Verified Physiologically Based Pharmacokinetic Model Research Articles

Physiologically Based Pharmacokinetic Modeling to Identify Physiological and Molecular Characteristics Driving Variability in Drug Exposure.

Prospectively defining the physiological and molecular characteristics most likely driving between-subject variability (BSV) in drug exposure provides the opportunity to inform the assessment of biomarkers to account for this variability. A physiologically based pharmacokinetic (PBPK) model was constructed and verified for dabrafenib. This model was then used to evaluate the physiological and molecular characteristics driving BSV in dabrafenib exposure. The capacity to discriminate a steady-state dabrafenib trough concentration >48 ng/mL was also evaluated. The mean simulated/observed ratios for the parameters describing dabrafenib exposure in single-dose, multiple-dose, and drug-drug interaction studies were between 0.78 and 1.23. Multivariable analysis indicated that consideration of baseline weight, body mass index, and CYP2C8, CYP3A4, and P-gp abundance strongly predicts steady-state dabrafenib trough concentration above 48 ng/mL (ROC AUC = 0.94; accuracy = 86%). This is the first study to use a verified PBPK model to identify baseline physiological and molecular characteristics driving BSV in drug exposure.

Physiologically-based pharmacokinetic modelling of a CYP2C19 substrate, BMS-823778, utilizing pharmacogenetic data.

Previous studies demonstrated direct correlation between CYP2C19 genotype and BMS-823778 clearance in healthy volunteers. The objective of the present study was to develop a physiologically-based pharmacokinetic (PBPK) model for BMS-823778 and use the model to predict PK and drug-drug interaction (DDI) in virtual populations with multiple polymorphic genes. The PBPK model was built and verified using existing clinical data. The verified model was simulated to predict PK of BMS-823778 and significance of DDI with a strong CYP3A4 inhibitor in subjects with various CYP2C19 and UGT1A4 genotypes. The verified PBPK model of BMS-823778 accurately recovered observed PK in different populations. In addition, the model was able to capture the exposure differences between subjects with different CYP2C19 genotypes. PK simulation indicated higher exposures of BMS-823778 in CYP2C19 poor metabolizers who were also devoid of UGT1A4 activity, compared to those with normal UGT1A4 functionality. Moderate DDI with itraconazole was predicted in subjects with wild-type CYP2C19 or UGT1A4. However, in subjects without CYP2C19 or UGT1A4 functionality, significant DDI was predicted when BMS-823778 was coadministered with itraconazole. A PBPK model was developed using clinical data that accurately predicted human PK in different population with various CYP2C19 phenotypes. Simulations with the verified PBPK model indicated that UGT1A4 was probably an important clearance pathway in CYP2C19 poor metabolizers. DDI with itraconazole is likely to be dependent on the genotypes of CYP2C19 and UGT1A4.

Evaluation of Cytochrome P450 3A4-Mediated Drug-Drug Interaction Potential for Cobimetinib Using Physiologically Based Pharmacokinetic Modeling and Simulation.

Cobimetinib is eliminated mainly through cytochrome P450 (CYP) 3A4-mediated hepatic metabolism in humans. A clinical drug-drug interaction (DDI) study with the potent CYP3A4 inhibitor itraconazole resulted in an approximately sevenfold increase in cobimetinib exposure. The DDI risk for cobimetinib with other CYP3A4 inhibitors and inducers needs to be assessed in order to provide dosing instructions. A physiologically based pharmacokinetic (PBPK) model was developed for cobimetinib using in vitro data. It was then optimized and verified using clinical pharmacokinetic data and itraconazole-cobimetinib DDI data. The contribution of CYP3A4 to the clearance of cobimetinib in humans was confirmed using sensitivity analysis in a retrospective simulation of itraconazole-cobimetinib DDI data. The verified PBPK model was then used to predict the effect of other CYP3A4 inhibitors and inducers on cobimetinib pharmacokinetics. The PBPK model described cobimetinib pharmacokinetic profiles after both intravenous and oral administration of cobimetinib well and accurately simulated the itraconazole-cobimetinib DDI. Sensitivity analysis suggested that CYP3A4 contributes ~78% of the total clearance of cobimetinib. The PBPK model predicted no change in cobimetinib exposure (area under the plasma concentration-time curve, AUC) with the weak CYP3A inhibitor fluvoxamine and a three to fourfold increase with the moderate CYP3A inhibitors, erythromycin and diltiazem. Similarly, cobimetinib exposure in the presence of strong (rifampicin) and moderate (efavirenz) CYP3A inducers was predicted to decrease by 83 and 72%, respectively. This study demonstrates the value of using PBPK simulation to assess the clinical DDI risk inorder to provide dosing instructions with other CYP3A4 perpetrators.

Physiologically based pharmacokinetic modeling to predict drug-drug interactions involving inhibitory metabolite: a case study of amiodarone.

Evaluation of drug-drug interaction (DDI) involving circulating inhibitory metabolites of perpetrator drugs has recently drawn more attention from regulatory agencies and pharmaceutical companies. Here, using amiodarone (AMIO) as an example, we demonstrate the use of physiologically based pharmacokinetic (PBPK) modeling to assess how a potential inhibitory metabolite can contribute to clinically significant DDIs. Amiodarone was reported to increase the exposure of simvastatin, dextromethorphan, and warfarin by 1.2- to 2-fold, which was not expected based on its weak inhibition observed in vitro. The major circulating metabolite, mono-desethyl-amiodarone (MDEA), was later identified to have a more potent inhibitory effect. Using a combined "bottom-up" and "top-down" approach, a PBPK model was built to successfully simulate the pharmacokinetic profile of AMIO and MDEA, particularly their accumulation in plasma and liver after a long-term treatment. The clinical AMIO DDIs were predicted using the verified PBPK model with incorporation of cytochrome P450 inhibition from both AMIO and MDEA. The closest prediction was obtained for CYP3A (simvastatin) DDI when the competitive inhibition from both AMIO and MDEA was considered, for CYP2D6 (dextromethorphan) DDI when the competitive inhibition from AMIO and the competitive plus time-dependent inhibition from MDEA were incorporated, and for CYP2C9 (warfarin) DDI when the competitive plus time-dependent inhibition from AMIO and the competitive inhibition from MDEA were considered. The PBPK model with the ability to simulate DDI by considering dynamic change and accumulation of inhibitor (parent and metabolite) concentration in plasma and liver provides advantages in understanding the possible mechanism of clinical DDIs involving inhibitory metabolites.