Physiologically Based Pharmacokinetic Modeling for Maribavir to Inform Dosing in Drug-Drug Interaction Scenarios with CYP3A4 Inducers and Inhibitors.

Maribavir, an orally available antiviral agent, has been approved in multiple countries for the treatment of patients with refractory post-transplant cytomegalovirus (CMV) infection and/or disease. Maribavir is primarily metabolized by CYP3A4; coadministration with CYP3A4 inducers and inhibitors may significantly alter maribavir exposure, thereby affecting its efficacy and safety. The effect of CYP3A4 inducers and inhibitors on maribavir exposure was evaluated based on a drug-drug interaction (DDI) study and physiologically-based pharmacokinetic (PBPK) modeling. The effect of rifampin (a strong inducer of CYP3A4 and moderate inducer of CYP1A2), administered at a 600 mg dose once daily, on maribavir pharmacokinetics was assessed in a clinical phase 1 DDI study in healthy participants. A full PBPK model for maribavir was developed and verified using in vitro and clinical pharmacokinetic data from phase 1 studies. The verified PBPK model was then used to simulate maribavir DDI interactions with various CYP3A4 inducers and inhibitors. The DDI study results showed that coadministration with rifampin decreased the maribavir maximum plasma concentration (Cmax ), area under the plasma concentration-time curve (AUC), and trough concentration (Ctrough ) by 39%, 60%, and 82%, respectively. Based on the results from the clinical DDI study, the coadministration of maribavir with rifampin is not recommended. The PBPK model did not predict a clinically significant effect of CYP3A4 inhibitors on maribavir exposure; however, it predicted that strong or moderate CYP3A4 inducers, including carbamazepine, efavirenz, phenobarbital, and phenytoin, may reduce maribavir exposure to a clinically significant extent, and may prompt the consideration of a maribavir dosing increase, in accordance with local approved labels and/or regulations.

Failure time regression with continuous informative auxiliary covariates.

In this paper we use Cox's regression model to fit failure time data with continuous informative auxiliary variables in the presence of a validation subsample. We first estimate the induced relative risk function by kernel smoothing based on the validation subsample, and then improve the estimation by utilizing the information on the incomplete observations from non-validation subsample and the auxiliary observations from the primary sample. Asymptotic normality of the proposed estimator is derived. The proposed method allows one to robustly model the failure time data with an informative multivariate auxiliary covariate. Comparison of the proposed approach with several existing methods is made via simulations. Two real datasets are analyzed to illustrate the proposed method.