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

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: 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
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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: 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

“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: www.linkedin.com/in/kent-rapp
Biolinco website: www.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
Support the show

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: www.linkedin.com/in/kent-rapp
Biolinco website: www.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
Support the show