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270: Simplify Complexity with Ben Larman

Ben Larman, Scientific Founder and Chief Scientific Officer (CSO) of Infinity Bio, is driven by a passion for advancing human health by decoding the complexities of the immune system.

We discuss Ben’s Antibody Reactomics Framework and his Complex Data Delivery Framework. The Antibody Reactomics Framework leverages DNA barcoding to analyze immune responses, providing researchers with groundbreaking insights into human health and disease mechanisms. The Complex Data Delivery Framework streamlines the process of communicating scientific findings by focusing on understanding customer needs, analyzing data, presenting positive results, and combining findings to deliver customized insights. He also shares how AI is transforming biomedical research and the importance of refining a biotech company’s narrative to effectively engage diverse audiences.

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Listen to the podcast here

Simplify Complexity with Ben Larman

Good day, dear listener, Steve Breda here with the Management Blueprint podcast. And my guest today is Ben Larman, the Scientific Founder and CSO of Infinity Bio, a technology company that measures individual immune responses against all known human viruses, autoimmune conditions and allergies. Ben, welcome to the show.

Thanks, Steve. Great to be here.

My favorite question for you. So what is your personal “Why” and what are you doing to manifest it in your company?

Great. Well, yeah, I’ll start by saying I guess I became an immunologist by training because I believe there is an incredible opportunity to improve human health by better understanding immune responses and the immune system. The immune system is incredibly complex. It’s sort of, we’re just scratching the surface now in terms of our understanding and how it’s connected to so many different health conditions ranging, as you mentioned, from infections to cancer, to autoimmune disease, to allergies. The ability to move technology forward in a way that allows us to take better measurements and understand human immune responses at a population scale, that’s always motivated me. And so that’s what we formed Infinity Bio to accomplish by being able to read antibodies.

Okay. And when you read antibodies, so how does that inform you? How does it help you fight immune conditions? I guess you can call it.

Yeah, so antibodies, I think we all sort of have seen the cartoons of these Y-shaped molecules. I think what a lot of people don’t appreciate is the massive abundance and diversity of these molecules in our blood at all times. So there’s roughly 100 trillion antibody molecules in every drop of blood, and they really store the information from all of our prior immune responses, going back to your vaccinations in childhood. And one way to think about them, they’re expressed by cells in our immune system in response to very specific targets. They have these ridges and bumps on them, and that’s what allows them to very specifically recognize their target, whether it’s flu or a cancer antigen. And so what we’ve done is to create a technology that you can think of like a record player where the record, your old vinyl records with those grooves and bumps read out by a needle, that’s sort of what we’re doing with molecules, reading out those bumps and grooves to know what they bind to and get a picture of your immune system. Antibodies are really a wonderful window into your immune system and everything that it has tried to accomplish over your lifetime.

So that’s an amazing complexity. You said trillions of antibodies in a drop of blood.

Yeah.

That’s very, very minuscule and very numerous. So how do you measure this data or process even this data? How do you then communicate this data is about?

And actually you develop a framework around this. So can you tell me a little bit about how that came about and what’s its significance is and how does it work?

Yeah, so the approach that we’ve taken and that I’ve sort of worked on since grad school is to try and convert the antibody target interaction question into a DNA synthesis and sequencing problem, which is something that is much more approachable because DNA technologies have advanced so dramatically over the past couple of decades, both in our ability to create DNA molecules and our ability to read them. And so without going into too much technical detail, we basically have created a system, a technology, for producing large collections of proteins that could be targeted by antibodies and putting DNA barcodes on them. So, these are just sequences that associate what that target is. And so we can take someone’s blood sample and we basically mix it with this collection of molecules and we pull out the molecules that are bound to the antibodies and use DNA sequencing to read it out. It allows us to take advantage of those advances in DNA technologies to measure all the different things that someone’s antibodies bind to. And so, just to give you a sense for the scope of a particular test that we offer at Infinity Bio for all the viruses, as you mentioned, we test antibodies against about 285,000 different viral targets. These are proteins, fragments of proteins that are associated with all the viruses known to infect humans. And so, for each sample, we get a result that says, okay, these are the proteins that are recognized by that sample’s antibodies, and these are the proteins that are not recognized by that person’s antibodies. So, at the basic level, we’re generating sort of like a report for each sample. We typically work with investigators who have sets of samples, some from individuals with a particular disease, for instance, like type 1 diabetes, and a set of samples from individuals that don’t have that. And they would like to know what is the difference between these groups of individuals in terms of which viruses they’ve been infected with, and maybe to an even more detailed level, during those infections, what were the specific targets of those antibodies? And by comparing the groups, we could identify things that may actually be mechanistically causally associated with the ultimate disease that these individuals developed. And so communicating that data is a challenge. It’s a very deep data set that we typically will deliver in a way that can be opened with Excel, but they can be very large files depending on the number of samples that we’ve tested. So we have an amazing informatics team at Infinity Bio, but it’s really been something of a learning process for us over the last year as we’ve been operational and returning data sets to folks is how can we do it in a way that provides them the most insight into the question that they’re really trying to ask. And I think we’ve learned several lessons that we’ll be able to improve the way that we deliver data and insights to our customers going forward.

I think that AI will be able to provide a really important resource in this area. Share on X

Because really what people want to know a lot of times is what is the difference between the groups and then how is that related to all the scientific research about whatever that difference is? So, it may be the case that we identify three different viruses that are associated with the disease. What’s known about that already? It may take someone a long time to go in and mine the literature to figure out what is that connection, whereas an AI, generative AI, can help dig out the papers that are most meaningful, most cited, or whatever it is, to help them really interpret that information.

So, Ben, what are the steps then of your process to communicate this really complex information that could potentially overwhelm the recipient?

Yeah, great question. So, it’s a process that will change because up till now, we’ve really been returning the datasets without as much conversation upfront as we should have been having to really understand what they want to learn from their data. And going forward,

we'll want to engage our customers in a more thorough way and prepare an analysis in that first data return. Share on X

Right now, we’re returning data, we’ve been returning data in a way that’s sort of generic. And we say, okay, take a look at your data. Let’s get together and figure out how to answer your question. Going forward we’ll put more of an emphasis on let’s come up with an analytic plan ahead of time so that when we return those results in the first instance, you get your generic reports and your generic data, but we also give you an analysis that’s specific to your question.

Now, one of the things you mentioned in our earlier discussion is that when you present a mix of positive and negative results, then customers often get confused and sometimes less is more. The more you give them, the more confused they are and you have to somehow curate the information so that they can actually use it. How does that work?

Yeah, we’ve all had the experience of going to the grocery store and looking for butter and seeing 13 different options and being overwhelmed. And that has definitely been the case for some of our customers returning multiple sets of files that they’re sort of not sure which ones to go into first if they want to be very thorough. And we need to do a better job of distinguishing for them. Here’s the key results. And in antibody reactome profiling, we take these very large libraries, these collections of proteins, most of the data is actually negative. And so these big files that we’re giving to people, it’s mostly negative, but what they really care about is the positive data. And we’ll be doing a better job of integrating that positive data across the different service offerings because sometimes people will combine services for certain projects. So we’ll just be doing a better job of distilling down the positive data that provides the most insight and separating out, making it available to them, the data that can potentially lead to rabbit holes, but making it harder for, or less easy for people to accidentally start going down rabbit holes, doing their best job to understand complex data.

So you are doing this research, you’re providing data and other companies, they, I guess, they use this data to create medical solutions or drugs. What do you see is the future? How is AI going to change this whole process and how is it going to make it more efficient?

Yeah, yeah, so there’s an enormous potential for integrating this kind of data with other types of these high throughput technologies. They’re often referred to as omics technologies. Most people are familiar with genomics, sort of DNA sequencing of genomes and looking for associations between the inherited genes and a disease risk or something like that. And so there’s genomics, but there’s also proteomics, metabolomics, there’s the microbiome, and more and more large studies are involving these types of omics data and integrating across them to extract the insight, the mechanistic insight.

That's a huge opportunity, for AI to assist the statisticians and the biologists that are currently doing that research. Share on X

A lot of that research is being done in academia. A lot of that research is being done at pharmaceutical companies. And this is how the next generation of drugs, I think, are going to be discovered and tested in populations where these measurements are made. So, different archetypes of responders and non-responders will be better understood. I think that’s certainly going to be true about the immune system. We already know many drugs can stimulate an autoimmune response. Many drugs rely on stimulating an immune response to a cancer in order for them to work. And so really getting a better understanding of the immune system in conjunction with all these other omics is really going to be transformative for biomedical research and incorporating AI to integrate those analysis with the scientific literature, I think is going to be a critical element of that going forward.

Yeah, it’s really fascinating how the immune system can be stimulated to overcome certain illnesses. Because when I was a child, and even maybe up until recently, I thought that the drugs were actually overcoming the disease, but more and more what I understand is that the drugs are not overcoming anything. The drugs are just helping the immune system overcome the disease. So it’s a fascinating subject. Now, talk about different omics, and your area, you mentioned is the antibody reactomics. So is this the science of how the immune system reacts to certain inputs? What is it about?

Exactly, yeah. So, we have these very complex repertoires, molecular portfolios of antibodies that we all carry around with us. And the antibody reactomics is really an attempt to extract as much of that information as we can. It’s using the record player to read out the targets of those antibodies. So it’s referred to as an antibody reactivity. When an antibody binds to something, it’s a true target. And so, the reactome is the set of all those potential reactivities that we carry in our blood. And so, we’re trying to capture that information as comprehensively and as accurately as we can.

So, how does DNA sequencing come into the picture. And you mentioned in our earlier conversation that it’s a new gold for healthcare technologies. So why is DNA sequencing so important and how does it help?

There has been so much incentive to advance the ability to read DNA for a number of fields, that the technology, it’s mostly been led by Illumina technologies that people are now trying to convert, as we are, different types of questions into a DNA sequencing readout to leverage the advance in DNA sequencing. And so, there’s just so many different technologies now. Many of them have, there’s sort of a hyphen and then SEQ (S-E-Q). There’s RNA S-E-Q, DNA S-E-Q, and there’s single-cell RNA S-E-Q, and all these different S-E-Q’s out there now that are just giving an incredible wealth of information to researchers. And it sort of is all riding on the back of this wave of DNA sequencing technologies.

What is your exact approach to leveraging these technologies? What is unique about Infinity Bio that you do that helps other companies?

Yeah, so the uniqueness at Infinity Bio is basically the way that we create this reagent, this collection of proteins and DNA barcodes. We use a technology called MIPSA for molecular indexing of proteins by self-assembly. It’s the self-assembly part that makes it super efficient. As an example, I mentioned the viral service that we do, 285,000 peptides. To make each of those individually DNA barcoded would be not feasible, or at least not economically feasible.

So what we do is use self-assembly in a single reaction. We do a process that allows these protein fragments to barcode themselves. And so everything is just happening in one tube, and then we can use that reagent to test individual samples. Share on X

So it’s the efficiency with which we make that library, that collection of proteins that allows us to do it in a high throughput testing format. And to be able to do it at a cost that actually enables researchers to test a lot of samples. Because one of the key things about human immune systems is that they’re so different. Even identical twins have very different immune response profiles. And so to get at a true disease association, you need to test large numbers of individuals. And so the efficiency with which we can make this material and use it for testing is actually the core special sauce at Infinity Bio.

Wow. So how did you guys come up with this idea? It sounds so esoteric for a layperson. What is the process of stumbling upon this idea or developing this approach, this concept?

It’s been a natural evolution attempts to read the antibody repertoire using different approaches. And the approach before this I worked on as a graduate student and that interestingly uses, they’re called bacterial phage. They’re basically viruses that infect bacteria to connect to the DNA. So it’s again, using DNA barcoding, DNA attaching to proteins, but we were using this virus system to attach the DNA to the protein fragment. And that had some great advances over all the previous technologies, but had some important limitations. And so, MIPSA was the next step to overcome those limitations and really bring to market for the first time a really reliable solution that could be affordable for running large numbers of samples and provide proteins in a way that antibodies naturally recognize them in solution. So there’s several sort of step function advances that allowed us to bring this to market.

Okay, so we are getting close to the end and I’d like to ask you the question that what is the most important question that the biotech entrepreneur should be asking themselves?

Hmm. That’s a tough one. I guess I mean,

telling the story is so important and building the team around that story is so important. Share on X

So I think the entrepreneur really needs to ask themselves, are they ready to tell the story? And you can never be too ready, I guess. So talking to people who have different perspectives is so important in telling your story and refining your story and then asking yourself, are you ready to take the leap? Because it is not for the faint of heart to do a startup company. Once you are ready, do you have the right team with you? Are you able to rally a team around you that is going to be as passionate as you are, as resilient to the setbacks that you’re definitely going to experience? But I do think it is the most rewarding thing that you can do is to start a business or make a discovery that advances human health.

Yeah, I agree. What is the challenge in telling that story? You explained this with some hesitation that you said that you have to be ready, you’re never quite ready. So is this about having to show conviction for something that is uncertain? Or what is it that makes it difficult to tell that story?

So, every story will have its own unique challenges. For us, there has not been as much advance in this area. And so, in telling our story, we have to do a lot of education as well to convince the audience that this is a worthwhile pursuit. When you drink your own Kool-Aid, you can oftentimes take that for granted. Of course, we have this beautiful solution for reading antibodies, but if the audience is not already convinced that you need to read antibodies, then you’re not going to win over that audience.

You really need to understand your audience and how to tell your story to be heard. Share on X

I totally relate to this, because just this past year, I started going to trade shows and exhibiting my company with a booth. And I thought that the biggest benefit was going to be the number of clients I find in this trade show. But actually, it was different. It was being able to refine the story, being able to articulate it, because I had so many interactions with people, so many times I explained in a simple way what it is I do, that I managed to refine the message and it actually became a lot more attractive. So I really resonate with this thought. And, luckily, my business is a lot less complex than yours. It’s easier to get to the story, but still it was a struggle. So thank you for coming to the show. So Ben Larman, the scientific founder and CSO, what’s CSO stands for?

Chief Scientific Officer.

Chief Scientific Officer, of course, of Infinity Bio, a technology company that measures individual immune responses against all known human viruses, autoimmune diseases, and allergies. So thanks for doing what you do. Thanks for coming on the show. If people would like to learn more, where should they go and how can they find you?

You can always go to infinitybio.com or send me an email through the website or my Johns Hopkins email, hlarman1@jhmi.edu

Okay, super easy. We’ll put it in the show notes so people can pick it up. Thanks for coming and thank you for listening.

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