Alex Zhavoronkov: Beating Aging, Designing Drugs and Betting on China | Frameshifts Episode #12

Logan Thrasher Collins: The Trojan Horse That Could Fix Gene Therapy | Frameshifts Episode #14

On this show, we’ve talked a lot about gene editors. These are little molecular machines derived from or inspired by the Nobel-prize winning CRISPR system that put gene editing on the radar of the general public. Once these editors get into a cell, they can cut out DNA, change the DNA sequence, insert entire copies of genes or delete faulty genes that cause illness. Think of them as tools to cut, paste, backspace and overwrite the code of life. But the primary challenge with these gene editors, besides off-target edits and low-but-steadily-improving editing efficiencies, is that they need to get into the cell to be able to do anything. Getting gene editors into cells is an entire field of biomedical engineering called gene delivery. And in comparison to the rapidly flourishing field of gene editor tech dev, the field of gene delivery is both under-appreciated and relatively stagnant. And that’s a massive problem. Because without effective delivery vectors, gene editors are limited to a very small number of conditions for which our delivery vectors happen to be okay at. Current clinical successes are concentrated in situations where delivery is unusually favorable: * Liver diseases, where lipid nanoparticles can efficiently deliver cargo to hepatocytes after IV injection * Blood disorders and some hematological cancers, where cells can be removed from the patient, genetically modified ex vivo using lentiviruses and then re-injected into the patient (e.g. HSC therapy for sickle cell anemia or CAR-T therapy for leukemia) * Diseases that only require a very small percentage of cells to be edited in order to be functionally cured * A limited number of tissues, such as the retina or CNS where viral vectors can achieve therapeutically meaningful gene transfer That's the ceiling today. But if we crack delivery, the potential of this field is disgustingly profound. Gene Delivery Vector + Gene Editor = Gene Therapy If humanity can solve both sides of this equation, we can, literally, cure anything. You have a genetic disease? Here’s an injection that gives all of your cells the healthy copy of the corrupted gene you inherited from your parents. You’re prone to cardiovascular disease? Here’s an injection that gives you new human-designed molecular machines that clear out your clogged arteries. You have a family history of cancer? Here’s an injection that replaces your genome maintenance and DNA machinery with engineered supernatural versions that can protect your cells from cancer-causing mutations for at least 1000 years. Worried about keeping up in a world of artificial superintelligence? Here's an injection that carries the morphological instructions for a population of your cortical neurons to rewire themselves for compatibility with high-bandwidth brain-machine interfaces, connecting you directly into Dario or Sam’s frontier models. Now, I know a lot has to go right for humanity to achieve mastery over the code of life and transcend the limits imposed by “fit enough to have offspring” evolutionary biology. So I’ll get my head out of the clouds for a moment. Currently, the most clinically successful delivery vehicle in the emerging field of gene therapy is a family of viral variants that we call Adeno-Associated Viruses, or AAVs. AAVs are DNA viruses that scientists have hijacked to make little viral couriers that carry a working gene into your cells. Within this group are many serotypes and engineered capsid variants, each with tropism for specific organs and tissue types. Significantly oversimplifying: AAV9 can penetrate the brain, AAV8 is strongly liver-tropic and other capsids are tuned for tissues like skeletal muscle or retina. But somewhere between a third and two-thirds of people already have antibodies against AAVs, left over from ordinary childhood infections with the wild versions. If you’re one of them, a gene therapy that could save your life may simply not be available to you, because your immune system will neutralize the courier before it arrives. And even if you’re in the clear, most AAV therapies work exactly once. The first dose teaches your body to recognize the vector, so a second one gets destroyed the moment it touches your immune system. For a whole field built on delivering genes into people, the delivery itself is one of the deepest unsolved problems there is. Logan Thrasher Collins has spent his career on that problem. As a PhD student in biomedical engineering at Washington University in St. Louis, he invented a new gene-delivery modality called vaultAAV, which does something a bit strange: it hides the AAV inside a protein vault. Wow. Incredible. But what the hell is a protein vault? Vaults are odd, barrel-shaped organelles our own cells churn out in huge numbers, and nobody is entirely sure what they’re for [https://www.cellgs.com/blog/cell-vaults-the-next-big-thing-in-biology.html]. What Logan realized is that because the body recognizes a protein vault as self, they act as immunologically invisible Trojan horses. Cells take them up, the vault disassembles in the acidifying endosome, and the AAV escapes to deliver its DNA to the nucleus, all without the immune system ever getting a look at the AAV courier hiding inside. In this episode, Logan walks us through his entire scientific journey, from designing an aggregating antimicrobial peptide as a high school science fair project (which won the top prize at ISEF and got a minor planet named after him), to the insight that connected the protein vault literature with the AAV field’s antibody problem. We discuss the full landscape of gene delivery: AAVs, adenoviruses, LNPs, virus-like particles, and why the field’s relationship with each vector is driven more by reputation and vibes than by rigorous analysis of dosing and safety data. Logan also explains why vaultAAVs may enable something no other approach can: redosability. Currently, AAV capsids are engineered with surface mutations to escape antibodies, but each new variant can only be used once before the immune system learns the new epitope. Vault-shielded AAVs, because their outer surface is self, could theoretically be administered multiple times, allowing lower doses, reduced toxicity and lower manufacturing costs. Logan has since taken the vaultAAV work out of the lab and into a company. He co-founded Cathedral Therapeutics [https://www.cathedraltx.com/], a gene-delivery engineering company, alongside Prof. David Curiel, MD, PhD [https://radonc.wustl.edu/people/david-t-curiel-md-phd/] at Washington University, to build exactly this kind of stealth delivery system. In this episode, we also get into: * How 30 to 60% of people are locked out of AAV gene therapy by the time they reach adulthood * How hiding a virus inside one of your own organelles gets it past the immune system * Why vaultAAVs don’t just dodge antibodies but also boost transduction up to 5x (in some cell lines) * Why gene therapies cost $850K to $3.5M a dose, and how synthetic biology could make them cheap * How a high-school science fair peptide led to Logan getting a minor planet named after him * How we can engineer adenoviruses to cross the blood-brain barrier in order to treat neurodegenerative disease and mental illnesses * What it means that our cells are full of a mysterious organelle nobody can fully explain Gene editing technologists like David Liu, Feng Zhang and my friends in the AbuGoot lab have spent two decades proving that CRISPR-based systems can fix faulty genes. The much harder problem is how we can get gene therapies into everyone who needs them, and eventually make them as safe, cheap and ubiquitous as Ozempic. To do that, we need to be able to inject them more than once, and get them to our target tissues without the immune system getting in the way. That’s the problem Logan is working on. Watch on YouTube, listen on Apple Podcasts or Spotify. GUEST INFORMATION: * Logan Thrasher Collins, PhD, Washington University in St. Louis [http://logancollinsblog.com] * Cathedral Therapeutics [https://www.cathedraltx.com/] * VaultAAV preprint [https://doi.org/10.1101/2023.11.29.569229] * Synthetic Biology for AAV Manufacturing [https://doi.org/10.1021/acssynbio.2c00589] * X (Twitter) [https://x.com/LoganTCollins/status/2067740366820786503] CONNECT WITH US: * Website [https://frameshifts.org/] * Substack [https://frameshifts.bio/] * YouTube [https://www.youtube.com/@Frameshifts] * X (Twitter) [https://x.com/frameshiftspod] * LinkedIn [https://www.linkedin.com/company/frameshifts/] * TikTok [https://www.tiktok.com/@frameshiftspod] Get full access to Frameshifts with Benjamin Arya at frameshifts.bio/subscribe [https://frameshifts.bio/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Joe Betts-LaCroix on Retro, Reprogramming, and the First Real Longevity Drugs | Frameshifts Episode #13

Most longevity talk is still stuck within two bad schools of thought. On one side, weak-sauce incrementalism: supplements, vibes and tiny effect sizes. On the other, immortality rhetoric that asks you to throw out the data altogether. Joe Betts-LaCroix has very little patience for either. He’s the co-founder and CEO of Retro Biosciences, one of the most ambitious longevity companies in the world. Launched with an initial $180 million of capital from Sam Altman’s personal wealth, the company has since raised new round led by 4P Capital at a $1.8 billion valuation. Retro’s mission is to add ten healthy years to human life by going after the cellular mechanisms of aging directly. Betts-LaCroix is an unlikely person to be running a longevity company. He left school with a D average and spent six years in a shared house with a “bunch of musicians and artists and weirdos”, doing electronics work to pay rent. That somehow became biophysics at Harvard, MIT and Caltech, then building the world’s smallest personal computer (OQO, acquired by Google), and later founding an automated animal-research company (Vium, acquired by Recursion), before landing on the question he couldn’t walk away from, which is why we age and what we can actually do about it. A lot of our conversation is about what a serious longevity biotech company looks like once you strip the noise away. We dig into how Retro chose its three core programs, autophagy, partial reprogramming and blood stem cell replacement, out of an initial six. We talk about how the company walked away from their blood plasma program despite promising early data, and how their liver reprogramming results, dramatic as they were in mice, became commercially unviable once GLP-1 drugs reshaped the entire liver-disease market. Their first drug, a small-molecule autophagy therapy aimed at neurodegeneration, has since begun dosing patients in a Phase I trial in my home country of Australia. Then we go deep on GPT-4b micro, the protein-design model Retro built with OpenAI. Joe lays out what it actually is and isn’t, how it can rewrite up to 80% of a protein’s amino acids while still preserving fold and function, and why the redesigned Yamanaka factors it produced have already made their way into FDA submissions. It’s striking to me that this model (among several others in its class) keeps proposing designs no human would ever have come up with, and that general-purpose protein generation carries a biosafety tension Joe doesn’t try to dodge. From there, our conversation opens up into the bigger questions: why aging may not need to be formally recognized as a disease for meaningful therapies to win approval, what it takes to build a company around a problem this hard, and, near the end, what happens to human ambition and our sense of time when people stop living only for tomorrow. In this episode, we cover: * Why the target isn’t immortality but ten additional healthy years * Why one of its first clinical programs targets autophagy in neurodegeneration * How partial reprogramming evolved from whole-body proof of concept toward tissue- and cell-type-specific intervention * Why Retro walked away from a promising liver reprogramming program despite dramatic mouse data * How GPT-4b micro works as a protein-design model, and why it’s more than “an LLM for Yamanaka factors” * Why protein design may unlock improved versions of our own endogenous maintenance machinery * How Joe thinks about regulation, aging endpoints, and what will eventually force the FDA to adapt * Why meaningful missions tend to produce stronger companies and stronger teams * Why he sees longevity not as a billionaire side quest, but as one of the most important things a civilization could work on The most important problems in biology might also, if you’re careful about which ones you take on, turn out to be the solvable ones. GUEST INFORMATION: * Joe Betts-LaCroix, Co-Founder & CEO, Retro Biosciences [https://en.wikipedia.org/wiki/Joe_Betts-LaCroix] * Retro Biosciences [https://retro.bio/] * OpenAI x Retro Biosciences, GPT-4b micro [https://openai.com/index/accelerating-life-sciences-research-with-retro-biosciences/] * Defining a longevity biotechnology company (Nature Aging) [https://www.nature.com/articles/s41587-023-01854-0] * X (Twitter): @bettslacroix [https://x.com/bettslacroix] * LinkedIn [https://www.linkedin.com/in/bettslacroix] CONNECT WITH US: * Website [https://frameshifts.org/] * Substack [https://frameshifts.bio/] * YouTube [https://www.youtube.com/@Frameshifts] * X (Twitter) [https://x.com/frameshiftspod] * LinkedIn [https://www.linkedin.com/company/frameshifts/] * TikTok [https://www.tiktok.com/@frameshiftspod] Get full access to Frameshifts with Benjamin Arya at frameshifts.bio/subscribe [https://frameshifts.bio/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Alex Zhavoronkov: Beating Aging, Designing Drugs and Betting on China | Frameshifts Episode #12

Plenty of people will tell you AI is about to design our drugs. Alex Zhavoronkov already has. His company’s lead candidate, rentosertib, is the first drug with both its biological target and its molecule discovered by generative AI to post real Phase 2 results in humans. That gap, between the pitch deck and a drug in an actual person, is the whole reason this conversation is worth your time. Alex started out in GPUs and high-performance computing, then left to bet that AI could design drugs better than people can. He retrained in biomedical science, and nearly two decades on, his company Insilico Medicine has gone public in a US$292 million Hong Kong IPO and signed a collaboration with Eli Lilly worth US$2.75 billion. That track record is what makes his bluntness about the rest of the field worth hearing. We get into the only benchmark he thinks matters: the time it takes to go from target identification to a real developmental candidate, and why he treats funding rounds, papers and patents as mostly noise. We talk about how Insilico initially stalled while chasing aging “in the most hardcore way possible”, why he’s convinced that experimental validation rather than model size is still the real bottleneck in biology, and the claim that tends to make founders in Boston and San Francisco uncomfortable: that if you’re not competing and collaborating in China, you’re already behind. In this episode, we also get into: * What “pharmaceutical superintelligence” actually means, and what it doesn’t * Why medicinal chemists aren’t getting replaced any time soon * Why humanoid robots are the wrong bet for the lab right now * Where embodied AI actually fits into biology * Why owning the best software doesn’t make a drug company defensible * How small, validated models end up training the bigger ones * Can you still compete in biotech without building in China? * When AI can design the drug, who actually captures the value? In Alex’s ideal version of all this, you wouldn’t even open a drug discovery platform. You’d just tell a language model what you want cured, for whom, and with what tradeoffs, and let it go to work behind the scenes. It sounds like science fiction, right up until it starts working. GUEST INFORMATION: * Alex Zhavoronkov, CEO Insilico Medicine [https://insilico.com/team] * X (Twitter) [https://x.com/biogerontology?lang=en] * LinkedIn [https://www.linkedin.com/in/zhavoronkov/] * Deep learning enables rapid identification of potent DDR1 kinase inhibitors (Nature Biotechnology, 2019) [https://www.nature.com/articles/s41587-019-0224-x] * Rentosertib Phase IIa results in idiopathic pulmonary fibrosis (Nature Medicine, 2025) [https://www.nature.com/articles/s41591-025-03743-2] CONNECT WITH US: * Website [https://frameshifts.org/] * Substack [https://frameshifts.bio/] * YouTube [https://www.youtube.com/@Frameshifts] * X (Twitter) [https://x.com/frameshiftspod] * LinkedIn [https://www.linkedin.com/company/frameshifts/] * TikTok [https://www.tiktok.com/@frameshiftspod] Get full access to Frameshifts with Benjamin Arya at frameshifts.bio/subscribe [https://frameshifts.bio/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Building Science's Missing Infrastructure — Adam Marblestone | Frameshifts Episode #11

Many of science’s most important projects fall through the cracks between academia and industry. These are the foundational tools that could accelerate entire fields, and yet don’t fit neatly into a PhD thesis or a venture-backed startup. And to humanity’s credit, there has been growing interest in the last few years in building alternative systems for accelerating science beyond academia and industry. Groups like Episteme [https://episteme.com/], the Arc Institute [https://arcinstitute.org/] and Astera [https://astera.org/mission-and-vision] differ in structure and ambition, but they share a common premise: some of the most important work in science requires institutions that neither universities nor venture-backed companies are built to support. They also share something else. None of them would exist without billionaire philanthropy: Altman and Masa in the case of Episteme, Jed McCaleb in the case of Astera, and the Collison brothers in the case of the Arc Institute. And yet when I bring these organizations up with friends and colleagues in science, I often sense the same underlying skepticism: impressive as they are, can any of them really become durable drivers of scientific progress or are they structurally incapable of becoming anything more than donor-dependent experiments? Bell Labs changed the world, but it was built on an economic foundation, a telecommunications monopoly, that no longer exists. So, what’s left in humanity’s armament for progress? Well, there’s the NIH, DARPA and ARPA-H in America, ARIA in the UK, university-affiliated research institutes around the world, a dense ecosystem of startups concentrated in the major entrepreneurial hubs, and then a handful of billionaire-backed nonprofit research orgs. But there is another model that has been gaining traction in recent years: the Focused Research Organization, or FRO. These are nonprofit research organizations built around tightly scoped scientific milestones, typically with 10 to 30 person teams and budgets in the $20-30 million range. Adam Marblestone is the founder of Convergent Research and the architect of the FRO model. Late last year, in what many saw as validation of the model, the National Science Foundation announced a new initiative to “launch and scale a new generation of transformative independent research organizations to advance breakthrough science” [https://www.nsf.gov/news/nsf-announces-new-initiative-launch-scale-new-generation]. In my chat with Adam, he traces his path from graduate training in George Church’s lab to DeepMind’s neuroscience team. He came to believe that science needs a third institutional model, one that complements rather than replaces academia and industry. We discuss the idea of “intellectual dark matter”, the promising ideas researchers have but rarely get the chance to pursue. Adam explains why mathematicians need robust software infrastructure just as much as astronomers need telescopes, and how Convergent Research is systematically identifying more than 100 missing “Hubble Space Telescopes” across scientific fields. Adam argues that many breakthrough ideas remain invisible not because they are wrong, but because the shared infrastructure needed to test them does not yet exist. Topics we cover include: * Why progress in fields like mathematics and neuroscience is often bottlenecked by missing shared infrastructure (e.g. proof verification, connectome mapping, ultrasound brain interfaces) * How “intellectual dark matter” exposes systemic blind spots in the way science is funded, evaluated, and organized * How the Gap Map [https://www.gap-map.org/] is systematically cataloging hundreds of missing foundational capabilities across scientific disciplines * Why building scientific infrastructure often requires industry-style execution inside nonprofit structures * Why some of the most ambitious deep-tech efforts are too infrastructural for venture capital, yet too operational for academia GUEST INFORMATION: * Adam Marblestone, Founder & CEO of Convergent Research [https://www.adammarblestone.org/] * Convergent Research [https://www.convergentresearch.org] * Gap Map [https://www.gap-map.org/] * X (Twitter) [https://twitter.com/AdamMarblestone] CONNECT WITH US: * Website [https://frameshifts.org/] * Substack [https://frameshifts.bio/] * YouTube [https://www.youtube.com/@Frameshifts] * X (Twitter) [https://x.com/frameshiftspod] * LinkedIn [https://www.linkedin.com/company/frameshifts/] * TikTok [https://www.tiktok.com/@frameshiftspod] Get full access to Frameshifts with Benjamin Arya at frameshifts.bio/subscribe [https://frameshifts.bio/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Why 92% of Your Proteins Are Invisible — Parag Mallick | Frameshifts Episode #10

Parag Mallick is a Stanford Professor and Chief Scientist at Nautilus Biotechnology, a publicly traded biotech company. He’s also a professional magician and circus performer, which might sound random until you realize that his company does the closest thing to proteomics magic I’ve ever seen. The things that make you weird, he argues, are exactly what let you see data differently than anyone else.Here’s what caught me off guard: we can only measure ~8% of human proteins with current mass spectrometry tools. The other 92%—what Parag calls the “dark proteome”—is essentially invisible to us.Why is proteomics so much harder than genomics? Three reasons. First, protein concentration. DNA is basically uniform—roughly the same amount per cell. Proteins range from one copy to a billion copies in a single cell, and no analytical tool can handle that spread. Second, dynamics. Your genome is relatively static over your lifetime. Your proteome changes every second of every day. Third, there’s no equivalent of amplifying proteins like PCR can do. You get what you get.Nautilus is tackling this with single-molecule detection. The technical approach is fascinating: they use DNA origami nanoparticles. Picture a Ritz cracker with a flagpole sticking out the top. That flagpole has exactly one attachment point. They coat sample proteins with one half of a click chemistry reagent (methyltetrazine), the nanoparticle has the other half (trans-cyclooctene), and they bind together. One protein per nanoparticle. These then self-assemble onto a flow cell with 100-nanometer landing pads, creating a hyperdense array of billions of individual protein molecules.Now comes the really clever part: protein identification. Traditional proteomics tries to build one highly specific antibody per protein—an intractable problem when you’re dealing with millions of proteoforms. Nautilus does the opposite. They intentionally build cross-reactive affinity reagents. The system uses ~300 different affinity reagents, each one recognizing just a three-amino-acid epitope. That’s deliberately non-specific. Then they run 300 cycles of iterative mapping. The primary data looks like fluorescent NGS—you get a light-up at each location on the array or you don’t. Binary. Yes or no.The median protein only needs ~12 epitopes to be uniquely identified, but each protein gets touched 10-30 times across the 300 cycles for high confidence. It’s exactly like playing Guess Who: “Do you have glasses? Brown hair? A hat?” Each question alone tells you almost nothing, but together they pinpoint exactly who you are. Same principle here: “Do you have this 3-amino-acid sequence? What about this one?”The 300 binary measurements create a point in 300-dimensional space. Each protein has a characteristic signature in this space. The machine learning layer compares your observed pattern against the reference proteome and asks: what protein is compatible with this specific binding pattern? If you find a totally new protein, it’ll occupy a new point in that space—something not in the database.But here’s the thing about building something this audacious: you can’t prove it works before you start. Parag shared what the early days were actually like—renting a single lab bench at Stanford’s StartX incubator, trying to convince people to join when he couldn’t demonstrate single-molecule deposition yet, couldn’t show them they could run 180 cycles because they didn’t have an instrument. The first automation system was literally called “Parag” because he was pipetting by hand. How do you hire people to believe in something impossible? You share the vision of what it could mean—bringing the proteome to everyone—and see if that resonates. Some people thrive in that uncertainty. Others are brilliant at early-stage innovation but different people excel at scaling and productization. That evolution isn’t failure, Parag argues. It’s healthy. It’s part of the journey.We’re really just at the beginning of understanding biology. The genomics revolution, as transformative as it’s been, was the opening act. The era where we can actually see what proteins are doing—that’s what comes next. And in case you’re short on time, here’s a quick teaser: Watch on Youtube [https://www.youtube.com/@Frameshifts/videos]; listen on Apple Podcasts [https://podcasts.apple.com/us/podcast/frameshifts-podcast/id1829721662] or Spotify. [https://open.spotify.com/show/4TTOptQbhlNeT7BMTGpz3S] Guest Information * Parag Mallick [https://med.stanford.edu/profiles/parag-mallick]. Professor at Stanford University & Chief Scientist at Nautilus Biotechnology. * LinkedIn [https://www.biorxiv.org/content/10.1101/2025.06.26.660445v2] * X (Twitter) [https://x.com/paragmallick] * Nautilus Biotechnology [https://www.nautilus.bio/] * Large-scale single-molecule analysis of tau proteoforms [https://www.biorxiv.org/content/10.1101/2025.06.26.660445v2] * Proteomics Toolkit Paper (Nature, 2012) [https://www.nature.com/articles/nbt.2377] * High-density and scalable protein arrays for single-molecule proteomic studies (bioRxiv, 2022) [https://www.biorxiv.org/content/10.1101/2022.05.02.490328v3] Connect With Us * Website [https://frameshifts.org/] * Substack [https://frameshifts.bio/] * YouTube [https://www.youtube.com/@Frameshifts] * X (Twitter) [https://x.com/frameshiftspod] * LinkedIn [https://www.linkedin.com/company/frameshifts/] * TikTok [https://www.tiktok.com/@frameshiftspod] Get full access to Frameshifts with Benjamin Arya at frameshifts.bio/subscribe [https://frameshifts.bio/subscribe?utm_medium=podcast&utm_campaign=CTA_4]