Leveraging Mechanistic Static Models for DDI Waivers and Regulatory Approval

This is session 1 of the proposed symposium title "DDI Predictions: Static vs. PBPK Models in Discovery and Development".

Prediction of drug-drug interactions (DDIs) is critical in drug development and regulatory approval to ensure patient safety and therapeutic effectiveness. Basic static models (R-value), and mechanistic static approaches (net effect), are valuable tools in early development for initial DDI risk screening. While basic models serve simple purposes, such as identifying low-level inhibition or induction, mechanistic static models (MSM) can account for the disposition of both perpetrator and substrate drugs. Dynamic mechanistic models, including PBPK, incorporate time-varying system- and drug-specific parameters to guide decisions on when and how to conduct clinical DDI studies. Proper use and interpretation of these models improves data integration, dose optimization, and can help avoid unnecessary studies. A recent publication demonstrated that mechanistic static models can support regulatory filings and study waivers, while another emphasized the value of PBPK models in capturing time-dependent changes and inter-individual variability.

The session titled "Leveraging Mechanistic Static Models for DDI Waivers and Regulatory Approval" will explore how utilizing unbound average steady-state concentrations in MSMs can provide results comparable to PBPK models for non-dynamic parameters like AUC ratios, when identical input data is applied. Additionally, the recently released ICH M12 guidance seems to endorse the use of MSMs for quantitative DDI predictions and study waivers.