Manifold Bio Adds COO to Drive Its AI + In Vivo Targeted Biologics Platform
Dec 16, 2025 - Manifold Bio has appointed Paula Cobb as Chief Operating Officer to scale its AI-guided, in vivo-first drug discovery engine for tissue-targeted biologics. The move signals a clear execution push: turn a strong technical platform into a durable business with internal programs and meaningful partnerships.
Why this matters for executives
Most discovery platforms stall at translation. To address translational gaps, leadership often invests in cross-functional skills-see AI Translation Courses. Manifold Bio is betting that massive in vivo data, paired with AI-driven protein design, shrinks that gap by testing what actually works inside living systems-not just in a dish.
The company already signed an initial deal with Roche. Bringing in an operator like Cobb points to a shift from proving the science to scaling the engine, the pipeline, and the partnerships that fund long-term advantage.
The leadership lineup (and what each role solves)
- Paula Cobb, COO: Former Board member at Prothena; senior roles at Affinia Therapeutics, Decibel Therapeutics, and Biogen. Charter: operational scale-up, partnering strategy, and translating platform output into pipeline value.
- Gleb Kuznetsov, Ph.D., CEO: Co-founder focused on platform and company strategy; pushing internal programs and external collaborations.
- Pierce Ogden, CTO: Co-founder leading technology development at the interface of AI and protein engineering.
- Shane Lofgren, Head of Strategy: Co-founder aligning platform capabilities with market opportunities and deal structure.
- Kimberly Scearce-Levie, Ph.D., CSO: Driving translational application with deep experience in neuroscience and delivery challenges.
- Steve Holtzman, Executive Chair: Platform-first company builder, guiding corporate strategy and capital efficiency.
- Doug Williams, Ph.D., Chair of Strategic Advisory: Long-term R&D leadership shaping scientific direction and partnerships.
The platform thesis
Manifold Bio integrates large-scale in vivo measurement with AI-guided protein design to develop tissue-targeted biologics across multiple therapeutic areas. Teams scaling those design capabilities may find practical guidance in AI Design Courses, which cover AI-driven design principles and workflows.
With added operational leadership, the focus turns to throughput, cycle time, and decision quality-moving from interesting datasets to assets that clear clinical and commercial hurdles.
What to watch next
- Throughput and cycle time: How many variants can the platform test in vivo per cycle, and how quickly do those data shift designs?
- Hit quality and progression: Clear criteria for moving from binders to targeted, functional biologics with clean delivery profiles.
- Pipeline milestones: Named programs, preclinical readouts, and IND timelines.
- Partnership depth: Beyond the first Roche deal-scope, economics, and data-sharing models.
- Data moat: Unique in vivo datasets and how they feed model performance over time.
- Operating model: How the team aligns biology, ML, and development under one accountable plan.
Strategic takeaways for leadership teams
- Pair platform ambition with operational muscle early. Translational bottlenecks aren't solved by science alone.
- Build a data advantage that compounds-own the loop from design to in vivo readout to next design.
- Use partnerships to validate, pressure-test the model, and fund scale-without diluting the core thesis.
- Set explicit KPIs (throughput, cycle time, success rates) and review them like a product company, not just an R&D shop.
- Keep the end state in mind: tissue-targeted biologics that hit clinical endpoints and make it to market.
Bottom line: Manifold Bio is moving from promise to execution. Adding a seasoned operator makes the intent clear-build an enduring platform and convert it into medicines that matter.
If you're building AI capability across your leadership team, you may find this helpful: AI courses by job role.
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