A method for estimating pharmacokinetic risks of concentration-dependent drug interactions from preclinical data

This article evaluates a novel approach for estimating the pharmacokinetic risks associated with drug interactions in populations. Preclinical pharmac okinetic and metabolism data are analyzed with a stochastic differential eq uation-based pharmacokinetic model that recognizes that the risks associate d with known drug interactions involve deterministic and stochastic compone nts. Specifically, a Bernoulli jump-diffusion pharmacokinetic model that ac counts for the pharmacokinetics, the variability inherent in the pharmacoki netics, and the idiosyncratic nature of the possibility of drug interaction s is proposed. In addition, the variability inherent in the extent of drug interaction is explicitly accounted for. The approach provides useful mecha nistic insights into the stochastic processes that "drive" drug interaction s in populations because it yields analytical results. The validity of the model predictions was tested with experimental data from two previously inv estigated systems: N-1 and N-3 caffeine demethylation in populations with s mokers and in the terfenadine-ketoconazole system.