Smart Biotech Scientist | The CMC and Bioprocessing Podcast for Process Development and Manufacturing Leaders

The entire public record of antibody affinity data comes to about half a million measurements, and by David Younger's estimate, upwards of 75% of it is AlphaSeq data from his own company's publications. His conclusion is that no single company can close the gap alone. So he is asking pharma and AI labs to pay for an experiment and share every result with the other members.
In Part 2 of this two-part conversation, David Brühlmann talks with Younger, founding CEO of A-Alpha Bio, whose AlphaSeq platform uses engineered yeast to measure protein interactions at scale. Part 1 set out the shortfall: about 10,000 antibody-antigen structures in the Protein Data Bank against a need of hundreds of thousands to millions. Here Younger explains how the consortium keeps proprietary targets out of the shared pool, who has joined, and why he believes it differs from federated learning.
Topics discussed:
Challenges faced by individual companies in generating sufficient data for effective AI models (02:17)
How a data consortium enables pre-competitive data sharing while protecting proprietary information (06:13)
Profiles of ideal consortium members and examples of current participants from pharma and AI model builders (08:36)
The consortium’s current focus on VHH antibodies and plans for expansion to broader datasets and applications by 2030 (10:00)
Comparison between the consortium approach and federated learning models in data sharing and collaboration (12:01)
The main takeaways about the quantity and quality of data needed for machine learning in protein engineering (14:09)
Smart insight: As David Younger envisions it, successful maturation of these consortia could fundamentally shift the field by 2030. Multiple consortia could address different scaffolds, data types, and problem statements, alleviating the bottleneck that has long constrained progress. The hope is that abundant, high-quality data will enable breakthrough therapeutic advancements while revealing new gaps that, in turn, inspire the next wave of innovation. Notably, David Younger sees collaboration—not just data scale—as the true secret weapon. Unlike federated learning, consortia incentivize members to share not just data, but experimental insights and best practices, effectively lifting all boats and accelerating collective progress.
Curious how others are tackling the data problem in protein design? These episodes pick up right where David Younger leaves off.
Episodes 185 - 186: How Generative AI Accelerates Protein Design for Biotech Discovery with Elise de Reus
Episodes 115 - 116: Revolutionizing Biologics Development with Hyper Throughput Screening and AI with Jeremy Agresti
Episodes 123 - 124: Manufacturability: Why Most Protein Candidates Fail (And How to Pick Winners Early) with Susan Sharfstein
Episodes 219 - 220: From 10,000 Structures to 1.8 Billion Interactions: Breaking the Data Bottleneck to Engineer Efficacious Therapeutics with Troy Lionberger
Connect with David Younger
 Company website: www.aalphabio.com
LinkedIn: www.linkedin.com/in/david-a-younger/
Data Atlas website: www.atlas.aalphabio.com
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The Protein Data Bank holds about 10,000 antibody-antigen structures and grows by roughly 1,000 a year. David Younger's scaling work suggests that generalizable de novo antibody design needs hundreds of thousands to millions. At that pace, even 200,000 structures is nearly two centuries away.
In this episode, David Brühlmann hosts David Younger, founding CEO of A-Alpha Bio and the mind behind the AlphaSeq platform. Trained at the intersection of synthetic biology and computational protein engineering, David Younger cut his teeth at the Institute for Protein Design and sharpened his focus on the stubborn gap between theoretical promise and practical performance. His approach rewrites the rules of data generation—scaling up both the volume and the insight from protein interaction data to deliver the kind of information AI needs to actually make an impact in therapeutic design.
Topics discussed:
Bottlenecks caused by insufficient and low-quality data in AI-enabled protein engineering (02:53)
The limitations of traditional protein discovery methods and the persisting role of serendipity in antibody development (08:02)
An overview of the AlphaSeq platform and how it enables large-scale measurement of protein interactions using engineered yeast (10:50)
The differences between measuring protein-protein affinities with conventional techniques and AlphaSeq’s batch approach, including cost and speed advantages (16:54)
Strategies for generating training data for machine learning models in protein engineering, including the need for structural and affinity data (18:23)
The value and limitations of the Protein Data Bank (PDB) in training de novo design models (20:17)
The challenge of benchmarking foundational models for protein engineering and the need for universal industry standards (23:11)
Smart insight: The future of biotherapeutic innovation is intertwined with a simple truth: more and better data unlock AI’s true potential. With breakthrough wet-lab technologies like AlphaSeq, and commitment to openness and benchmarking, the dream of efficient, precise, and truly rational protein engineering may soon become reality. The next big leap in biotech is not just smarter algorithms—but the scale, quality, and transparency of the data that powers them.
Curious how others are tackling the data problem in protein design? These episodes pick up right where David Younger leaves off.
Episodes 185 - 186: How Generative AI Accelerates Protein Design for Biotech Discovery with Elise de Reus
Episodes 115 - 116: Revolutionizing Biologics Development with Hyper Throughput Screening and AI with Jeremy Agresti
Episodes 123 - 124: Manufacturability: Why Most Protein Candidates Fail (And How to Pick Winners Early) with Susan Sharfstein
Episodes 219 - 220: From 10,000 Structures to 1.8 Billion Interactions: Breaking the Data Bottleneck to Engineer Efficacious Therapeutics with Troy Lionberger
Connect with David Younger
 Company website: www.aalphabio.com
LinkedIn: www.linkedin.com/in/david-a-younger/
Data Atlas website: www.atlas.aalphabio.com
Free 5-day email course, The CMC Failure Chain: the five recurring CMC mistakes that put your promising program at risk → Get it here
Support the show

Cardiovascular disease has long been managed by slowing its relentless progression. But Matthew O'Connor, CEO of Scientific Affairs at Cyclarity Therapeutics, is betting on an outright paradigm shift: treating atherosclerotic plaque with therapies designed to actively reverse disease, not simply manage symptoms or risk.
In this episode, David Brühlmann explores the strategy, regulatory journey, and manufacturing realities behind Cyclarity's unconventional cyclodextrin-based approach—a molecule sitting at the crossroads of small and large molecule drug development.
Topics discussed:
Insights into the company's clinical development progress, including completion of a first-in-human study and what comes next (02:47)
How the FDA views drugs that don't fit traditional categories and how this influences development pathways (04:35)
The challenges and considerations in demonstrating efficacy for drugs designed to reduce heart attacks and strokes (05:22)
Cost considerations in manufacturing the cyclodextrin dimer and how it compares to biologics and GLP-1 drugs (08:14)
Vision for how cardiovascular disease may be diagnosed and treated a decade from now, including the role of imaging and prevention (10:24)
Broader implications for treating age-related diseases by targeting toxic metabolites in the body (14:25)
Why next-generation therapeutics may be built not by sticking to old modalities but by tackling root causes with custom solutions (16:10)
Reflections on making new drugs both safe and fundamentally beneficial, minimizing side effects (17:10)
Smart insight: Start with the problem, not the modality. Matthew O'Connor argues that instead of defaulting to an antibody, a small molecule or a gene therapy, developers should build a custom solution to the problem they're trying to solve. A drug that suppresses a pathway across the whole body brings side effects you have to manage. A drug that removes something harmful, like oxidized cholesterol, can have side effects that are actually beneficial. His goal for the next generation of drugs is ones that simply make people healthier.
When a drug is too big to be a small molecule and too small to be a biologic, every CMC decision gets harder. These episodes look at what it takes to develop, characterize and manufacture a modality that doesn't fit an existing box.
Episodes 281 - 282: When Your Delivery Vehicle Contains a Membrane Protein: CMC Decisions With No Regulatory Precedent with Jitendra Kumar
Episodes 207 - 208: Cryogenic Infrared Ion Spectroscopy: From Mass Spec Limitations to Molecular Precision with Tom Rizzo
Episodes 197 - 198: Mastering ADC Development: CDMO Strategies for Analytics and Scale-Up with Amanda Hoertz
Episodes 231 - 232: From IND to BLA: The Biologics CMC Decisions That Determine Regulatory Success with Henri Kornmann
Connect with Matthew O'Connor:
Website: www.cyclaritytx.com
LinkedIn: www.linkedin.com/in/droki
Email: matthew.oconnor@cyclaritytx.com
Free 5-day email course, The CMC Failure Chain: the five recurring CMC mistakes that put your promising program at risk → Get it here
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Why does CMC development still expect every drug to be one molecule with one peak, when new therapeutics don't always fit neatly into existing regulatory or chemical categories?
Matthew O'Connor, CEO of Scientific Affairs at Cyclarity Therapeutics, is developing a drug that sits between a small molecule and a biologic. On the Smart Biotech Scientist Podcast, Matthew shared how his team engineered a cyclodextrin dimer that binds and removes 7-ketocholesterol, an oxidized form of cholesterol that builds up in artery plaque and is linked to a higher risk of heart attack and stroke.
Topics discussed:
Matthew O'Connor's motivation for focusing on aging and molecular damage (03:39)
The biological problem of oxidized cholesterol buildup and its role in atherosclerosis (04:42)
Mechanism of action: how the cyclodextrin binds and removes 7-ketocholesterol (06:23)
Approaches to designing a highly specific cyclodextrin therapeutic (09:14)
The "medium-sized molecule" characteristics and the question of regulatory classification (12:16)
Why cyclodextrin starting material is cheap and not a manufacturing bottleneck (15:37)
Manufacturing strategies and the role of specialist CDMOs in complex carbohydrate chemistry (17:04)
Methods for demonstrating identity and consistency in isomeric mixtures (18:43)
Drug formulation considerations and why an injectable route is necessary (21:46)
Smart insight: Matthew O'Connor's work shows what happens when you start with the problem rather than the drug format. His team's cyclodextrin dimer doesn't fit neatly as a small molecule or a biologic, and that makes manufacturing, analytics and regulatory work harder. But if it can remove oxidized cholesterol from the body, it could help reverse plaque instead of just slowing it down.
When a drug is too big to be a small molecule and too small to be a biologic, every CMC decision gets harder. These episodes look at what it takes to develop, characterize and manufacture a modality that doesn't fit an existing box.
Episodes 281 - 282: When Your Delivery Vehicle Contains a Membrane Protein: CMC Decisions With No Regulatory Precedent with Jitendra Kumar
Episodes 207 - 208: Cryogenic Infrared Ion Spectroscopy: From Mass Spec Limitations to Molecular Precision with Tom Rizzo
Episodes 197 - 198: Mastering ADC Development: CDMO Strategies for Analytics and Scale-Up with Amanda Hoertz
Episodes 231 - 232: From IND to BLA: The Biologics CMC Decisions That Determine Regulatory Success with Henri Kornmann
Connect with Matthew O'Connor:
Website: www.cyclaritytx.com
LinkedIn: www.linkedin.com/in/droki
Email: matthew.oconnor@cyclaritytx.com
Free 5-day email course, The CMC Failure Chain: the five recurring CMC mistakes that put your promising program at risk → Get it here
Support the show

Scale-up doesn't fail because the science was wrong. It fails because a cell's environment in a shake flask has little in common with its environment in a bioreactor. Christian Witz has spent years building the tools to close that gap: simulating exactly what a cell experiences as it moves from bench to plant, before a single liter is wasted finding out the hard way.
Continuing from Part 1, David Brühlmann and Christian Witz, CEO and founder of SimVantage, go deeper into how CFD moves from theory to daily practice: validating simulation results, scaling real cell therapy and Streptomyces processes, and an active FDA-backed project on residence time distribution in continuous bioprocessing.
Topics discussed:
Why data validation is crucial in CFD simulations and how to interpret and trust simulation results (02:39)
Real-world examples of scaling up from shake flasks to stirred tank reactors using CFD, including specific collaborations and cell therapy case studies (05:40)
Technical and validation challenges in simulating fluid dynamics in shake flasks and integrating these results into scale-up strategies (08:14)
Ongoing FDA-backed project to model residence time distribution in continuous bioprocessing and its regulatory significance (11:49)
The increasing openness of regulators like the FDA to include CFD data in submissions and relevant guidelines for simulation credibility (13:23)
Advice for small biotech teams on when and how to start using CFD in CMC development and scale-up planning (15:06)
Insights on the future of bioprocessing, including virtual scale-up, digital reactor repositories, and the democratization of CFD tools (18:20)
Smart insight: Christian Witz advised that teams begin CFD modeling as early as concept development. CFD not only helps choose the right equipment—even when working with contract manufacturers—but creates a digital process record that reduces experimental burden, shortens timelines, and impresses investors with its risk mitigation.
If this conversation got you rethinking your scale-up strategy, here are a few past episodes that go deeper on the pieces Christian touched on:
Episodes 91 - 92 : Mass Transfer Secrets: Mastering Bubbles and kLa from Bench to Large-Scale Production with Lars Puiman & Rik Volger
Episodes 245 - 246: Why Your Shake Flask Culture Doesn't Scale: OTR, Shaking Diameter, and How to Fix It with Tibor Anderlei
Episodes 173 - 174: Mastering Hybrid Model Digital Twins: From Lab Scale to Commercial Bioprocessing with Krist Gernaey
Episode 243: Turn Tech Transfer from a Gamble into a Managed Process: The 6-Pillar Biologics Scale-Up Framework
Connect with Christian Witz:
Website: www.simvantage.com
Christian’s LinkedIn: www.linkedin.com/in/christian-witz-84aa9b162/
Christian’s email: christian.witz@simvantage.com
Free 5-day email course, The CMC Failure Chain: the five recurring CMC mistakes that put your promising program at risk → Get it here
Support the show