A Genetic Algorithm-Based Approach for Quantitative Prediction of Drug-Drug Interactions Caused by Cytochrome P450 3A Inhibition or Induction in Horses

Introduction: A genetic algorithm (GA)-based approach was designed to predict drug–drug interactions (DDIs) triggered by cytochrome P450 3A (CYP3A) inhibition or induction in horses. Methods: Area under the concentration-time curve ratios (AUCRs), obtained from published in vivo DDI studies in horses, were used to compute the following parameters: (1) the contribution ratio (CR), i.e., the fraction of the substrate dose metabolized via the CYP3A pathway, and (2) the interacting drug’s inhibitory potency or inducing efficacy (IR or IC, respectively). Results: AUCRs for 9 substrates, 12 inhibitors, and 1 inducer of equine CYP3A were predicted and validated with the developed method. More than 96% of predictions fell within the commonly accepted range of 50–200% of observed values. Conclusions: The proposed GA-based method may be a useful tool to estimate possible clinically relevant DDIs when co-administration of a CYP3A substrate and a CYP3A-interacting drug is anticipated.