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

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: www.amelabiosciences.com
Company LinkedIn: www.linkedin.com/company/amela-biosciences-ag
Sandra’s LinkedIn: 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
Support the show

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: www.lewislab.uga.edu
LinkedIn: 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

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: www.lewislab.uga.edu
LinkedIn: 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