A genetic algorithm-based approach for quantitative prediction of drug-drug interactions caused by cytochrome P450 3A inhibitors and inducers in dogs and cats

A genetic algorithm (GA)-based framework was developed to predict drug-drug interactions (DDIs) caused by cytochrome P450 3A (CYP3A) inhibition or induction in dogs and cats. Area under the plasma concentration-time curve (AUC) ratios, obtained from published in vivo DDI studies, were used to calculate the following parameters: (a) the contribution ratio (CR), which represents the fraction of the dose of the victim drug metabolized via CYP3A, and (b) the inhibitory potency (inhibition ratio; IR) or inducing potency (IC) of the perpetrator drug. AUC ratios of 3 substrates, 4 inhibitors and 1 inducer of CYP3A in cats, and the AUC ratios of 10 substrates, 12 inhibitors and 3 inducers of CYP3A in dogs were successfully predicted and validated by the developed methodology within 50-200 % of observed values. This approach could represent a useful resource to predict the extent of DDIs in clinical scenarios requiring the simultaneous administration of a CYP3A substrate drug with a CYP3A perpetrator.