The Bioverge Podcast: Changing Drug Discovery with Transomics

On this episode, Neil sits down with Samantha Dale Strasser, co-founder and chief scientific officer of Pepper Bio, to discuss the company’s computational drug discovery platform and how its transomics approach can lead to the discovery and development of safer and more effective drugs.

On this episode, Neil sits down with Samantha Dale Strasser, co-founder and chief scientific officer of Pepper Bio, to discuss the company’s computational drug discovery platform and how its transomics approach can lead to the discovery and development of safer and more effective drugs.

Available on SoundCloud, here.

Available on iTunes, here.

Full Transcript:


Danny Levine (Producer)
You're listening to the bio verge podcast with. Yeah, we've got Samantha Dale Strasser on the podcast today for listeners who are not familiar with Samantha, who is she?


Neil Littman (Host)
I am thrilled to invite Samantha to the show today. She is the chief scientific officer and co-founder of Pepper bio. Before Pepper, she was a national science foundation, graduate research fellow, a Churchill scholar, or a Goldwater scholar. She pioneered various omits related analysis and approaches in electrical engineering and computational science at MIT. So she has a wealth of knowledge that she brings to Pepper. Pepper describes themselves as the world's first transomics drug discovery company. They use a stack of proprietary transomics data and analytics to identify promising first-in-class therapies, rediscover new uses for existing therapies there. Talk about being able to rescue drugs that have maybe failed in their own development path. And so what does all that mean? Well, to me, in very basic terms, that means they are leveraging a stack of technology and data and attempt to try to improve the probability of successfully developing a new drug as frequent listeners of the show.


Neil Littman (Host)
Know, that's a common theme we talk about, and I'll just give a couple of data points about why it's a common theme that we talk about, because I think this is really important, right? That the cost of developing a new therapy, I think it's currently estimated at about $2.6 billion, right? That includes the cost of not only developing that approved therapy, but all the drugs that failed along the way, the success rate of developing a drug going from preclinical to FDA approval is somewhere in the single digits. I've seen stats ranging anywhere from like five to 9% range, but not very good. So if a company like Pepper can move the needle, even by a percentage point or two, right, that has a tremendous impact on the timelines, on the value creation and ultimately on patient lives at the end of the day. So I'm really excited to talk to Samantha about the stack of technology that they're implementing to try to improve drug development.


Danny Levine (Producer)
We're seeing drug discovery more and more becoming a data science. As you look at the landscape of companies bringing various data streams and AI into the drug discovery process, where does Pepper sit, how unique is what it's doing.


Neil Littman (Host)
That's really what I'm excited to talk to Samantha about what are the inputs of their platform? What are the outputs, what are the types of data that they're analyzing? How are they going about building up? What is common these days for these types of tech bio companies is this data moat. Samantha has described, what they're doing is as analogous to that, the traffic navigation app ways. And, and I love that. I'm excited for her to talk about that, but, th the AI or machine learning, being applied to drug development, drug discovery, right. It's very buzzy these days, but I think there's, there's a lot of there that I'm excited to dig into with Samantha and particularly around this notion of this transomics platform that they built. I, I think, that, that is there to differentiate or so, like what does that mean? What are the types of data sets that they're collecting?


Neil Littman (Host)
How are they analyzing that and how is that leading to better informed drug discovery and development efforts?


Danny Levine (Producer)
Well, if you're ready,


Neil Littman (Host)
Let's do it, Danny. I Samantha, I'd like to say a big, thank you for joining us on the show today.


Samantha Dale Strasser (Guest)
Thank you very much for having me pleasure to be here.


Neil Littman (Host)
So, Samantha, today, we are going to explore how new technological advances, things like artificial intelligence and machine learning are bringing about cures to diseases that we've previously thought to be incurable. This is a topic that is near and dear to my heart and something that I really love discuss as most of our listeners on the show. Now you're one of our major focuses at bio verge is this notion of bringing science fiction to life. So I'm really excited to talk to you about your experiences and what you're working on at Pepper bio. Before we jump into some of the more technical parts of the discussion, if you're comfortable talking about it, I'd love to start on a more personal note and talk about the experience you had with your father's illness and how that has influenced your career. Would you be open to discussing that experience?


Samantha Dale Strasser (Guest)
Of course, most certainly. It's a topic that's very near and dear to my heart. That really has been a huge driver to the impact that I've really focused on for my career and specifically for Pepper itself as it's been growing. Just to kind of round out in the color in the stories. I grew up in a small town in central Wisconsin was an only child in a close-knit family of three. My mom, my dad and myself were really a, a tight group. It was something that, when I learned in grad school that my father had frontal temporal dementia, was it really, frankly, my name is earth-shattering aspect for my family. We had never heard of the disease. We quickly went into our usual problem solving mode, right. We went, okay, we're going to find treatments. We're going to talk to doctors. We, we're optimistic on the onset to be completely honest and was the first experience that I, myself and my really had with the reality of many diseases that there just simply aren't treatments.


Samantha Dale Strasser (Guest)
We went to some of the best hospitals in the country and kept finding the same narrative that like with many neurodegenerative diseases, they could track the disease, they could do imaging, but there wasn't an option. That for me, really drove the problem I wanted to solve. I had loved for years science and the work that I was doing, but just how much that feeling of knowing how much your world stops when there are no treatment options really led me to see that's where I wanted to make my impact. And so in the work that I had done during graduate school and the work that we're doing now at Pepper was really focused on that end facet of how can we ensure that it's impactful to reach a person, a human, a patient at the end of the day. And so it starting Pepper bio that's been central for both myself and my co-founder.


Samantha Dale Strasser (Guest)
Unfortunately, he also had experiences within areas that were seizes, had no options, and it's really been a facet for us. That's the foundation to our goal of treating the untreatable.


Neil Littman (Host)
Samantha, thank you for sharing that very personal story. And I think like so many entrepreneurs in our field, I get this almost overwhelming sense of how deeply personal and meaningful your work is to you and the mission that you're pursuing at Pepper and before Pepper. And, and I don't want to go off on a tangent here, but I will for a moment. So parallelism and.


Samantha Dale Strasser (Guest)
That's okay.


Neil Littman (Host)
I just finished rereading a wonderful book called a man's search for meaning by Viktor Frankl. I think I probably read it for the first time in college, but, long story short, he talks about his experience being in, I think, several concentration camps during the Holocaust, and even during a worst situation imaginable for human beings or one of the worst situations manageable, right? Some prisoners were still able to find meaning or a sense of purpose in their life. So, as I think about tying this back to our discussion, right, as an investor, when I'm looking to invest in a new company, a new entrepreneur, new team, right. One of the characteristics that I always look forward to this idea of grit, right. Passion and perseverance. Another way to think about that is through this idea of a purpose, right. Do you drive a sense of purpose from the work that you do?


Neil Littman (Host)
I think, your story certainly highlights, that is a critical component of your day-to-day work. I think that we see that oftentimes in the field that we're in. So I think it's critical. So, so thank you for sharing that story and just, I guess, getting back to your story, how do you see, you know, that, that experience that you went through with your father's illness, you know, directly influencing the work that you're doing today at Pepper.


Samantha Dale Strasser (Guest)
Yeah. So, I mean, I, it all comes down to, I think, when we're making a decision within pepper and for the work that we do, it comes to asking, well, will this impact a patient at the end of the day for choosing a, say, a area to focus on it's focused on impact. It's a focus on, can we make a tangible leap forward in this space to drive towards that end clinical impact will, are the science and the experiments that we're doing informed a decision that will be adopted clinically. I mean, I think that's something that definitely is a common thread in all of our discussions is the patient impact side. That again, facet personally of seeing, that part really drove that home. For a lot of our team, I mean, I think, you mentioned the grit aspect. That's why, that's why we've all really come to the medical space of seeing the need and wanting to help people at the end of the day.


Neil Littman (Host)
Yeah. I think it's a stark reminder of why we all do what we're doing, right. There's a patient at the end of the road. And, I don't need to remind you, but at some point in our lives, we're all patients.


Samantha Dale Strasser (Guest)
Yeah. It's very true. It's a very central of the human condition in many ways. I think a lot can come from, you mentioned the grit facet for the book you read of humanity sometimes it's that pressure that really drives a lot of innovation and change and growth in humanity.


Neil Littman (Host)
Absolutely. I couldn't agree more. So as I think about, and look at the way drug discovery is conducted today, what you're doing a Pepper is, is slightly different. You have an approach that is focused on, I believe what you call transomics and in particular phospho proteomics, before we dive into the nuances of what those particular terms mean, which I do want to make sure that we describe for our audience, let's make sure we're on the same page and start with what's wrong with the drug discovery process today.


Samantha Dale Strasser (Guest)
This is a super question. And when we talk about a lot of Pepper, because if we think about, you know, people often talk about pharma as being the super expensive industry, right? Like how much does it cost to make a drug is astronomical in the billions, but let's, if we really think about the problem, it really comes to flipping one's perspective and less about the, this year costs itself becomes two. Why is it so expensive? Why is it so expensive? Why do we not have the breadth of treatments that we'd like to have for many diseases? If we break this down, a lot of it comes down to simply the probability of success of a given drug. I mean, it's a stark number being, say throw 3% is an estimate that's usually given. It really comes down to biology is really hard. We have a problem in the industry of how bringing the right information to the table at each step and seeing are we really bringing in the most recent advances to do so and so to overcome that challenge of that probability of success at each stage really comes to the breadth of technologies that can really start to improve our decision-making at each step.


Samantha Dale Strasser (Guest)
This can stem from, in drug discovery. The first step is usually finding a target what to go after to treat the disease. That's the first foundation to really make sure that starting point that Dr. Wen is going after is correct. The more wet each step, either finding the target, the next step of finding the drug to best often inhibit that target is a really key facet at each stage to having the true disease complexity at every step that leads towards a high probability of success along the way, which can lead us not only to having more treatments, it can also lead to more effective treatments with lower toxicities. That's something that for us at Pepper is really key that when we talk about treatment for us, that really means that when someone goes to the doctor, right, and they're diagnosed that they have versus an option to begin with and an option that will be effective for them, that one of the problems also in the industry stems from some treatments, just not working for a fraction of patients.


Samantha Dale Strasser (Guest)
It again comes circles back to that same, having the right, early definition of a disease to have that correct probability of success at each step along the way.


Neil Littman (Host)
Yeah. And I think that's critical. And, and Samantha, you alluded to some of the numbers here, but just to give our audience some context, I think the latest data points about, from it costing about $2.6 billion to successfully develop a new therapy. Of course that includes not only that drug, but all the failures along the way. You threw out the number about 3% for, probability of success, right. I've heard, 3% up to about nine or 10%. Varies depending on what stage of development you're talking about, but regardless, single digits, very low probability of success. So then it sounds like the real focus of Pepper and your platform is really trying to increase the probability of successfully taking drugs through each stage of development. With, it sounds like a particularly focused on the earliest stages. I think this would be a nice time to circle back to my prior question about this idea of transomics and fascial proteomics as part of your platform.


Neil Littman (Host)
What do those terms mean? You can describe them to the audience.


Samantha Dale Strasser (Guest)
Yeah, absolutely. I guess just to also, to comment on the, the past of helping in every stage that you had mentioned, I want to just to mention that it is from early in preclinical, but through, to the clinical stages, it's something that we're excited to bring breadth of understanding and increasing that probability of success, even down to the level of what patient do we give this drug to. At something that, with those, terms all next delve into is a really central part to the platform facet of our technology and trans omics, and phosphoproteomics yes, to rather big words and there, it just syllables themselves. And it's, I'll start with trends. Transomics is I think that's, you know, we talk a lot about how we're the world's first transomics company. What we mean by that is it's a fully integrated, comprehensive view of biology that really tells you the context of what's happening at a systems level within a disease system or within a drug.


Samantha Dale Strasser (Guest)
This transatlantic approach, it integrates across a range of Omex data types. Many folks are familiar with genomics, and we had an over 20 years ago, the first sequencing of the human genome phenomenal advance for the field and the industry and says that it's essentially been tracking closer and closer with data types to get towards biological function. So genomics. Those underlying instructions of what can happen, there's transcriptomics, which are really kind of the next stage along that biological information path, which then leads to proteomics. A measurement of looking at proteins within a system, the molecular actors within a cell, but that's, again, still only looking at how much of a given actor is there. The last key step is really what is that protein doing? That's where the word fossil proteomics comes to play. Phosphorylated proteins are a type of modified protein, specifically a phosphate group that's added on and off of that molecular actor of a protein.


Samantha Dale Strasser (Guest)
It's with that modified protein that we drive at really understanding what that proteins function is doing. Adding these molecular groups can affect a protein shape, which then affects what it can do with NSL, what molecules it can bind to what effect it has on say a facade lives or a cell dies. It's something that molecular impact of understanding that functional state through looking at the data type of fossil proteomics unlocks this whole new set of understanding of really what is happening in a disease or when a drug is applied. We often liken it to the ways for drug discovery, by bringing in this type of approach. That what that means is we're essentially taking the biological equivalent of real-time traffic data. Mean if you think about it, many of us have, over time been familiar with the level of, we used to have big paper maps that gave instructions of where roads were, but say weighs itself was a huge advance in the tech space for how we navigate around.


Samantha Dale Strasser (Guest)
That now, by having context of where cars are on the road, where traffic gems are this much more kind of systems, level facet of what's going on and a dynamic facet, it's the same dynamic facet that we through using trends Elmax and phosphoproteomics can understand and biology to navigate towards the most effective decision and most effective drug down the line for a patient.


Neil Littman (Host)
I, I love that analogy, linking it to the, the ways app and, just like ways there's a community aspect to that technology. If I understand correctly, there's a community aspect to your technology as well. The more it's used, the more it can do. In other words, how can you talk about that? How customers use the platform and its capabilities?


Samantha Dale Strasser (Guest)
Absolutely. I guess, I mean, first, just a point of note, our, I mean, our collaborations are a huge aspect of what we do and building understanding of biology as a whole. I mean, that's something that we see, in our own technology and for it, for the field itself. For us, the community aspect comes down to the data that we generate. We in working on our own programs or in partnerships with pharma, that data that we gleaned from particular studies only works to fuel and further improve our platforms, understanding itself, that feedback loop, that we've actually, in some sense, we've seen in the tech industry that we're now migrating over into this kind of tech bio space of a feedback loop that brings together understanding really enables, improving our platform technology over time.


Neil Littman (Host)
I want to come back to this notion of data, because I think this is really critical and this concept of building a data mode. Cause I think you're right. I think this is relatively novel and it's a playbook that a lot of the, as you said, tech bio companies are using before we get to that though, I want to drill down into of the nuances of the platform itself. Specifically what types of data does the platform draw from, how was it used? What are you analyzing? Right. Walk me through just some of the details in terms of the inputs and outputs of, of the platform.


Samantha Dale Strasser (Guest)
Yeah. First, just in terms of the, I mean for inputs and outputs is kind of a crisp answer. There is fundamentally our input is this trends, anemic data of genomics, transcriptomics proteomics, and fossil proteomics back and modified protein data. It's through that data that we're able to analyze, to provide what we call a transatlantic signature, which really gives this context of understanding of the disease or the drug of interest of functional changes within a system to identify the impact of disease to drop after better targets or the other end of looking at, what is the drug doing to best characterize it's even on and off off target effects of a drug, but kind of collectively, what does that mean? Right. I think it's important to take a step back for a minute and think about, why we're bringing in these data types and what this approach solves for the industry in terms of key problems for the platform itself.


Samantha Dale Strasser (Guest)
If we think about, the industry itself, we spent a lot of time talking with folks in pharma and learned of kind of what they're were most hungry for, right? Like what are the, kind of going after the scientific side is what are they really looking for? What are the big gaps? That's where there were really three pillars that are, that we found were needed. This is where the technology that I've been building since my time in grad school, we really started to see, a pattern of what the need was and that were able to go after solving with this technology. Those pillars, the three are functional global and causal. What I mean by that is functional brings us to the bringing in of this more, the data types that are closest to it and biological response. This is where that phosphoproteomics data layer comes to play.


Samantha Dale Strasser (Guest)
By integrating this data itself, it brings in a more function, the most functional understanding, frankly, the biotech stack with integrating them still the context of what we see, what genomic transcriptomics and proteomics to capture that full complexity of a biological system. The second one that I've mentioned is global. This is in many ways, it's, in some ways I think a very clear choice once described, if it's wanting to look at the entire breadth of all of the signaling pathways that we can measure historically in the field, folks would, might look at say their, they often talk about, and we know they have a favorite pathway, right? Which is historically for how science just, began. That makes sense in terms of where technique started. Now we're at a point in the industry where we can measure, hundreds or thousands and tens of thousands of analytes, which this is where I get really excited on the technical side, because it's a phenomenal point.


Samantha Dale Strasser (Guest)
Now this insight we have into how biological system functions. By on our approach of ensuring everything we do is global and it's measurement it's unbiased, keeps the system wide perspectives. We don't miss something key that might be in a pathway that hadn't been considered previously. Lastly, is this aspect of being, bringing, driving towards causality. So, folks would often talk about how, a challenge or historically in the field would say, they'd look at individual measurements. They it's, look at what's significantly changed and have a list of maybe, 50 to a hundred interesting things to follow up on, but not a clear linking of this data together into a singular analysis. What we're driving for on our own approach is now in trends, omics, linking together these data types to really drive towards the understanding. That's closer to causality, not just say a correlation of the analytes themselves.


Neil Littman (Host)
And Samantha. I always like concrete examples. How, how are you using the technology today? I mean, I think all the things that you just went through are, are, are really amazing, right? I think there's definitely a long-term play there particularly around, the generation of that, that data and all the insights that you can glean from that data that you're collecting over time. You mentioned a partner or a couple of customers that you're working with today. How are you using the technology with them or on your own today? Are you using it more in the preclinical setting and the clinical setting? What types of diseases or therapeutic areas are you currently focused on?


Samantha Dale Strasser (Guest)
Yes, absolutely. It ends up where I was being used today. It's both for our own internal programs, as well as with partners and in both scenarios, it's understanding drivers of disease and better characterizing drug candidates themselves. To kind of add some specificity on each of those internally, this is with actually three pipeline programs that we're building out two of which are actually partnered with Dean Belcher's lab at Stanford. Where here using our technology to identify novel targets within oncology. We're building out in a MC driven cancers, lymphoma and liver, as well as the third program being within EGFRs mutant non-small lung cancer in parallel with those internal programs. We also have a partnership that we've done with a pharma partner. This is actually where it's been exciting on towards a looking at a clinical stage compounds. Again, showing breadth of application of our platform. With this partner, they came to us seeking additional depth of understanding of a clinical stage drug that they were working with and wanted to do have a broader context of the mechanism of action to enable both understanding better what they've seen in the clinic and to consider indication expansion opportunities.


Samantha Dale Strasser (Guest)
We with worked with them to collect data on samples, the in vivo samples that were treated with our drug of interest, we carry out our trends, ohmic analysis, and we're through the trends and MC signatures we created from those that data itself. It was a beautiful example of one it aligned with what was already understood about this clinical stage compound. It was frankly, fairly well studied to date, but it's provided with that, the context again of this global functional and causal view, new understanding above and beyond what they had known. This kind of new potential mechanism of action that they had yet to have identified, provided context to really seeing new indications that they could go after.


Neil Littman (Host)
I think this is a good segue into a discussion around your business model. You know, you had mentioned this idea of the tech bio sort of, you know, applying the technology playbook to, you know, you biotech in general, how do you tend to think about Pepper bio? It sounds like you have some customers, you have some partnerships in terms of helping others glean more insights into their pipeline. Compounds sounds like you are working on developing your own pipeline of drug candidates. How do you think about the business model for Pepper. It to be a, a drug developer on its own? It to really have a, a full stack of writing of different partnerships? Is it some combination thereof?


Samantha Dale Strasser (Guest)
Yes, no, it's actually, it is a combination thereof. We have a hybrid business model that actually focuses on both our internal pipeline. This is, as I mentioned, we're focusing oncology where we have three drug programs and that's in parallel then with our business model that is partnerships with pharma and this ladder stems from the fact that we have a unique ability to support pharma's key questions and challenges from the technology that we're already developing in house. This has actually been the beauty of a platform technology, where with this technology, we can continuously out new insights, new drugs for our own internal programs while in parallel applying that same approach to the questions that folks have within pharma. From that, it's something that, again, we can really contribute in two ways to bringing forth new opportunities for patients.


Neil Littman (Host)
And Samantha. I always like to ask this question, you, so you came from academia, right? You're, you're now chief scientific officer at Pepper. I mean, how is what you're doing at Pepper similar or different than the way that you worked in academia?


Samantha Dale Strasser (Guest)
What is different? I think it stems from really what questions are asked to some extent and what the end application is to an extent. I think there's, in both scenarios, there's a really big push towards, advancing forward new and exciting science and identifying new concepts within biology that can lead towards helping patients. But I think in I've found for a Pepper, we have a more, time-sensitive focus on finding options for treatments that can have an impact in a shorter timescale, I think has been a really key facet. Some, I think just stems from, I mean, academia versus a startup there's, somewhat different, methods of how things are translated at the end of the day in terms of some of those goals. That's for me, how and why I was really excited to found a company was to keep a focus on being the implementer of taking this technology and translating it to the end of the day, service that can be provided for patients.


Samantha Dale Strasser (Guest)
I think it's, on both sides, I think there's some similarities, right? As scientific condor, I have the, the fortunate scenario of being able to still push forward publishing and keeping, the same degree of scientific rigor. That's something I've liked about the role itself is the, maintaining the scientific side while also having a strategic angle towards building a company that can really translate that science to reaching patients at the end of the day.


Neil Littman (Host)
Yeah. Samantha and I think that that's key, I, I even remember when I was at that store, right. We worked a lot with academic investigators and, really licensing academic IP into building, early stage biotech companies. To my knowledge, there has never been a commercialized product that has reached patients that has not been in the hands of pharma or biotech company. Right. It just, it doesn't happen in academia.


Samantha Dale Strasser (Guest)
And they're both key. I think they address different questions at something there's questions. You'd ask an academia, you wouldn't see and say the industry startup side and vice versa. It's definitely contrasting but helpful to have both. So.


Neil Littman (Host)
I couldn't agree more. I think there are a tremendous compliment to one another. Absolutely. I, I want to circle back to the platform and maybe you could talk about the types of therapeutic modalities that the platform is best suited to. Are you mainly focused on small molecules? Does the platform work with other novel types of maybe cell or gene therapies or biologics?


Samantha Dale Strasser (Guest)
Absolutely. This is actually something that's been exciting for us for opportunities as a company, because we are in principle modality agnostic. It comes on at the end of the day towards we can characterize within our trends on my platform, modalities everything from small molecule, tar and AI. There's a range of different modalities that we can go after. That's partly even seen for, potential partners that we meet with even seeing now for our discussions we've had, but our own pipeline, that there's a lot of opportunity that exists that we can contribute to across the industry. I will say one thing that, we have, kind of honed in on within our platform is, the potential for therapeutic area itself. That's where we've identified, the broad areas of oncology where our internal programs are focused as well as inflammatory and neurodegenerative. That stems from really, what's already been identified of the role of phosphorylation in the basically, the Genesis of those diseases themselves.


Samantha Dale Strasser (Guest)
For kind of us really that facet of therapeutic areas been more, what's been focusing kind of how we identify potential in our own programs, as well as potential partners.


Neil Littman (Host)
Samantha, I want to circle back and dig in a little more and this notion of what it means to be at tech bio company. I think this is a question I always love to ask folks and get different perspectives. What is, what is being tech bio mean to you?


Samantha Dale Strasser (Guest)
Tech bio is a phrase that I've loved the adoption for the, because it's it really emphasized? I think the best of both worlds of bringing in a, a tech perspective, right. Of taking large amounts of data, really having a very, more kind of applique of a data focus kind of start that then drives towards looking at applications to questions in biology. I think it flips a perspective and an industry that had a similar pattern over the last several decades in many ways of how drugs are looked at. Tech bio, I think, starts to say, okay, let's, let's look, take a different approach towards going after identifying treatments. Let's take all of these new technologies from AI to mean machine learning, to looking at a, flipping the question of how we go about identifying new drugs and treatments. I think it also brings a lot of cultural facets from the tech industry that can beneficial of greening new insights to the drug discovery industry.


Samantha Dale Strasser (Guest)
Now that's something we've seen in growing our team, as well as bringing in perspectives that start to bring, what's been really effective from even just the tech industry itself over the last 10 years.


Neil Littman (Host)
Yeah. I, I think that's a critical point. Well, so I mean, you mentioned the, the technology aspect of it, but I think that the cross disciplinary teams is also a critical aspect, right. And, and that cultural shift really within the biotech landscape and what it means to develop drugs. I, and I think that's often not talked about enough, but I think that cultural aspect is super important here. And, culture plays a key role in building a sustainable and successful company. So I don't know if you have any comments around the, sort of the culture that you're looking to build at that Pepper, but I always find it useful to talk about some of the more qualitative aspects as well.


Samantha Dale Strasser (Guest)
Yeah, absolutely. I mean, it's something when my, so my co-founder, and I would go back over almost over 10 years now. We've talked a lot about, what really we wanted to grow as a company, the type of team we want to, to build around us and the type of culture that, it goes along with that. For us, a lot of that, I think stems from a, a really curious data-driven group of people. I think that's something that was what, for my co-founder and I, when we first started to get to know each other and come back, this was back when were in undergrad that we first met during our time at Northwestern. We saw early on just our own synergy of seeing new perspectives come to coming towards a common goal. That's something of having that common vision, but coming at it from different perspectives and angles has been part of how we've grown the team itself and wanting to keep that thread to challenge ourselves, to kind of push forward and building something that's broader than what we could do independently.


Neil Littman (Host)
Yeah. And I love that. I actually just brought up a tangential question for me. I want to circle back to the business model and in particular, the, the IP strategy here. Right. Obviously you're developing, your own therapeutic candidates, there's IP around that, whether it's, composition of matter or method of use, do you also think about having IP for the data or for the platform itself?


Samantha Dale Strasser (Guest)
Yes. On both in the context, I'd say, I mean, for the data, we do have proprietary data in house. That is something that is our own, we've talked about the data Motown and then the platform itself as that grows and that develops further. That is also something that is protectable as well. There's, I guess, and that sounds kind of a three-pronged strategy from the data itself to the platform, as well as the assets themselves.


Neil Littman (Host)
Yep. And, and if I remember my timeline correctly, you emerged from stealth in the fall of last year with support from NFX as a, as a, as a venture firm here in the bay area. W what are your plans going forward in terms of, either financing or , growing the business, in other words, like, where would you see yourself, let's say the next two to three years.


Samantha Dale Strasser (Guest)
Absolutely. In the next two to three years, I mean, our, one of our big pushes is our own internal pipeline itself. Building that out further as we also then advance the platform with both our investments and tracking with that pipeline, as well as through partnerships. That leads us to really, driving towards that vision of having assets that lead us towards treating the untreatable. I think it really, again, drives towards that end goal, but to build around really how, how we're doing about that a bit, and kind of be, the realm of where we started, you mentioned and effects as the investor that we had in our pre-seed round for us. They've been, what have you been thrilled with that partnership? It's one of the things that we've been really excited about that's come together and just the last year now, and that a lot came from, the alignment of really them knowing and us aligning on the platform capability to optimize for patient impact and to be able to go after really complex diseases.


Samantha Dale Strasser (Guest)
Within, since that investment in the last year, so kind of where we've, gone, we've accomplished a lot. It's been a, it's an, a phenomenal year in the sense of both growing the teams to five full-time team members, six phenomenal advisors, and initiating those three internal pipeline programs along with the partnerships that I've mentioned. Working with Dean Felsher, his lab at Stanford, going after, and reveling MC driven cancers, as well as partnerships with pharma as well. This, to moving forward, it's been really exciting just to see the sheer demand for approaches like ours, which has really launched that aim that I've mentioned of our ambition of, expanding our program those next couple of years to have assets of our own, that we really are moving forward. That's, from what we've seen of the need from both pharma, as well as investors, they've seen the draw of a product engine to really provide, continually kind of building forward these new insights in their own, right.


Neil Littman (Host)
That was Samantha. I think we could probably talk for the next two days about these topics, but I want to be cognizant of your time. I want to wish you the best of luck in your endeavors at Pepper. If the company is successful and even moving the probability of successfully developing drugs, by a few percentage points, right. That has a huge impact, not only on patients, but creates a tremendous amount of value along the way as well. Thank you so much for joining me on the show today. I really appreciate your time and a great discussion.


Samantha Dale Strasser (Guest)
Thank you, as well, Neil, it's been a pleasure, really enjoyed the conversation.


Danny Levine (Producer)
Well near, what did you think?


Neil Littman (Host)
I thought that was a really great discussion with Samantha. I mean, I think we hit a lot of these key points. I was hoping to dive into around the, sort of the details and the nuances of what is, what does transomics mean exactly? What does, they talk about this idea of fascial proteomics. W what is that? How are they utilizing those types of datasets to better inform drug development, either in their own pipeline or for their pharma partners? So, yeah, I, I really appreciate that, that discussion, and also around the notion of what it means to Samantha, to be at a tech bio company,


Danny Levine (Producer)
We've long seen technologies enter drug discovery that are supposed to improve accelerate, reduce the cost of drug discovery. Although every indication is that continues to rise. How long do you think it'll take to see technologies like Peppers provide cost benefits?


Neil Littman (Host)
Well, that's the billion dollar question, Danny, if only had.


Danny Levine (Producer)
Africa.


Neil Littman (Host)
Yeah, exactly. $2.6 million question, it's hard to say. What I will say is that there has been a tremendous amount of funding going into companies that are pursuing this type of, relatively novel drug development approach that are applying these pretty sophisticated, computer-based models, computational biology that incorporates AI or machine learning, or whatever buzz you want to throw in there into the process. It is still relatively young. So, it's hard to say when some of these efforts are going to bear fruit, but just judging by the sheer number of companies that are pursuing this pathway, the amount of capital and, and brilliant people that are working on this problem. I think it's a matter of when and not if, and, we've seen the first wave of some of these companies are now public companies. Some of the initial, products that they came up with as , lead compounds are now entering the clinic or are in the clinic.


Neil Littman (Host)
So we'll see how they perform. It's still relatively in its infancy, but I think the future is very bright.


Danny Levine (Producer)
Pepper talks about producing actionable insights, others miss, as we're able to dive deeper into the complexity of biology, do you think there's a risk of being lost in the data? Do you have confidence in the ability of AI to deal with the complexity and separate, meaningful from non meaningful data?


Neil Littman (Host)
Yeah. I mean, another really good question, Danny. I mean, I think there's a lot that, sophisticated computers are very good at doing that. People are not necessarily so good at doing. That is, that is crunching large data sets right now, obviously the computers are only as good as the people who program them at this point. Right. I think, having the, the human knowledge base and the human element being paired with a powerful computer that can more readily crunch, the large sets of data, I think is the evolution of where we're going in general, not just in the biotech world and drug discovery and drug development, but I think I would argue you could apply that across industries and across disciplines. So, yeah, I, I think there is going to be a lot of noise. There's going to be a lot of trial and error, but I do see this as a viable pathway to improving the efficacy and improving the probability of successfully developing novel drugs.


Neil Littman (Host)
Don't forget, we don't necessarily, we don't need to go from, a nine or 10% success rate to 90%. If we go from, whatever nine or 10% to 12% to 15%. I mean, even those incremental changes have a huge net positive benefit, both in terms of value creation, but also in terms of patient impact.


Danny Levine (Producer)
Companies with compelling platforms like Peppers often set out with a dual business model to both be a service provider and also build its own pipeline often though they ended up just building their own pipeline because it becomes more compelling way to realize value. There's been some exceptions to that, but they're very few, what do you think Pepper will need to do to be successful with this type of dual business model?


Neil Littman (Host)
Yeah, I think you're exactly right, Danny. I mean, I think there's a lot of businesses emerge and they start out in this or the partnership model and in some cases that can work well in terms of pursuing non-core therapeutic areas or non-core indications into the future. At some point I think it oftentimes can become competitive between the company's internal pipeline and the pipeline of their partners. At the end of the day, it all comes down to resources and focus, right? You need to focus your, your capital and your resources and your efforts on what you think will yield the highest value for your business, for your shareholders and what will have the highest probability of, of successfully getting something to the market. So, if you look at things on a, a risk adjusted return basis that is often developing your own therapies, right? Cause that has the highest risk, but also has the highest reward and on a risk adjusted NPV basis, that is often the better path, not always, but often is the better path.


Neil Littman (Host)
I think that's why we often see companies pursue that direction. That being said, there's, it's commonplace for biotech companies to have their own therapeutic pipeline and then partner a bunch of, their own products or have partnerships around other company's products as part of their pipeline as well. Again, and that's typically around non-core therapeutic areas or non-core indications. It's also just a way to number one, help de-risk what they're doing to some degree in sharing the resources of, each other's core strengths and, and oftentimes leverage some of the resources of a larger company.