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

How can small biotech companies navigate increasing molecular complexity, tight budgets, and the ever-present pressure to accelerate timelines while ensuring long-term process success?

In this episode, David Brühlmann welcomes Lena Tholen and Lars Stöckl of FyoniBio GmbH—two scientists who’ve dedicated their careers to optimizing cell line and process development for biopharma clients.

Topics covered in this episode:

• The trend toward outsourcing CMC development and the reasons behind it for both large pharma and small biotechs (07:27)
• How specialized CDMOs innovate and continuously improve cell line development platforms, giving clients access to new options and efficiencies (10:36)
• Real-world experience navigating project pitfalls from discovery to clinical studies—including differences between cell lines and processes (13:38)
• The importance of retaining key activity assays and critical knowledge in-house—and the risks of over-reliance on consultants (15:04)
• Key factors that help biotech companies thrive when partnering with CDMOs, including internal expertise, flexibility, and milestone planning (17:32)

Smart insight: Product quality and success are designed in early through careful choices about cell lines, processes, and analytical strategies. Streamlined outsourcing and platform innovation can turbocharge development, but only when matched with committed, knowledgeable in-house leadership and measured, forward-thinking investment. The next time a CMC decision seems minor or a shortcut tempting, remember the long game—your product quality, speed to clinic, and future cost may all depend on it.

Lars and Lena’s main point is that early choices about the host cell line, the analytics and the partner decide what’s possible later on. The episodes below take that idea further. One looks at the mechanics of cell line development, one at why promising molecules fail to manufacture, and two at outsourcing CMC work from the sponsor’s side.

• Episodes 117 - 118: Cell Line Development Secrets: Eliminating Critical Bottlenecks for Faster Timelines with Andrea Gough
• Episodes 123 - 124: Manufacturability: Why Most Protein Candidates Fail (And How to Pick Winners Early) with Susan Sharfstein
• Episodes 103 - 104: One-Stop Shop vs. Specialist CDMO: A Scientist’s Guide to CDMO Selection with Sigma Mostafa
• Episodes 71 - 72: Effective Outsourcing: How Small Biotech Companies Can Thrive with Mark Melville

Connect with Lars Stöckl and Lena Tholen:
Website: https://www.fyonibio.com/
LinkedIn:https://www.linkedin.com/in/dr-lars-stoeckl/
LinkedIn: https://www.linkedin.com/in/lena-tholen-80642a11a/

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Is your process model gathering dust while real-world decisions march on without it? Many technically strong digital tools—no matter how powerful—end up sidelined in bioprocess development. Why is that, and what does it actually take for digital modeling to earn its place at the lab bench?

In this episode, David Brühlmann sits down with Moritz von Stosch of HINA Bioventures, a scientist-turned-investor who’s navigated both the technology provider and pharma end-user sides. Moritz von Stosch brings firsthand insights from building digital solutions, deploying them in pharma, and evaluating what actually sticks when the clock is ticking and the stakes are high.

Topics discussed:

• The importance of starting with your purpose when selecting a modeling strategy (03:00)
• Experimental design: when to use factorial versus random space-filling designs, and the inherent biases each can introduce (04:22)
• The future of agentic workflows, AI, and autonomous labs in bioprocessing, including the importance and challenges of sensor technologies (06:02)
• Practical hurdles to implementing fully autonomous laboratories, and the non-obvious value humans still bring (09:09)
• Adapting mindsets for the evolving role of the bioprocess engineer in a more automated, agent-driven workflow (11:48)
• Advice for biotech professionals on how to assess the real value in new technologies versus hype, and the role of proprietary algorithms and company stability (13:33)
• How cultural factors, not just technology, shape the adoption of modeling and digital tools in biotech (16:02)
• Strategies to help the bioprocess community adopt more innovation while ensuring compliance in regulated environments (17:14)
• Three key takeaways for staying relevant—including generating data for tomorrow’s questions and balancing curiosity, experimentation, and oversight (19:14)

Smart insight: Three takeaways from Moritz von Stosch:

• Generate data for tomorrow’s questions, not just today’s.
• Be curious—try, adapt, don’t over-plan.
• Stay relevant: Use digital tools but keep your human edge (creativity, critical thinking).

To keep exploring how modeling and prior knowledge can speed up process development, check out these related conversations:

• Episodes 05 - 06: Hybrid Modeling: The Key to Smarter Bioprocessing with Michael Sokolov
• Episodes 173 - 174: Mastering Hybrid Model Digital Twins: From Lab Scale to Commercial Bioprocessing with Krist Gernaey
• Episodes 277 - 278: Your Bioprocess Data Already Holds 35% More Yield: From End-to-End Models to Digital Twins with Ignasi Bofarull-Manzano
• Episodes 215 - 216: From Data Silos to Autonomous Biomanufacturing: Digital Twins and AI-Driven Scale-Up with Ilya Burkov

Connect with Moritz von Stosch:
Website: https://hinabioventures.com/
LinkedIn: https://www.linkedin.com/in/movosto/

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Are you frustrated with reinventing the wheel every time a new molecule enters bioprocess development? Is there a better way to harness data and modeling to accelerate breakthroughs in biotech? The bioprocessing industry is constantly searching for ways to streamline development, reduce costs, and speed new therapies to market—yet leveraging existing knowledge remains a significant challenge.

In this episode, David Brühlmann welcomes Moritz von Stosch, partner at HINA Bioventures and a pioneer in digital bioprocess development. Spanning roles from academic research in hybrid modeling to solution design at DataHow and now biotech investment, Moritz von Stosch’s journey traces how mechanistic, hybrid, and machine learning models have (and haven’t) changed process development over the last two decades.

Topics discussed:

• Moritz von Stosch’s career: from early interest in modeling to investment in digital bioprocess tools (03:36)
• The reality and limitations of platformization and knowledge reuse in large pharma (07:37)
• Historical context and effectiveness of QbD (Quality by Design) and PAT (Process Analytical Technology) initiatives (10:24)
• Where modeling can reliably deliver value today in process and clinical development—and where it still overpromises (12:17)
• The importance of starting to build models early and collecting data that can be reused and repurposed across the process lifecycle (14:17)
• Approaches to transferring learning between molecules, cell lines, platforms, and the fundamental limitations of this transfer (16:40)
• Opportunities and difficulties in transferring process knowledge across scales (from microtiter plates to large bioreactors) (18:31)

Smart insight: David Brühlmann and Moritz von Stosch agree that models are only useful when they are built on reliable data and interpreted with expert judgment. Models can help biotech teams make better decisions, but they don’t replace human expertise. The real value comes from looking at the whole process, focusing on the data that matters, and making sure knowledge is shared effectively. This can help teams improve bioprocess development and bring new treatments to patients sooner.

To keep exploring how modeling and prior knowledge can speed up process development, check out these related conversations:

• Episodes 05 - 06: Hybrid Modeling: The Key to Smarter Bioprocessing with Michael Sokolov
• Episodes 173 - 174: Mastering Hybrid Model Digital Twins: From Lab Scale to Commercial Bioprocessing with Krist Gernaey
• Episodes 277 - 278: Your Bioprocess Data Already Holds 35% More Yield: From End-to-End Models to Digital Twins with Ignasi Bofarull-Manzano
• Episodes 215 - 216: From Data Silos to Autonomous Biomanufacturing: Digital Twins and AI-Driven Scale-Up with Ilya Burkov

Connect with Moritz von Stosch:
Website: https://hinabioventures.com/
LinkedIn: https://www.linkedin.com/in/movosto/

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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: http://aalphabio.com/
LinkedIn: http://linkedin.com/in/david-a-younger/
Data Atlas website: https://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: http://aalphabio.com/
LinkedIn: http://linkedin.com/in/david-a-younger/
Data Atlas website: https://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

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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: http://cyclaritytx.com/
LinkedIn: http://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: http://cyclaritytx.com/
LinkedIn: http://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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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: https://simvantage.com/
Christian’s LinkedIn: https://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

You've perfected your process at the bench. Now comes the part that keeps CMC scientists up at night: will it still work at 2,000L? Right now, most teams find out the hard way, through failed runs, wasted batches, and months of delay to patients waiting on a therapy. Christian Witz believes there's a better way: use computational fluid dynamics to see how your cells will actually behave at full scale before you ever run the process, and stop treating scale-up as a guessing game.

On this episode of the Smart Biotech Scientist Podcast, David Brühlmann talks with Christian, founder of SimVantage, about why he treats CFD as a tool every process engineer should use directly, not something you outsource to a specialist. Christian's path into biotech ran through heat pump simulations and GPU-based particle flow modeling before he rebuilt a legacy Fortran bioreactor simulation program in modern C++ and CUDA, work that became the foundation of SimVantage.

Topics discussed:

• Christian’s path from simulating heat pumps and particle flows to pioneering GPU-based bioreactor simulations (03:54)
• The surprising lack of full equipment characterization in the biotech industry compared to fields like oil, gas, and iron & steel (06:40)
• The interdisciplinary challenges and steep learning curve when entering biotech from other industries (08:07)
• Why academic spinouts matter and the practical realities of founding a company based on university-developed software (09:44)
• Reasons behind CFD’s low adoption in bioprocessing—perceived complexity, need for specialists, and historical limitations in algorithms and computing power (11:58)
• How recent advances in GPU power, algorithms, and cloud computing remove barriers for routine use of CFD in process development (14:35)
• Strategies for characterizing and scaling processes across various reactor types, from shake flasks to manufacturing reactors (16:39)
• Practical considerations and limitations when setting up comprehensive CFD characterizations, including simulation parameter choices (19:49)

Smart insight: The degree of reactor characterization depends on a company’s needs. Startups might analyze a scale-up project once, while contract manufacturers—handling diverse products—benefit from comprehensive, reusable characterization across all reactors and operating conditions. Simulations are guided by physics (density, viscosity, surface tension) rather than biological specifics, allowing quick adaptation to new media or cell types.

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: https://simvantage.com/
Christian’s LinkedIn: https://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

Biotech founders often pour everything into building the “perfect” product only to realize their real customers have walked out the door. Chasing perfection can burn money and stall even the most promising CMC innovations.

In this episode, David Brühlmann continues his conversation with biotech entrepreneur and investor John Bonham-Carter. John shares his insights on the realities of building and scaling successful biotools companies, from the challenges of fundraising and the importance of authentic storytelling, to knowing when to stop perfecting a product and start selling.

Topics discussed:

• How fundraising is less about money and more about the story, team, and real commitment behind a startup (02:33)
• Practical advice for founders: obsess over the customer, not just the technology (07:14)
• The dangers of spreading yourself too thin as a startup founder and lessons from parenting applied to startups (07:09)
• Choosing business partners and collaborators whose skills complement your own—and making peace with imperfection (10:10)
• John’s approach to building teams and why accepting mediocrity in some areas can lead to collective excellence (11:16)
• The story behind John joining Stellion Biosystems, and what makes their cell counting technology different in the market (11:51)
• Using creative, low-budget tactics for visibility—why John chose a lizard mascot for Stellion Biosystems (15:19)
• Thoughts on when (if ever) to stop and the value of keeping multiple life and career doors open (17:26)
• John’s main takeaway: “Try everything”—professional advice drawn from experience (and Zootopia) (19:01)

Smart insight: John leaves us with a simple yet powerful credo: “Try everything.” In biotech, that means embracing experimentation not just in the lab, but in business strategy, partnerships, hiring, and technology adoption. By collaborating, iterating, and listening closely to customers, you can grow, succeed, and—perhaps—build something that truly makes a difference. In a field where the only constant is change, remember: launch before you’re ready, find your people, and keep your story genuine.

Some ideas are ahead of their time; a few people make a career of being early. This is part bioprocess history, part founder's playbook: why the best technology doesn't automatically win, how to move a conservative industry, and when to sell rather than scale. To go further on both the technology and the business behind it:

• Episodes 191 - 192: Process Intensification Secrets: A Process Engineer's Decision Framework with Andreas Castan
• Episodes 183 - 184: From Lab to Market: Secrets to Commercializing Cutting-Edge Biotech Innovations with Chervee Ho
• Episodes 259 - 260: Why Strong Science Isn't Enough to Get Funded: What Investors Actually Look For with Michael Rome
• Episodes 165 - 166: Why Your Funding Pitches Fail Despite Brilliant Science (And How to Fix It)

Connect with John Bonham-Carter:
Website: http://www.stellion-biosystems.com/
LinkedIn: https://www.linkedin.com/in/johnbc/

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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Biotech loves talk of disruptive technology, but few have repeatedly shifted the industry's direction. This episode of the Smart Biotech Scientist Podcast focuses on an idea once dismissed as a fool's game: process intensification through perfusion—now the backbone of leading biomanufacturing platforms.

Meet John Bonham-Carter, a serial entrepreneur whose fingerprints are on some of biotech's most transformative tools. From putting the once-ignored ATF system and perfusion on the global stage to pushing intensified cell therapy bioprocessing with ERBI, John's story isn't about chasing the next big exit. Instead, it's about spotting unmet needs and building and selling biotools companies more than once.

Topics discussed:

• The story behind the ATF system, and how John Bonham-Carter and his collaborators convinced a conservative industry to adopt perfusion (04:43)
• Early career influences, including family ties to bioprocess equipment manufacturing (05:03)
• Reflections on the biotech industry’s culture of collaboration and the dual motivation to deliver medicines and achieve commercial success (07:18)
• Realities of scaling a startup, trading income for equity, and the role of persistence in sales (09:34)
• Lessons learned from global travel and building professional networks in biotech (10:16)
• The importance of recognizing what drives different biotech companies to adopt new technology (12:25)
• The transition from ATF to co-founding Erbi, a bioprocessing startup focused on small-volume cell therapy (19:05)
• Strategic acquisitions: experiences of being acquired by Repligen and Merck Millipore (22:50)
• Advice for founders on whether to build a company for exit or long-term operation (23:07)
• The limits of leading with technology, and why customer understanding is critical to success (26:10)

Smart insight: For founders, John Bonham-Carter offers practical guidance anchored in self-awareness. Should you build a company for the long haul or for acquisition? The answer: “There isn’t really a right answer...it is, what is the life you want to lead?” Some entrepreneurs thrive at the scale of ten-person teams and relish the handoff to a strategic acquirer. Others yearn to build and steer a 200-person enterprise. Regardless, serving customers and employees well, and being attuned to the market’s shifting demands, matter most. Exit opportunities arise from building value and strategic fit—not from fixating on selling out.

Some ideas are ahead of their time; a few people make a career of being early. This is part bioprocess history, part founder's playbook: why the best technology doesn't automatically win, how to move a conservative industry, and when to sell rather than scale. To go further on both the technology and the business behind it:

• Episodes 191 - 192: Process Intensification Secrets: A Process Engineer's Decision Framework with Andreas Castan
• Episodes 183 - 184: From Lab to Market: Secrets to Commercializing Cutting-Edge Biotech Innovations with Chervee Ho
• Episodes 259 - 260: Why Strong Science Isn't Enough to Get Funded: What Investors Actually Look For with Michael Rome
• Episodes 165 - 166: Why Your Funding Pitches Fail Despite Brilliant Science (And How to Fix It)

Connect with John Bonham-Carter:
Website: http://www.stellion-biosystems.com/
LinkedIn: https://www.linkedin.com/in/johnbc/

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

What if building a world-class biotech company didn’t require owning a single piece of manufacturing equipment or even a lab of your own?

Most startups obsess over bricks, mortar, and stainless steel. Not Sandra Núñez. She’s challenging the status quo as CEO of Amela Biosciences by running a “virtual plant” model: zero in-house production, everything outsourced, and agility baked into every decision. It’s a bold experiment in redefining what a biotech company can look like. One that’s already delivering real, animal-free products to customers today.

Topics discussed:

• Key considerations and pitfalls in tech transfer from academic labs to external CDMO partners (02:34)
• The reasoning behind choosing a virtual plant model with no in-house manufacturing (04:57)
• Criteria for selecting external manufacturing partners, including technical capabilities and cultural fit (06:28)
• Stakeholder communications and managing misconceptions about outsourced manufacturing (07:44)
• Process development challenges when scaling products for commercial use, including impurity profile and scalability of purification methods (08:53)
• Early preparations for future GMP manufacturing and building a foundation for regulatory compliance (11:20)
• The impact of regulatory shifts (like the FDA Modernization Act 2.0) on manufacturing strategy and product reproducibility (12:47)
• Hopes for the company’s future role in the transition to animal-free and regenerative medicine (14:19)
• Practical advice for startup founders considering a leap from corporate biotech to entrepreneurship (15:09)
• Reflections on the importance of addressing batch-to-batch variability and building reproducible systems (16:40)

Smart insight: Don’t accept batch-to-batch variability. Reproducibility is non-negotiable. Batch-to-batch variability, whether in raw materials or process design, is a critical risk that forward-thinking should design out, not accept as inevitable. As Sandra concludes, reproducibility isn’t just a lab problem, it’s a company-building problem, one that shapes strategy, manufacturing partnerships, and regulatory success.

Here are four episodes that pick up the same threads: animal-free matrices, reproducibility by design, and building a biotech without a factory:

• Episodes 271 - 272: From Static Scaffolds to Dynamic Matrices: Hydrogels for Animal-Free 3D Cell Culture with Jan Hunik and Matt Baker
• Episodes 221 - 222: From 2D Cultures to Advanced 3D Cell Models for Preclinical Research with Catarina Brito
• Episodes 275 - 276: From Lab-Scale Molding to GMP: Manufacturing a Collagen Implant for the Clinic with Eva-Maria Balet
• Episodes 279 - 280: Why Nanovesicles Outperform Exosomes: Scalable Drug Delivery Beyond Injectable Vaccines with Christopher Locher

Connect with Sandra Núñez:
Website: https://amelabiosciences.com/
Company LinkedIn: https://www.linkedin.com/company/amela-biosciences-ag/
Sandra’s LinkedIn: https://www.linkedin.com/in/sandra-nunez-ch/
Sandra’s email: s.nunez@amelabiosciences.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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Batch-to-batch variability isn’t just a minor annoyance in bioprocess development; it’s often a systemic design flaw hiding in plain sight.

That’s the challenge Sandra Núñez, CEO of Amela Biosciences, set out to eliminate. Drawing on 15 years across Lonza, Medinova, Baxalta, and Biogen, she’s engineered a new generation of animal-free, recombinant protein matrices that promise researchers experimental consistency on demand.

Topics discussed:

• The widespread issue of batch-to-batch variability and why Sandra argues it’s a design failure, not the norm (02:49)
• The mechanics and biological tunability of Amela’s engineered protein matrices, and how this technology works (06:34)
• How Amela’s recombinant protein bioinks differ from traditional animal-sourced extracts, enabling reproducibility (08:53)
• Manufacturing insights: why the proteins are relatively straightforward to produce for research use (12:05)
• Engaging with users to optimize and customize protein matrices for diverse tissue and assay applications (13:33)
• The creation and use of protein libraries for specific user requirements and the flexibility this provides (16:20)
• Definitions and roles of ‘bioinks’ in tissue modeling and 3D cell culture (16:52)

Smart insight: Reproducibility remains the cornerstone of reliable science. With innovative platforms like Amela Biosciences’ engineered protein matrices, the future of tissue engineering and in vitro biology looks not just reliable, but radically empowering. If you’re striving for consistency, customizability, and cutting-edge technology in your workflows, the era of animal-free, batch-consistent bioinks has arrived.

Here are four episodes that pick up the same threads: animal-free matrices, reproducibility by design, and building a biotech without a factory:

• Episodes 271 - 272: From Static Scaffolds to Dynamic Matrices: Hydrogels for Animal-Free 3D Cell Culture with Jan Hunik and Matt Baker
• Episodes 221 - 222: From 2D Cultures to Advanced 3D Cell Models for Preclinical Research with Catarina Brito
• Episodes 275 - 276: From Lab-Scale Molding to GMP: Manufacturing a Collagen Implant for the Clinic with Eva-Maria Balet
• Episodes 279 - 280: Why Nanovesicles Outperform Exosomes: Scalable Drug Delivery Beyond Injectable Vaccines with Christopher Locher

Connect with Sandra Núñez:
Website: https://amelabiosciences.com/
Company LinkedIn: https://www.linkedin.com/company/amela-biosciences-ag/
Sandra’s LinkedIn: https://www.linkedin.com/in/sandra-nunez-ch/
Sandra’s email: s.nunez@amelabiosciences.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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Manufacturability challenges in biotech are becoming more complex as therapies become more potent, formats diversify, and timelines compress across the industry.

On this episode, David Brühlmann sits down with Sigma Mostafa, Chief Scientific and Technology Officer at KBI Biopharma. Sigma brings deep expertise in early-stage CMC decision-making and has guided countless programs—from upstart startups to established pipelines—through the traps and trade-offs of process development. Her ground-floor perspective spans in silico modeling, innovative cell line engineering, and the gritty realities of tech transfer.

Key topics discussed:

• Practical advice for startups on selecting robust cell lines and avoiding long-term lock-in to problematic platforms (03:07)
• Managing risks in process development, such as high oxygen demand and filter loading, before tech transfer to manufacturing (04:47)
• The value of pressure testing bioprocesses at scale and identifying potential failure modes, including filter clogging and narrow feeding windows (07:41)
• Balancing speed, robustness, and regulatory expectations when advancing new molecules (09:23)
• How fast-tracking from transfection to IND is changing timelines, and the associated risks of accelerated development (09:54)
• Trends and caution in applying AI and in silico tools to protein and process modeling, and the limits of digital solutions (12:01)
• What first-time founders need to get right, including early analysis of molecular issues and careful cell line selection (15:48)
• Shifting modality trends—growing numbers of ADCs/XDCs, more complexity, and the move toward smaller scale and more potent molecules (16:55)
• The overarching lesson: invest early in the areas you cannot change later—especially cell line and understanding of molecule challenges (18:23)

Smart insight: Startup founders often wonder which fires to fight first. Sigma’s advice: focus on deep molecular assessment and making informed, scalable cell line choices above all else. These are the “few things you cannot change later” and the investments that separate enduring programs from cautionary tales.

This episode unpacks what that means in practice and where speed and robustness pull against each other. If it resonated, these conversations expand the picture: how to spot manufacturable candidates early, how in silico tools predict stability and aggregation before the lab, and which early CMC decisions quietly become permanent.

• Episodes 123 - 124: Manufacturability: Why Most Protein Candidates Fail (And How to Pick Winners Early) with Susan Sharfstein
• Episodes 213 - 214: From Developability to Formulation: How In Silico Methods Predict Stability Issues Before the Lab with Giuseppe Licari
• Episodes 231 - 232: From IND to BLA: The Biologics CMC Decisions That Determine Regulatory Success with Henri Kornmann
• Episodes 103 - 104: One-Stop Shop vs. Specialist CDMO: A Scientist's Guide to CDMO Selection with Sigma Mostafa

Connect with Sigma Mostafa:
Linkedin: https://www.linkedin.com/in/sigma-mostafa-79180817
KBI website: https://www.kbibiopharma.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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A breakthrough in drug discovery can be derailed in an instant if manufacturability is left as an afterthought. Too many biotech programs hit bottlenecks at scale-up because key decisions in cell line and process development get kicked down the road.

On the Smart Biotech Scientist Podcast, David Brühlmann spoke with Sigma Mostafa, Chief Scientific and Technology Officer at KBI Biopharma. She’s spent 25+ years converting early-stage discoveries into commercial biomanufacturing success and she’s adamant: manufacturability decisions belong at the candidate selection stage, not after.

Topics discussed:

• Why manufacturability should be assessed at the candidate selection stage, not later (03:02)
• Sigma’s background in bringing math and biology together and her path into bioprocess engineering (04:18)
• The "art" and complexity of bioprocess development, especially with new molecule types (05:54)
• A case study of how switching cell lines revealed hidden manufacturability issues (07:33)
• Key properties affecting manufacturability, such as aggregation and thermal stability (10:10)
• Reasons companies delay manufacturability assessments—timing pressure, costs, and lack of early deep characterization (11:02)
• Why CMC should be integrated from day one and concerns with treating scalability as 'just' engineering (12:08)
• Critical decisions and pitfalls in cell line development, including robustness, media choices, and adapting after the master cell bank is made (12:48)
• Regulatory aspects of cell line development: demonstrating clonality, avoiding animal source materials, and documentation requirements (15:04)

Smart insight: Manufacturability is not just a box for the CMC team. It’s a proactive mindset, to be embraced from day one. Early, cross-functional scrutiny—examining both molecule and cell line—preempts disasters during scale-up and accelerates timelines to market while minimizing costly surprises.

This episode unpacks what that means in practice and where speed and robustness pull against each other. If it resonated, these conversations expand the picture: how to spot manufacturable candidates early, how in silico tools predict stability and aggregation before the lab, and which early CMC decisions quietly become permanent.

• Episodes 123 - 124: Manufacturability: Why Most Protein Candidates Fail (And How to Pick Winners Early) with Susan Sharfstein
• Episodes 213 - 214: From Developability to Formulation: How In Silico Methods Predict Stability Issues Before the Lab with Giuseppe Licari
• Episodes 231 - 232: From IND to BLA: The Biologics CMC Decisions That Determine Regulatory Success with Henri Kornmann
• Episodes 103 - 104: One-Stop Shop vs. Specialist CDMO: A Scientist's Guide to CDMO Selection with Sigma Mostafa

Connect with Sigma Mostafa:
Linkedin: https://www.linkedin.com/in/sigma-mostafa-79180817
KBI website: https://www.kbibiopharma.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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What does it take to crack the code of protein production and why do some proteins stubbornly refuse to cooperate, despite the best efforts of scientists and engineers? Biotech’s ambitions are often limited not by vision, but by the real-world bottlenecks of host cell lines and the unpredictability of post-translational modifications.

Nathan Lewis, GRA Eminent Scholar at the Center for Molecular Medicine, Complex Carbohydrate Research Center, and Department of Biochemistry and Molecular Biology at the University of Georgia, has made a career out of asking impossible questions about glycosylation, cell line selection, and the hidden machinery at work inside every productive cell. He’s moved beyond academic curiosity—translating discoveries into applications and even launching a company, Augment Biologics, that’s taking glycoengineering from theory to practice.

Topics discussed:

• A proximity proteomics approach to identify supporting machinery for challenging-to-express proteins like rituximab (02:36)
• Findings from expressing the full human secretome in CHO cells, and the correlation between host cell gene expression and protein productivity (05:00)
• Clarifying when host cell characteristics matter more than the protein construct itself (05:46)
• Emerging evidence that protein sequence and structure influence glycosylation patterns (contrary to previous dogma) (06:49)
• Engineering point mutations to precisely tune glycan features for improved therapeutic efficacy (09:25)
• The vision and activities of Augment Biologics in custom glycosylation control for drug discovery (10:32)
• The importance and barriers to open data sharing in bioprocessing, and thoughts on overcoming them (11:11)
• The shifting landscape as technology advances and the need for high-quality, annotated data (13:54)

If this got you thinking about the data already sitting in your freezer, and what it would take to actually use it, start here. These four conversations dig into AI-ready data, actionable omics, hybrid-model digital twins, and the cell-engineering biology underneath it all.

• Episodes 263 - 264: Why AI and Automation Tools Won't Deliver Until Your Lab's Data Is Connected with David Hardy
• Episodes 173 - 174: Mastering Hybrid Model Digital Twins: From Lab Scale to Commercial Bioprocessing with Krist Gernaey
• Episodes 169 - 170: Why Your DNA Is a Terrible Disease Predictor (And How Multi-Omics Changes Everything) with Mo Jain
• Episodes 77 - 78: Cell Factories Explained: How Synthetic Biology and AI Revolutionize Protein Production with Mauro Torres

If you'd rather follow the glycosylation thread, check Episodes 69 - 70: Glycoanalytics Explained with Róisín O'Flaherty

Connect with Nathan Lewis:
Website: https://lewislab.uga.edu/
LinkedIn: https://www.linkedin.com/in/nathanelewis

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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Dormant omics data are a goldmine for CMC innovation waiting to be unlocked. But legacy structures, poor annotation, and spreadsheet chaos hold most biotech teams back from the real breakthroughs.

Nathan Lewis, GRA Eminent Scholar at the Center for Molecular Medicine, Complex Carbohydrate Research Center, and Department of Biochemistry and Molecular Biology at the University of Georgia, has a clear message: actionable data is now within reach thanks to hybrid modeling, advanced study design, and AI as a true scientific collaborator.

Topics discussed:

• Rethinking the dogma: controlling protein glycosylation quality from the inside out, not just by bioprocess conditions (03:00)
• Nathan Lewis’s journey into science and bioprocessing, from unexpected college choices to pivotal advances in CHO cell engineering (05:12)
• The evolution of omics in bioprocessing: why actionable insights, not just big datasets, should be the goal (10:49)
• Strategic advice for structuring, annotating, and making old and new datasets ready for AI and LLM analysis (17:34)
• The balance between mechanistic and machine learning models—when each makes sense, and why hybrid modeling is gaining ground (22:20)
• The current and future role of digital twins in process development and why foundation models and data consortia matter for scalability (26:24)

Smart insight: The real revolution isn’t in making new data, but in unlocking the value of what already exists. Advances in AI, hybrid modeling, and collaborative standards promise to turn decades-old data into a catalyst for innovation—enabling faster, smarter, and more reliable bioprocess development.

If this got you thinking about the data already sitting in your freezer — and what it would take to actually use it — start here. These four dig into AI-ready data, actionable omics, hybrid-model digital twins, and the cell-engineering biology underneath it all.

• Episodes 263 - 264: Why AI and Automation Tools Won't Deliver Until Your Lab's Data Is Connected with David Hardy
• Episodes 173 - 174: Mastering Hybrid Model Digital Twins: From Lab Scale to Commercial Bioprocessing with Krist Gernaey
• Episodes 169 - 170: Why Your DNA Is a Terrible Disease Predictor (And How Multi-Omics Changes Everything) with Mo Jain
• Episodes 77 - 78: Cell Factories Explained: How Synthetic Biology and AI Revolutionize Protein Production with Mauro Torres

If you'd rather follow the glycosylation thread, check Episodes 69 - 70: Glycoanalytics Explained with Róisín O'Flaherty

Connect with Nathan Lewis:
Website: https://lewislab.uga.edu/
LinkedIn: https://www.linkedin.com/in/nathanelewis

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

“Does DNA barcoding actually work?” It's the first question Kent Rapp hears from prospective customers when he pitches Biolinco's DNA barcoding platform. By his own admission, the technology can sound too good to be true.

In Part 2 of this conversation, Kent rejoins the Smart Biotech Scientist Podcast to address that skepticism head-on, unpack how subclone variability and bispecific antibody purity concerns shape customer trust, and share what it actually took to move Biolinco from a Johns Hopkins postdoc project to a company with its first paying customer.

In this episode:

• Common pushbacks and questions from industry regarding new cell line development technologies (03:18)
• Practical advice for resource-constrained startups developing cell lines, emphasizing efficiency and data quality over brute force automation (08:42)
• The role and value of DNA barcoding in screening and developing robust cell lines for therapeutics (08:56)
• Lessons learned from translating scientific innovation into a commercial product, including securing early support and customer trust (10:27)
• Insights on adapting messaging, leveraging feedback, and understanding market needs as part of the entrepreneurial process (13:52)
• Key differences between academic research and industry requirements for reliable, repeatable biotech tools (15:26)
• Kent’s most important takeaway for successful cell line development: focus on collecting the right data at the right scale for informed decisions (17:14)

Smart insight: Translating innovations from academia to industry brings a new wave of challenges. Kent describes the “valley of death” separating a publishable prototype from a reliable, commercial-grade product. Academic success is often tied to novelty, publications, and grants, while industry demands repeatability, robustness, and consistent performance across labs and operators. Securing early champions and funding, iterating based on tough industry feedback, and building trust with initial partners all require resilience, and a willingness to pivot as needed. Feedback, even when harsh, becomes a “gift” that helps refine the product and business model. The secret is not a single breakthrough, but adaptability and relentless customer focus.

If this got you rethinking how you screen clones, you'll want these next. We've tackled cell line development, high-throughput screening, and the art of spotting manufacturable candidates early from a few different directions — here are four worth queuing up.

• Episodes 117 - 118 : Cell Line Development Secrets: Eliminating Critical Bottlenecks for Faster Timelines with Andrea Gough
• Episodes 09 - 10: Revolutionizing Cell-Line Development: Unleashing the Power of Nanopens and Microenvironments with Tanner Nevill
• Episodes 123 - 124: Manufacturability: Why Most Protein Candidates Fail (And How to Pick Winners Early) with Susan Sharfstein
• Episodes 115 - 116: Revolutionizing Biologics Development with Hyper Throughput Screening and AI with Jeremy Agresti

Connect with Kent Rapp:
LinkedIn: https://www.linkedin.com/in/kent-rapp/
Biolinco website: https://biolinco.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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What if the bottleneck in cell line development isn’t how many clones you screen, but how you track them?

Cloning workflows have long relied on brute force: screen more cells, automate harder, and hope that small-scale performance predicts manufacturability. But too often, the “perfect” clone in a 96-well plate turns into a dud when it reaches the bioreactor. That disconnect costs time, money, and promising therapies.

This week, host David Brühlmann welcomes Kent Rapp, Co-founder and CEO of Biolinco, an entrepreneur who’s turning the classic approach to cell line development inside out. Drawing from his background in chemical engineering and his work in biomanufacturing at Johns Hopkins University, Kent teamed up with DNA barcoding experts to pioneer a new workflow: barcode every cell, pool them, and track their true performance in the environment that matters.

Topics discussed:

• The pitfalls of brute-force screening in traditional cell line development (03:05)
• Kent’s background and how he was drawn to combine science, startups, and biomanufacturing (04:37)
• Overcoming discrepancies between small-scale and large-scale screening environments (08:30)
• How DNA barcoding allows for high-resolution, pooled clone screening (10:34)
• Sensitivity advantages of sequencing over plate-based detection (14:21)
• Methodology for tracking and recovering individual high-performing clones from pools (15:13)
• Impact on speed and workflow efficiency in cell line development (17:31)
• Regulatory and safety considerations related to DNA barcodes in cell lines (19:04)

Smart insight: According to Kent, biotech as an industry has a tendency to "automate problems instead of solve them". Rather than addressing the root causes—like lack of meaningful measurements at relevant scales—companies often throw more robots and more plates at the issue, hoping brute force will finally yield the magical clone. But real process improvement requires a rethinking of what is being measured and how those insights are generated—not just a higher throughput of the same flawed assay.

If this got you rethinking how you screen clones, you'll want these next. We've tackled cell line development, high-throughput screening, and the art of spotting manufacturable candidates early from a few different directions — here are four worth queuing up.

• Episodes 117 - 118 : Cell Line Development Secrets: Eliminating Critical Bottlenecks for Faster Timelines with Andrea Gough
• Episodes 09 - 10: Revolutionizing Cell-Line Development: Unleashing the Power of Nanopens and Microenvironments with Tanner Nevill
• Episodes 123 - 124: Manufacturability: Why Most Protein Candidates Fail (And How to Pick Winners Early) with Susan Sharfstein
• Episodes 115 - 116: Revolutionizing Biologics Development with Hyper Throughput Screening and AI with Jeremy Agresti

Connect with Kent Rapp:
LinkedIn: https://www.linkedin.com/in/kent-rapp/
Biolinco website: https://biolinco.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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Your active ingredient is the nucleic acid. So why does a proteolipid vehicle filing include viral clearance studies, stability data and full characterisation of a membrane protein that is not the drug? Because that protein sits on the particle surface, and a component nobody has filed before is the agency's problem regardless of what you call it.

Proteolipid vehicles (PLVs), the platform Jitendra Kumar works on as Lead Scientist for Chemistry and Process Development at Entos Pharmaceuticals, represent a novel frontier in drug delivery, Instead of being taken up into an endosome and having to escape it, a PLV fuses with the cell membrane and releases cargo straight into the cytosol. That opens targets and patient groups that liver-tropic lipid nanoparticles and viral vectors have struggled to reach, and it leaves Kumar building a regulatory file with nothing on the shelf to copy.

Highlights from the episode:

• Strategies for communicating novel technology with regulatory agencies and ensuring robust science-driven submissions (02:34)
• What differentiates the analytical characterization of PLVs compared to standard recombinant proteins or antibodies (04:03)
• The development plan and regulatory pathway towards clinical and commercial approval for their lead leptin therapy (05:35)
• The evolving role of advanced techniques—such as cryo-EM—in supporting regulatory filings and product understanding (06:56)
• Jitendra Kumar's career journey: from agricultural research in India to protein science and neurodegeneration, and how these experiences inform current PLV technology development (09:00)
• Challenges of early diagnostics and product development in neurodegenerative diseases (14:29)
• Decision-making differences and focus in academic versus industry biotech research (15:32)
• Practical advice on the importance of honest technology assessment, building networks, and understanding both strengths and weaknesses (16:16)

Smart insight: A persistent challenge for groundbreaking delivery systems is the lack of established regulatory playbooks. Jitendra Kumar laid out a science-first approach: let data do the talking, supported by rigorous GLP toxicology studies and transparent communication with agencies. Regulatory bodies like Health Canada are receptive to innovation, provided that sponsors demonstrate safety, efficacy, and scientific rationale for any deviation from standard criteria.

If this conversation got you thinking about how novel delivery vehicles reach the cell — and what it takes to carry one from bench through CMC, scale-up, and regulatory review — these four episodes go deeper:

• Episodes 125 - 126: How to Enhance Cell Engineering Using Mechanical Intracellular Delivery with Armon Sharei
• Episodes 231 - 232: From IND to BLA: The Biologics CMC Decisions That Determine Regulatory Success with Henri Kornmann
• Episodes 89 - 90: Scale-Up Secrets: Cracking the Code of AAV Production with Ahmed Youssef
• Episodes 71 - 72: Effective Outsourcing: How Small Biotech Companies Can Thrive with Mark Melville

Connect with Jitendra Kumar:

• LinkedIn: https://www.linkedin.com/in/jkumar2/
• Email: jitendra.kumar@entospharma.com
• Website: https://entospharma.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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