This webinar is hosted by the International Society for the Study of Xenobiotics (ISSX) and is designed to provide an overview of emerging strategies transforming pharmacokinetic (PK) prediction in modern drug discovery. It will explore how high-throughput physiologically based pharmacokinetic (HT-PBPK) modelling, combined with machine learning, enables rapid, structure-driven prediction of PK properties, reducing reliance on experimental data in early discovery.

This session will highlight the application of HT-PBPK workflows for large-scale compound prioritization, alongside advances in modelling beyond Rule-of-Five (bRo5) compounds such as PROTACs and cyclic peptides. Through case studies, speakers will demonstrate how novel physicochemical descriptors improve the prediction of key ADME and in vivo PK endpoints, including permeability, solubility, and systemic exposure. Together, these approaches offer new opportunities to enhance decision-making and accelerate the design of developable drug candidates.

By the end of this webinar, participants will be able to:

1. Describe how in silico inputs can replace in vitro data to enable scalable pharmacokinetics prediction.
2. Assess the value of novel physicochemical descriptors in improving ADME and pharmacokinetics predictions
3. Interpret prediction accuracy (e.g., fold-error ranges) and identify key success factors (e.g., clearance pathway classification, ML confidence)
4. Understand the drug development challenges posed by PROTACs and cyclic peptides
5. Describe emerging strategies for modelling beyond Rule-of-Five (bRo5) compounds

Speakers:

• Jeremy Jones, Ph.D., Principal Scientist, Simulations Plus

• Davide Bassani, Computational DMPK and Translational PK/PD Leader, Hoffmann-La Roche

Moderators:

• William Weigel, Scientist, Molecular Design, Terray Therapeutics
• Daniel Scotcher, Lecturer, The University of Manchester