Archives: Agenda

• How the growth of domestic Korean CROs is reshaping vendor selection
• Supply chain capability comparisons between local and global CRO, CMO, CDMO offerings
• Where domestic CROs are closing the gap on international standards

• Addressing common operational and scientific challenges in Phase 1 and Phase 2 trials
• Managing complex protocol designs incorporating biomarkers and adaptive trial elements
• Evaluating the necessity of placebo groups in Phase 1 studies

• Embedding QbD principles across the clinical supply lifecycle to proactively manage risks
• Strengthening risk-based quality management for compliance and supply continuity
• Preparing quality-driven supply strategies for advanced therapies and complex trials

• Key operational lessons learned from conducting large-scale vaccine trials in (LMICs)
• Balancing rapid trial execution with regulatory compliance and Good Clinical Practice
• Strategies for successful patient recruitment, retention, and community engagement in resource-limited settings
• Ensuring data quality, oversight, and risk management across geographically dispersed trial sites

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