages.

Afshin Beheshti: And the diapod’s two stages, their most resistant, resilient stage, and my collaborator who’s done experiments on these embryos and these fish, like they sell, like for example, gamma radiations, which is different from, as I mentioned, the space radiation, you could expose them to 50 gray of gamma radiation, which will kill us if you got exposed to that. So don’t get exposed, force yourself to 50 radiation. They actually start fine. The embryos are fine, they hatch fine. Oh, okay, great. 4% hydrogen peroxide, which would be really toxic to us and damaging to us. They’re fine. So then you could go on the list. They have lots of really neat extremophile properties. So the idea is now, why we’ve done similar experiments already, we already launched from the space a few months ago, rather than new missions to do that, is capture why these things are so resilient, right?

Afshin Beheshti: And so, so resistant. And can we adapt that into human cells and figure out the same mechanism? So we’re not going to create a half fish, half human, although that might be cool. But the key would be to figure out the pathways, the mechanism it’s developed this extreme resistance and apply that to them. So what some evidence and some links were already discovering, and Jason’s the one who’s really running this by, and we’ve done like some sequencing, exposed to space radiation or abandoned space, or, and some things we see that he mentioned is that in that diapause to extreme state of resistance, their metabolism mitochondrial basically shuts down to zero. Basically it’s an extreme hibernation state. And so why, why would that be important? So I mentioned that mitochondrial dysfunction happens, right? It’s suppressed in your space.

Afshin Beheshti: When that happens, you get reactive oxygen species, and a lot of reactive oxygen species in your cells are bad news because that’s creates, perpetuates more and more damage in your body. Wouldn’t be like, that’s what, that’s what would help create this like lack of hypoxic or lack of oxygen in your cells and tissues, which are downstream. It ramp up your glycolysis activity and your metabolism is screwed up in the immune. But if your metabolism is shut down to zero, mitochondria is shut down to zero, it can’t do create the metabolism species. So, okay. So then the cells basically is a dormant sitting there. You radiate them with like the space ratios. Sure. You still get the damage done. The DNA, when things get radiated, your DNA gets damaged, right? But then in your body, you have a bunch of DNA repair protein activity that comes in to try to repair all that damage done.

Afshin Beheshti: But when there’s reactive oxygen species and things like mitochondria dysfunction, there could be dysfunction in that repair to perpetuate damage and cause all the health risk. And this is the case here with the killifish. If that’s shut down, maybe it has just your body, their, their system to repair the DNA is fine. Goes on it and functions fine. And it survives that damage done. So that’s the part where I think that then you might think about using that kind of idea. Can we, let’s say that could be another type of countermeasure. Is it maybe a hibernation model or something? Like you see in the sci-fi movies, they go in the hibernation chamber and they sleep and I have colleagues who are like researching hibernation as a thing for space.

Afshin Beheshti: So that’s, could that be another physical type countermeasure you might do, or maybe adapt the cells to slow down the metabolism space or do something. So that’s, there’s been other types of research people have done to figure out how resilience can be adapted to human biology. Right. So this is another example of it. So that’s one example we’re trying to send these killifish in, other than the sounds neat, we’re sending killifish to space, but the other part is you could figure out what the resilience of how, why this is so resilient, adapted to human. And then not only would that help us in space, but it could also make us adapt to extreme environments on earth since unfortunately things are getting warmer, right? Climate change is happening. So maybe this could be another way of figuring out how humans could adapt to changes that are happening on earth if we see it.

Grant Belgard: That’s pretty cool. So you, you co-authored a nature perspective and what you actually called this the second space age. What distinguishes this from the first space age of the space age of Apollo and the ISS?

Afshin Beheshti: Yeah. No, good question. So few things. So the first space age was really a two state, literally a two state problem is the USSR and United States. That was it. So the amount of things being launched in space was limited because the technology wasn’t there. And then also limited resources because of just that. And of course it’s just a base war between two countries that happened. So the advancements made were based on the, say, if this person did this, oh, this country did this. We’re going to do this in response. And then of course the US did well. They went on the moon first, right. And then the [Sputnik?] program, the USSR did well too. They had, but they’re US, of course, as we know the history. But the second space age now in that paper, you can see this little chart.

Afshin Beheshti: And as you can imagine, what happens, there’s been a kind of a steady line of things from the beginning, from the Apollo mission in the late fifties and go on Apollo mission in the sixties. But the first things lost to space in the late fifties until about 10 years ago, it’s been a steady state of things being launched in space. A steady state amount of things from satellites to manned missions and so on. But in the past 10 years, all of a sudden there’s this huge explosion, like an exponential increase, even bigger exponential where, you know, a thousand fold increase of things being lost in space.

Afshin Beheshti: Because now not only it’s not just two countries, it’s lots of countries above now, like all those European countries or the European space agency, for example, every country in Europe has their own space agencies, Japanese space agencies, the Indian space agency, just go on the list, it’s Australian, so I don’t want to leave out anyone to get mad at me, but everyone has, every country has this space agency, whether some are more active than others, right? If some have more resources than others, but everyone’s involved now because they see the benefits of how space can help humanity. So that’s one thing. So then more things gets launched in space because now more countries are launching things in space that way. In addition to countries, government agencies, now you’ve got all these commercial companies, right?

Afshin Beheshti: I mentioned earlier, obviously everyone knows SpaceX, that’s always in the news, because they’re launching things consistently. But there’s all these other ones, like Axiom Space, for example, is building, the International Space Station has been up there for now, 25 years, I think, is it? And the government could decommission by, I think, estimated by 2030, something like that. But these room, maybe some commercial companies can use what’s there. So Axiom Space is building a module for a new space station there, and then, see the ISIS decommission by the government, they could maybe some of the orders they could detach and be their own space station now. Vast Space is another one that they’re launching. They’re going to launch the first commercial space station up. I think they’re doing very well for themselves and they’re beating everyone.

Afshin Beheshti: And they’re going to have their own space station up now in the next year, supposedly tentative what they’ve been planning. And there’s all these space labs, you just go down, I think there’s at least four or five space companies planned to run space stations up in low Earth orbit. And then there’s other plans for other countries to get together. Gateway was kind of a joint, ESA, NASA, and other, JAXA and other government agencies to be a space station in deep space closer to the moon, as it sounds like. Now, I don’t know if NASA is still part of this approach, but ESA still would be probably going full force with all the European space agencies that are in JAXA and so on. The eventual goal is to have a moon base again, before people are going to start thinking how to do research there. And of course, if we want to go to Mars, right now it’s probably a one-way ticket.

Afshin Beheshti: So maybe the people who are really advocates, send them there. It’s okay. They could be our guinea pigs, a few people in mind, but that’s okay. Eventually, then once it’s safe, then everyone can do that long year trip. But you have to do the baby steps. You have to figure out the condiment. So this is why it’s the second space age, because you got not only just two countries, you got this huge explosion of things being lost in space currently. And then it’s even more things planned to go. As I said, some agencies are planning to do a space hotels or space tourism. Although I would say maybe if you’re up in space for three, four days, that might be okay. You’re still, from some of our work we did in that nature package, that looked like 95% of signals were coming back to normal. But there’s still 5% of signals that don’t, which one of them was a mitochondria.

Afshin Beheshti: So still, even though three days in space, a lot of things come back to normal space, there’s still 5%. Those 5% could be pretty detrimental for you if it’s your mitochondria. I don’t know if I would recommend space tourism just yet, but by the time, let’s say, these space hotels are made, maybe we’ll have the cocktails to make it safer. Do I want to go to Hawaii or do I want to go to space? So that might be the discussion you have with your family in five years. So that’s why it’s the second space age, because it’s the explosion of everyone just watching things in space and wanting people to go.

Grant Belgard: Has anyone looked at frequent flyers? Obviously they’re getting as irradiated as you would in space, but do they accumulate mitochondrial deficits as well?

Afshin Beheshti: I haven’t looked at that. That’s a good question. I haven’t asked that before too. Oh yeah, the frequent flyers. Yeah, you’re getting closer to, of course there’s an atmosphere. I travel a lot, so I’m probably a frequent flyer. And the thing is though, the ozone layer protects you from the galactic cosmic rays. So it’s a different kinds of radiation that you’re impacted then from these. But nonetheless, you do get a higher dose of radiation than non-frequent flyers, right? Although, granted, it’s very low. So my dad was a commercial pilot, so obviously he was a frequent flyer. So there are higher incidents of cancer risks in commercial pilots. Now, is it because of the radiation? I think there’s colon cancer is one. But also one of the things is that the pilots are sitting on the radar without shielding. So if people sit on radar, there’s a cancer risk normally with that.

Afshin Beheshti: But if you’re over your entire career of being a pilot, you sit on it constantly can that contribute to cancer risk? I don’t know. Research has to be done for that. Maybe, maybe not. Again, and for the frequent flyers, I don’t think they should panic that they’re going to get an increase of cancer risk or health risk because the research is either way at this point because no one has done that research. So the short answer to your question is no. No one has really conclusively said, are you going to get increased health risk if you try? That’s a good question to actually explore because that could be another avenue of increased health risk due to maybe more exposure to radiation at that level. And then the next step is now you go to space and get the bigger dose and more damaging radiation.

Grant Belgard: Can you walk us through your career? Tell us how did you end up here?

Afshin Beheshti: Yeah, so I don’t have a, my career is not a straight path. So some people who go into science, they studied some factor in the graduate school and then did that for the postdoc. And then they keep going that career path, that trajectory. Mine’s kind of been all over the place. I started one place and then randomly jumped to somewhere else. I started in undergrad. I got a, I’m a physicist, so I got a bachelor’s degree in high energy. I was looking at high energy physics. So high energy physics is basically when people go into high energy physics, smash particles together. And it’s basically trying to figure out, the basic question is to figure out the fundamentals of life, universe, and everything. And I guess Douglas Adam would say it’s 42, but I don’t know if his high energy physicists will agree with that.

Afshin Beheshti: So then in the graduate school, I switched to, I’m going to put biophysics in quotes because I was looking at, now I was trying to get closer to biology. I was still a physicist. So my PhD was looking at how DNA moves through objects and look at, I didn’t even care about the biology of DNA, but I want to know how they move. As a physicist, we model things. So I was modeling how things, DNA moves, stretches, gets through different networks, because it’s going to answer different kinds of questions about how your body can function or how drugs can behave, things like that. So I did that. And then at one point, I was getting close to the end of my PhD to figure out the next steps to do a postdoc. I said, well, I want to do more things that are clinically and human relevant. This is getting there. So I only took one biology course my entire, the freshman biology course, that was it.

Afshin Beheshti: But I said, you get your PhD, it trains you to think. We all know how to read at that point, hopefully. Maybe some don’t. But at that point, you know how to read and think. And the main thing in graduate school, I think I’ll tell you, you learn your critical thinking. And half the things you learn in biology are wrong 10 years from now, too. It’s not like physics, you get the fundamentals of gravity and the fundamental forces, right? In biology, and this is the nature of biology, it’s just so complex. We learn some things and then some new discovery comes along. Oh, that’s the true mechanism behind it. So they’re almost right. But now it’s a ball to this part, right? So that’s then when I switched to a microbiology lab. I moved to Boston and worked at a place called Foresight Institute, which they concentrate on oral microbiology.

Afshin Beheshti: Now, why they, why the principal investigators, the people in the lab hired me as a postdoc is because the techniques I had, I just go about looking at how DNA moves and separates, apply it to what they want to do with the microbial work. And then after that postdoc, I did another postdoc where I joined the cancer systems biology. [Under?] actually the director. Her name is Lynn Halaki. She was a physicist by training, too. So she understood, oh, the physics is mine. And I joined the cancer systems biology. But systems biology at the time was a newer term. And what that means is biologists have been trying to solve things by their own cancer and things like that for centuries or not, well, decades, we’ll say decades, right? But for centuries, but decades. So in that case, they haven’t really solved the ideas that you haven’t solved as much on your own.

Afshin Beheshti: So and the goal is really to solve complex diseases. You need a multidisciplinary. You just don’t need biologists. You need mathematicians. You need physicists. You need biologists. You need computer folks and computer scientists. And so you go down the list. And once you get all these different ways of thinking together, this is where you might come up with the new discoveries and use all the different tools from the different fields to actually really tackle like a complex thing like cancer, for example. So that’s that’s the system biology. And it’s a top down approach thing. The biologists who look at mechanism, they’ll start the nitty gritty like molecule, how that helps, which is important. But they also need the top down approach. How do you connect all the different nitty gritty details that are there? So that’s where I joined the cancer system biology lab.

Afshin Beheshti: And we’re looking at cancer. I was doing a lot of wet lab work and also computation works, physicists can do work. And then she had a large NASA grant that got us out of the space field. She had NASA grants, started working on NASA work and cancer work and eventually ended up at Tufts Medical Center where I was working on some more on cancer. But then one of my colleagues and friends joined NASA Ames Research Center in Silicon Valley area where they’re starting to develop this tool called GeneLab, which is a platform available for free for everyone to use in the public and the world. And this is where all like the big data, the omics data, which is the bioinformatics sequencing data ends up free and it’s deposited the one resource that the whole world can use. So my colleague’s name is Celan Koss who was the project manager for this. Now it’s called the NASA Open Science Data Repository.

Afshin Beheshti: And there that whole platform was there for the public to use. And now it’s a great resource. So there I joined NASA Ames Research Center and eventually start helping with that because I’ve been a lot of space research now. And then eventually I got my own grants in the past few years of NASA Ames Research Center working on topics of the mitochondria research or also other things like microRNAs, trying to figure out how to make a safe basically for humans travel. And then this is how I ended up at Pittsburgh. Was that about a year before I joined, I was at a meeting, there was some data being presented and then meet some people here and they started recruiting me here because they said, oh, what I’m doing can be applied to that. Just starting the Center for Space Biomedicine, a lot of different things like I mentioned earlier, everything I do applies to many different fields.

Afshin Beheshti: So cancer, COVID, trauma, things like that. So eventually that’s how I ended up now in the Center of Space Biomedicine, but also still working on all the different fields out there because a lot of things you do is plug and play.

Grant Belgard: So what do you think are the key skill sets that tomorrow’s space biomedicine scientists will need?

Afshin Beheshti: I think it’s multiple things. One is there’s still a lot of unknowns in space, right? So that’s what makes space biomedicine really fun because there’s always novel things to discover so far. So one of the key things I always say in science in general, not just space, but space always works is that, yeah, don’t lose your inner child. That’s keep your inner child. So I think the more creative you are, which kids are very creative and imaginative, right? And so that’s the key. I think in space more than others fields, maybe keeping that inner child and creativity is a key because you have to come up with a lot of out of the box thinking. Sometimes it’s design experiments. How do you do it in space? Because when you go on your bench here on earth, you could pipette, you could do this, or you can set up a cell. Now we don’t have any gravity. How do you do that same experiment?

Afshin Beheshti: So that’s the part. Creativity is key, not just design experiments, but also coming up with novel questions to ask. The other part is having, I think in general in science, not just spaces, you could be a wet lab bench scientist, but having the key computational skills is key because now there’s a lot of computational algorithms, AI tools, ML tools, machine learning tools, bioinformatics, the whole sequencing data. This is all integrated now. It’s a lot of times people just focus on be that and computational biologists, and then they collaborate the ones, but understanding the language between the two is key because sometimes they might not, the competition biologists might not fully understand the biology and the wet lab biologists might not fully understand how the computation biology is done.

Afshin Beheshti: So having the inter cross-lingual language, diverse computation and wet lab is key for them to have. And I think that’s a true success for the scientists to have. And the space biomedicine, of course, you have to understand radiation biology because that’s one big thing that happened in space, having understanding how micro impacts and just really understanding the differences between space and earth. But in general, I think any kind of disease focus you have can be applied to space, but understanding the fundamentals there for space is key. And also just having the, if you’re open-minded and want to work on many different subjects, space might be the thing for you because all the different health risks out there is really key. And solving that is like putting the jigsaw together, puzzle systemically, why are these things dysfunctional?

Afshin Beheshti: Maybe there’s one key thing connecting things together like mitochondria.

Grant Belgard: What do you think is the most underappreciated health risk for a Mars mission?

Afshin Beheshti: One that is right now is in the space biomedicine field, most everyone knows what this is called SANS, space-associated neuro-ocular syndrome. But non-space people might not know what that’s saying, non-space biomedicine people. SANS is a space neuro-ocular syndrome. And it’s the case where some astronauts, not all, but some lose their vision or not lose their vision, but they have vision decline. So no one like in the ISS, the International Space Station has lost their vision. But what happens is that they might cut, their vision slowly gets worse and worse. And they come back to Earth. Some of them who didn’t wear glasses, they’re now wearing glasses. Again, it doesn’t happen to everyone. So that’s what classically is thought of.

Afshin Beheshti: Maybe since of the gravity, you get the flattening of the deme, you get like pressure changes, fluid shifts that happen that can contribute to the vision loss. I think it’s mitochondria because I’m a mitochondriac. That’s what we call ourselves when everything’s mitochondria. So because in the mitochondria, there are diseases that due to mitochondrial mutations, patients would lose their vision, the kids would lose. But we’re showing that it could be, but it could be a combination of mitochondria. So I think that’s one, if you’re going to Mars and no one’s been in that deep space condition outside the Earth’s magnetic field, that reduces the dose due to the physics. But going to Mars is about a year, year and a half trip, round trip. So no one’s really done that.

Afshin Beheshti: So if you’re doing that, what happens if your vision declines to a point when you want to be blind by the time you get there or in the middle of it, that’s bad news. So I think that might be one. I think also one of my colleagues, Keith Seward, he’s at University College London. He’s looking at kidney effects. He published a really good paper in that Nature Package looking at comprehensively what happens to your kidney. And he’s finding, yes, health risks related to kidney, the renal tubes in your kidney start collapsing and other things. But more importantly, he thinks there’s going to be a potential risk of kidney stones. So imagine if you’re halfway to Mars and you get a kidney stone out in space, how do you resolve that problem? That’s going to be a huge issue. You do it on Earth, that’s a problem, right? On Earth, when you get a kidney stone, that’s a hard thing to deal with.

Afshin Beheshti: But in space, how does that happen? We could keep going. But I think some of the more interesting things are that I would say all of this is probably heavily related to mitochondria. So that would be the maybe the central focus of a health risk, meaning mitochondrial diseases or like that. So maybe mitochondrial disease would be my number one pick for people at Target because it could maybe impact a lot of these health risks and improve conditions.

Grant Belgard: Mitochondrial deficits are obviously a big area of overlap between what you’ve been discussing, the longevity space. What are other areas of overlap between space biomedicine and longevity research?

Afshin Beheshti: Yeah, there’s a lot. So I don’t research this. I have colleagues and collaborators who do. My collaborators, Susan Bailey and Chris Mason, they’ve looked at telomere length. So, you know, the telomeres that kept the chromosome and as you grow older, they get shorter and shorter. Right. So then that means decline of your aging that happens and that could be the health risk. So interestingly, in space, what happened is when this was first, they did this study The twins study Scott Kelly, who went to space for a year, and his identical twin, Mark Kelly, was on Earth. And now Mark Kelly is a senator, of course. So the idea is comparing genetically identical twins, what might change, what not, you know, what changed. But what they saw was that with Scott Kelly, your telomeres actually got longer. So people were like, what, did he get younger in space? It came back.

Afshin Beheshti: He actually, what happened was it got, it got to the normal length, but then it got shorter than it should have been. So that means it made me, because aging got a little excited. But when he was in space, it got longer. And it’s been told that also he lost weight and he got taller. So it was like, oh, great, a great diet plan. But fortunately, when he came back to space, the telomere thing that happened, and then luckily, well, luckily for science and the reproductions, the NF1, what they’ve done is they’ve looked at telomere links, how they are for other astronauts and other cohorts, like other 10 and more astronauts, and they see the same pattern happen.

Afshin Beheshti: So they have some ideas, maybe potentially this could be like some potential other factors like these non-coding RNAs that could be involved that could be causing this or other factors that, and it’s not a sign that you’re growing younger, it might be a sign that it’s causing damage to your chromosomes and your telomeres because of the environment you’re in. And this could maybe contribute to potential, not as long, for longevity, it could maybe reduce it. So how do we stop that impact? For other longevity kind of type of work, one of the area focus I work on is microRNAs I mentioned earlier. MicroRNAs, the Nobel Prize was won on that last year, people had discovered it. And microRNAs are basically small RNA that’s 22 nucleotides. And before the people discovered won the Nobel Prize, people thought they were just deprived because of the size. They thought, oh, it’s RNA fragments.

Afshin Beheshti: These are not important. So one person’s garbage turned into this Nobel Prize, this huge thing. This is a lesson for people in science and in general, don’t discard things that seem like garbage because they become very important, at least in the scientific world. But anyway, these microRNAs, as I said earlier, they could bind to genes because of this one region called the seeding region. And again, there’s good microRNAs that bind to genes that would cause detrimental impact on your body and accelerate aging and health risks. But then there’s microRNAs that diseases like cancer produce that would bind to tumor suppressor genes for that. Or an aging, there’s a whole set of microRNAs related that are expressed as you get older and older that start binding to genes that would make you age faster, would decline the mitochondria, would decline your immune function.

Afshin Beheshti: This is people have studied this in aging that show only these microRNAs are both there. So my work, I’ve identified certain set of microRNAs that might be related with what happened in space. And then I said, what happens if I inhibit these microRNAs in like mouse models, 3D organ models, human tissues in the chip? And what happens if I bind a set of microRNAs with cardiovascular risk that with aging also occurs? And indeed, when you stop that, the set of microRNAs I identified that would be involved, increase the risk of cardiovascular risk in space, you stop those microRNAs and mitigated the damage done. So that could also then translate to longevity because I think about all these microRNAs that if you inhibit.

Afshin Beheshti: But the key with microRNAs are tricky because some of these microRNAs that are being increased due to the damage, there’s a basal level in your body that microRNAs should exist. So if you inhibit them too much, now you’re going to cause the side effects, detrimental effects that you’re going to not necessarily make you for aging is involved, but it’s going to for your health. It’s important. So this is where the tricky balance is. So this is where you have to figure out exactly what the important microRNAs are, where to inhibit it, how much to inhibit it. And then this could potentially be a way not only to prevent space damage done, but also reduce. It may not make you age as fast, right? But there are people working on microRNAs as a clinical therapeutic on Earth.

Afshin Beheshti: But the issue is there has been no FDA-approved inhibitor for a microRNA because I think sometimes they’re inhibiting the wrong microRNAs involved or sometimes they’re inhibiting the wrong group of microRNAs or they’re inhibiting it too much. So eventually I think someone’s going to come up with a good microRNA therapeutic, but that’s not happened yet. But that’s another example of space research longevity.

Grant Belgard: To wrap us up, what in the space biomedicine field are you most excited about?

Afshin Beheshti: The chance that you and I get to go to space. My wife says I have to get good life insurance before I go to space. So currently I agree that right now you probably should get good life insurance. But that’s the key. Like, I think just going to space right now, people think, oh, we’re here on Earth, why do it? Well, the reason we could do it is because it’s not only the fact that we can, humans want to do things that we can, the fact of how do we push humans forward, but the advancements in science that we can make, all the great things that can be achieved that a space exploration can do. I think that’s exciting. And the chances that it’s getting cheaper and cheaper to do it and maybe more safer and safer once we figure out the cocktail of pills you could take or cocktail or hibernation to prevent that, then the unknown universe is at our disposal, kind of like we’ve seen Star Trek.

Afshin Beheshti: I think that’s what everyone wants in space. Of course, we’re all sci-fi fans. So I think that’s the ultimate goal. The excitement of going past where we are at and expanding humans to new boundaries, that’s I think really good. And then also the excitement of if we are able to make it safer, we’re able to maybe cure a lot of diseases on Earth, too. I think that’s the other part that’s very exciting for me because ultimately we got to help humanity and I think this is a key space.

Grant Belgard: That is a great, optimistic way to end. Thank you so much for joining us.

Afshin Beheshti: Thanks for having me. It’s been fun.

Transcript of Episode 63: Kenny Workman

Disclaimer: Transcripts are automated and may contain errors.

Grant Belgard: Welcome to the Bioinformatics CRO Podcast. I’m Grant Belgard, and joining me today is Kenny Workman, co-founder and chief technology officer of LatchBio, the data infrastructure making large streams of raw molecular data human interpretable for computational biologists and bench scientists alike. Since we last spoke with his co-founder Alfredo in 2022, Latch has started working with the solution providers developing and distributing new technologies to measure molecules, partnering with spatial omics and single-cell innovators to bring analyses straight to the browser. Kenny studied electrical engineering, computer science, and bioengineering at UC Berkeley before jumping headfirst into biotech entrepreneurship.

Grant Belgard: Today, we’ll explore what’s new at Latch, what drew Kenny to build in this space so early in his career, and the advice he’d give to the next generation of computational biologists. Kenny, welcome.

Kenny Workman: Thank you so much for having me.

Grant Belgard: So for listeners who remember Alfredo’s episode, give us the what’s changed at Latch since 2022 elevator pitch.

Kenny Workman: Yeah, I think the most profound shift has been that away from biotechs into the folks directly generating data. So the solution providers measuring molecules, concretely, these are single-cell spatial providers and working with them directly rather than the original biotech folks.

Grant Belgard: What’s the single biggest misconception newcomers still have about Latch?

Kenny Workman: Oh, it’s a great question. I think biotech data analysis is really more of a project management and human orchestration project than a single point analysis solution. And, you know, what I mean by that is the practical problems are not running a workflow and getting an output. They’re really like, how do you coordinate diverse groups of chemists, immunologists, software engineers, computational biologists to come together and analyze data and speak the same language. And so what that means in practice is the data infrastructure’s, you know, true role is to capture and expose data over a long timeline to people with different levels of computational fluency, and not necessarily like run a workflow.

Kenny Workman: So yeah, I think a lot of the misconceptions are around Latch or similar types of products or technologies being workflow orchestrators, rather than a kind of like more complex and long living thing that brings people together to understand science.

Grant Belgard: If you met someone at a conference and had only 30 seconds, how would you describe your own role at Latch?

Kenny Workman: That’s a great question. I primarily work on engineering a product. I would say that the core of what I do is synthesizing information from customers, you know, scientists and engineers, and trying to figure out how to improve the product and move it in a direction that has the highest probability of impact on the pace of science.

Grant Belgard: And what’s a picture of a typical customer, size of the team, data types, pain points.

Kenny Workman: Yeah. So, you know, within the solution provider buckets, there’s a variety of very interesting emerging technologies. And this is actually one of the super exciting stories of modern biotech is that of increasing data generation on a variety of like different molecular types. So you can look at facial biology, and within that you have several sub-trees, you have imaging-based spatial, sequencing-based spatial, which differ in how they actually read out the molecular information. And within each of those buckets, you have a unique group of customer that Latch works with. And the reason I make this separation is like the actual details of how they use the product, how they distribute data to their customer, you know, it looks quite different depending on like how they’re actually capturing molecular information. A great example of a sequencing-based spatial provider is AtlasXomics.

Kenny Workman: They’ve developed this technology called DBiT-seq, so deterministic barcoding in tissue, where they basically place a chunk of tissue on a purpose-built microfluidic chip and flow barcodes from the X and Y direction over the tissue and resolve them computationally post-fact to understand or basically associate sequencing information with spatial information on the tissue. And so, you know, working with folks like AtlasXomics, their primary concerns are how do we get the raw sequencing information and imaging information from the tissue processed first into a state that’s immediately human interpretable? And then how do we like layer in rich tertiary analysis? So allowing scientists to pan, zoom, manipulate, do things like look at spot A and spot B on the tissue and, you know, look at differential genes and cell types that emerge between those two spots.

Kenny Workman: So it really depends on like the type of customer we work with. And that type is kind of induced by the technology type that the solution provider is. But, you know, AtlasXomics could serve as a great flagship example of like the type of solution provider we do spend time with.

Grant Belgard: What problem keeps you up at night?

Kenny Workman: Yeah. I think, you know, the pace of technology adoption within the biotech industry and, you know, we’re in a tough spot right now in terms of the funding environment, the number of companies trading below cash. You know, more recently, there’s the narrative of the cannibalization of market share by Chinese companies, clinical market share, that is. So the amount of like in licensing behavior, that proportion of that, that is taken up by China versus American biotechs, et cetera. And. Well, especially the delta on that, right. Kind of the rapidity with which that that’s increased versus an all zero baseline a few years ago. Like the rate at which that, that trend is occurring.

Kenny Workman: I forget the exact numbers here, but I think we’re at about a third to maybe as much of the half as to which phase of one, one and two assets can be attributed to Chinese R&D versus, you know, a decade ago is as low as I think sub 10%. Um, so yeah, totally the rates of growth there is what’s staggering. It’s that derivative rather than the absolute number. And, you know, I think a big reason for this is the pace of innovation, the like drug mechanism level. And a lot of that is due to it. We’re still not using the best methods to generate data and go after like new targets or disease mechanisms at a level that perhaps exceeds, you know, human context and still leaning on old guard, rational techniques to develop drugs. So I, it was like very vague hand wavy claims, but, uh, I think at the pace of adoption of, you know, new technologies that really let you measure more molecules.

Kenny Workman: And then the second order effect of data generation is, is really adopting data driven discovery as a result of like having more volume that like lets you discover things that you cannot have interpreted manually. So, so that is one, one thing I think is interesting and, uh, detrimental for the industry.

Grant Belgard: So Latch now claims to offer a single pane of glass for data compute and visualization. What does that look like when the user logs in?

Kenny Workman: Yeah. Um, what that looks like is, you know, biotechs are very different from each other. When you look at the types of experiments they’re running, the team structure, the communication style between those teams, you end up with these like very complex organizations that are in many ways, snowflakes. So building a generalizable data life cycle, uh, that like can be plugged into different teams, given the, the amount of those variabilities, it’s quite a challenging task. Like what we’ve arrived at, I think is, is pretty durable. Like, uh, there’s a high, you know, a high chance that it will work with all these factors. And so concretely, uh, when you get data from an experiment, you know, you can, you can trace out its life cycle. Step one would be, you, you put it in what we call Latch Data, which is a, the Blob-Store-backed distributed file system.

Kenny Workman: And that has graphical accession as well as like programmatic ways to, you know, read out files and do these sorts of things. And you’ll see the theme of having both graphical and code-based controls for each step of the data life cycle is, uh, something we worked heavily on because one of the grand challenges in biotech data analysis, as much as like, like I mentioned earlier, getting the result is, is coordinating diverse groups with these different backgrounds and different fluencies with, with computational tools. So that first step is just getting data, putting it in a Blob-Store-backed distributed file system called Latch Data. The next step is usually, Hey, we usually, we want to, uh, run batch compute.

Kenny Workman: So we have some raw molecular data in its raw form, completely unusable, but we need to turn it into something that’s a little more human interpretable, or at least fit for down to tertiary analysis. And this is where, um, workflows come in. And so bioinformatics workflows at this point are, are in many ways, like a solved problem, but you still need infrastructure, especially at, with scale of data to manage their, their orchestration.

Kenny Workman: So taking those raw files, fanning them out on lots of computers, keeping track of, you know, the containers that are run on those computers, the logs, the versions of the workflows, um, making sure that you have all this information audited and stored for long-term use, especially, you know, if you’re a biotech with an eye for the clinic, which many people are, the third step after you process, you know, information in a batch well-defined way is usually storing it with contextual metadata from the lab, you know, structuring the raw outputs with information about the cell line that was used to produce this data, the temperature in the lab. Um, you could perhaps the name of the, the tech who, who ran the experiment, and these just look like tables.

Kenny Workman: But the fourth step is usually some sort of, um, ad hoc sandbox compute for folks downstream who are interested in running whatever code they want to analyze the data. Um, but often the problem here is, is access to large resources and the, the outputs of the workflows previously. So you can imagine like if you spin up a naked machine, having that original file system mounted to the machine. So you have full access to all the information your team generated is very important and that’s a hard problem, as well as making sure that machine is large enough for your task at hand. And then potentially as the cadence of your analysis goes up and down, shifting those resources with it to making the machine smaller and making the machine larger, um, as your work progresses, the fourth step, we call that Latch Pods. And the fifth step is dashboarding.

Kenny Workman: You need to build visualizations and serve information scientists. So we built a dashboarding framework, um, which is the result of a nearly a year and a half of work. Uh, it’s a reactive Python kernel with widgets that can be specified in Python, as well as the ability to put count matrix based file formats on very large computers to serve information to scientists. So that was a very long winded explanation, but in essence, there’s five steps. There’s data storage, there’s workflows, there’s registry or tables, contextual metadata. There’s ad hoc compute, uh, sandbox compute called Pods, and there’s dashboarding or plot generation called Plots. And over four years, we’ve really added to and extended the platform so that these five components are a pretty good, uh, way to tackle this, this problem of like high variance between analysis life cycles and biotech.

Kenny Workman: They usually work for most companies.

Grant Belgard: Which features did you remove or sunset because they weren’t working?

Kenny Workman: Yeah. Um, it’s a good question. I think very early on, we tried to build a lot of, not a lot of, we, we tried to build some assay specific features before our teams really had a good understanding of the, the real problems with those assays. And we’ve since come full circle. And I think given the amount of years we’ve been in the industry and working with scientists on these problems, come to understand and re-tackle those problems. Very concrete example here is with single cell. You know, single cell analysis, many listeners are probably very familiar with both the biological goal and then the outputs of the data. You’re usually working with count matrices. We know some, some N number of observations and M number of genes. So you end up with like N by M matrix with all sorts of metadata about both the genes, um, more importantly, the cells, things like cell type, disease and tissue.

Kenny Workman: Often scientists need to run a series of pretty data intensive count transformation steps and shove this high dimensional matrix object into basically a two dimensional embedding and then do a whole host of essentially artisanal ad hoc operations to subset, lasso, annotate this point cloud to drive biological meaning from the data. And it, it, this is still quite a hard problem primarily because the footprint of this object is a very large and the ecosystem of tools, you know, while there’s been a lot of progress made is still primarily active open source, um, driven project. So we two, two or three years ago, tried to build a browser that was very similar to cell X gene, which is a very popular browser built by the chance of biohub. And we actually kind of fell flat because we missed the mark of what was important.

Kenny Workman: And more recently, especially with a lot of these, those operations overlapping with spatial retackle this problem. And I think, you know, did things, uh, more correctly. So that’s perhaps a big example of how we taken a stab at a problem, sunsetted it because we realized, you know, we weren’t approaching it correctly and people were really taken to it and coming back to it as, you know, new solution providers are generating, um, a lot more data and having demand for these, these sorts of visualizations.

Grant Belgard: So you’ve expanded from next gen sequencing to imaging and mass spectrometry. How did you pick those verticals?

Kenny Workman: Yeah. Um, we do less with mass. We do a lot, increasingly more with imaging, especially imaging based spatial. The, the primary kind of like selection mechanism we use to identify good customers is how much, how constrained are they likely to be by true computational bottlenecks? And you know, what I mean by this is I’m sure listeners can relate is until recently, there have been very few problems that are truly constrained by a software computers in an end to end experimental cycle at R&D. So, you know, you do have examples of, you know, these single cell assays or like more traditional NGS assays requiring two or three days to process raw data into a more human readable state with computers.

Kenny Workman: But you take those two or three days and you compare it to like the two to three months, six months, sometimes of like an end to end planning the experiment, getting resources that are zero in order, running it in vivo study, getting readouts, you know, playing tag with all these vendors and like kind of organizing resources in the real world, those two to three days pale in comparison to that, to that timeframe. However, you know, with new spatial assays, especially as the, the way that they’re distributing them and packaging them and making kits testable and there’s a direct market incentive to decrease hands on time to run these kits. All the while the data coming from them is truly increasing at a scale that I think many people don’t appreciate. Um, these new spatial assays actually have some concrete delay, um, sometimes weeks to months from data itself and from computation itself.

Kenny Workman: So we tend to look for experimental outputs of that type. And that, that is like usually the heavy approach that we do. And so, and sorry to that point, a mass spec tends not to be one of those. Um, whereas like a lot of the spatial based assays tend to be one of those.

Grant Belgard: So where, where does Latch draw the line between no code and code; do advanced users ever hit a wall?

Kenny Workman: Yeah, we, uh, this is actually maybe another misconception about the platform is we’re very code first and pretty much any aspect of the platform, any feature on the platform has both code and no code tools. And we find this necessary kind of given what I introduced earlier, which is at any given point, a piece of data needs to be interpreted by different folks with different scientific backgrounds. Um, and then, you know, most, you know, some of those folks are going to want to load it into their favorite or a Python library and like play around with a little bit. Some folks are going to want to play with it in the command line. Some folks are going to need click controls and graphical components. So we really don’t build for one or the other. Uh, it’s, it’s very much a hybrid endeavor.

Kenny Workman: And that has been one of the more interesting and challenging aspects of building out the product is like, Hey, anything you, you build needs to speak to not only code and no code cohorts, but kind of like hard mode code, easy mode code, and no code. And whatever you buy hard mode and easy mode is like you tend to have, um, computational biologists who are more of the data scientists flavor who use their domain knowledge and background and like G stat to like write basic code, but they’re not familiar in like a naked Linux environment. And so, yeah, like even you can even stratify code, code tools into, uh, those two, two pockets as well.

Grant Belgard: So walk us through your AI protein engineering launch. Uh, what problems did that solve?

Kenny Workman: Yeah. Um, that was interesting. So, and it, I would say we even do less of this now, but you’re having something of an explosion and new models for variety of molecular prediction tasks. Yep. Post maybe unnecessary long-winded history, but obviously post AlphaFold 1, AlphaFold 2, as people realize a, you know, the scaling of a lot of, basically the base transformer architecture with basic modifications that are fit for the domain, adding things like multiple sequence alignment, et cetera, actually have a quite strong performance and a variety of like concrete, concrete things that are useful in drug discovery. You know, more recently, we’ve seen models like Boltz, Boltz-2 go all the way from, you know, protein structure prediction to a protein ligand binding with comparable accuracy or efficiency to, um, that original problem.

Kenny Workman: And so it’s feeling clear that these, that this approach to training very large models, lots of parameters, applying some expertise in the domain to tweak the architecture and tweak the training process is extending to lots of different practical tasks. The, the same problem of like, Hey, I’m a scientist. I have a lot of domain knowledge and running experiments in the lab, but I’m unable to readily access these amazing new methods, um, can be extended to running a model just as much as it applied to running a bioinformatics workflow. Um, so we definitely mostly by popular demand customers, uh, just started uploading the models that they asked for. So they just had to know, but I will say at least with our customer cohort, these are kind of like nice to have or, or just like fun things that they’re trying out.

Kenny Workman: And very few of them are using them to like actually drive experimental campaigns. So yeah, like they’re, they’re not the source. You can, you can think of like early pre-R&D, what you’d call campaign is like hit discovery, hit [?] maturation, um, these well-defined steps. And at any point within any one of those steps, we didn’t really see the, the driver of new targets or new external structure from like model outputs. It was more like folks getting their feet wet and seeing if it’s something they wanted to use.

Grant Belgard: So the platform’s now HIPAA and SOC 2 Type 2 compliant. What surprised you the most about that journey?

Kenny Workman: Yeah. Getting compliant is important, but it’s super difficult. Yeah. It was a multi-year endeavor. Um, we have absolute beast compliance team led by actually someone that used to work under Obama in the White House. So assembling that team has been fun, but the process is no joke. These compliances exist for a reason. And it was, it was just very rigorous and difficult. Something that was needed as you start, you know, traveling up market and working with more, more and more mature companies.

Grant Belgard: What roughly speaking, what percentage of your revenue come from sales that, that hinge on being, offering compliance with these frameworks?

Kenny Workman: I would say it comes up like 80% of the time, um, in like the evaluation process of a sale. And so, I mean, it’s, it’s, I would say like in the vast majority of those cases, it’s like a need to have, so absolutely an essential thing, you know, for other folks, like considering building data software product in the space. Yeah. Very, very ubiquitous.

Grant Belgard: Not naming names, but a few of your competitors that, that were created around, uh, the time Latch was created, uh, have gone out of business. Why are you guys still around? What, what makes you different?

Kenny Workman: That’s a great question. I think we approach company building as an engineering problem. And what I mean by this is we’ve never had some, you know, deep prescriptive, visionary insights and how people should do things. From the beginning, we’ve always been very intentional about finding deep need, collecting information, doing research, talking to users, talking to customers and always growing in proportion with like the value that we’re creating in the market and aggressively, you know, raising capital and like hiring brilliant people to work with us, but always in step with, um, you know, where we were at the time. I don’t think we ever grew or overextended, um, or, you know, did, did silly things with resource allocation for the most part. I mean, we definitely made our share of mistakes, but none of them were, um, existential.

Kenny Workman: And so I think that the biggest thing here is like, we’re currently a team of under 20 people, mostly engineers, and we’re doing, you know, seven digit revenue. Like it, there’s many companies, I think that were at that stage and probably grew a little bit too fast. And so that like data-driven approach and like careful, slow, you know, and as we, as we grow, we’ll ramp accordingly, but that approach is something we’ve taken. I’d also say like, we focus a lot, maybe to an extent that’s underappreciated on engineering and product. And like those being the sole drivers, not sole, but like the primary drivers of like value we create. And that lets us stay incredibly lean. In computer engineering, it’s especially possible for small teams of highly exceptional people to really create outsized amounts of value.

Kenny Workman: Because at the end of the day, it’s like an information munging discipline where you can have folks that have an incredible amount of context on stack relevant technologies with great ability that can like ship and build quickly. So those two things are top of mind.

Grant Belgard: And how has your ideal customer profile evolved from your seed round to now?

Kenny Workman: It really is that, um, that shift from servicing biotechs directly to servicing the companies that service biotechs. That is the main story I’m really here to tell. And maybe getting a bit into how we discovered that because I consider it an earned secret. It’s quite interesting. You know, we threw, like I told you, we were a very data oriented engineering company. All we always try to be honest with ourselves in terms of results. We, we threw a lot of stuff against the wall and at the edges of the industry for years. And a few things made us realize that biotechs are not actually the best customers. I mean, for starters, I mentioned earlier, the structure of their analysis varies widely between them, you know, because each of them are kind of procuring their own kits, their own machines, their own experiments. It’s really hard to find consistency in the types of problems that they have.

Kenny Workman: Um, another problem is especially pressure. Now the ecosystem is volatile. Uh, R&D is very overrepresented in small and medium startups. And so that means that like a lot of these customers are going out of business, um, a lot of people are getting merged and the company acquiring them no longer, it’s a different, you know, uh, entity. They might have no interest in working with you. And then, uh, another point and perhaps most controversial is like, I actually observed pretty, uh, irrational, like an economically irrational behavior from many biotechs, uh, by virtue of large capital raises and long cycles towards like feedback from, from market forces. And I’ve seen millions of dollars wasted on internal tool build out because it’s kind of like unclear to leadership what the best thing to do at the time.

Kenny Workman: And folks are not like running proper vendor evaluations because, you know, the vendor ecosystem perhaps was quite mature or there wasn’t like a lot of consensus in the field around, you know, is it better to build versus buy? And so there’s a certain amount of like, as a company recognizing truths about your market that you can and cannot control. We control our technology. We control building our product. We cannot control like a broad behavior, like from biotechs to not use a product, even if it might make economic sense. So we can’t fight that. Our original thesis is that data generation uniquely is restructuring a lot of the industry around computation and computers and sources of data generation are where the problems lie. So we did what made sense to us and we went to the source.

Kenny Workman: And solution providers have been a source of most of our growth over the past year and are also directly producing these problems because that way we’re kind of the original thesis of the company. So it’s quite interesting.

Grant Belgard: Have there been any usage patterns that surprised you? Features that people love that you thought were minor?

Kenny Workman: Yeah, we’ve. So that last step in the platform plots, which has really been the focus of our team’s energy over the past year, year and a half, is kind of a Python based dashboarding framework where you can build cells with widgets and compose them with transformations on data that you control and code. integrating basically language models and exposing those language models and like a basic chat interface to biologists has, has been something that both works quite well and has gained a lot of adoption. Much to our surprise, you know, building in this space has been a constant slog of, hey, we think this is a good idea and what, no scientists don’t adopt it. So the amount of, after a number of those cycles, you build a lot of scar tissue and, and you, uh, you get very surprised when things work.

Kenny Workman: So yeah, basically the adoption of asking a language model to generate code and having like scientists like pick that up to translate tasks. Like I want to pull on a table and like make basic plots of that table. And then, you know, maybe, uh, write some statistics or run some transformer of like a column in that table and feed it into another plot. These like frontier models are quite adept at performing those tasks. And then the scientists are actually using them. But that is something that’s like quite interesting. I think that that will only continue.

Grant Belgard: So you raised 5 million in your seed round in 2021 and 28 billion series A in 2022, I think. What milestones unlocked each round?

Kenny Workman: Yeah. So 5 million, you know, back in 2021 really was, we had no idea we’re going to raise that much. We never had any intention of raising that much. The impetus for that was we did 200, 300 customer interviews. I’m in the space reaching out to any scientist who would talk to us.

Grant Belgard: Yeah. I remember those calls.

Kenny Workman: Yeah. I think it’s probably one of the, yeah. Um, it’s a, I mean, you probably remember both our energy and I have a day all in one, but we came to raise with, uh, a pretty concrete thesis or framework about like how companies would both that the state of generation problem was happening. It was unclear at what pace, but it was happening and that companies would need to build out or restructure their companies around, you know, processing it. Uh, and we had like hundreds of pages of notes on like the details of these problems. We didn’t really expect to raise that much money. We ended up doing so. And with that money, we, we built, uh, the first version of our products and sort of working with customers and iterating with users, et cetera. And then the series A was, it was preempted.

Kenny Workman: I mean, it was in a very different market climate, but it sounded the result of like real traction and, and real use and folks continuing to believe in this trend and taking a bet on our team. I’m sure. Yeah. Alfredo covered this two years ago and nothing, nothing’s changed much, but I remain, you know, very grateful and fortunate for those events. And I think at this point we’ve just been very efficiently using that capital to continue growing as a team.

Grant Belgard: So as, uh, I’m sure all our listeners are well aware, uh, in 2022, the tech bio funding climate was frothy and, uh, it is comparatively harsh now, but how has that affected your, your hiring plans and strategic roadmap at, at launch?

Kenny Workman: Yeah. I mean, maybe, um, as I alluded to prior, because I don’t think we’ve really grown too quickly or yeah, forward resources into something haphazardly. We haven’t adjusted too much based on like the biotech industry’s state. I will say, I mean, it’s definitely made, we, we feel the constriction at Latch. Like it is harder to find deals and close deals than it was a year ago, but we continue to grow quite a bit, mostly by working with existing customers and ramping their usage. And it’s a very exciting time, um, in technology for these molecular measurement teams. And despite, and this should be like, you’ll put some optimism for folks that are experiencing the, uh, the, the very negative effects of like the current capital climate, the people building new molecular measurement technologies are growing fast.

Kenny Workman: They’re gaining adoption and they’re doing that despite like the arid funding environment. And, um, yeah, and broad strokes. I think that that is reason to be excited. And the second order of effects of that will be profound across basic research and translational research and industry biotech, et cetera. As data becomes cheap and abundant, a lot of new discoveries will come out of this, but we’re seeing like the leading indicators of that would continue to grow on our side and primarily from this cohort that builds tools. So it’s definitely very exciting.

Grant Belgard: What criteria will dictate when you go to raise a series B?

Kenny Workman: Yeah, really don’t think about this that much. I mean, we’ve only, you know, we’re a series A company and we haven’t, we’ve only been around for four and a half years, but so far we’ve tried to think about technology customers, products, and it’s usually in that order. I mean, depending on if you’re talking to me or Alfredo, the order might change or Kyle, the order might change a little bit. But yeah, just by focusing on like creating real things that scientists use, the funding has always come later. And especially at this stage, like there isn’t really a way to, if we wanted to like hack another fundraising round series B and beyond, it’s like kind of like big company growth territory. And you really have to have something that’s profound to continue raising.

Kenny Workman: And you know, our idea of what’s profound is kind of a ubiquitous data infrastructure that if you go to your top 20, top 50 market cap pharma, you go to, um, any of like these major solution providers, the [Parse Bio?]s, the, the 10Xs, the VizGens, are they using Latch? That’s all I think about. That’s all most of our team thinks about. And funding comes downstream of that.

Grant Belgard: How does a realistic path to profitability look for Latch?

Kenny Workman: There is an argument to be made that profitability in of itself, uh, with a company like this is not the best path. You kind of always want to be a little bit in the red because otherwise you’re not, um, you’re not pouring a fuel on the fire. You’re not working with the best talent, you know, developing technology fast enough. You’re not growing in the market fast enough. Um, so I would say like, we definitely focus on keeping finances tight and having a strong control over our balance sheet, but our goal is never to like hit profitability per se. Uh, we can always leave that as, as an option. And we do have like a plan where that could be an option because our primary goal is to survive and make sure that the product’s around for a long time. But yeah, in not so many words, the short answer that is not a focus focuses on growing in a controlled way and being a little bit in the red.

Grant Belgard: What’s what have you found to be the single hardest cost to predict for Latch?

Kenny Workman: I think I definitely underestimated how expensive good people are. And then second to that, I underestimated how expensive computers are. So another misconception about Latch is we’re not a software, we’re an infrastructure company. So we have like six figure compute bills every month. Uh, it’s not cheap. And then, yeah, you know, good engineers, they really are expensive, but that’s because they are so rare. There’s a huge difference between, you know, someone who studied CS, uh, the average person who studied CS at even like a top university and like someone who approaches engineering computers is like their craft and life blood. They spend like their nights and mornings reading about it. And it really shows in the work that they do. So yeah, those people, uh, they need to be rewarded, you know, appropriately based on like their, their value in the capital markets.

Kenny Workman: And so they’re expensive.

Grant Belgard: How big is the team now and how is it split between engineering, customer success and science?

Kenny Workman: The team is quite tight, quite small. Um, it’s under 20 people. I would say, you know, it’s three quarters of that is, is engineering. And our sales team is very small. We’ve actually found like a lot of information about how to do sales as a software company and other industries did not translate to, um, to biotech very well. And I think a lot of this comes down to the really high technical scientific buffer of, of communication and both like recognizing, you know, problems and like communicating what you do to the people who have those problems, as well as like the heterogeneity and like, uh, you know, the variability between companies I mentioned prior.

Kenny Workman: So having salespeople that can like recognize problems, uh, amongst what is like a lot of noise, like cut through and like find what is the important bit amongst like a lot of scientific jargon and technical complexity, as well as, um, to communicate that, uh, is, is difficult. The other thing that’s interesting is we’re a usage based compute product. And so like most of our like recurring and growing revenue comes from working with, you know, large clients for like long, long periods of time. And so the name of our game isn’t like, let’s go out and have a lot of sales people that are slamming LinkedIn and emails. Um, although it is important to do good outbound. It’s how do we understand existing customer science intimately forecast their problems, work with them on their problems that are related to computational stuff, and then really grow with them over time.

Kenny Workman: And that allows like single traditional account executives to hold much larger quotas than they would in other industries where most of the quotas come from like bringing in contracts for like upfront costs, um, like traditional SaaS. But for us, it’s like, Hey, you can have like a single person working with a lot of like essentially forward deployed engineers or bioinformaticians. That’s just holding this huge basket of business or a larger basket of business than other companies.

Grant Belgard: What’s a cultural mistake you made and had to unwind?

Kenny Workman: We were, we were and continue to be young. So we definitely made a variety of mistakes. Fortunately, none yet existential. I think the, the biggest one we’ve made was rewarding. And we’ve since corrected this, I think quickly, but rewarding experience and rewarding credentials more than rewarding slope, intelligence, and hunger. A few, yeah, like, you know, pretty concrete examples that I probably won’t get into now are top of mind and pretty painful, but from here on out, I think most of the teams aligned here is really closely, I would say core value to the company. It’s reward, hunger, reward, intelligence, reward, um, desire to create and make rather than, you know, what is on your resume. And people that lie in the former camp tend to in many other ways be aligned with, um, uh, you know, like me, the founding team engineers outside of work in so many ways too.

Kenny Workman: It’s interesting, you know, what we’d like to talk about. We like to read on the weekends, et cetera. But yeah, professionally, we definitely were results oriented company and we made mistakes about bringing people who like looked very good on paper, but you know, when rubber met the road, did not execute as well as this, another archetype.

Grant Belgard: Explain your datalake architecture.

Kenny Workman: Yeah. So I think the word datalake is actually quite funny because it’s said it’s a enterprise, it’s like enterprise technology terminology that is pretty disjoint from like what engineers would use to describe things. Uh, we’ve built, like our datalake is basically a blob store backed distributed file system, which is a pretty cool, uh, I would say like innovation that we, we, we built accidentally as the platform unfolded. Uh, it’s basically like a place to store molecular blob data and a central system that can be mounted into, you know, the environments running and workflows into the sandbox pods, into these dashboarding environments. So basically anywhere on the platform, you can mount this central file system, uh, usually with, you know, what you call a fuse implementation. So file system user space that lets you translate normal file operations.

Kenny Workman: So like read, write list, et cetera, POSIX based file operations into like, you know, fetching and loading chunks from the central system. And you, a lot of great ideas and kind of like systems. So transactional stuff on consistency stuff, how do you, uh, make sure that operations are like safe if you are running many of them at the same time is the basic idea with these things went into like making something that was like safe. So you don’t like have data loss or a data corruption when people are like trying to read and write from it, from all these different mountain points. So pretty cool stuff. I think we wrote a little, like not super technical, which is pseudo technical blog post on it. We call it L Data, but yeah, something that emerged from like having a very systems heavy engineering culture and like people just like going deep.

Grant Belgard: Where do you still rely on off the shelf, open source tools? And what have you rewritten from scratch?

Kenny Workman: Our philosophy is generally to support open source tools indirectly by building frameworks that are generalizable and let people write whatever code they want. So they can just like bring whatever open source tool they want to drop it in. Actually, one of our mistakes and then the corrections as a company was like not supporting like the predominant bioinformatics workflow languages of the community and trying to like force some new language that we like half wrote onto people. There’s a lot of like earned wisdom and like kind of latent knowledge shoved both into the languages and the code bases that are built on top of them that we would never want to rip out. If you know, that is not even getting into the the amount of inertia that that lies in a critical mass of people using a set of tools or languages.

Kenny Workman: So with that workflow, for example, instead of like, you know, shoving this like Python based domain language onto folks and like having that be the only option we’ve since supported, you know, like nextflow and snakemake and all these things. And that trend of just like, how do you build like a framework that people can drop in whatever tool they want into it and like play around freely is something we’ve tried to do everywhere, especially with like plotting stuff or like writing code on computers, the pods tool. Um, yeah, definitely, uh, try to support the science.

Grant Belgard: What’s the most common performance bottleneck you notice when onboarding a new user’s pipeline?

Kenny Workman: Yeah. So while I have mentioned that in many cases, you do not see, you know, large bottleneck for compute with new solution providers, you get just because of like often the same data output scaled a lot because that’s like one of the features of these solution providers can just turn to three day workflow run times to like week plus workflow run times. And a lot of, and then our team can go in and works with many of the internal black positions of these solution providers to optimize things a bit.

Kenny Workman: It’s hard to pick out, um, general lessons from those optimization efforts, but I would say like generally there’s inefficiency in file IO or inefficiency in the implementation of like the core algorithm that is running for the majority of that, like two to seven day, like a window for the former, there isn’t, there’s sometimes not a whole lot you can do, but like using modern, you know, filed storage devices that just like have higher IOPS and higher throughput, making sure that you’re like reading and writing from things correctly. You’re doing things async when you need to, these are like useful tools and ideas and broad strokes. And then for the latter, you’ve actually had a decent amount of success. I’m rewriting, um, algorithms for accelerated hardware, GPUs.

Kenny Workman: So, um, most, most of these like batched algorithms are like single instruction, multiple data, so classic SIMD stuff that can be parallelized and rewritten for CUDA and like run much faster. So, um, it’s early days here because there’s like a few technologies that actually experienced is significant enough to justify resources in our rewrite, but yeah, we are seeing them. Actually, I think we re-released, we re-released some of our work here with Chroma Biosciences or Chroma Medicine with, um, GPU implementation of epigenetic peak calling, um, shrinking runtime quite a bit. It’s an example of like, there’s not a lot of people like developing epigenetic peak calling tools. So like the code there ends up being like not very optimized. So it’s low hanging fruit for a GPU re-write.

Grant Belgard: What technical skills did you not have when you started Latch that you had to learn on the fly?

Kenny Workman: Yeah. I mean, like honestly, most of them, I mean, you could say like, I studied, I, I pretty strong coursework and so like applied math, CS on these things, but most industry engineering, um, you really only develop concrete abilities by like doing industry engineering. So, and that doesn’t even get into like company building, sales, marketing and management, all these things, the whole gamut continued to and forever will be learning. But most pressure and probably to listeners is you only get good at, uh, you know, building the system by doing it. And, um, yeah, from the outset, certainly did not have enough years of experience doing it. But at this point, we have a team of incredibly, incredibly competent systems engineers have been building the same system in this highly focused, uh, vertical for over four years.

Grant Belgard: What personal productivity system or tool do you use?

Kenny Workman: I honestly don’t really believe in that stuff too much. I keep, the only thing is I keep, uh, daily notes on like what I’m doing in them. And I have just like a flat file of text files or a flat directory of text files named after like, with each one named after the date, it’s like a lot of what I’m doing in there. And I use like basic Linux tools, like crap to like find past dates or contents of the files within, within, um, the data files. And that system works pretty well. I find like the more complexity you impose on like, at least for me, personal management systems, like severe diminishing returns. And like, you need to kind of structure your life around like getting into the work and just like, yeah, sitting down and doing the thing. So that that’s what I would say very minimal.

Grant Belgard: Describe a day when you thought the company might fail. How did you handle that?

Kenny Workman: It’s like kind of a, there hasn’t been a singular event that was existential that I can think of by not remembering it probably means it has to happen. It’s more just like, you know, most of these things don’t work for reasons that are like a little less acute. Uh, it’s like, you don’t grow fast enough, you know, as you hear a lot about fragility and relationships between the co-founders or like kind of team members, breaking things up, like fraying slowly. And then like, you know, like eventually leading to, um, some sort of yeah, the departure, none of those in the former, the former camp, like the growth camp. Um, I certainly think about that every day. The whole team thinks about it every day. It’s there, there’s a pretty high bar to hit as a company, especially like we aspire to be like a top tier growth company.

Kenny Workman: And especially with some of the numbers coming out of like recent, recent like AI native companies, it can be overwhelming. But yeah, I don’t, I don’t know. Like I don’t, we in broad strokes think about growing quickly and building the best possible product every day, but there hasn’t been a single month. It caused me to like like worry about the long job you watch a cute moment.

Grant Belgard: If you could redo your undergrad years, what would you do? What would you do differently?

Kenny Workman: I’m mostly pretty happy with the path I took, which, um, I had an idea that I would leave early or at least like not, um, despite having a domain interest in biology, um, a lot of like fields in CS, I chose the roots of mostly taking applied math. And in some cases, pure math, pure math, more minimal. It’s like really just the applied math EECS at Berkeley gives you. Uh, I had a pretty influential seminar from one of the folks who re-architected with the EECS curriculum. His name was Anam Sahai and he basically said, if you want to make, you know, like long, durable, long, long standing durable progress in a field, you got to like drill down to its core elements. If you want to study machine learning, which I did at the time, he said, don’t study, don’t take the machine learning class.

Kenny Workman: It’s like take linear algebra, take stat, take like the building blocks that are like going to have longevity and are going to be here to stay and kind of get more of the core of what the subject entails. And so I, I, I think I took a lot of those courses. There’s always more. I would have loved it, but in terms of like compressing, I was in school for two and a half years. Um, I feel like I compressed a lot of really useful coursework and, and in that time it spent a lot of time on fluff and it worked quite a bit while I was at school too. And I think if anything, I would go back and completely not take any of like the prereqs, like English, et cetera. Cause I didn’t need to, I wasn’t going to graduate. That would, that would be one thing I would do concretely.

Grant Belgard: What misconceptions do software engineers have about biology and vice versa?

Kenny Workman: Most of the things you think will be important end up not being important. And you really have to understand the full context of like a biotech’s problems and like all the elements that go into like making a drug before you can have confidence that, um, whatever idea you have around like a system or like a language or like a tool, like it really moving the needle. We were certainly guilty of this. I was guilty of this. And it’s taken years of kind of like growing alongside or being embedded in many cases within biotech organizations, like understand what was important. Um, in many cases, like building a tool that makes mathematics work faster or building a tool that like makes something faster, like shows more data, um, having some more intuitive visualization kind of like misses the mark in terms of what moves a needle for, for, um, big picture progress.

Kenny Workman: And a lot of the most useful tools in many cases are the boring ones, things that aggregate, synthesize, store over a long period of time. And I don’t think this will always be true, but I think if you are a computer scientist interested in building tools, folks here, and we need as many of you as we can get, there should be a lot of kind of studying and mirroring and like following the path of the scientists and not just the scientists, but like a biotech org or complex piece that go along with you develop.

Grant Belgard: What’s the biggest shift you expect in biology R&D tooling by 2030?

Kenny Workman: I think in broad strokes, we are approaching an inflection point, whether it’s now, whether it’s sometime soon, where volume of data generated will fundamentally like reorganize how people design experiments and reach for results. And, and you see in like pretty much every major field of science, when the data generation techniques reached a certain scale, folks weren’t just like doing the same experiments at a bigger scale. They were just conducting fundamentally new types of experiments. Um, I’m, you know, I’ve been a student of, um, attempted student. It feels like immunology over the past few years and digging into how, I mean, I’ll just think about learning things. It’s very artisanal. It’s very micro. In many ways, it’s very hacky.

Kenny Workman: You look at like techniques like adoptive transfer, where you’re trying to like basically take groups of cells, sometimes engineered in small ways, move them from one mice to another mice, look at their effects. Usually looking at their effects in quotes is a very precise, tailored readout of a handful of proteins, a handful of cells, handfuls of tissues. So you’re both on the, you know, write side, like what you’re able to control is small. And then from the read side, you’re getting a very small window, often biased by what you think you should be looking for. Right. All’s that, all that’s to say is like, as you can just read everything in many, in not so many words, and as you can write everything, the, the types of experiments you will run will just be different.

Kenny Workman: So as these things play out, driven by better tooling, the types of drugs, the types of disease mechanisms, mechanisms that are, you know, intertwined between multiple pathways that drastically exceed what a human mind can like shove into their own context, right? Drugs that go after like that thoroughly cover like the, the full chemical space of like what that molecular modality like allows, um, rather than like claiming to do that in the instance of like antibody engineering, claiming to engineer the whole antibody, but really focusing in on the few hundred base pairs that make like the CDR3 region in many cases. There’s actually a very controversial paper from [?] a few years ago for, for HER2, on this exact topic.

Kenny Workman: Um, yeah, so long with it, but data generation driven by better tools will cause companies to reorganize how they develop experiments around measuring everything and then covering the space of both drugs and what they target. And like, this isn’t a particularly profound prediction and many people are building towards this, but that is the broad theme that people should be aware of.

Grant Belgard: Okay. So, uh, wrapping up, what’s, uh, the best place for listeners to follow your work?

Kenny Workman: We have a great website, uh, latch.bio, and then everyone on the team for the most part tries to engage pretty actively on like socials. Well, not all socials. I think it was number that, but Twitter. I personally don’t like LinkedIn that much, but we, we do post on LinkedIn. But yeah, I think Twitter is probably like the best place. And then we have a substack that is, uh, is, I think we regularly try to produce long form content that dig into both how we’re thinking and then the details of the products we’re building. Yeah. We just have an absolutely incredible team. You guys should go check out each and every, uh, one of them co-founders, Kyle and Alfredo, as well as Hannah and the whole engineering team.

Grant Belgard: Cool. Kenny, thank you so much for joining us. It was a nice conversation.

Kenny Workman: Yeah. I really appreciate you having me on. Had a lot of fun. Thanks.

Transcript of Episode 62: Don Alexander

Disclaimer: Transcripts are automated and may contain errors.

Grant Belgard: Welcome to The Bioinformatics CRO Podcast. I’m your host Grant Belgard and joining me today is Don Alexander, a certified personnel consultant who has spent more than two decades helping life science companies recruit and retain top talent. In 2018, he launched GeneCoda, an executive search firm headquartered in North Carolina’s research triangle, focused exclusively on biotech, pharma, medtech, and diagnostics. Welcome to the show, Don.

Don Alexander: Hey, it’s great to be with you today, Grant.

Grant Belgard: Great. So how would you summarize the 2025 hiring climate in biotech in one sentence?

Don Alexander: Yeah, I would say that I’m cautiously optimistic post-labored heading into 2026. I mean, beyond that, while overall volume is down, we’re seeing fractional hiring is up and companies are being far more strategic in areas like outsourcing, principally due to capital constraints.

Grant Belgard: And you track over 50,000 job ads each quarter for the Pulse report that you put out. What did Q1 of 2025 tell you that headlines missed?

Don Alexander: Yeah, great question, Grant. So Pulse, backing up just a step, Pulse tracks a cumulative job ad, which are posted in the life sciences sector. We do that, as you mentioned, on a quarterly basis. It’s really what we think of as a leading indicator in hiring, not a rearview mirror, something that’s tracking postings. The Q1 headlines focused on layoffs, but I would say Pulse shows kind of a rebalancing of the market. There was, in fact, a 5% net year-over-year total postings drop, but commercial roles, project management, data-adjacent functions actually grew during the same period. So, you know, I think that’s important to point out, and those kinds of things don’t always grab headlines, if you will.

Don Alexander: So it’s important, I think, the takeaway is that it’s important to understand that although total posts can be increasing or decreasing on a quarter, well, on a year-over-year or quarter-over-quarter basis, certain skill sets remain in favor, irrespective of changing markets.

Grant Belgard: And since this is The Bioinformatics CRO Podcast, I have to ask, how about bioinformatics and comp bio postings?

Don Alexander: Yeah, I think that’s a great question. Computational biologists are expected to be and remain really in high demand, especially those that can kind of bridge the in-silico modeling with experimental validation. We do have a situation where the focus for most venture funds right now is asset-centric companies that are already, you know, maybe they already have products in the clinic, and those are still drawing what I would call the lion’s share of the funding. But I think those companies that are getting funded that might be prior to being in the clinic, a lot of the money that’s flowing or is flowing into kind of those data-first computational platforms. So that would kind of get the background of why, broadly speaking, data analytics, machine learning, and the like, bioinformatics are all part of that ecosystem.

Grant Belgard: And in terms of seniority levels of roles, are you seeing those tracked together for the bioinformatics, comp bio space, or are you seeing a divergence between demand for, you know, more senior level versus more junior level roles?

Don Alexander: You know, I think that’s a great question. I think it’s really a hybrid mix. To be candid, I haven’t looked like what I would call deeply into stratifying those specific layers, but from what we’ve seen, it’s really a mix of skills and seniority levels.

Grant Belgard: So we’ve talked about types of jobs that, you know, have seen hiring rebound and others that haven’t. What about subsectors of the space, for example, cell and gene therapies, CDMOs, MedTech, et cetera, which of those are seeing an increase in net hiring despite the overall dip?

Don Alexander: Yeah, that’s a great question again, Grant. I would say that MedTech overall has been surprisingly resilient, especially in diagnostics and connected devices. CDMOs with more diversified client bases have remained stable, and I would say we’re going to see in some level of growth in areas like cell therapy manufacturing as select players really scale. I also think that, you know, one of the other underpinnings of recent on-shoring, if you will, discussions in biopharmaceutical manufacturing, that may be also kind of factoring into, I guess, the broader manufacturing hiring trend.

Grant Belgard: And how are funding cycles and IPA windows shaping demand for computational biologists? So you mentioned earlier a lot of the funding going towards companies that have assets in clinic, but then also a lot of data-centric and presumably, you know, AI-focused companies getting some large rounds.

Don Alexander: Yeah, absolutely. I mean, again, I think some of the rounds that we’ve seen, in particular with companies that are pre-clinical, have, there have been some sort of constitution about AI machine learning capabilities within the platform constructs themselves. So being able to develop therapeutics in record time at a fraction of the cost, we actually have a client that is doing this by way of example, where they’re examining, in this case, small molecule libraries, really compounds that have made it into the clinic and failed for one reason or another. And they’re looking to machine learning technologies and very rapid iterations to see if they can get products into the clinic. And that, of course, reduces cycle times from, you know, what, seven to 10 years, maybe to get a product into the clinic, oftentimes to, you know, maybe just a year or two and a small fraction of the cost.

Don Alexander: So more shots on goal was kind of the net-net of where we fall, I suppose.

Grant Belgard: Quick ball of that, lower cost, quicker cycle times. And quicker to fail, too, if the asset’s going to fail. And pre-COVID remote work was pretty uncommon. Obviously, with COVID, it became a lot more common. And there have been a lot of headlines about it’s always kind of getting less popular. What are you seeing? Is that a fad that is fading? Are we kind of asymptotically approaching some new normal that’s higher than the pre-COVID norm?

Don Alexander: In terms of, like, the remote and hybrid postings and things like that?

Grant Belgard: Yeah.

Don Alexander: Yeah. You know, I would say hybrid’s here to stay for areas like G&A, technical teams, and various functional components like clinical operations, for example. Wet labs and manufacturing not surprisingly remain primarily location-centric. You know, as a sector, I think what we saw was we experienced rapid growth pre-COVID to about 14% of all roles advertised as being remote or hybrid during COVID’s peak. And that’s since fallen to about 12.5% this past quarter. That still has, in our view, a room to grow, but it’s not growing nearly as quickly as it did by, you know, COVID forced everybody into a specific set of circumstances. So, you know, we saw an enormous rise, and it’s kind of leveled off, tapered off.

Grant Belgard: Have you seen any differences in the prevalence of roles advertised as remote-friendly based on where the company’s center of gravity is, right? So, are companies headquartered in Boston, you know, less likely to be open to that, the companies headquartered outside of major biotech hotspots, or do you see not much difference there?

Don Alexander: Yeah, we don’t, I think it’s really not uncommon. Maybe one way to look at it is it’s really not uncommon to see, you know, the CEO of a company located in Boston, the chief medical officer, be located in the research triangle where I live, and maybe the CSO located in the Bay Area. So it was just, it’s really a hodgepodge. I think, you know, especially when you’re thinking about virtual and early-stage company, you’re thinking about a model that really isn’t quite as location-centric as, you know, if you were later stage, you know, perhaps with the manufacturing facility, you’re going to, you know, kind of building those harder assets.

Grant Belgard: And what impacts are you seeing, the market turbulence on salary expectations?

Don Alexander: Yeah, salaries, this is interesting, I think. Salaries for junior roles, I would say within the last 18 months, have kind of plateaued to some degree for proven commercial or development leaders, those that can lead across maybe functional areas, regulatory, clinical, business development, licensing. I would say that, you know, I don’t want to call them bidding wars, but there certainly hasn’t been no fall-off in remuneration. And, you know, so those tend to be people that can bring, say, investor or FDA credibility. So, we saw the median advertised salary, for instance, on the Pulse report in G1 to 2025 was about $108,000. And that’s across all disciplines, all seniority levels. And remote hybrid roles command a higher median, actually, at $134,000. So, if you think, you know, kind of maybe like, why is that?

Don Alexander: It’s because those are some of those senior roles that tend to be in GNA in some of the areas that I mentioned earlier that can really be done anywhere. So, that would predicate basically a higher, probably a more experienced professional. So, we’ve seen some compression of wages, but not as much as you might expect, given some of the recent layoffs that we’ve also seen in the industry.

Grant Belgard: And what impact are you seeing so far of AI and automation, both in terms of the numbers of jobs that are being advertised and then also of the job descriptions and roles? Are you seeing a consolidation or other changes to what’s expected of candidates?

Don Alexander: So, within the job postings themselves? Yeah, I think that people are putting a little bit, well, I think there is a little bit more emphasis these days on, again, what we’re going to call cross-functional skills, the ability to, in some ways, do two jobs, do more than one job. But I think there’s also an emphasis on, I guess, what people have historically called softer skills as well. So, people that are intellectually curious, they’re natural problem solvers. I think that in our industry, it’s pretty important to have some of those attributes as well.

Grant Belgard: And shifting gears a bit to your career journey, you began an investment in strategy consulting. Can you walk us through your journey into executive search?

Don Alexander: Yeah, absolutely. I appreciate the question. I mean, it basically is somewhat serendipitous. It started with a friend that recruited me into the recruiting field. As you mentioned, I started in the financial services industry. And so, I guess the real, you know, parallel there that I saw is companies, you know, they tend to make million-dollar hiring mistakes, and it’s not necessarily infrequent. And I guess I realized recruiting, you know, it’s not just HR, but it’s capital allocation as well as marketing. And those are both things I was pretty intimately familiar with from my days in the financial services industry.

Grant Belgard: What were the biggest aha moments that convinced you to launch GeneCoda?

Don Alexander: Yeah, Grant, good question again. I’ve always really wanted to own or be part of business ownership. And I wanted to affirm where deep domain expertise, not necessarily just speed or volume, was a differentiator. And when a client told me at one point, you understand our business better than most of our partners, that was kind of my signal to get going with GeneCoda.

Grant Belgard: Nice. Can you walk us through some of your early missteps building a niche firm? What are some things you would do differently if you could transport back to 2018?

Don Alexander: Yeah, no, I think this is a really great question, and it’s kind of reflective in the drug development paradigm as well. So a lot of times in the drug development companies, they tend to go after maybe too many assets rather than focusing on, you know, a single asset. And right, you know, rightfully so. But I think the spirit of it is trying to be everything to everybody. I’ve kind of learned over time that niche focus is really more powerful in concept. And I think I also underestimated how critical marketing would be, even in retained search. So, you know, you’ve got to be continuously marketing, continuously out there in front of people, and don’t try to be everything to everybody.

Grant Belgard: And from your vantage point, how has recruiting and life sciences evolved over time?

Don Alexander: Yeah, I think the biggest single shift that I’ve seen from, you know, perhaps earlier on in my career anyway, a couple decades ago, it’s really shifted from what I’m going to call more pedigree focused and more impact focused. So we’ve really moved away from the idea of where did you go to school to what did you build, launch, or fix. And I would say that’s especially true post-COVID. It’s interesting.

Grant Belgard: Yeah, I guess there’s been a lot of discussion about that same trend in other industries as well. And moving on to what advice you would have for employers. What’s the biggest delay that in closing candidates that you see repeatedly?

Don Alexander: You know, it would be bottlenecks generally in the hiring process, which might include approval. Candidates go cold or get hired elsewhere. When hiring managers wait for things like budget sign-offs, so pre-aligning internal stakeholders, setting expectations with candidates up front to avoid looking unresponsive, those are, there’s a pretty critical ghosting candidates is really bad for a company’s brand.

Grant Belgard: And what advice would you have for employers on building a compelling employee value proposition without overspending on perks?

Don Alexander: Yeah, it’s really interesting. My framework on that is really to lead with purpose and impact. Why does this role matter to science, and why does it matter to patients, ultimately? Always consider your end audience. And then if you add things like flexible work, clear growth paths, a culture where smart risks are in fact encouraged, and managers who mentor, these types of aspects I find matter more to most scientists than, say, a break room with ping-pong tables.

Grant Belgard: And when should the startups start mapping succession plans?

Don Alexander: Yeah, we actually wrote a guide on this and invite anyone to explore it in more detail, but I would say as early as possible for mission-critical roles. What I find in the industry, and I don’t know that this is specific to life sciences, but succession planning is often an afterthought in startups. So waiting until, say, a key executive resigns, or even worse, passes away, it has happened. That’s like trying to buy life insurance post-mortem, truly. One overlooked point that I also think about is demographics. So in our paper, in our reference succession planning, in 2024, we figured out that chief scientific officers and chief medical officers of publicly traded companies in the industry, their average age is skewed to the late 50s. So basically they’re all in their late 50s.

Don Alexander: So what that tells me is that of those that have some of the deepest scientific knowledge in the entire country, you know, if you think about normal retirement age being around 65, I mean, they’ve got about 7 to 10 years of work left. My question is, who’s going to replace them?

Grant Belgard: Who do you think is ultimately going to replace them?

Don Alexander: Well, I hope they’re doing a good job of bringing up the ranks. Because if they’re not, we’re going to be in trouble. No, I do think, as a lot of people do, they continue to matriculate into part-time or mentorship roles, even within a quote-unquote retirement construct. So, you know, I think there’s a lot of different answers to that question. But I do look, cast that rod forward and see that we’re, you know, we may have a bit of a brain drain as an industry on our hands in a few years.

Grant Belgard: So shifting to advice you’d have for job seekers, what are the resume red flags that you still frequently see today?

Don Alexander: Yeah, the biggest thing, well, there are a few, but the biggest thing, well, one that I see pretty consistently is a list of responsibilities with little intrinsic results. So when I talk to people about resumes, I always like to use the premise of a which resulted in statement mindset. So, in other words, I did X, which resulted in Y. That’s really important to think about that formulaically and not just tell someone what you did, but what the results of what you did meant. I would also say frequent job changes, they’re more acceptable, I think, today than they were in the past. But the context matters, so tell the story before someone has to guess would be my advice on frequent job changes.

Grant Belgard: Yeah. And I guess somewhat on that note, it’s not uncommon for people to have an interdisciplinary pivot in the industry, for example, wet lab to bioinformatics. What advice would you have for job seekers on creating a narrative for those who want to pivot?

Don Alexander: Yeah, I would say to own your hybrid skill set. Your career is ultimately up to you. All those startups or interdisciplinary scientific programs are great labs for learning. You know, a bench scientist moving into data science should know not just Python skills, but how they’ve translated biology into code with measurable outcomes.

Grant Belgard: And what advice would you have for job seekers right now on negotiating compensation given the investment challenges of the industry?

Don Alexander: Yeah, I would say don’t anchor to 21, 22 peak salaries. Bring data. Data is king, as it is in many parts of our life. Focus on the value that you create, not just what you want. Then I would say evaluate the total package, including the resume trajectory over the next two to three years. You know, don’t, I would say don’t proactively take a job that doesn’t offer 20 to 30% stretch from what you’re currently doing.

Grant Belgard: And what advice would you have for job seekers when networking?

Don Alexander: Pick five people that you admire. Send a short message. It could be just appreciation, a tip, or an offer to help. Don’t ask for anything. Most people won’t respond, but one might open the door that changes everything if you made that a standard practice every week.

Grant Belgard: And how should employers and employees use labor market data like Pulse to make career hiring decisions?

Don Alexander: Yeah, I would say follow the data, not the headlines. If the top 10 in-demand skills are turning in one direction, that’s where opportunity is growing. If your area is flat, it may be time to reskill or reposition.

Grant Belgard: Are there areas in the comp biospace that you see that might be flattening out relative to what you saw a few years ago?

Don Alexander: You know, I, no. I think this is an area that continues to experience increasing demand from everything I’ve looked at. And I think it will into the foreseeable future.

Grant Belgard: What books, newsletters, podcasts, or other resources would you recommend to our readers, to listeners, to stay sharp?

Don Alexander: Yeah, there are quite a few, but I’ll try and narrow it down. Some of my favorites are The Seven Habits of the Highly Effective People, Stephen Covey, if you haven’t read that. And a couple of other, what I’m going to call business books. I see this, even with people that don’t own businesses, I think could get a lot out of both of these books because they can help, I would say, in a professional career in a general sense. So the first is 10x is Easier Than 2x by Ben Hardy. And Dan Sullivan. And the second is Disciplined Entrepreneurship by Bill Aulet. And of course, I have to put a shameless plug in for my own book, The Unwritten Rules. And that was really written for job seekers and those transitioning careers. In terms of podcasts, of course, The Bioinformatics CRO Podcast.

Don Alexander: My second, and I promise, final shameless plug is for a podcast that we do called Exclusive Insights for Life Sciences Innovators where we focus on innovative scientific founders, people that are running those companies. They’re kind of doing what I think of as the next generation scientific work. Newsletters, you know, things like Endpoints, Fierce Biotech, BioPharmaDot are great resources to kind of stay in touch with the industry.

Grant Belgard: Great. What’s a tool you can’t live without?

Don Alexander: Well, I have to default to HubSpot because it’s my memory, basically marketing engine and relationship map. But I do have to add that more recently, I would have to also include ChatGPT and [the Harpa?].

Grant Belgard: What do you think is the most underrated skill in biotech hiring?

Don Alexander: Storytelling. You know, whether you’re pitching a role as, you know, as a client might or your resume as a candidate by narrative lens.

Grant Belgard: I guess, wrapping it up on a very lighthearted question. So, what’s your go-to way to decompress after back-to-back calls?

Don Alexander: Yeah, absolutely. I am a guitarist. I’m not a great one, but I do enjoy it quite a bit. So, Stairway to Heaven still gets airtime in my home office.

Grant Belgard: Great. Well, Don, it’s been a great conversation. And where can our listeners go to learn more about GeneCoda?

Don Alexander: Absolutely. So, I maintain a LinkedIn profile that’s at D Alexander. My first initial D as in Don, last name Alexander. So, we just type that in the front end of the URL LinkedIn stream. You can also find us online at Gene, G-E-N-E, Coda, Charlie Oscar Delta Alpha, Coda.com, GeneCoda.com.

Grant Belgard: Great. Thank you so much.

Don Alexander: Thank you, Grant.