Prediction of maximum exposure in poor metabolizers following inhibition of nonpolymorphic pathways

C Collins1, R Levy, I Ragueneau-Majlessi

  • 1Department of Pharmaceutics, University of Washington, Seattle, 98195, USA. carolc3@u.washington.edu

Insights

Drug exposure can significantly increase in individuals with poor drug metabolism when taking certain inhibitors. A new predictive model helps identify high-risk substrates, improving drug safety during clinical trials.

Area of Science:

  • Pharmacokinetics
  • Drug Metabolism
  • Clinical Pharmacology

Background:

  • Inhibitors of nonpolymorphic enzymes can cause substantial increases in drug exposure in poor metabolizers.
  • Clinical trials show variable increases in drug exposure ratios, raising safety and ethical concerns.

Purpose of the Study:

  • To develop a predictive model for maximum drug exposure in poor metabolizers when co-administered with inhibitors.
  • To identify drug substrates susceptible to large increases in exposure.

Main Methods:

  • Literature data mining to identify relevant clinical trials.
  • Development of a theoretical approach to predict maximum exposure in poor metabolizers.
  • Validation of the predictive model using existing clinical trial data.

Main Results:

  • The predictive model achieved a mean percentage difference of 11.9% between predicted and observed maximum exposure.
  • Substrates with a high fraction metabolized by polymorphic enzymes (fm(POLY) > 75%) are at increased risk for > tenfold exposure increases.

Conclusions:

  • A theoretical model can effectively predict drug exposure in poor metabolizers.
  • Identifying substrates with high fm(POLY) is crucial for assessing risks associated with enzyme inhibitors.

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