Finding Your Position as a Biotech Startup: Atlas Biotech
Podcast
In this episode, Dr. Josh Reynolds, Founder & CEO of Atlas Biotech, joins host Maxwell Murray to explain how functional genomics can de-risk cancer drug development before a single patient is dosed. Atlas builds large-scale libraries of mutations, mapping the resistance landscape of a drug target so teams can predict which patients a therapy will help — and where it is likely to fail.
They trace Josh's path from a neuroscience undergrad and a family cancer diagnosis to spinning Atlas out of Dr. Justin Pritchard's lab at Penn State on the strength of an NCI grant. The conversation covers why roughly 95% of oncology candidates still fail in the clinic, how Atlas makes deep mutational scanning quantitative enough to translate into patient doses, and why the company is building a public resource to guide treatment for rare mutations. Josh also reflects on commercializing deep science — and the Radian Grant rebrand that finally gave Atlas a story to match its research.
Key Takeaways
Atlas Biotech uses functional genomics to map how mutations drive drug resistance, predicting which patients a cancer therapy will help before it ever enters a trial.
Josh's motivation is personal: his grandfather's lymphoma diagnosis during his undergrad years set him on a path toward cancer research.
Atlas spun out of Dr. Justin Pritchard's lab at Penn State, launched on the strength of an NCI small-business transition grant awarded the same month Josh defended his PhD.
Roughly 95% of oncology drug candidates fail in the clinic; Josh argues the real gap is translational models that don't predict real patient outcomes.
The old approach is retrospective — waiting for a drug to fail. Atlas flips it: forecast resistance pathways up front to engineer more durable therapies.
Atlas's edge isn't the mutation libraries themselves — it's making deep mutational scanning quantitative, translating data into patient doses via a dose-response curve for every mutation.
Atlas positions itself as a nimble collaborator, not a vendor: same-day execution and custom solutions versus the paperwork and lead times of large CROs.
In a risk-averse funding market, early biotechs need fast, affordable data that moves investor conversations forward — a gap in the U.S. market Atlas is built to fill.
Beyond commercial work, Atlas is building a free public database to help clinicians and patients with rare mutations find likely-effective therapies.
The Radian Grant gave Atlas language to match its science — turning "we build mutation libraries" into a clear story of de-risking the path to the clinic, and giving this discovery-stage grant recipient the commercial momentum to carry into its next stage of growth.
"If you wait until you're giving a patient medicine to look for failure points, you've already lost."
Maxwell Murray: Welcome back to the Radian Podcast, your go-to source for digital product–focused discussions on design, technology, and digital health advancements. I'm Maxwell Murray, your host, and I hope today's episode inspires your next big idea. Joining us today is Dr. Josh Reynolds. He's the Founder and CEO of Atlas Biotech. The company uses functional genomics to reduce risk in cancer drug discovery, matching therapies to patients using unique genetic profiles. Atlas Biotech builds tools that help teams anticipate resistance; their technology predicts clinical outcomes and supports smarter decisions in drug development. Josh's background is in biomedical engineering and translational research, and he took Atlas Biotech from an academic spinout to a valuable partner in the biotech space. Welcome, Josh.
Josh Reynolds: Thanks for having me, Max. I'm happy to be here — this is an episode I've been excited to record. It's been really nice to get to know you over the past few weeks.
Maxwell Murray: We've got a good amount of questions here, and we kind of want to start with the origin — the founding of Atlas Biotech. What originally pulled you toward working on cancer drug resistance and precision oncology?
Josh Reynolds: Yeah. I actually did my undergraduate work in neuroscience and spinal cord injury work. And while I was at my undergraduate studies at Texas A&M, my grandfather got diagnosed with lymphoma and pretty quickly passed away. I think that was the inception of my motivation for trying to work in cancer and help patients avoid a similar fate — give them a happy, healthy life if we can. So that's probably the source of my motivation. I came up here to Penn State, found some really good professors to work with, got involved with some good projects, and that's how I got to where I am today.
Maxwell Murray: I hadn't realized the personal connection there. And I've noticed that more and more as I get to interact with really brilliant, passionate, motivated people like you. There's this family layer, this deeper pursuit. It's not just academic.
Josh Reynolds: It's unfortunately common, right? I think everyone's been touched by cancer in some way. It's exciting to see so many people working on it, but there's still a lot of work left to do. There are always opportunities to help contribute to what will one day, we hope, be a cure. So that's what we're working towards — trying to improve how we develop and administer these medicines to patients.
Maxwell Murray: So let me ask you this: when did Atlas shift from an academic idea into something you knew had to become a company?
Josh Reynolds: Again, a little bit of a personal aspect to it. I was finishing up my PhD here and looking for what I wanted to do next. I initially planned to move to Boston and work at a company, or start a startup there. And then I met my now-wife, and she still had a few years left in her degree. So I said, "Well, I'm not moving anymore, I'm staying here." I had to figure out what I wanted to do locally. We're in State College, Pennsylvania — not a lot of biotech around here, unfortunately. So I was working on a pretty cool project in my academic lab, and we'd already talked about the possibility of it being a commercial product or service. We saw that there was a gap in the market in functional genomics — specifically looking at these large-scale libraries of mutations and a drug target. People had not done it commercially, and hadn't really done it well even in academic research. So we saw a gap of potential value we could provide, and I wrote a grant on it — a small business transition grant that I sent off to the National Cancer Institute while I was also trying to work on my dissertation and finish my degree. Not really thinking much of it — I was like, "We'll see what happens. I'm going to shoot my shot and see where it lands." And as I was preparing to defend, I got the notice of award. So I got this notice of award the same month I was defending my degree. I had about a week between my PhD being done and having to start the company, because the grant was kicking off and I was then out of the university. I had to find the space to rent and build a lab. It was a bit of a whirlwind. But that was the real ignition for the company — the NCI looked at it and thought there was something valuable there, gave us some support to get going, and we've been building ever since.
Maxwell Murray: Awesome. I mean, talk about a quick turnaround and juggling a lot. That sounds intense.
Josh Reynolds: It was. This was May 2024, and when I look back on it, I don't think I slept that entire month. I was just non-stop working, trying to get things done. I was also doing some national training programs that the NSF provides, and one that Penn State works on, so I was doing a lot of startup-related things too. I was not a human for a solid month there.
Maxwell Murray: Man, that's crazy. On autopilot a bit, I bet.
Josh Reynolds: I don't know how I made it through. I will say, give my wife a little credit — she just brought me food and put it next to me as I was working, like, "Please eat something."
Maxwell Murray: So besides the whirlwind of it all, what surprised you the most about the transition from academia to running a company?
Josh Reynolds: The speed at which things need to get done was the biggest transition. I've always been pretty good about making lists of tasks and then checking them off — I've always enjoyed that aspect of work. But in academic labs, typically you think on things, you talk things over, you try a few things out; it's a little more relaxed of an environment. And then once I was at the company — and I think this is not just startups but industry in general, especially in biopharma — there is a drive to get things done fast and efficiently. Especially when you have a clock: we have a certain amount of funding for a certain amount of time, and I don't know what's going to happen after that. So I was like, "If I don't start building now, we could run out of time." I've always been a little motivated to be quick, do a good job, and be efficient with our funds. That was the biggest difference I instantly noticed — I have to get things done now, I can't wait.
Maxwell Murray: There's pressure to execute in a different way, and it seems like you experienced that. We've had a few folks come over from academia to join the company, and one of the things they've said is the pace at which we operate is much faster — which is good. We're trying to be careful, do a good job, but also get things done and execute. There's a stat that kind of blew me away in previous conversations with you: 90% of drug candidates fail after entering clinical studies, and 50% of those failures are due to preventable efficacy gaps. There's a problem here. So from your perspective, why do so many oncology drugs fail in clinical trials?
Josh Reynolds: Well, it's really challenging to treat cancer in general. These are your human cells — your patient's cells — that have had some often very small mutation that has caused them to become cancerous and oncogenic. So finding a drug that is both effective against that cancer and safe for the patient's healthy tissue is hard. That's why it's been such a challenging disease to treat. What's staggering to me is that, despite all these advances in technology, we've stagnated in terms of our success rates for clinical trials in oncology. It's actually a little lower — about 5% success in oncology, so even worse than the average. I think the biggest gap is in translational models. People do a lot of really good work on the discovery side, on the biology, trying to understand the mechanisms of these drugs. But once you're trying to take a hopeful drug candidate that you want to put into patients — we haven't developed a lot of new assays in the past couple decades that are more predictive of patient outcomes. That's the big gap right now: these translational models. There's a lot of evidence suggesting that the classic in vivo studies are not very predictive of whether a patient will have adverse events, or whether a drug will actually treat the disease in a human. So that's where there's the biggest opportunity to improve our systems and be more predictive of whether a patient will actually benefit from a medicine.
Maxwell Murray: We're talking about a pretty big gap there, and I'm sure there's a lot of confusing information out there. One question we have for you: what are some misconceptions people have about predicting drug response and resistance?
Josh Reynolds: The classic way people have done it is retrospective. And that's never going to be successful when you're battling cancer specifically, because cancer is always evolving. It's always trying to find escape paths for whatever treatment you're using. So if you wait until you're giving a patient medicine to look for failure points, you've already lost — the cancer is going to figure out how to evade that medicine, and it'll probably happen faster than you can design new drugs to combat it. This is the whole idea of the lab I was in at Penn State, Dr. Justin Pritchard's lab: to predict and forecast where these failure points in therapies will happen, so we can engineer the minimum pathways of resistance and maximize the durability of these medicines. In our case, we're trying to look at a patient population where there are lots of mutations in a drug target. We have a good drug, we know it works, but we want to figure out what mutations will cause that drug to fail. We want to model all the potential mutations that might cause that drug to fail a patient, then predict which ones will actually be treatable and which ones won't. That way we know we're giving patients medicines that actually work — and we can define the treatable patient population for a new drug before putting it into a trial.
Maxwell Murray: So it sounds like the biggest gap there is having to be retroactive rather than proactive.
Josh Reynolds: Exactly. Historically, companies have been retroactive — probably a little bit trying to prop up their drugs. They don't want to look for failure before they have to. If they can get their drug approved and into the clinic, they're going to make some money and they're pretty happy. But what we're seeing now is that, as more medicines are developed, you need differentiation from what already exists. Being able to say which patients you can actually treat — and to forecast that — is going to be very powerful, as opposed to the classic retrospective analyses people do.
Maxwell Murray: And that ties into some phrasing I see you using in your communications and website material: you map the genetic landscape before your clinical trial. Proactive, not reactive.
Josh Reynolds: Yep. We want to get as much information as we can on a drug or a drug series before making that decision — it's a lot of risk to take a drug and put it in a clinical trial. So before making that pivotal decision, you want to have as much information as you can. That's what we're trying to do: improve our decision-making and the quality of data we have prior to escalating a drug into phase one.
"We can get data — but does that data matter? We're not just getting you a spreadsheet. We're trying to help you make a good decision on what to do with your assets."
In plain language: what Atlas actually does
Maxwell Murray: In plain language, how do you describe what Atlas Biotech does? I feel like it's a loaded question — "in plain language, how do you do this amazing thing?" Go ahead.
Josh Reynolds: It's been a challenge for me coming from academia. That was one of the biggest critiques I'd get starting out: "Okay, you said a bunch of words, no one knows what they mean." So it's something I've worked on over the past couple years. But in plain language: we make a cellular model. We take human cells, or some other model cell line, grow it in culture, and introduce a bunch of mutations into that cell line to represent what might happen in a patient. We're trying to cover as much of the genetic diversity a patient could have as possible. We use next-generation sequencing to keep track of how these mutations interact and respond to drugs, and we can do this very quantitatively with the technologies we've developed. After we run these assays, we can look and say: how does each mutation we engineered into these cells cause a drug to work, not work, or maybe work a little bit worse? We can be very quantitative and say, given a certain dose of drug in a patient, is it likely to actually help a patient with mutation X, or is it going to be ineffective — in which case we should look for a different drug?
Maxwell Murray: That's the mapping aspect we were referencing earlier, right?
Josh Reynolds: Yep. We call the libraries of cells a "library of mutations," and we're looking at the landscape of mutations in a certain drug target — a protein. So we're mapping the resistance of that protein.
Maxwell Murray: Excellent. How does functional genomics help predict how drugs will perform in the clinic?
Josh Reynolds: Functional genomics is a pretty broad term, and that's what we specialize in — functional genomic tools. This library of mutations, looking for drug resistance and predicting patient outcomes, is one application of it. But really what it means is we're making intentional changes to a genome — to a cell, to its genetic code — that we think might represent some aspect of a patient population, or a mutation, or a disease. And we're doing a functional assay: how does that mutation change how these cells behave? That could be a lot of things — does the cell grow faster or slower? Does it respond to this drug differently? It's a broad term, but we're using it as a tool to model the genetics and the diversity of humanity, of patients.
Maxwell Murray: This is all fascinating science and technology. From my standpoint, it's always fun to then think about the value proposition — what does it mean in the marketplace? What makes Atlas Biotech's approach different from existing CROs or research providers? And I'd even layer in another level: are you even a CRO? How would you define yourself?
Josh Reynolds: I've gotten a lot of different input on what we actually are, from advisors and people we've worked with. CRO is a fair term, but it's a broad umbrella that can apply to people helping run clinical trials, or people doing sponsored or contract research at the pre-clinical level. The way we want to interact with our clients is as a collaborator and a resource — not just a vendor or service provider where you say, "Hey, I want this cell line, do this mutation," and then you don't hear back for a month and you get something in the mail. We want to actually work with people to solve interesting questions and design new tools to do that. That's what we have fun with — we like the engineering and design. So that's what we try to present ourselves as: almost a sidekick biomedical engineering firm specializing in functional genomics. And we develop tools that we want to have as platforms — modules we can use to solve very common problems. To answer the first part of your question — what differentiates us from some of the larger vendors — some of them are adopting functional genomics, but they're not very quick to do so. Having seen the steps they've taken, they're using NGS, trying to build internal pipelines, but it's not to the point where I'd say they're near as good as us at building really high-quality libraries, thinking quantitatively about the data, and making translational predictions. These are aspects no one has done as well as we have. And — I'll toot our own horn a little — that's what we focused on. We can get data, but does that data matter? Can we actually use it to make decisions that are correct when it comes to treatable patients? So we're not just getting you a spreadsheet with some data. We're trying to say this actually can help you make a good decision on what to do with your assets.
Maxwell Murray: Anyone in business should always contemplate: am I just providing a solution, or am I providing impact? And that's what I think I just heard you say — as a partner in the biotech space, you can pump out data, share a bunch of information, but if it's not put through that lens of "how does this de-risk the program?"... So, coming to you for what you deliver versus the rest of the marketplace: do you anticipate a certain standard turnaround time, or will your operations facilitate quicker, richer updates that help scientists and program leads figure out how to make certain their target advances all the way to the finish line?
Josh Reynolds: One of the things we try to focus on is having more touch points — interacting frequently, working with people to solve problems. So yes, we're going to be able to be more nimble and work with teams to build a solution that solves their problem, not something one-size-fits-all. That's one of the challenges of working with the larger CROs and providers of pre-clinical services. Now, I don't want to say we can turn things around lickety-split — we want to do a good job. So the quality of the data means we might take longer to get there, especially on a custom solution. But that data will help make a more robust, stronger decision for a pipeline or program than you'd get with a slightly faster turnaround that ends up hurting your program in the long term. So maybe a little inverse logic there: we might not be as fast to get you data as some of the big CROs, but we can get you the right data faster — and that's what matters.
Maxwell Murray: Another thing we've discussed in the past: as you take on more clients, there's actually a compounding improvement in your value to the marketplace, which I find so exciting. When you address one project, that data helps your library improve — and there are even future ideas where you're not only a partner in biotech, but there's potential for clinical impact when you think about matching drugs to patients.
Josh Reynolds: Whenever we work on a project, we typically have to build a library of these mutations, like we talked about. And every time we build a library, that's another asset we add to our bank — we can use that library for other studies. We also always try to look at existing drugs as controls, at the very least. One of the things that motivates me is looking at how we can use this information to improve treatment decisions happening today. So one branch of what we're doing is building a database — a website that clinicians and patients can go to and say: if my patient has a rare mutation that there's no clinical guidance on — which happens a lot, especially for diseases where targeted therapies are used — they need help knowing which drug is going to be effective. We're trying to make an available public resource for these people to reference. We can't say "use this drug" — we're not clinicians — but we can say, "Here's evidence that strongly suggests, on a biological, molecular level, this drug is going to be more effective for your patient if they have this mutation. Maybe shy away from this one." That's going to be very helpful for patients with rare mutations, and it isn't currently available. It's a solvable problem, and that's pretty motivating for me. I don't know if we'll make a lot of money off of it — it's not really a commercial product — but it's something that will help improve treatment decisions and hopefully help patients have more successful outcomes. So that's something we want to add to over time, to give back a bit.
Maxwell Murray: I think that's incredibly admirable. And I constantly think about the age we live in — this age of artificial intelligence — which will only become more useful as high-quality, impactful, accurate data is made available for those models to leverage. So you heard it here, people: watch and continue to monitor Atlas Biotech as they build these libraries. This company is positioned to impact not only drug discovery but also patient-level outcomes, which is really incredible. You sit in two very interesting and impactful worlds, Josh.
Josh Reynolds: Thanks, Max. Helping patients is the motivation for us. I'd love to have a successful commercial business — I think what we're doing is valuable and will help biopharma companies — and, as you said, AI models need robust data sets that are built for purpose. That's another area we're trying to facilitate. But at the end of the day, what's the objective? The objective is to get better medicines to patients and get them the right medicine. The personalized medicine aspect of all this is the real goal. Us building pre-clinical models that are more accurate in translation to patients — that's hand-in-hand with having personalized medicine that actually delivers successful outcomes, gives people a better quality of life, lets them live longer. That's the real goal. So whatever we can do — every time we design a new assay, that's what we have in mind: is this actually going to model a patient well, get them a better drug, help them have more success with their treatment?
"We might not be as fast as some of the big CROs, but we can get you the right data faster — and that's what matters."
From qualitative to quantitative
Maxwell Murray: I want to circle back to de-risking. As I understand it, when looking at this type of data, the marketplace has been highly qualitative, and you're able to shift that to quantitative. What examples can you give of when someone engages with Atlas Biotech in a way that leads to different decision-making, as a result of having access to the type of data you facilitate?
Josh Reynolds: This specific tool we've been talking about — these libraries — is part of a field called deep mutational scanning. It's a technology where you look very closely at all the mutations in your library in a specific protein; that's what the field refers to as DMS. And DMS in a lot of functional screens is qualitative by nature, because you don't know exactly what you're measuring. It's a very relative measurement, and often there's a lot of uncertainty due to technical aspects. So what we've worked on and engineered over the last few years is: how do we take this really powerful tool that is qualitative by nature and make it quantitative — to where we can say, this isn't a relative evaluation, this is a measurement we can take and translate. That's the key word: translate. We want to translate that data into patient doses. We want to say, given how much of this drug is available in a patient, what's its half-life, how long is it there — we can tie those together, and that's where we get our translational value. We can be very accurate with our predictions because we have quantitative measurements, with units and everything. That's the goal, and this is the technology we developed. We can say with a high degree of accuracy what the actual measurements of each cell are in our big pool — our DMS pool. We can measure every cell and its growth rate at different concentrations of drug, and that lets us build a dose-response curve for every single mutation in our library. That's the core engine of what we're doing, and we can use it to evaluate lots of different things.
Maxwell Murray: I can't resist an analogy — you've probably learned that in our conversations. It makes me think of turning on your GPS. Imagine if it said, "I can get you approximately to Target or Starbucks, and we'll see, maybe you'll get there." Versus what you're doing, which is: every roadblock, every delay, every place you need to reroute — there's a high-fidelity picture, a data set that specifically points out where you're going to see resistance and how you're going to increase the durability of the dose. It never stops fascinating me, because I realize cancer is kind of a shape-shifter when it comes to treating patients. So for us to finally be having discussions of, "Okay, it can try to escape, it can try to move out of the way, it can try to avoid dosage, it can try to nullify what we're doing" — and we say, "No, no, no. Predictive. Recalculating to get to the destination of healing that patient." That's just really interesting.
Josh Reynolds: I like the analogy. We're trying to look along the route at all the different pathways the disease might take — and in this case, we're trying to make sure that there is no pathway. That would be the ideal outcome: no pathway for this cancer, this cell, to evade treatment. One fun aspect of what we're doing: the cells we're studying are modeling patients, and some are derived from humans. The fact that we can take a 2D culture — this is a little technical mumbo-jumbo, and there's a lot of cool work being done in 3D model spheroids — but we can take a 2D culture of cells in a dish, and just with some careful engineering and thinking about drug availability, drug binding, and pharmacokinetics, we can take a very simple model and make really accurate predictions of patient outcomes. And that's just the start. That's what gets me excited — this is step one to having a really good picture of personalized medicine, of pharmacogenetics. The way we can take this tool and apply it to other systems and models — I'm excited to see what we can build in the future. We're going to take this beyond just targeted therapies for very specific types of cancer. We want tools that work for all cancer types, for any drug modality. That's the potential of what we can do. And it's not going to be just us — a lot of people are working on cool ideas here. But we're in an age of really rapid advancement, and I'm hoping we see this translational gap — that 5% success — start going up. We want to help contribute to that. The potential of all these technologies is exciting.
Maxwell Murray: That actually checked off something I was going to ask — what excites you about this space and where it's headed. But now I'll ask another question, since you just inspired one. It sounds like there should be a platform developed for companies like yours to collaborate. Let me know if you agree, or if I'm behind the curve and there's already something out there. And here's your moment to point out some peers — "Look, guys, we think we're developing a protocol, a system you could hook into, and we could do some really great stuff together."
Josh Reynolds: There are some websites people use to find research services — like scientist.com and a few others. Those exist and are relatively well used. But most of our success and interactions have come through word of mouth, or just meeting people. This is a bit of an aside, but given the amount of outbound messaging from companies these days — whether AI-generated or whatever — there's a lot of noise in the digital world when it comes to marketing. So the face-to-face, organic, personal interactions are where I see a lot of value in engaging with people. That's something I want to keep core to what we do: working with people to solve their problems. It's one of our core fundamentals. If people have interesting problems, or a cool drug they want to get more information on, we're looking for collaborations and we're happy to help out. You can reach out to me — atlasbio.tech is our website, little plug. That's how I'd reach out to us. We're excited to work on cool projects with folks.
"The objective is to get better medicines to patients — and get them the right medicine. That's the real goal."
Communicating deep science
Maxwell Murray: There's a lot of data and information that comes out of the analysis and assay development you do. How do you communicate complex scientific results to teams that need to make strategic decisions?
Josh Reynolds: That's one of the more challenging aspects of what we're doing, especially if it's virtual — if we're trying to interact through a slide deck. So one of the things we've been developing is an interactive, website-based viewer for our data. I think that's relatively novel. Some of the larger institutes have things like this — if you look at some of the stuff at the Broad Institute, they'll have online resources to look at all their screens — and that was kind of the inspiration. But we want it as a data-delivery platform too. So instead of just sending someone a file with a bunch of Excel sheets, we want to give you actually interactive, useful information, where you don't have to say, "All right, I'll go do my own analyses on this." We can say, "Here's what matters out of this last data set we got for you. Here's how that's going to impact what will be successful in the patient population you want to treat." We want to take that to actual outcomes — can we tie this data set to patient outcomes? How does it impact your treatable patient population when you're post-clinical-trials and actually treating people in the market? So we want to do more than just get you data — we want to get you answers. That's what we're working on with this visual, interactive data-delivery platform.
Maxwell Murray: And when working on this company and building it out, what has been uniquely challenging about commercializing a service built on deep science?
Josh Reynolds: Well, aside from having to learn how to talk less jargon-y — that was the first hurdle for me. In the biopharma space, you're typically working with people who are very well-versed with the jargon of the current day, so that isn't too much of an issue. But when it comes to marketing, there's a lot of noise in the marketing space. So really finding those organic, meaningful connections — I don't want to say it's been the biggest challenge, because we've had some success there, but it's definitely one I'm always working on. It takes effort. I'm not just trying to get someone to give me money — we're trying to help people solve problems, and we want to make sure there's a good fit both ways. Besides that, I'm coming from an academic background; I didn't even know what marketing meant when I started the company. I was like, "Oh yeah, Google Ads or something." So there's a whole aspect of presenting the company professionally — disseminating what we do concisely and effectively. What is it we do in 30 seconds? That's something I've had to work on. So the whole business side, as opposed to the science, has been a learning step for me. That was probably the biggest challenge — thinking about it in a different way. We can do the science, but what does that mean for the people making financial decisions for these companies? That's what I have to understand and help improve.
Maxwell Murray: One of the things that really impressed me when we first met was that, even though you'd begun this journey not long ago, you already, as a newer biotech partner, had people who were, low-key, clients. You got to work and people started to engage you for your services organically. Can you speak to that?
Josh Reynolds: We started out in 2024 — I got booted out of the university, started up the company — and within about six months we had work we were finishing the planning for with a couple of companies. That just shows there's a need for help. And a lot of it is trust — these were people we'd been introduced to or met through video calls or a mutual contact. I'd chat with them, learn about their problem, and say, "Here's what we can do to help." And the ability to execute same-day was what sealed the deal. They were like, "Oh, you can get this started today?" And I was like, "Yeah, sure, I'll go collect cells now and get to work." So that aspect made it really easy. Like I said, we're small and nimble — there's not a lot of paperwork to go through, which is a big issue with some of the big CROs. And when it comes to the current funding environment, it's not great for biotechs. A lot of these earlier, Series A, seed-funded companies need answers, but they need them quick and kind of cheap. They don't need a full-blown in vivo study — they need a couple of data sets to help convince investors for the next round. That's where there's a bit of a gap, especially in the US marketplace. There are overseas solutions, but in terms of boots on the ground here in the US, there aren't a lot of options for small, nimble biotech.
Maxwell Murray: Perfectly timed and at the right level — again, being nimble, being that partner. We talk to biotech firms and observe the market, and we see a reservation to invest. There were stronger investments a number of years ago, and during the pandemic there was a big uptick — "biotech's hot." That's cooled off. Working with companies like yours, the discussion is about how to position you to better serve those standing up against the challenges of "where are we going to get funding, look at our burn rate, how are we going to get our ideas to clinical trial and eventually commercialize." And investors are saying, "I'm going to take it easy right now because of the market, and be very careful who I select." So anything you can do to de-risk — by engaging someone like Atlas Biotech — can provide that evidence, that reassurance, when people are more apprehensive to write the check.
Josh Reynolds: It's a very risk-averse market right now when it comes to funding. There's a lot of work that goes into getting a potential drug to the point you want to put it in a trial, and it's not cheap. Right now, funders and investors — and even the big pharma companies — want to see things as de-risked as possible. They're like, "Show me the success from your phase 2 trial and we'll talk." But people have to get there somehow. So we want to reduce that technical risk as much as possible. It's going to have to change, because right now there's just not enough money going into the early-stage work. As it stands, you need very cash-conscious, affordable, efficient partners to help you get at least the next step down on your pipeline.
"The ideal outcome is that there's no pathway for the cancer to evade treatment."
The Radian Grant & the rebrand
Maxwell Murray: I've got to ask this: Atlas came through the Radian Grant program. We're curious what made you open up and invest your time and energy into engaging with the program and diving into commercialization and positioning.
Josh Reynolds: Yeah — I don't know about correlation there, we won't make any assumptions. But again, I'm coming from an academic, research background. I don't know much about branding, marketing, that entire aspect. And I'd been told — this is a little revealing — by some of my advisors, "You should really think about redoing your brand; you don't use, like, medtech/biotech color schemes." I was like, "Oh man, well, I like my logo." But I wanted us to look professional. I'd given it the old college try to make a website and realized that's not my expertise. So I'd been looking for help already. And I think you might have reached out to me and suggested I look at it, and the timing was very serendipitous. I said, "Oh wow, this would be great — I can get some help with the weaker aspects of my branding, marketing, and website design." I saw it as an absolute win for me. And the help you all provided was fantastic. I really enjoyed working with you and Lindsay — that was a blast. Now we've got a very professional, clean website, and it's been great.
Maxwell Murray: All kidding aside, it's very important from a Radian aspect. These ideas are complex and not always easy to digest. It's hard to tell the story of what's taking place so that not only the highly scientific, technical mind can observe and realize the opportunity, but also everyday people. How can we make things as plain-spoken as possible without diluting the science or the impact to the point where the message is lost? It's so important for us to be scientifically accurate yet approachable and understandable, and also to underscore the value and upside to investors or future customers. Were there any "aha" moments as we looked at the material and thought about how to approach positioning and a bit of a rebrand for Atlas Biotech?
Josh Reynolds: I don't remember the exact language you used, but one of the things you wrote as copy for the website just distilled down what we did into a nutshell. The copy was perfect — punchy, but accurate and concise. That's the kind of stuff I've struggled with: taking this pretty complex product, service, research — whatever you want to call it — and making it into a very distilled but still meaningful piece of information that's accessible to people. That's essentially what you're trying to do: "Here's this complex idea in a nutshell, and here's what it means for you." And that's something you were able to do quite well — understand the science enough to put it into language that was concise and meaningful. Maybe the "aha" moment was: I'm not that good at this, I need help.
Maxwell Murray: Well, you gave us a great starting point. There was a brand and a website in place. And the substance always drives the ability to tell a story — without the work you do, without the innovative ideas you're bringing, there's no story to tell. My initial thought was, "Okay, so they make libraries." But then we went on this journey of what does that mean to the audience? And there was this shift of, "Yes, libraries are created, but what we're doing is de-risking the very expensive, very challenging path to clinical trial and getting your drug approved." That clicked for me.
Josh Reynolds: It's a bit of a human aspect, right? You're trying to think about what's going to matter to the person looking at this, and how it helps them. What catches their eye? What color scheme is going to look nice? There's a very human aspect to what you're doing, and that's perhaps challenging for a lot of researchers — to take the science and make it something personable. That's an art form, for sure. Watching the way you were able to take the data and the science and make it into something that is — honestly, I think our website's beautiful. I think it's like art. That was really cool, and it takes a lot of talent to do.
Maxwell Murray: Thank you so much — we'll relay those very kind words to the entire team. One of my favorite parts of what we worked on was the genetic helix work, and how we iterated and went back and forth. We wanted to nail that scientific accuracy — we even have sketches from you giving us guidance on how to handle it.
Josh Reynolds: That was one of the more fun conversations to have. I was trying to make sure there was the artistic aspect, but that it was still honest with the science — and maybe I was a little more particular about it than I needed to be. I was like, "We have to make sure it's three nucleotides in a row to make a codon." And Max was like, "Okay, but is it pretty?" So that was really fun to work back and forth — sketching out the designs was cool.
Maxwell Murray: It was fun to merge the science and the visualization, the graphic design, the art, into a final product. Follow-up: since launching the new Atlas Biotech website and brand, how do you feel the company is represented differently, and where do you think this takes things going forward?
Josh Reynolds: I'm hoping it really solidifies what we do in a way that's like a marketing tool. I want people to be able to visit the website and get a good sense of who we are, the science we do, and how we work. To me, it's a resource — something I can point to that's kind of self-explanatory for folks. I think we did a good job getting it to that point. So I'm hoping we can use it for outreach, marketing, and education too. And as we ramp up to what we think is going to be a full commercial deployment of some of these initial libraries, we're looking for people to work with, to do pilot projects with for new libraries. This is a really useful tool in our toolkit of outreach and discussions with people.
"Honestly, I think our website's beautiful. I think it's like art — and that takes a lot of talent to do."
Looking ahead
Maxwell Murray: That was the future of Atlas Biotech, which I think is genuinely bright and filled with opportunity. On a macro level, what shifts do you expect to see in oncology drug development over the next 5 to 10 years?
Josh Reynolds: That's very tough to predict — there are a lot of opinions on this space, so grain of salt with whatever I say. But as new modalities are being evaluated — to use the toolkit metaphor again — we have what we work on, which is targeted therapies, chemical medicines: inhibitors, modulators, binders. That's a whole class of medicines, and they're a cornerstone of oncology, so I don't think they're going away. That's a tool we'll continue to rely on and develop. But we also have the whole class of biologics. We have RNA-based therapies. We have cell therapies — people are doing in vivo cell therapies now, which is really exciting. We have gene therapies that are becoming, I hope, more accepted; I think that's going to be a really good tool, especially in more deadly diseases like aggressive cancers. There are lots of options, and who knows what it'll look like in 10 years. I think there are going to be a lot of different tools we need for different situations. But my hope in 10 years is that we've improved our ability to not just say, "We have a patient with this disease, give this medicine." I want us to be able to say: we have this patient, with this specific genetic background, this specific disease set — not an umbrella disease, but their disease. What's going to help them? I want us to get to personalized medicine and have that actually be the standard of care. That's what we need to move towards to have improved outcomes for all patients, not just some percentage. So that's my hope, and I think we are moving that way. It'd be great to see that in the next five or ten years.
Maxwell Murray: Ladies and gentlemen, we had Josh on the show — Atlas Biotech, a great company to check out. We'll share out their website and contact information on our channels. Josh, thanks for your time. Enjoy the rest of your day.
Josh Reynolds: Thanks, Max. I appreciate it — always a pleasure, and thanks again for all your help. It's been awesome working with you and the Radian team. Have a good one.
Maxwell Murray: You too. Thank you for joining us on this episode of the Radian Podcast. If you found this episode insightful, we'd love for you to subscribe on your favorite podcast app or YouTube, and follow us on LinkedIn or X for more updates and insights.