Archives: Agenda

The last mile of patient recruitment—connecting interested patients to sites—often breaks down. This session explores how PRPs such as Trialbee’s Honey Platform™ strengthens that handoff through invisible, backend infrastructure that reduces missed connections and improves enrolment at scale.

• Where patient-to-site handoffs fail
• Why a single backend matters across studies and regions
• How the Honey Platform improves conversion without changing the patient experience

• Building flexibility into outsourcing partnerships for scalability
• Cost, quality and efficiency trade-offs between different models
• Identifying which model fits different phases of clinical development
• What does transition look like to you and how feasible is it?

Moderator: Anca Copaescu, CEO, Clinical Maestro

• Define a clear indication and targeted patient population early by aligning the targeted product profile (TPP) with meaningful clinical outcomes and measurable endpoints.
• Build a comprehensive development roadmap that anticipates key inflection points from IND through pivotal trials, ensuring evidence generation stays aligned with approval requirements.
• Engage regulators early (e.g., Pre-IND meetings) to support gap analysis, de-risk development decisions, and refine a practical regulatory strategy.
• Embed Quality by Design (QbD) principles from the start to protect data integrity, ensure fit-for-purpose operational execution, and improve inspection readiness.
• Strengthen the likelihood of approval success by integrating indication clarity, regulatory alignment, and quality systems into a cohesive, proactive development approach.

• Decoding what FDA BIMO/PAI inspectors actually look for and why
• Designing inspection-ready systems that withstand real-world pressure
• Data integrity and story coherence: The real drivers of first-cycle approvals
• Communication choreography: Before, during, and after the inspection
• Tailored guidance: Differentiating strategies for small biotechs vs. mid-size companies

• Understand the foundational requirements for AI
• Differentiate between realistic and unrealistic expectations of AI and agents
• Identify strategies for prioritizing AI use cases

• Addressing recent site performance concerns and rebuilding trust when site credibility is questioned
• Identifying common gaps in oversight, governance to avoid actively monitoring site work
• Understanding policies and competing with limited resources at sites
• Enhancing site staff training, culture and accountability of clinical research

• Overcoming the hype: Moving from pilot projects to real-world applications of AI in pharma
• In a conservative and cautious industry, are we ready for AI?
• Reskilling for the AI Age: Preparing today’s workforce for the future, equipped with data-centric skills
• Unveiling but disparaging AI myths in clinical research

• Overcoming financial constraints when budgets are limited and resources tight
• Fierce site competition: How start-ups can compete with established companies for sites
• Utilizing effective planning to improve study and program efficiency without compromising patient safety
• Agile and adaptable: Being prepared to adjust your strategy and approach as needed