The successful execution of a clinical trial relies on the expertise and collaboration of every team member involved. This session will focus on the pharmacovigilance team and its pivotal role within clinical research. With patient safety as the foremost priority, we will examine how effective cross-functional collaboration contributes to achieving this objective. In the latter portion of this informative session, a panel discussion will feature experts from biostatistics, clinical operations, pharmacovigilance operations, and related disciplines. Panelists will share insights from their professional experiences, highlighting effective collaboration with pharmacovigilance teams at various stages of clinical trials.

The focus of BR assessments is shifting from a peri- and post-approval activity to early in clinical development, aiming to establish a lifecycle framework for the continuous assessment and optimization of a product's BR profile as early as first-in-human. In this presentation we are looking at opportunities at various lifecycle stages and at important considerations for successfully performing and communicating BR assessments.

Safety integration projects require strong partnership & collaboration with a technology vendor & internal company lines. Designing user requirements requires mutual understanding of each other’s differences in business processes/regulations with the goal of improving the overall user experience for sites and sponsors. Team sharply focused on improvements including enabling data capture to push/trigger delivery of safety data cases for required PV reporting, elimination of SAE reconciliation, & automation of case delivery to Safety to avoid late reporting.

Machine learning (ML) is increasingly being used to predict specific adverse events (AEs) using real-world data (RWD), such as electronic health record (EHR) and insurance claims. In this session, you will learn how AE prediction using ML can help optimize clinical trial design, improve patient recruitment and pinpoint at-risk clinical sites needing more oversight, ultimately leading to faster, more successful trials.

AbbVie modernized patient safety surveillance by replacing static, delayed reports with an interactive, self-service analytics platform. This approach enabled real-time engagement with safety data, streamlined regulatory reporting, and improved overall monitoring during study conduct. The talk will share lessons from building a scalable solution and ongoing challenges in predictive signal detection.

This session will showcase how a cross-functional team at Pfizer developed an internal generative AI copilot to streamline the authoring of Pharmacokinetics (PK) sections in Clinical Study Reports (CSRs). By leveraging content from the Study Protocol and Tables, Listings, and Figures (TLFs), the tool can generate a high-quality draft in approximately 10 minutes. In blinded evaluations by subject matter experts, the AI-generated drafts scored 80–90% compared to finalized human-authored reports, demonstrating the potential of generative AI to accelerate CSR development without compromising scientific rigor. The presentation will provide an overview of the technology, highlighting the development journey and key operational learnings.

PV Professionals bring a unique set of skills focused on patient safety but also regulatory compliance. The stage of product development is one consideration for bringing in PV support; the safety profile and patient profile is another. The CRO and Medical Monitor capabilities are other parameters. So when is the right time?

Gene therapy presents unique safety challenges, requiring risk management across three dimensions: the investigational product itself, its delivery to target organs, and the delivery device. This talk will examine strategies to address these complexities and ensure patient safety in the rapidly advancing field of gene therapy.

This important aspect of clinical trial safety is often performed inadequately by the investigational sites and can affect the success or failure of a product's development. A methodology to improve the quality and reliability of this process is discussed.

The ICH E6(R3) concept of “acceptable ranges” broadens the use of Quality Tolerance Limits (QTLs), including for early-phase studies and those with safety/exposure endpoints. But applying QTLs in these settings requires a different approach. This session shares practical tips and considerations for implementing QTLs in safety/exposure-focused, early-phase studies—and why building a consistent framework across all trials can benefit the broader portfolio.

This presentation will focus on how risk-based data management methodology has been introduced with consistency across several trials within an oncology program. Key topics of discussion will include how study teams identified critical data for endpoints early in protocol discussions, the importance of cross-functional collaboration (Central Monitoring, Data Management, Stats, Clinical, etc.), and improvements to monitoring strategies to increase efficiencies.

As Risk-Based Quality Management (RBQM) matures, organizations are expanding its impact beyond individual studies. This session explores how RBQM principles can be strategically integrated at the asset level to drive portfolio-wide insights, optimize oversight, and align with broader business objectives. Learn how to leverage cross-study data, enhance decision-making, and elevate quality management from a study-centric function to an enterprise-wide value driver.

This talk explores how the effectiveness of central monitoring in clinical trials is critically dependent on what happens after an alert is raised. The talk emphasizes that the true value of data lies not just in the initial signal, but in the subsequent execution, communication, and clarity of the actions taken. It highlights best practices for ensuring high-quality, clear interactions with study sites, stressing that the precise communication and clear execution of follow-up actions are paramount to mitigating risk effectively and ensuring data integrity. The presentation will reveal how optimizing these post-signal processes is essential for translating central monitoring insights into meaningful, real-world site improvements and overall study success.

Early phase studies benefit from a risk-based approach but present unique challenges due to limited data, smaller size and decision-making required throughout the trial. Assessing risks during protocol design, careful selection of CtQs and choosing fit-for-purpose, flexible tools are areas where RBQM can have the most impact towards success of these studies.

At Amgen, we are transforming our analytics ecosystem with AI-driven tools that enable near real-time, self-service insights for Global Development stakeholders. Our next-generation analytics engine features a rebuilt metrics platform, centralized data governance, and a streamlined dashboard landscape leveraging Power BI with Co-Pilot and custom AI interfaces. By implementing AI/ML bots and natural language queries, we are shifting from static reporting to a dynamic, AI-driven experience.