Key Topics:

• The evolving role of new approach methodologies in drug discovery and regulatory toxicology
• Why human-relevant in vitro models complement existing preclinical approaches
• The importance of context of use, assay qualification, and fit-for-purpose model design
• Navigating assay standardization, inter-laboratory benchmarking, and leveraging biologically meaningful variability in 3D cell culture
• How industry consortia and collaborative initiatives are advancing qualification and regulatory acceptance
• The growing role of artificial intelligence in analyzing complex in vitro data and improving decision-making
• Current opportunities, such as advanced drug modalities (e.g. cell therapies, monoclonals), and remaining challenges for broader adoption of advanced in vitro models

Speakers

• Daniela Cornacchia, PhD - Co-Chair, ISSCR Consortium on Advanced Cell-Based Models in Drug Discovery and Development
• Clive Roper, PhD - Director, Roper Toxicology Consulting Limited
• Paul Vulto, PhD - CEO and Co-Founder, MIMETAS
• Ludovico Buti, PhD - Senior Research Lead, Discovery and Safety Assessment, Charles River Laboratories
• Magdalena Kasendra, PhD - Director of Research and Development, Center for Stem Cell and Organoid Medicine (CuSTOM) at Cincinnati Children’s Hospital Medical Center
• Guisy Tornillo, PhD - R&D Senior Scientist, Molecular Devices
• Riya Sharma - Senior Scientist, Liver Biology, STEMCELL Technologies
• Jenna Moccia, PhD - Director, Product Management, STEMCELL Technologies

Frequently Asked Questions (FAQ)

What is a Context of Use (CoU) for NAMs and why is it important for NAM adoption in drug discovery?

A Context of Use clearly defines how and why a method will be used to inform a specific decision in drug discovery or development. It describes the scientific question the method is intended to address, how the resulting information will be used, and the decision it is intended to support. Defining the Context of Use helps establish the performance requirements a method or assay system must meet to be fit for that purpose.

How do in vitro NAMs complement traditional preclinical models?

In vitro NAMs can augment and complement the insights accessible through traditional preclinical models by providing human-relevant lines of evidence that strengthen confidence in drug discovery and development decisions. For example, they can provide insights into mechanisms of action or toxicity in the context of human biology, including responses that may not be adequately captured by animal models. Rather than relying on any single study to answer every question, NAMs can be integrated with animal and other preclinical data as part of a weight-of-evidence approach incorporating multiple, complementary lines of investigation.

What role does AI play in advancing New Approach Methodologies?

Artificial intelligence (AI) and machine learning can complement in vitro NAMs in several ways. AI-enabled analysis can help generate actionable insights from the rich, multiparametric datasets produced by complex in vitro models and advanced readouts such as imaging and ’omics. Conversely, biologically relevant in vitro systems can provide experimental evidence to test and validate predictions generated by in silico models. Together, these approaches can connect computational predictions with experimental biology while helping researchers interpret complex biological data and identify patterns that may otherwise be difficult to discern.