This study aimed to construct a widely applicable method for quantitative analyses of drug-drug interactions (DDIs) caused by the inhibition of hepatic organic anion transporting polypeptides (OATPs) using physiologically based pharmacokinetic (PBPK) modeling. Models were constructed for pitavastatin, fluvastatin, and pravastatin as substrates and cyclosporin A (CsA) and rifampicin (RIF) as inhibitors, where enterohepatic circulations (EHC) of statins were incorporated. By fitting to clinical data, parameters that described absorption, hepatic elimination, and EHC processes were optimized, and the extent of these DDIs was explained satisfactorily. Similar in vivo inhibition constant (K ) values of each inhibitor against OATPs were obtained, regardless of the substrates. Estimated K values of CsA were comparable to reported in vitro values with the preincubation of CsA, while those of RIF were smaller than reported in vitro values (coincubation). In conclusion, this study proposes a method to optimize in vivo PBPK parameters in hepatic uptake transporter-mediated DDIs.

The aim of the present study was to establish a physiologically based pharmacokinetic (PBPK) model for coproporphyrin I (CP‐I), a biomarker supporting the prediction of drug‐drug interactions (DDIs) involving hepatic organic anion transporting polypeptide 1B (OATP1B), using clinical DDI data with an OATP1B inhibitor rifampicin (300 and 600 mg, orally). The in vivo inhibition constants of rifampicin used as initial input parameters for OATP1Bs (K i,u,OATP1Bs) and multidrug resistance‐associated protein two‐mediated biliary excretion were estimated as 0.23 and 0.87 μM, respectively, from previous reports. Sensitivity analysis demonstrated that the K i,u,OATP1Bs and biosynthesis rate of CP‐I affected the magnitude of the interaction. K i,u,OATP1Bs values optimized by nonlinear least‐squares fitting were ~0.5‐fold of the initial value. It was determined that the blood concentration‐time profiles of four statins were well‐predicted using corrected individual K i,u,OATP1B values (ratio of in vitro K i,u(statin)/in vitro K i,u(CP‐I)). In conclusion, PBPK modeling of CP‐I supports dynamic prediction of OATP1B‐mediated DDIs.

This study aimed to construct a physiologically based pharmacokinetic (PBPK) model of rifampicin that can accurately and quantitatively predict complex drug‐drug interactions (DDIs) involving its saturable hepatic uptake and auto‐induction. Using in silico and in vitro parameters, and reported clinical pharmacokinetic data, rifampicin PBPK model was built and relevant parameters for saturable hepatic uptake and UDP‐glucuronosyltransferase (UGT) auto‐induction were optimized by fitting. The parameters for cytochrome P450 (CYP) 3A and CYP2C9 induction by rifampicin were similarly optimized using clinical DDI data with midazolam and tolbutamide as probe substrates, respectively. For validation, our current PBPK model was applied to simulate complex DDIs with glibenclamide (a substrate of CYP3A/2C9 and hepatic organic anion transporting polypeptides (OATPs)). Simulated results were in quite good accordance with the observed data. Altogether, our constructed PBPK model of rifampicin demonstrates the robustness and utility in quantitatively predicting CYP3A/2C9 induction‐mediated and/or OATP inhibition‐mediated DDIs with victim drugs.

It is essential to estimate concentrations of unbound drugs inside the hepatocytes to predict hepatic clearance, efficacy, and toxicity of the drugs. The present study was undertaken to compare predictability of the unbound hepatocyte-to-medium concentration ratios (K) by two methods based on the steady-state cell-to-medium total concentration ratios at 37°C and on ice (K) and based on their initial uptake rates (K). Poorly metabolized statins were used as test drugs because of their concentrative uptake via organic anion-transporting polypeptides. K values of these statins provided less interexperimental variation than the K values, because only data at longer time are required for K K values for pitavastatin, rosuvastatin, and pravastatin were 1.2- to 5.1-fold K in rat hepatocytes; K values in human hepatocytes also tended to be larger than corresponding K To explain these discrepancies, theoretical values of K and K were compared with true K (K), considering the inside-negative membrane potential and ionization of the drugs in hepatocytes and medium. Membrane potentials were approximately -30 mV in human hepatocytes at 37°C and almost abolished on ice. Theoretical equations considering the membrane potentials indicate that K values for the statins are 0.85- to 1.2-fold K, whereas K values are 2.2- to 3.1-fold K, depending on the ratio of the passive permeability of the ionized to nonionized forms. In conclusion, K values of anions are similar to K when the inside-negative membrane potential is considered. This suggests that K is preferable for estimating the concentration of unbound drugs inside the hepatocytes.

PurposeTo establish a physiologically-based pharmacokinetic (PBPK) model for analyzing the factors associated with side effects of irinotecan by using a computer-based virtual clinical study (VCS) because many controversial associations between various genetic polymorphisms and side effects of irinotecan have been reported.MethodsTo optimize biochemical parameters of irinotecan and its metabolites in the PBPK modeling, a Cluster Newton method was introduced. In the VCS, virtual patients were generated considering the inter-individual variability and genetic polymorphisms of enzymes and transporters.ResultsApproximately 30 sets of parameters of the PBPK model gave good reproduction of the pharmacokinetics of irinotecan and its metabolites. Of these, 19 sets gave relatively good description of the effect of UGT1A1 *28 and SLCO1B1 c.521T>C polymorphism on the SN-38 plasma concentration, neutropenia, and diarrhea observed in clinical studies reported mainly by Teft et al. (Br J Cancer. 112(5):857-65, 20). VCS also indicated that the frequency of significant association of biliary index with diarrhea was higher than that of UGT1A1 *28 polymorphism.ConclusionThe VCS confirmed the importance of genetic polymorphisms of UGT1A1 *28 and SLCO1B1 c.521T>C in the irinotecan induced side effects. The VCS also indicated that biliary index is a better biomarker of diarrhea than UGT1A1 *28 polymorphism.Electronic supplementary materialThe online version of this article (doi:10.1007/s11095-017-2153-z) contains supplementary material, which is available to authorized users.