Archives: Agenda

Statistical computing environment (SCE) is the analytical backbone of drug development — yet many organizations still operate across disconnected platforms, inconsistent language ecosystems, and fragmented data architectures that undermine reproducibility, traceability, and speed to insight. This session traces Regeneron’s end-to-end journey of engineering a validated, cloud-native statistical computing environment that unifies SAS, R, Python and Databricks, under a single governed platform using SAS Viya and Posit. Central to this transformation was reimagining how data is stored and made accessible using a shared storage layer and future ready for establishing coherent data flows and fit-for-purpose data management practices that serve both GxP-regulated submissions and exploratory analytics without duplication or drift. We examine the architectural decisions that shaped the computing and data layers, the governance and validation strategies anchored in a product-based framework, and the change management required to migrate teams from legacy, siloed workflows to a modern, standardized environment.

• Exploring practical strategies for outsourcing CDM functions to support successful clinical study delivery
• Establishing clear governance and communication models with vendors to enhance efficiency and maintain data quality and oversight
• Leveraging vendor capabilities effectively while sticking to budget and without compromising operational control

As clinical trials grow more complex across CROs, vendors, and data sources, sponsor teams are under increasing pressure to review data faster while maintaining clear oversight, traceability, and control. At the same time, many review workflows still depend on fragmented tools, manual listings, and programming queues that slow issue detection and make decisions harder to defend.

This session will explore how sponsor organizations can operationalize agentic AI in a governed, human-in-the-loop model to strengthen clinical oversight without compromising quality or compliance. We will examine a practical approach that connects data review, issue management, and decision documentation in a sponsor-controlled environment, while using AI to accelerate custom listings, triage, and review preparation.

Attendees will learn how this model can help teams move from manual, disconnected review to faster, more traceable oversight across studies and partners. The discussion will also highlight how AI-augmented workflows can support earlier signal visibility, clearer follow-up, and more inspection-ready documentation in the context of modern GCP expectations.

Visit our exhibitors’ booths throughout the day and collect stamps in order to enter our Prize Draw and be in for a chance of winning Apple devices or Amazon giftcards. The Prize Draw will take place in the Exhibition Hall. Make sure you don’t miss out!

• Where RWD/RWE fits in the evidence lifecycle: Understand use cases before trial, during trial, and after trial.
• Role of RWD in trial execution
• Benefits of RWD/RWE in strengthening submissions

• Discuss the concept of reimbursable file objectives incremental to regulatory file objectives guiding clinical program development and execution
• Understand how the market access function is collaborating much more frequently with clinical development teams and why this trend is accelerating
• Share examples of what ‘good looks like’ and some of the pitfalls, challenges with collaboration between market access and clinical development

• Automate the routine: Apply intelligent, rules-based automation against imported study data and bi-directional workflows to streamline the opening and closing of issues and queries.
• Focus on what matters: Reduce manual effort and enable data management teams to focus their expertise on critical data, exceptions, and decisions that require human judgment.
• Drive impact today: See how Integrated Data Review Plan (IDRP) capabilities work together with this automation engine to speed the path from plan to action – for a more proactive, efficient, and scalable data review process that teams can put to work today.

Concept: This is the practical second half of the workshop, where attendees use real examples to test and refine the trend forecasting framework. Working through guided scenarios, participants will identify signals and translate outputs into concrete decisions.

Takeaway: Attendees will leave having validated the framework through hands-on practice, with a completed sample forecast and a reusable template they can take back to apply to their own clinical data management processes

Traditional clinical trial execution suffers from costly efficiency losses and heavy monitoring burdens due to manual data transcription. This session introduces a simplified, integration-free EHR-to-EDC approach that streamlines the clinical data flow. Attendees will discover how this solution enables universal site adoption, cuts transcription time by up to 80%, and achieves near 100% data accuracy at the point of entry.