Prediction of Drug-Drug Interactions with Crizotinib as the CYP3A Substrate Using a Physiologically Based Pharmacokinetic Model

Abstract

An orally available multiple tyrosine kinase inhibitor, crizotinib (Xalkori), is a CYP3A substrate, moderate time-dependent inhibitor, and weak inducer. The main objectives of the present study were to: 1) develop and refine a physiologically based pharmacokinetic (PBPK) model of crizotinib on the basis of clinical single-and multiple-dose results, 2) verify the crizotinib PBPK model from crizotinib single-dose drug-drug interaction (DDI) results with multiple-dose coadministration of ketoconazole or rifampin, and 3) apply the crizotinib PBPK model to predict crizotinib multiple-dose DDI outcomes. We also focused on gaining insights into the underlying mechanisms mediating crizotinib DDIs using a dynamic PBPK model, the Simcyp population-based simulator. First, PBPK model-predicted crizotinib exposures adequately matched clinically observed results in the single-and multiple-dose studies. Second, the model-predicted crizotinib exposures sufficiently matched clinically observed results in the crizotinib single-dose DDI studies with ketoconazole or rifampin, resulting in the reasonably predicted fold-increases in crizotinib exposures. Finally, the predicted fold-increases in crizotinib exposures in the multipledose DDI studies were roughly comparable to those in the singledose DDI studies, suggesting that the effects of crizotinib CYP3A time-dependent inhibition (net inhibition) on the multiple-dose DDI outcomes would be negligible. Therefore, crizotinib doseadjustment in the multiple-dose DDI studies could be made on the basis of currently available single-dose results. Overall, we believe that the crizotinib PBPK model developed, refined, and verified in the present study would adequately predict crizotinib oral exposures in other clinical studies, such as DDIs with weak/ moderate CYP3A inhibitors/inducers and drug-disease interactions in patients with hepatic or renal impairment.

Physiologically Based Pharmacokinetic Modeling for Olaparib Dosing Recommendations: Bridging Formulations, Drug Interactions, and Patient Populations

“…However, we have shown improved agreement with observed clinical data (predictions were within 1.9‐fold of observed olaparib monotherapy clinical data) when the PBPK model incorporated all necessary and adequately characterized system and drug parameters. Recent studies have also used robust PBPK‐modeling approaches to successfully predict the clinical drug interactions for mixed CYP3A inhibitors and inducers, further supporting our mechanistic PBPK‐modeling approach to quantify the net effect of olaparib drug interactions.…”

Optimized Cocktail Phenotyping Study Protocol Using Physiological Based Pharmacokinetic Modeling and In silico Assessment of Metabolic Drug–Drug Interactions Involving Modafinil

“…All parameters were based on published literature values or were model predicted based on the physicochemical characteristics of the drug. Modafinil hepatic microsomal intrinsic clearance (CL int ) was back-calculated from clinically observed oral clearances (CL PO ; Wong et al, 1998a) using the retrograde model function in Simcyp (Yamazaki et al, 2015). The interaction (‘inhibitor’) profile was created based on published in vitro microsomal inhibition and hepatocyte induction data for CYP 1A2, 2C9, 2C19, 2D6 and 3A4 (Robertson et al, 2000).…”

Physiologically Based Pharmacokinetic Modeling of Palbociclib

“…This renders an understanding and predicting possible DDI with palbociclib to be important for its clinical use. In this regard a useful mechanistic modeling tool that can be applied to accomplish this objective involves physiologically based pharmacokinetic (PBPK) modeling and simulation, which provides a quantitative framework to assess potential DDI . In addition, recent advances in PBPK modeling and simulation have enabled this approach to be used as a mechanistic modeling tool for the evaluation of the possible impact of various intrinsic and extrinsic factors affecting the pharmacokinetics of drugs such as drug‐disease interactions in organ impairment patients, pharmacogenetics, drug formulation, and pediatrics …”