676 Chris Moxham: Co-Founder & CEO of Transcripta Bio
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Hi, everyone. Welcome back to the Kara Golden Show. Super, super excited to have our next guest, Chris Moxham, who is the CEO of Transcripta Bio. He is the founder and CEO of Biotech Company on a mission to revolutionize drug discovery through the power of, ready for this, transcriptomics and AI. Hi.

Chris is what you'd call a true drug hunter with over 25 years of experience in pharma and biotech. He spent a couple decades at Eli Lilly, where he helped shepherd more than 10 molecules into the clinic.

He was most recently CSO, Chief Science Officer at Fulcrum Therapeutics, where he led scientific strategy and early development, including a promising treatment for sickle cell disease. Now at the helm of his company, his first company that he's founded, Transcripta Bio, Chris and his team are harnessing the power of high-resolution technology

Transcriptomic Data and Frontier AI to Identify Entirely New Ways to Tackle Human Disease. That's a mouthful. So exciting that he's here to explain it all in plain English to everybody who has not heard of this incredible, incredible Bay Area company that he's founded. So welcome, Chris. So excited that you came on.

Well, thank you, Kara, so much for the invite. And it's great to be here and excited to talk with you. So let's start at the top, I guess, with Transcripta Bio. How do you describe the company and what makes it different? Yeah, you know, we are a platform drug discovery company. And so we have a platform where we have a lab here in Palo Alto where we're screening

drugs and new novel drugs and surveying them against the transcriptome. And we'll talk more about what that is, but it's just. You should know what that sound is.

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phenomenal blueprint that dictates what your cells do in your body. And we're different in the sense of your traditional biotech where, you know, starting from the beginning, trying to discover new medicines and take them all the way into the clinic and to an approval. Well, that is a long process, usually 12 to 15 years, very expensive, over $2 billion. Failure rates are high. And so what we have done is using our platform, we said, well,

Let's see if we can find fast opportunities to take into patients quickly and more cheaply. And that is using already existing FDA approved drugs, for example. And we have three examples now where we've done that, but also use the platform to identify molecules that have already been in clinical trials and find new indications and move those into the clinic quickly. And so the premise of the company really is to use advanced technology in an applied way

to make the cycle time shorter in terms of getting molecules into the clinic, to do this across all therapeutic areas. So not just one area like oncology, for example, we work across all therapeutic areas and to do it in a cheaper way as well. And so really trying to address all the issues that currently kind of plague the pharma industry in terms of productivity.

and do it in a very scalable way, combining both a wet lab that we have here with the application of AI to do this faster, cheaper, and better. So how did the idea, but also for you to go and run this kind of come together? This episode of the Kara Golden Show is brought to you by Rula, affordable online therapy that fits your life and your schedule.

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Yeah, I think it's been a culmination of, you know, throughout my career, I've always tried to be a catalyst to make things go faster, either in terms of speed or scale. And while I was at Fulcrum Therapeutics, as we were evolving the platform, I just had this kind of aha moment where I was like, you know, I think we could actually scale this now. Instead of traditionally just thinking of one idea at a time per project, we could actually screen across hundreds of potential projects all at once.

And that was due to advances in technology. And to me, this was the idea of like, I could go out and build a drug machine, basically, where all at once I could interrogate hundreds of diseases and find potential solutions and also build what we can refer to as a lab in a loop, where we have a traditional lab that everyone might envision with benches and you're doing work in the lab generating data. And we're doing that, certainly. And then you pump that data over through the loop to our AI engine.

where that AI engine is now predicting novel drugs. And that information from the AI engine flows back into the lab to test those hypotheses. And so we now have this loop running. We've done a billion experiments in our wet lab in just two years, which still kind of blows my mind as well. The amount of data we can generate is really incredible. And that feeds into our AI component of this lab in a loop.

So you talked about three cases that you've taken existing FDA-approved drugs. Can you share any of those stories and kind of what you've realized in testing this so far? This episode of The Kara Golden Show is brought to you by Rula, affordable online therapy that fits your life and your schedule.

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Yeah, I mean, one certainly to highlight is this in autism, working with this family with a seven-year-old child who has severe autism, nonverbal, needs 24-7 care. And through this work, we first identified, well, what's the genetic alterations at the gene level in this child? So we understood what genes became affected.

And then we took cells from her skin and her parents' skin and generated neurons, nerve cells in the lab and started testing the different drugs that we have here in our lab and tried to identify ones that would reverse sort of the deficits in gene expression that this patient had. And through all of that, we identified an FDA-approved drug.

as well as a disease gene signature that characterized this patient's disease state. And their pediatrician got access to this FDA approved drug using off-label administration. This young girl started receiving the drug and after about six months started to show cognitive improvement, was much more settled down. And

People who didn't know that she was receiving this drug, caretakers as well as teachers at school, all started to comment to the parents, like, wow, we're seeing such profound changes in your daughter. And even at home, just her ability to play with things and even things like we take for granted, like self-toileting. But what's also really amazing from the science side of things is not only did we discover something that seems to be effective, but

that, that disease signature that we identified from her cells, we also were able to observe when we took blood samples. And after we started to give her this drug, that disease gene signature became normal. Uh, and so we could connect the dots in a way as a drug hunter, you're trying to connect those dots, both in terms of efficacy or the effects, the positive effects, but is it working as you might intend? Uh, and so this is just one of these really exciting examples that, um,

It's why I got into drug discovery, right, to try to find medicines to help people. But as a scientist using applied technology to really understand a disease and find solutions and then be able to do that over and over again. So we're now trying to take a bigger swing in the autism space, trying to replicate the approach that we took.

So in the case of that, was the drug anything that pediatricians typically had used? Yeah, the drug was approved for pediatric oncology. So for treating cancer in pediatric patients. And what's really intriguing to me, at least, is that, you know, the doses that you would normally use for treating a pediatric oncology with this drug were fairly high.

And what we discovered is that it was at lower concentrations or lower doses where you saw this positive benefit in the context of autism. And so it turns out that you can use much lower doses that are less toxic than those higher doses. And so it really becomes kind of a sweet spot in terms of balancing the positive effects and not having as much negative effect.

So what is the typical process for somebody, use this as an example for autism. So somebody goes to a doctor, they, you know, they read their pediatrician, they read up on all of these things. Maybe they hear about a doctor somewhere else in the world who is, you

creating a cocktail of some sort that might be helpful. But there just doesn't really seem to be anybody that a consumer could go to, right? Or a patient could go to, to

Is that where your company is helping people? I guess, how does this scale or how do consumers deal with their problem? That's a great question. It's one of the ways in which we started with the company. And we've worked with over probably 40 different patient foundations at this point in these rare genetic disorders, trying to apply that same formula. What's the genetic cause of

Is there an opportunity where a small molecule drug could either treat the root cause of that disease? And are there existing FDA approved drugs? And that's a great way for us to, one, help patients, but also create very rapidly proof of platform examples, right? That the platform translates into the clinic and we have something that could be effective. But what we've also done now is beyond that, as I said, we're going into other therapeutic areas.

including obesity and oncology and others, because the platform lends itself to scale in this way. And so, yes. And there's a whole database in rare diseases, for example, where people have captured what are the genetic alterations that occur in many over 7000 diseases. And so we've used that as a database to intersect with our data to see, are we uncovering opportunities in rare diseases?

But as I said, that's part of the business model, but it's not the entirety of our business model today. So interesting. So AI is a hot topic in healthcare. How exactly are you using AI at Transcriptive Bio? We are using it simply to say that we are using it to predict novel drugs that would tune gene expression for therapeutic benefit. And how we do that is...

We're recording drug response data in our wet lab, right? As we screen drugs in cells, we're recording data across over 15,000 genes at a time that are expressed in a cell. So you and I, each of our cells express roughly 20,000 genes at a time. So we're capturing drug response across virtually every genes that's expressed in the cell. And that creates a really powerful database, right? To understand how different drugs affect over 20,000 genes. And, and,

That information creates a training set. Again, these billion experiments that we've conducted, it gets fed into and trains machine learning models for individual genes where we can now use AI where you can input a new chemical structure and it will predict whether that chemical structure will increase the gene expression or decrease it or have no effect. And this is really a novel application that we're taking here that I'm not aware of many other people doing this.

That allows us to virtually screen three and a half billion compounds at a time across these models, predict new structures. So that's how we're using AI today. And so we're using it to try to predict for efficacy in terms of modulating gene expression. But we're now in the midst of trying to see if we can predict safety issues as well. Right. Because those those you need to understand both sides of that equation.

I feel like what you're doing is you're really working across different drug companies, right? You don't, you're agnostic to whether you had worked at

Eli Lilly, you had also worked at Fulcrum, you know, doing rare diseases and early stage development. But now you are agnostic. We are. And I mean, if you think about it, right, if you just collected all the pharma companies together and just asked what are the major disease areas that they work across, oncology, metabolic disease, cardiovascular, immunology, neurology. Well, our platform is

can work in all of those spaces, right? And so the whole ecosystem of pharma and other biotech companies are really our potential partners. And the way I think about it is we're going to get multiple bites at that apple, if you will, right? We're not just in the oncology space, right? We're across all of those areas. And so as a business and a business model, we have a large opportunity to have partnerships with multiple pharma companies across multiple different areas.

What is the biggest challenge in, I'm sure there's multiple challenges, but what's one of the biggest challenges that keeps you up when you think about all of the amazing work that you guys are doing? Yeah, I think one is, of course, how do we remain focused, right? We can do a lot with this platform, but how do we focus on what's really necessary today for the stage of company that we are?

And so we spent the last couple of years really in kind of this origination phase, I would call it, where you have to hire the team and build the platform and generate the data and generate some proof points. And now we're really entering into what I would describe as this takeoff phase where I think we're really going to scale. And so for me personally,

It's, you know, again, what are the necessary steps we need to take to scale appropriately without diluting ourselves out over too many things and not really achieving much on any one of them? So that's one. I think the other is just we've made a lot of headway in neuroscience with the early data sets that we've generated.

How do we now start to generate some other data sets in other disease areas to further substantiate that business case where we can work across multiple therapeutic areas? Definitely. So in working with previously FDA-approved drugs, is there now you're –

you're educating people on how they can actually be used for other diseases out there. Is it faster to actually use those or how is that? You can certainly, you know, so two things, one, you know, we definitely, we certainly work with FDA approved drugs and that's not the only thing we do. We discover novel drugs, but working and working with FDA approved drugs is

It certainly can go faster because these are, in fact, approved. And so there's a lot of data that's been created in humans already that can support your clinical development in your new area. So you definitely can go faster. It's already known how to make these molecules at scale. And so it can be cheaper as well. So that really is the attractive part about repurposing, which is it just can go faster.

Yeah, definitely. So the regulatory landscape for biotech is tough. AI obviously is helping you to go faster and faster. But as you start to navigate these technologies,

new boundaries, I guess, like what's going to stop you from being able to, I mean, is it these regulations? Is it what, I mean, how do we make this faster when somebody, when you're actually seeing that something is working yet, you might not be able to have somebody use it yet, right? Yeah, I think the FDA certainly is, I think in the last several years made great strides to try to really streamline drug development.

particularly in areas like rare diseases, for example, but overall, I would say. And so I don't really necessarily view the FDA or the regulatory process as holding us back. I think you have to do the right experiments, right? To demonstrate first and foremost safety, right? We don't want to hurt people, but you also want to, when you have something that's effective, how can we streamline getting that to patients and in a cost effective way? Well, we'll hold us back large. I think as a company, as we grow, we,

It's just I think we're going to have so many opportunities before us to pursue that. That's why partnering becomes such a big component of our business model. And there's already a whole ecosystem of pharma companies out there that can do drug development very well. And so rather than us trying to rebuild the wheel, right, reinvent the wheel, right.

let's leverage that ecosystem of really well-qualified partners to take all of the, I think, amazing ideas that we're going to come up with and opportunities to take those things forward. How much of this is genetic as you start to look at diseases versus, I don't know, like the environment? I mean, you hear about a lot of different aspects of it, but do you believe that most of these diseases are genetic?

I think the basis of most disease is in fact genetic, but certainly your environment plays a part in that, right? And how that disease manifests itself and when in some cases. But what we have tried to do is really index like anybody would, I think, where the genetics tell you that this gene causes that disease and it's unequivocal. You've now discharged a lot of risk in terms of I'm focused on the right thing. And so-

When you can start from that place, and it's not just my position, the industry and people who have been in this space for a long time would say the same thing. That is, when the human genetics really inform you in terms of what causes a disease and are there examples where if you alter that, it would be effective, that's a great place to start from. And fortunately, the knowledge around the genetic causes of disease has really grown. And so it creates a really fertile ground for companies like us to jump in and get started.

So you've worked in large companies. You've worked in smaller companies. This is your first company that you founded. You're the CEO. What's the hardest part of taking on that challenge that you've seen?

I would say the hardest part, it's not like building teams with people, whether you're in a large company like Eli Lilly or a smaller size biotech like Focum or a 15 person company like Transcriptive Bio. Building teams with those principles, I think are all the same. I think the hardest part or the biggest challenge has been

the concept of cash runway and managing to that, right? And really trying to extend it in a small company and being the CEO of the company, you know, that really that responsibility falls on me. When I was at Eli Lilly, no, I've never thought about cash runway of Eli Lilly or did we have the money to do it? But certainly being the CEO at Transcriptive Bio, I think about that a lot. And being the CEO, you really feel responsible for everybody at the company and you don't want to let them down.

Yeah. And I think also the focus part. So no one else is going to focus you, right? I'm sure you have plenty of people who are saying, hey, let's go do this. Let's go do this. And you've got to hold on to your North Star. You do. I mean, I'm really fortunate to have a very supportive board. More

We're all very much aligned in terms of the company we want to build and the direction we're going. So I'm really fortunate I can reach out to them and just kind of bounce ideas off of them as well. So one exciting thing that has happened at Trends CryptoBio that maybe you didn't anticipate would ever happen, right? Obviously, you had a big idea, a big North Star for what this company could be, but maybe something that happened that you were just like,

oh my gosh, this has happened and this is really great. I think the, you know, building this AI platform in conjunction with the wet lab data, I always knew from the wet lab data, we would find opportunities and be able to move those forward. But to build this companion AI capability and not only to build

see it build and come along. So in the matter of months, right, we had this machine learning models. We did our virtual screen. We had some of them molecules made and tested back here. And the molecules were active the way in which we predicted that just kind of blew me away and like, Holy cow, this works. And now we're really, we're off to the races basically.

That's amazing. How many, like are a lot of drug companies using AI at this point, or do you think it's still in its infancy? I think every drug company is using AI in some form, right? And some in guiding, you know, synthesis of compounds, right? Or manufacturing and incremental applications there.

We just happen to be using it in a way that suits our mission. But I think every pharma company, every drug biotech is using AI in some format.

So if a scientist came to you and said, I want to be an entrepreneur just like you, Chris, and build something and launch something, what's the first thing that you tell them? Beyond, you got to have a great idea that's going to scale, etc. It sounds great to be the CEO, but there's a lot that comes with that.

Yeah, I think there's a lot of the thing you have to be resilient, right? And prepare yourself for this roller coaster ride of the ups and downs. Because yes, if you have a great idea, right, and you have people that will support that, and you've got a plan to kind of move that along, you can do those things. But I think you have to prepare yourself as an entrepreneur, just kind of emotionally and to be ready to go on these ups and downs. And, you know, there are moments when you got a couple months of runway, and you're trying to

figure it out. But then all of a sudden, right, things happen that they happen because you did the things that led up to that. But I think you have to prepare yourself for kind of that up and down roller coaster and, and don't necessarily take it personally, I would say, right. It's just, I think everybody goes through it. Uh, but it's just part of just prepare yourself for that. So what is last question? So what is success for transcript of bio and what is the, uh,

five years from now, maybe it's two years from now, maybe it's six months from now. What is really success for Transcriptive Bio that you are looking forward to? To me, success is really bringing to life this idea of this drug machine. That is, we will be discovering new potential medicines across virtually all therapeutic areas all at the same time.

and partnered with virtually every pharma company, and perhaps even taking some of our own molecules into the clinic and developing them ourselves. And in five years, I would imagine that, and I believe we will either be a public company that is generating a lot of revenue through this type of model and doing things faster and better, or we will develop something so fantastic that some pharma company will probably want to buy us up.

I love it. So Chris Moxham, founder and CEO of Transcriptive Bio, everyone needs to pay attention to this incredible, incredible company that Chris is building, a powerful reminder of the impact innovation can have when it's driven by both

Science and Purpose. So Chris, thank you so much for coming on and for doing all you're doing. I'm very, very excited about what you're doing and can't wait to continue watching you. Well, thank you so much, Cara. It was great to be with you today. Thanks so much. Thanks again for listening to The Cara Golden Show. If you would, please give us a review and feel free to share this podcast with others who would benefit. And of course, feel free to subscribe so you don't miss a single episode of our podcast.

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