Building Resilient Biotech Teams in Cell and Gene Therapy with Nelly Viseux

What It Covers

Nelly Viseux, VP of Cell Therapy Development at Puageneron, outlines how biotech leaders build resilient organizations in cell and gene therapy by embedding adaptability, empowerment, and intentional innovation into team structures, talent strategy, quality systems, and data-driven decision-making across the full development lifecycle.

Key Questions Answered

• •Intentional Innovation Crossover: Separate innovation teams from execution teams structurally, but create a formal gate process where leadership decides when an innovation is ready to enter the manufacturing pipeline. This prevents premature implementation while keeping scientific progress moving, and gives regulators documented evidence of deliberate, confidence-backed process changes rather than reactive decisions.
• •Tiered Escalation with Rapid Response: Build manufacturing operations around a tiered daily escalation system where frontline teams hold decision-making authority, with issues elevated only as severity warrants. Layer in a dedicated rapid response team for critical failures. In autologous cell therapy, where patient material turnaround is time-sensitive, this structure directly reduces batch loss risk.
• •Talent Hiring Minimums: Define non-negotiable baseline skills before recruiting — for example, aseptic manufacturing technique for production roles — then train modality-specific knowledge internally. This narrows candidate pools to those with transferable fundamentals while preserving training resources for cell therapy specifics, and reduces onboarding time without compromising quality or compliance standards.
• •Cross-Lifecycle Data Mining: In autologous cell therapy, consolidate data across the full patient-to-patient product lifecycle into a shared data lake accessible to manufacturing, clinical, and scientific teams simultaneously. Each function interprets the same dataset differently, and cross-functional dashboard reviews — read-only, published regularly — surface patterns that siloed reporting would miss and inform process hypothesis testing.
• •Anticipatory Compliance Scaling: During Phase 1, actively evaluate whether current manufacturing technology — traditional CMO processes versus closed, automated, robotic systems — can meet the compliance bar expected at later development stages. Engage regulatory agencies early with comparative data showing equivalence between manual and automated outputs, rather than waiting until scale-up forces a reactive technology transition.