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. 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. The promise: faster learning cycles in a physiologically relevant setting, tighter feedback loops between design and outcome, and better odds of picking winners early.
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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