Archives: Agenda

Format and focus 
This will be a 40‑minute, highly practical, non‑commercial workshop built around a real FDA Warning Letter scenario in clinical research. Participants will be guided through a structured 4‑phase response framework: (1) problem analysis, (2) fact‑finding, (3) root cause analysis and (4) CAPA definition. We will then briefly show how the same scenario can be processed in our internal AI tool to compare human and AI reasoning, with the emphasis on methodology and lessons learned rather than on a product demo or sales content.

Learning objectives
By the end of the workshop, participants will be able to:

• Apply a structured 4‑phase approach to inspection findings (problem framing, fact‑finding, root cause analysis, CAPA design).
• Recognise common pitfalls seen in FDA Warning Letters (symptom‑level CAPAs, weak root cause statements, missing effectiveness checks) and how to avoid them in their own organisations.
• Understand where AI can realistically support, but not replace, human QA judgement in root cause analysis and CAPA design for inspection readiness.

• Embedding quality and governance principles earlier in digital and analytics initiatives
• Understanding how trusted data foundations enable faster, more confident innovation
• Exploring where governance accelerates innovation rather than slowing it down

Moderator: John Seman, CEO, Revitale Pharma

• Managing individualized supply models – moving beyond “one product, many patients” to patient-specific manufacturing, labeling, and release (e.g., CGTs, radiopharmaceuticals) while maintaining chain-of-identity and handling strict usability constraints
• Navigating DTP and logistic complexity – coordinating multi-stakeholder ecosystems (sites, patients, depots, logistics) while ensuring data privacy and adapting to country, site, and patient-level variability
• Enabling end-to-end control and compliance – leveraging RTSM solutions to ensure traceability, secure data handling, temperature and expiry management, and seamless oversight across the full supply lifecycle

• Questioning whether current AI tools are overpromising and underdelivering in real clinical operations
• Exploring where AI implementations are stalling due to data quality, integration and change-management challenges
• Examining regulatory, validation and inspection readiness concerns that are slowing adoption

• Embedding patient-reported outcomes (PROs) early to streamline oncology trial operations and control cost: How PROs in Phase I/II can deliver clearer tolerability and patient-experience data, supporting faster decisions and more efficient Phase III planning
• Operationalizing digital endpoints (eCOA) for speed, quality, and patient-centricity: Practical strategies for configuring eCOA to accelerate startup, and reduce patient burden
• Using digital endpoints to enable risk-based oversight and phase-to-phase continuity: How continuous, real-time eCOA data supports centralized/risk-based monitoring, and robustness of regulatory and scientific outcomes

• Exploring how communication styles, cultural mismatches and unspoken assumptions derail trials more than processes or systems
• Supporting teams by aligning expectations, roles and behaviour before contracting, not after issues emerge
• Strengthening long-term partnerships with transparent conflict resolution models and joint decision-making practices

Moderator: Peter Barschdorff, Vice President, GlobalData

• Exploring why innovations stall at pilot stage
• Supporting organizations to scale meaningful improvements
• Strengthening alignment and accountability across teams

Moderator

John Seman, CEO, Revitale Pharma

• Transforming Protocol to Execution: How AIKA uses advanced AI to streamline protocol generation, enhance feasibility analysis, and reduce study start-up timelines
• Data-Driven Decision Support: Leveraging real-time operational insights and predictive analytics to improve trial performance and risk mitigation
• Scaling Clinical Operations: Practical examples of AIKA in action, from automated task orchestration to cross-functional collaboration that drives efficiency and quality