299: Where Bioprocess Models Deliver and Where They Overpromise: Choosing Between Hybrid, Mechanistic, and Machine Learning with Moritz von Stosch - Part 1

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: www.hinabioventures.com (hinabioventures.com)
LinkedIn: linkedin.com/in/movosto
Support the show (buzzsprout.com/2263581/support)