A combined model for predicting CYP3A4 clinical net drug-drug interaction based on CYP3A4 inhibition, inactivation, and induction determined in vitro. Drug Metab. Dispos

ABSTRACT: Although approaches to the prediction of drug-drug interactions (DDIs) arising via time-dependent inactivation have recently been developed, such approaches do not account for simple competitive inhibition or induction. Accordingly, these approaches do not provide accurate predictions of DDIs arising from simple competitive inhibition (e.g., ketoconazole) or induction of cytochromes P450 (e.g., phenytoin). In addition, methods that focus upon a single interaction mechanism are likely to yield misleading predictions in the face of mixed mechanisms (e.g., ritonavir). As such, we have developed a more comprehensive mathematical model that accounts for the simultaneous influences of competitive inhibition, time-dependent inactivation, and induction of CYP3A in both the liver and intestine to provide a net drug-drug interaction prediction in terms of area under the concentration-time curve ratio. This model provides a framework by which readily obtained in vitro values for competitive inhibition, time-dependent inactivation and induction for the precipitant compound as well as literature values for f m and F G for the object drug can be used to provide quantitative predictions of DDIs. Using this model, DDIs arising via inactivation (e.g., erythromycin) continue to be well predicted, whereas those arising via competitive inhibition (e.g., ketoconazole), induction (e.g., phenytoin), and mixed mechanisms (e.g., ritonavir) are also predicted within the ranges reported in the clinic. This comprehensive model quantitatively predicts clinical observations with reasonable accuracy and can be a valuable tool to evaluate candidate drugs and rationalize clinical DDIs

A combined model for predicting CYP3A4 clinical net drug-drug interaction based on CYP3A4 inhibition, inactivation, and induction determined in vitro. Drug Metab. Dispos

Abstract Although approaches to the prediction of DDIs arising via time-dependent inactivation have recently been developed, such approaches do not account for simple competitive inhibition or induction. Accordingly, these approaches do not provide accurate predictions of DDIs arising from simple competitive inhibition (e.g. ketoconazole) or induction of P450s (e.g. phenytoin). In addition, methods which focus upon a single interaction mechanism are likely to yield misleading predictions in the face of mixed mechanisms (e.g. ritonavir). As such, we have developed a more comprehensive mathematical model that accounts for the simultaneous influences of competitive inhibition, time-dependent inactivation and induction of CYP3A in both the liver and intestine in order to provide a net drug-drug interaction prediction in terms of AUC ratio. This model provides a framework by which readily obtained in vitro values for competitive inhibition, time-dependent inactivation and induction for the precipitant compound as well as literature values for f m and F G for the object drug can be used to provide quantitative predictions of DDIs. Using this model, DDIs arising via inactivation (e.g. erythromycin) continue to be well predicted, while those arising via competitive inhibition (e.g. ketoconazole); induction (e.g. phenytoin) and mixed mechanisms (e.g. ritonavir) are also predicted within the ranges reported in the clinic. This comprehensive model quantitatively predicts clinical observations with reasonable accuracy and can be a valuable tool to evaluate candidate drugs and rationalize clinical DDIs

Use of Immortalized Human Hepatocytes to Predict the Magnitude of Clinical Drug-Drug Interactions Caused by CYP3A4 Induction

ABSTRACT: Cytochrome P4503A4 (CYP3A4) is the principal drug-metabolizing enzyme in human liver. Drug-drug interactions (DDIs) caused by induction of CYP3A4 can result in decreased exposure to coadministered drugs, with potential loss of efficacy. Immortalized hepatocytes (Fa2N-4 cells) have been proposed as a tool to identify CYP3A4 inducers. The purpose of the current studies was to characterize the effect of known inducers on CYP3A4 in Fa2N-4 cells, and to determine whether these in vitro data could reliably project the magnitude of DDIs caused by induction. Twenty-four compounds were chosen for these studies, based on previously published data using primary human hepatocytes. Eighteen compounds had been shown to be positive for induction, and six compounds had been shown to be negative for induction. In Cytochrome P4503A4 (CYP3A4) is the major drug-metabolizing enzyme in human liver and is responsible for the clearance of many commonly used drugs, including benzodiazepines, statins, calcium channel blockers, and HIV protease inhibitors. Certain drugs can modulate the level of CYP3A4 activity, thereby causing changes in clearance of coadministered drugs that are CYP3A4 substrates. Levels of CYP3A4 activity can be decreased by inhibition of enzyme activity, or increased by induction of new protein synthesis. Changes in CYP3A4 activity, either through inhibition or induction, can result in potentially serious drug-drug interactions (DDIs). Whereas assays for evaluating inhibition of CYP3A4 are routine and the relationship between in vitro data and in vivo effects relatively well understood Induction of CYP3A4 is thought to occur primarily through transcriptional activation of the gene. The 5Ј-regulatory region of the CYP3A4 gene contains elements that bind various transcription factors that can up-or down-regulate transcription. One such transcription factor is the pregnane X receptor (PXR). PXR is a ligand-activated transcription factor that is activated by a variety of drugs and endogenous compounds to increase transcription of CYP3A4 as well as other drug-metabolizing enzymes and transporter