Explore the Agenda
Workshop A
9:00 am Spearheading Regulatory & Industry Collaboration to Define 3D Tissue Model Validation & Qualification Standards
Regulatory bodies like the FDA are signaling support for New Approach Methodologies, yet clear guidance on model validation and qualification remains elusive. This workshop explores what data regulators expect for validated models and how leading biopharma can achieve confidence in model validation. Gain clarity on validation versus qualification
and uncover strategies to align with regulatory expectations for successful IND submissions.
- Understanding how biopharma validates and qualify models to secure FDA acceptance and ensure models answer critical research questions
- Gaining clarity on validation versus qualification to prioritize the right process for research goals
- Identifying what validation data and metrics FDA expects to improve clarity for in vitro model application and leadership buy-in
- Exploring ways to make reference data accessible to help regulators provide actionable guidance for biopharma
12:00 pm Lunch Break & Networking
Workshop B
1:00 pm Advancing Cell Sourcing Strategies to Minimize Variability & Overcome Primary Cell Scarcity
Even the most validated models fail without high-quality cells. Limited access to primary cells and batch-to-batch variability pose major challenges for reproducibility and scalability. This workshop examines sourcing strategies, IPSC applications, and centralized approaches to ensure sufficient, consistent cells for 3D tissue models. Learn how companies streamline cell acquisition and address patient heterogeneity for more predictive complex in vitro systems.
- Centralizing primary cell sourcing to reduce variability and achieve consistent readouts
- Evaluating iPSC benefits and limitations to expand cell availability and accelerate iPSC development timelines
- Streamlining cell acquisition from clinics to minimize human error and variability in cell quality
- Addressing low primary cell availability to capture patient heterogeneity and improve model predictability