Prediction of a Metabolic Drug-Drug Interaction in a Virtual Human Population using in vitro Enzyme Kinetic Information
Dickinson GL, Jackson KJ
Eli Lilly and Company
Introduction: The quantitative prediction of metabolic drug-drug interactions (mDDI) is of clinical benefit. In cases where there is potential for an interaction, in vitro ADME properties may be extrapolated to predict in vivo ADME behaviour in simulated virtual populations. These simulations can provide a valuable tool in the assessment of the probability of an mDDI and may facilitate the identification of patients most at risk of experiencing such interactions. Predictions such as these can assist in the design of clinical DDI studies and help make go/no go decisions.
Methods: An in vivo study was carried out to assess the interaction of the compound with ketoconazole. It was assumed that the substrate compound was metabolised entirely by CYP3A4 and 2C19 and in vitro kinetic data were obtained from various sources. Virtual trials (n = 10) were simulated (Simcyp Version 7.0, www.Simcyp.com), that mimicked the design of the in vivo study.
Results: In the in vivo study, observed changes in the area under the concentration-time curve (AUC) and the maximum substrate concentration (Cmax) were 4.5 and 2.5, respectively. Predicted values from the 10 simulated trials were an AUC ratio of 2.5 (range; 1.2 to 7.7) and a Cmax ratio of 1.5 (range; 1.1 to 2.9). The mean fold error in the prediction of Cmax ratios was 1.5 with 10/10 predicted values within 2-fold of the observed data. The corresponding mean fold error for AUC ratio was 2 with 7/10 predicted values within 2-fold of the predicted data.
Conclusions: IVIVE is a useful tool for assessing mDDI in early drug development; it was used successfully in the current study to accurately predict the interaction of a Lilly compound with ketoconazole. The simulations indicated a potentially significant interaction that was confirmed with in vivo studies.