Fireside chat: Data that holds up under pressure: How to generate Phase I–II efficacy data that survives scale-up, scrutiny and value inflection

Efficacy data as a scaling input, not an endpoint: How Phase I expansion and Phase II data should actively inform tech transfer, manufacturing readiness and comparability so scale-up and fundraising don’t expose hidden weaknesses later.

Stress-testing early efficacy before others do it for you: What parameters, thresholds and failure modes teams should be interrogating before investors, partners or regulators do and how structured checklists and smarter analytics reduce late-stage surprises.

AI as a discipline enabler, not a shortcut: Where agentic AI and advanced analytics can genuinely improve signal detection, cohort stratification and consistency and where over-reliance creates false confidence or audit risk.

Cleaner data, faster confidence: Practical lessons on improving data hygiene, standardisation and traceability in early trials how teams accelerate learning without increasing burden or compromising quality.

The data grey zones that derail value: Who really owns early efficacy data, how it contributes to IP and patent strategy, and where unclear data rights or provenance quietly undermine partnering and valuation discussions.