This session presents Dynamic Shared Ownership (DSO) as a disruptive operating model for clinical operations, replacing hierarchical, country-based structures with empowered, skill-based network teams. By shifting decision-making to the point of execution and enabling dynamic cross-border resource flow, Bayer achieved a 25% increase in productivity and efficiency, accelerated study timelines, and reduced management layers—while maintaining quality and delivery performance in a regulated clinical-trial environment.

Intelligent study design resembles a relay race: success depends on coordinated teams working both sequentially and in parallel throughout clinical development. Protocol decisions shape trial performance long before the first patient enrolls. This presentation shares insights into to key questions including -- Which protocol decisions most strongly influence enrollment, retention, cost, and site/patient burden? How can cross-industry benchmarks and predictive modeling inform design choices before first-patient-in? What practices best balance scientific rigor with operational feasibility while protecting data quality and timely execution. And how can teams coordinate sequential and parallel activities throughout clinical development?

Selective safety data collection (SSDC) streamlines late-stage and post-approval trials by reducing collection of common or non-serious adverse events when a drug's safety profile is well-established. This session examines ICH E19 guideline implementation experiences from regulatory and industry perspectives, identifying successes, lessons learned, and challenges. This session will focus on SSDC's role in advancing efficiencies in clinical trial design globally and share practical insights.

AbbVie uses capabilities to ensure that patients' and sites' voices guide every step of protocol development. These include but are not limited to: gathering patient and site input via surveys, optimizing protocols for efficiency and patient-centricity through data, and engaging external experts for real-world feedback. Together, these steps create a holistic, inclusive process that prioritizes continuous improvement and patient-centered research. 

Digital Data Flow (DDF) is reshaping clinical development by enabling design, conduct, and close-out information to digitally flow seamlessly across systems, functions, and partners. At Regeneron, we are pioneering the use of digital protocols as a machine-readable source of truth, transforming how studies are designed, built, and executed. Learn how protocol digitalization is reducing complexity, enabling downstream automation, improving study quality, and laying the foundation for scalable, AI-enabled clinical trial innovation.

Clinical trials are collecting more data than ever—but how much is truly needed? Unnecessary or misaligned data collection can increase burden on participants and sites, add operational complexity, and divert attention from the information that matters most. As the industry seeks to improve trial efficiency and participant experience, there is growing interest in approaches that better align data collection with study objectives, participant safety, and regulatory needs. Learn how TransCelerate’s work is helping advance more fit-for-purpose data collection in clinical trials. Through case studies, cross-industry collaboration, and dialogue with global regulators, speakers will demonstrate how Selective Safety Data Collection and pragmatic trial approaches can balance risk, data quality, and operational feasibility, along with practical resources and implementation considerations.

The U.S. FDA's CDER Center for Clinical Trial Innovation (C3TI) advances the use of artificial intelligence, decentralized features, digital health technologies, real-world data, and other pragmatic elements to enhance efficiencies in drug development. This presentation highlights C3TI's activities, emerging priorities, and the evolving regulatory landscape for innovative trial design and conduct. Attendees will learn about opportunities to engage with CDER to advance broader adoption of innovative clinical trial approaches.

This panel explores how AI can convert fragmented patient experiences, advocacy feedback, and engagement data into evidence that informs trial design, execution, medical, and labeling decisions. Industry leaders, patient advocates, clinical scientists, and site representatives will discuss how connecting the patient voice with clinical evidence creates more patient-centric trials, improves enrollment and retention, and drives better outcomes for patients and sponsors alike.

What if clinical trials were designed around the realities of clinical care, with smarter operations, seamless data flows, and study visits that fit where patients already receive care? As study designs, regulatory expectations, technology, and infrastructure evolve, a clinical research reset is underway. This session explores how organizations can empower research teams, clinicians, and patients to make trials simpler, more connected, more integrated with everyday care, and dramatically more scalable.

Historical clinical trial data offers an opportunity to make better use of data already collected and reduce reliance on traditional control arms. As clinical research becomes more patient-centric and data-driven, sponsors are exploring how previously collected, pseudonymized/anonymized data can help accelerate development, improve evidence generation, and extend the impact of past patient participation.

As AI becomes increasingly integrated into clinical trial design, organizations are exploring how generative AI and skill-based tools can support more efficient, consistent, and informed protocol development. This presentation will share a practical approach to building and applying AI capabilities across the protocol development process, including how targeted AI skills can support the creation and optimization of protocol content. The session will explore lessons learned from implementation, considerations for integrating AI into existing workflows, and how organizations can leverage widely available AI technologies to build scalable, transferable solutions for protocol development.

Rare disease trials face unique biospecimen challenges, including small and geographically dispersed patient populations, limited sample volumes, complex collection requirements, and the need to balance scientific objectives with participant and site burden. Integrating biospecimen strategy early in protocol and trial design can help address these challenges, improve study feasibility, reduce operational complexity, and support the generation of high-quality, fit-for-purpose data. This presentation will explore practical considerations for connecting trial objectives, sampling plans, visit schedules, logistics, and downstream testing needs from the outset.