Kota Toshimoto

Quantitative Analyses of Hepatic OATP‐Mediated Interactions Between Statins and Inhibitors Using PBPK Modeling With a Parameter Optimization Method

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 (Ki) values of each inhibitor against OATPs were obtained, regardless of the substrates. Estimated Ki 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.

Comparison of Methods for Estimating Unbound Intracellular-to-Medium Concentration Ratios in Rat and Human Hepatocytes Using Statins

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 (Kp,uu) by two methods based on the steady-state cell-to-medium total concentration ratios at 37°C and on ice (Kp,uu,ss) and based on their initial uptake rates (Kp,uu,V0). Poorly metabolized statins were used as test drugs because of their concentrative uptake via organic anion-transporting polypeptides. Kp,uu,ss values of these statins provided less interexperimental variation than the Kp,uu,V0 values, because only data at longer time are required for Kp,uu,ss. Kp,uu,V0 values for pitavastatin, rosuvastatin, and pravastatin were 1.2- to 5.1-fold Kp,uu,ss in rat hepatocytes; Kp,uu,V0 values in human hepatocytes also tended to be larger than corresponding Kp,uu,ss. To explain these discrepancies, theoretical values of Kp,uu,ss and Kp,uu,V0 were compared with true Kp,uu (Kp,uu,true), 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 Kp,uu,ss values for the statins are 0.85- to 1.2-fold Kp,uu,true, whereas Kp,uu,V0 values are 2.2- to 3.1-fold Kp,uu,true, depending on the ratio of the passive permeability of the ionized to nonionized forms. In conclusion, Kp,uu,ss values of anions are similar to Kp,uu,true when the inside-negative membrane potential is considered. This suggests that Kp,uu,ss is preferable for estimating the concentration of unbound drugs inside the hepatocytes.

Virtual Clinical Studies to Examine the Probability Distribution of the AUC at Target Tissues Using Physiologically-Based Pharmacokinetic Modeling: Application to Analyses of the Effect of Genetic Polymorphism of Enzymes and Transporters on Irinotecan Induced Side Effects

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.

Development of a Support Vector Machine-Based System to Predict Whether a Compound Is a Substrate of a Given Drug Transporter Using Its Chemical Structure

The aim of this study was to develop an in silico prediction system to assess which of 7 categories of drug transporters (organic anion transporting polypeptide [OATP] 1B1/1B3, multidrug resistance-associated protein [MRP] 2/3/4, organic anion transporter [OAT] 1, OAT3, organic cation transporter [OCT] 1/2/multidrug and toxin extrusion [MATE] 1/2-K, multidrug resistance protein 1 [MDR1], and breast cancer resistance protein [BCRP]) can recognize compounds as substrates using its chemical structure alone. We compiled an internal data set consisting of 260 compounds that are substrates for at least 1 of the 7 categories of drug transporters. Four physicochemical parameters (charge, molecular weight, lipophilicity, and plasma unbound fraction) of each compound were used as the basic descriptors. Furthermore, a greedy algorithm was used to select 3 additional physicochemical descriptors from 731 available descriptors. In addition, transporter nonsubstrates tend not to be in the public domain; we, thus, tried to compile an expert-curated data set of putative nonsubstrates for each transporter using personal opinions of 11 researchers in the field of drug transporters. The best prediction was finally achieved by a support vector machine based on 4 basic and 3 additional descriptors. The model correctly judged that 364 of 412 compounds (internal data set) and 111 of 136 compounds (external data set) were substrates, indicating that this model performs well enough to predict the specificity of transporter substrates.

A clinical quantitative evaluation of hepatobiliary transport of [11C]Dehydropravastatin in humans using positron emission tomography

Various positron emission tomography (PET) probes have been developed to assess in vivo activities in humans of drug transporters, which aid in the prediction of pharmacokinetic properties of drugs and the impact of drug-drug interactions. We developed a new PET probe, sodium (3R, 5R)-3, 5-dihydroxy-7-((1S, 2S, 6S, 8S)-6-hydroxy-2-methyl-8- ((1-[11C]-(E)-2-methyl-but-2-enoyl) oxy) -1, 2, 6, 7, 8, 8a-hexahydronaphthalen-1-yl) heptanoate ([11C]DPV), and demonstrated its usefulness for the quantitative investigation of Oatps (gene symbol SLCO) and Mrp2 (gene symbol ABCC2) in rats. To further analyze the species differences and verify the pharmacokinetic parameters in humans, serial PET scanning of the abdominal region with [11C]DPV was performed in six healthy volunteers with and without an OATP1Bs and MRP2 inhibitor, rifampicin (600 mg, oral), in a crossover fashion. After intravenous injection, [11C]DPV rapidly distributed to the liver and kidney followed by secretion into the bile and urine. Rifampicin significantly reduced the liver distribution of [11C]DPV 3-fold, resulting in a 7.5-fold reduced amount of excretion into the bile and the delayed elimination of [11C]DPV from the blood circulation. The hepatic uptake clearance (CLuptake, liver) and canalicular efflux clearance (CLint, bile) of [11C]DPV (544 ± 204 and 10.2 ± 3.5 µl/min per gram liver, respectively) in humans were lower than the previously reported corresponding parameters in rats (1800 and 298 µl/min per gram liver, respectively) (Shingaki et al., 2013). Furthermore, rifampicin treatment significantly reduced CLuptake, liver and CLint, bile by 58% and 44%, respectively. These results suggest that PET imaging with [11C]DPV is an effective tool for quantitatively characterizing the OATP1Bs and MRP2 functions in the human hepatobiliary transport system.

Comprehensive PBPK Model of Rifampicin for Quantitative Prediction of Complex Drug‐Drug Interactions: CYP3A/2C9 Induction and OATP Inhibition Effects

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.

Quantitative Analysis of Complex Drug-Drug Interactions between Cerivastatin and Metabolism/Transport Inhibitors Using Physiologically Based Pharmacokinetic Modeling

Cerivastatin (CER) was withdrawn from the world market because of lethal rhabdomyolysis. Coadministrations of CER and cyclosporine A (CsA) or gemfibrozil (GEM) have been reported to increase the CER blood concentration. CsA is an inhibitor of organic anion transporting polypeptide (OATP)1B1 and CYP3A4, and GEM and its glucuronide (GEM-glu) inhibit OATP1B1 and CYP2C8. The purpose of this study was to describe the transporter-/enzyme-mediated drug-drug interactions (DDIs) of CER with CsA or GEM based on unified physiologically based pharmacokinetic (PBPK) models and to investigate whether the DDIs can be quantitatively analyzed by a bottom-up approach. Initially, the PBPK models for CER and GEM/GEM-glu were constructed based on the previously reported standard protocols. Next, the drug-dependent parameters were optimized by Cluster Newton Method. Thus, described concentration-time profiles for CER and GEM/GEM-glu agreed well with the clinically observed data. The DDIs were then simulated using the established PBPK models with previously obtained in vitro inhibition constants of CsA or GEM/GEM-glu against the OATP1B1 and cytochrome P450s. DDIs with the inhibitors were underestimated compared with observed data using the geometric means of reported values. To search for better described parameters within the range of in vitro values, sensitivity analyses were performed for DDIs of CER. Using the in vitro parameter sets selected by sensitivity analyses, these DDIs were well reproduced, indicating that the present PBPK models were able to describe adequately the clinical DDIs based on a bottom-up approach. The approaches in this study would be applicable to the prediction of other DDIs involving both transporters and metabolic enzymes.

Physiologically Based Pharmacokinetic Modeling of Bosentan Identifies the Saturable Hepatic Uptake As a Major Contributor to Its Nonlinear Pharmacokinetics

Bosentan is a substrate of hepatic uptake transporter organic anion–transporting polypeptides (OATPs), and undergoes extensive hepatic metabolism by cytochrome P450 (P450), namely, CYP3A4 and CYP2C9. Several clinical investigations have reported a nonlinear relationship between bosentan doses and its systemic exposure, which likely involves the saturation of OATP-mediated uptake, P450-mediated metabolism, or both in the liver. Yet, the underlying causes for the nonlinear bosentan pharmacokinetics are not fully delineated. To address this, we performed physiologically based pharmacokinetic (PBPK) modeling analyses for bosentan after its intravenous administration at different doses. As a bottom-up approach, PBPK modeling analyses were performed using in vitro kinetic parameters, other relevant parameters, and scaling factors. As top-down approaches, three different types of PBPK models that incorporate the saturation of hepatic uptake, metabolism, or both were compared. The prediction from the bottom-up approach (models 1 and 2) yielded blood bosentan concentration-time profiles and their systemic clearance values that were not in good agreement with the clinically observed data. From top-down approaches (models 3, 4, 5-1, and 5-2), the prediction accuracy was best only with the incorporation of the saturable hepatic uptake for bosentan. Taken together, the PBPK models for bosentan were successfully established, and the comparison of different PBPK models identified the saturation of the hepatic uptake process as a major contributing factor for the nonlinear pharmacokinetics of bosentan.

Strategies to improve the prediction accuracy of hepatic intrinsic clearance of three antidiabetic drugs: Application of the extended clearance concept and consideration of the effect of albumin on CYP2C metabolism and OATP1B-mediated hepatic uptake

&NA; The antidiabetic drugs glibenclamide, repaglinide, and nateglinide are well‐known substrates for hepatic uptake transporters of the organic anion transporting polypeptide (OATP) family and metabolizing enzymes of the cytochrome P450 (CYP) 2C subfamily. The systemic exposure of these drugs varies substantially among individuals, impacted by genetic polymorphisms of transporters and metabolizing enzymes as well as drug‐drug interactions. The use of the conventional in vitro‐in vivo extrapolation (IVIVE) method was found to underestimate their hepatic intrinsic clearance (CLint,all); the clinically observed CLint,all values were ≥10‐fold higher than the predicted values from in vitro data. In order to improve the accuracy in predicting CLint,all of these drugs, the following modifications were implemented; i) the extended clearance concept was applied during IVIVE processes, ii) albumin was added to metabolic assays using human liver microsomes (to minimize the impact of intrinsic inhibitors on kinetic parameters for CYP2C‐mediated metabolism) and to hepatic uptake assays (to accommodate the enhanced hepatic uptake observed with albumin‐bound drugs), and iii) differing rates of efflux and influx via diffusion were used. The IVIVE method with these modifications yielded the predicted CLint,all values from in vitro data in closer agreement with the CLint,all values observed in vivo; the fold differences between the predicted and observed CLint,all values reduced from 13–15 to 5.9–6.7. Our current approach offers an improvement in the prediction of CLint,all and further investigations are warranted to enhance the prediction accuracy of IVIVE.

PBPK Modeling of Coproporphyrin I as an Endogenous Biomarker for Drug Interactions Involving Inhibition of Hepatic OATP1B1 and OATP1B3

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 (Ki,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 Ki,u,OATP1Bs and biosynthesis rate of CP‐I affected the magnitude of the interaction. Ki,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 Ki,u,OATP1B values (ratio of in vitro Ki,u(statin)/in vitro Ki,u(CP‐I)). In conclusion, PBPK modeling of CP‐I supports dynamic prediction of OATP1B‐mediated DDIs.