Archives: Agenda

• Ensuring your trial is both cost-conscious and efficient without compromising on quality and patient safety
• Why financial accruals for clinical and R&D processes are still so challenging despite years of work to alleviate these obstacles
• Strategies for cost-effective budget allocation to maintain long-term financial health and positive ROI
• Avoiding common mistakes to keep your clinical trial budget on track
• Supporting startups: Tackling investor’s lack of willingness to invest in smaller biotechs

Rare-disease trials face unique challenges – small patient populations, dispersed sites, and limited data. This session explores how AI-first protocol design transforms feasibility and speed in rare-disease research, enabling smarter, patient-centric, and regulator-ready studies from day one. Key Takeaways:

• How AI-first protocols reduce amendments and time-to-first-patient in small-population studies
• Using real-world and registry data to simulate feasibility and optimize site selection
• Building adaptive, patient-centric designs aligned with EMA and FDA expectations
• Integrating AI into cross-functional workflows — from clinical to regulatory teams
• Case examples showing measurable acceleration and cost savings in rare-disease trials

• Vendor Relationships in a Changing Environment
• Best Practices for Successful Vendor Collaboration
• From Vendor to Partner
• Keys to Establish Effective Oversight

• Key considerations for delivery of a large portfolio
• Challenges and opportunities in the implementation of a new delivery model/framework
• A case study: Internal Vs External Resourcing

• Understanding challenges faced when integrating AI into clinical data management processes
• Evaluating AI readiness and identifying red flags during development, implementation and deployment
• Taking away actionable insight how to use and incorporate AI, by learning from setbacks and successes

• Evaluating study plans effectively to strengthen vendor selection and enhance data management strategies
• Aligning data management goals with vendor capabilities to reduce workload
• Enhancing vendor selection though effective identification methods

• Human governance of AI: principles, boundaries, and a human-in-the-loop model for clinical data decisions
• Responsible adoption: how to train, measure, and scale without losing traceability (ATR: Audit Trail Review, RBQM: Risk-Based Quality Management, quality, and ethics)
• Practical architecture: rules + ML + agentic AI for clinical data review—what to automate vs. what to keep human