Can Mechanistic Static Models for Drug-Drug Interactions Support Regulatory Filing for Study Waivers and Label...

Area of Science:

• Pharmacokinetics and Drug Metabolism
• Computational Modeling and Simulation
• Drug Development and Regulatory Science

Background:

• Physiologically based pharmacokinetic (PBPK) models are crucial for assessing drug-drug interactions (DDIs) in clinical development.
• Dynamic PBPK models are used for quantitative DDI predictions supporting regulatory submissions, while static models are limited to initial screening.
• Significant resources are required for PBPK model development and regulatory review.

Approach:

• Investigated the utility of mechanistic static models for regulatory filing by analyzing representative cases of successful PBPK submissions to the FDA.
• Applied mechanistic static models to predict DDI risk using the same data and workflow as FDA clinical pharmacology reviews.
• Hypothesized that using unbound average steady-state concentrations of modulators in static models would yield comparable results to dynamic PBPK models for DDI risk assessment metrics like the area under the plasma concentration-time curve ratio.

Key Points:

• Mechanistic static models accurately predicted area under the plasma concentration-time curve ratios for DDIs across various application classes.
• Results from static models were largely comparable to those obtained using dynamic PBPK models in FDA reviews.
• The study supports using the most appropriate model for the intended purpose, optimizing resource allocation in DDI assessment.

Conclusions:

• Mechanistic static models demonstrate significant potential to support regulatory filings for DDI risk assessment.
• The findings encourage a routine comparison of static and dynamic PBPK models to establish best practices.
• Dynamic PBPK models should be reserved for applications leveraging their unique strengths, such as complex scenario testing or population extrapolations.