A physiologically-based pharmacokinetic (PBPK) model of niraparib and its primary metabolite using a relevant virtual cancer population is reported here. A series of in vitro experiments using liver S9, microsomes, and hepatocytes with various inhibitors and recombinant supersomes demonstrated that niraparib is specifically metabolized by carboxylesterase 1 via amide hydrolysis to an acid metabolite (M1). Available virtual cancer populations, along with reference populations, were applied to modeling simulations using fixed trial designs with demographic and clinical chemistry parameters from patients receiving niraparib in clinical studies. Simulations of niraparib and its metabolite M1 were verified across numerous available clinical studies and repeat dose ranges in cancer patients within 2-fold. The PBPK model was used to simulate exposures in moderately hepatic impaired, healthy Chinese and Japanese virtual populations as a surrogate of cancer comorbidity. The PBPK model confirmed minimal DDI liability with niraparib as a precipitant for most in vitro tested drug metabolizing enzymes and transporters. In vitro, niraparib lacks any CYP inhibition, induces CYP1A2 but not CYP3A4, and is not a CYP substrate, unlike some other PARPi's, which inhibit and induce numerous enzymes/transporters and are objects of CYP metabolism. At clinically relevant doses of niraparib ≥ 200 mg, a weak induction risk is predicted with sensitive CYP1A2 substrates, such as caffeine, and both niraparib and olaparib clinically increase serum creatinine in cancer patients, with up to a moderate inhibition risk predicted with MATE-1/-2K substrates, such as metformin, using a PBPK model of niraparib in the absence of a dedicated DDI study.

Background/Objective: Cabotegravir (CAB), available as an oral tablet and as a long-acting (LA) nanosuspension for intramuscular injection, is approved as a combination therapy for the treatment, and as a monotherapy for the prevention, of HIV-1 infection. People living with HIV may receive multiple concomitant medications, with the associated risk of drug–drug interactions (DDIs). CAB is an inhibitor of OAT1/OAT3 renal transporters and a substrate of the UDP-glucuronosyltransferase enzymes UGT1A1 and 1A9, in vitro. While the effect of induction of UGT1A1/UGT1A9 on CAB exposure had been investigated in the clinic, the effect of the risk of DDIs with CAB via inhibition of these enzymes, or as an inhibitor of OAT1/OAT3 transporters, had not been evaluated. Methods: A physiologically-based pharmacokinetic (PBPK) model was developed and verified for orally dosed CAB to investigate the DDI risks associated with CAB, using a matrix approach to extensively qualify the PBPK platform and the substrates and/or inhibitors of either OAT1/OAT3 or UGT1A1/UGT1A9. The effect of uncertainties in in vitro inhibition values for OAT1/OAT3 was assessed via sensitivity analysis. Results: A mean increase of less than 25% in systemic exposure for OAT1/OAT3 substrates was predicted, with the potential for an increase of up to 80% based on the sensitivity analysis. On co-dosing with UGT1A1/UGT1A9 inhibitors, the predicted mean increase in CAB exposure was within 11%. Conclusions: PBPK modelling indicated that clinically relevant DDIs are not anticipated with OAT1/3 substrates or UGT1A1/1A9 inhibitors and CAB. With maximal exposure of the LA formulation of CAB being lower than the oral, the results of these simulations can be extrapolated to LA injectable dosing.

Dolutegravir is a clinically confirmed inhibitor of organic cation transporter 2 (OCT2)/SLC22A2; however, its in vitro IC50 varies widely across studies. Using OCT2-expressing human embryonic kidney 293 cells, we investigated experimental conditions affecting IC50 determination. Based on clinical drug-drug interaction (DDI) data, an inhibition constant (Ki) of 0.0890 μM was estimated through pharmacokinetic (PK) modeling using both plasma concentration and urinary excretion profiles of metformin. In vitro, dolutegravir was found to be a noncompetitive inhibitor of OCT2. IC50 values increased with longer uptake times. A 2.9-fold difference was already observed between 1-minute and 5-minute uptake, and further extension to 30 minutes resulted in a 27-fold increase, attributable to the inhibition of both uptake and efflux processes. Preincubation of the cells with dolutegravir for 30 minutes additionally enhanced its inhibitory effect, resulting in a 5.8-fold decrease in IC50. Similar uptake time dependency was observed with other inhibitors (cimetidine and pyrimethamine). However, the extent of variability differed among metformin, N1-methylnicotinamide, atenolol, frovatriptan, and sumatriptan, likely reflecting differences in cellular kinetics. The lowest in vitro IC50 of dolutegravir for metformin uptake (0.126 μM), obtained under a 1-minute uptake and 30-minute preincubation condition, closely matched the estimated in vivo Ki. DDI predictions using in vitro IC50 values suggested repeated dolutegravir administration could raise the area under the curve of OCT substrates, such as atenolol, nadolol, and sulpiride beyond regulatory thresholds. In conclusion, we identified optimized in vitro conditions that yield IC50 values consistent with in vivo Ki estimates, offering valuable guidance for predicting OCT2-mediated DDIs. Significance Statement This study identified experimental conditions affecting the IC50 variability of dolutegravir as an organic cation transporter 2 inhibitor. These findings offer valuable insights for improving in vitro to in vivo drug-drug interaction predictions and optimizing inhibition experiments for transporter-mediated drug-drug interactions.

Objectives: The FDA recently announced the continuation of the MIDD Paired Meeting Program as part of the Prescription Drug User Fee Act VII. Here we aim to highlight the key take home message from recent publication [1] prepared by sponsors that are members of The International Consortium for Innovation and Quality in Pharmaceutical Development with the objective of providing relevant information that will optimize the value of the MIDD Program. The primary objective of the MIDD Paired Meeting Program is to support good decision-making in drug development. Secondary objectives can include the added benefits of substantial time saving and development costs. Methods: The report [1] describes a set of practices based on the collective experiences of industry participants involved in the MIDD Program to date, including all aspects of the process from meeting request submission to follow-up actions. The report elaborated on aspects involved in the MIDD paired meeting program including the choice of meeting topic, sponsor planning and internal involvement, meeting request, meeting information package (including the model credibility assessment) and planning for the second (Paired) Meeting.Results: With limitations on the number of meeting request that can be accepted by FDA as part of the MIDD Paired Meeting Program, the choice of topic is critical to ensure a high likelihood of being selected by FDA as part of the MIDD paired meeting program. The report [1] provided details on the eligibility criteria established by FDA and includes points to consider for selecting a qualified MIDD topic. The report discussed strategies for internal alignment and discussion before submitting a MIDD meeting request which provides a pathway for the necessary cross-functional alignment on model-based strategies and prioritization of potential MIDD topics. Several points to consider while framing MIDD strategy and questions to be included during a meeting request were discussed. Once the meeting request is approved, the section on the meeting information package provides explanation on how to prepare a successful package that includes the right amount of information allowing to perform appropriate model credibility assessments to make the best use of the allotted time. Finally, the report talks about the possibility for a follow-up meeting as part of the overall process for planning, discussing, and defining post-meeting actions. Conclusions: The FDA’s MIDD Paired Meeting Program has shown tangible benefits for industry participants. To facilitate optimal benefits for future participants, the co-authors have provided the guidance based on their collective experience with the program. Continued participation in the program is highly recommended for sponsors applying MIDD.Citations: [1] “Considerations for Industry—Preparing for the FDA Model-Informed Drug Development (MIDD) Paired Meeting Program”, Galluppi, G.R., Ahamadi, M., Bhattacharya, S., Budha, N., Gheyas, F., Li, C.-C., Chen, Y., Dosne, A.-G., Kristensen, N.R., Magee, M., Samtani, M.N., Sinha, V., Taskar, K., Upreti, V.V., Yang, J. and Cook, J. (2024), Clin Pharmacol Ther. https://doi.org/10.1002/cpt.3245 (available free through Open Access)

The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) model for conversion of phosphate prodrugs to active drug via intestinal alkaline phosphatase (IAP) implementing a generalized modeling strategy. Fostemsavir and fostamatinib were chosen as model drugs since there is extensive clinical pharmacokinetic data following administration of oral formulations. LUA scripting was used to develop an "in vitro" to "in vivo" extrapolation of the conversion rate of prodrugs derived from Caco2 cell lines using an absolute IAP abundance approach. The Simcyp v23 platform was modified to generate a virtual population to reflect gastric emptying rates following administration of a moderate fat meal. The PBPK model predicted the results of three different extended-release (ER) tablets of fostemsavir under fasted and fed conditions as well as for powder in capsule and tablet immediate release (IR) formulations of fostamatinib. Retrospectively, the model was also able to assess the clinical relevance of the in vitro dissolution method to rate changes of different microcrystalline cellulose-based IR tablets of fostamatinib, observed in acidic media. All predictions were within 2-fold of the observed Cmax, AUC, and Tmax, with 81% being within 1.25-fold. The developed modeling strategy can be effectively adopted to increase the confidence of using PBPK modeling to prospectively assess the in vivo performance of phosphate prodrugs and support the development of optimal oral extended-release formulations for this class of drugs.

Physiology Based Pharmacokinetic (PBPK) modeling is an established essential tool for predicting and/or analyzing drug–drug interactions (DDI). Uncertainty and variability associated with in vitro determined DDI‐related parameters have often been considered a limitation for predicting PBPK‐DDIs. Sensitivity analysis (SA) around DDI input parameters using PBPK analysis is often applied for assessing the relevance of clinical DDI predictions/prioritization/study designs. This perspective aims to explore and advocate practical approaches for precipitant (inhibitor/inducer) PBPK‐DDI SA for optimal clinically relevant evaluations.

Pediatric extrapolation strategies issued by health authorities have streamlined pediatric drug development and reduced the unnecessary burden of conducting pediatric clinical studies. In line with these strategies, physiologically based pharmacokinetic (PBPK) models have been utilized extensively for initial dosing regimen and sampling timepoint selection for pediatric studies, as well as dose validation throughout pediatric drug development. Here, the status and challenges of PBPK modeling in pediatric drug development have been summarized by the IQ Pediatric PBPK Working Group. Our work reviews current practices for pediatric PBPK modeling across various therapeutic areas. To enable best practice, we propose an optimized workflow for pediatric PBPK modeling recommendations. Two selected key pediatric PBPK case examples are also described, where modeling impacted the drug label extension to pediatric patients. Moreover, we analyze the current gaps and challenges in our understanding of drug absorption, distribution, metabolism, and elimination in pediatric PBPK model development. Since neonates are the least studied and the most medically fragile, the depth of our understanding of their rapidly evolving physiological processes is limited and so there exist significant modeling gaps which we summarize here. Finally, we provide recommendations, including building a public data repository, leveraging real-world data, and implementing microdose studies for addressing pediatric PBPK modeling challenges.

Physiologically-based pharmacokinetic (PBPK) modeling offers a viable approach to predict induction drug-drug interactions (DDIs) with the potential to streamline or reduce clinical trial burden if predictions can be made with sufficient confidence. In the current work, the ability to predict the effect of rifampin, a well-characterized strong CYP3A4 inducer, on 20 CYP3A probes with publicly available PBPK models (often developed using a workflow with optimization following a strong inhibitor DDI study to gain confidence in fraction metabolized by CYP3A4, fm,CYP3A4, and fraction available after intestinal metabolism, Fg), was assessed. Substrates with a range of fm,CYP3A4 (0.086-1.0), Fg (0.11-1.0) and hepatic availability (0.09-0.96) were included. Predictions were most often accurate for compounds that are not P-gp substrates or that are P-gp substrates but that have high permeability. Case studies for three challenging DDI predictions (i.e., for eliglustat, tofacitinib, and ribociclib) are presented. Along with parameter sensitivity analysis to understand key parameters impacting DDI simulations, alternative model structures should be considered, for example, a mechanistic absorption model instead of a first-order absorption model might be more appropriate for a P-gp substrate with low permeability. Any mechanisms pertinent to the CYP3A substrate that rifampin might impact (e.g., induction of other enzymes or P-gp) should be considered for inclusion in the model. PBPK modeling was shown to be an effective tool to predict induction DDIs with rifampin for CYP3A substrates with limited mechanistic complications, increasing confidence in the rifampin model. While this analysis focused on rifampin, the learnings may apply to other inducers.

AbstractIt is critical to understand the impact of significant physiological changes during pregnancy on the extent of maternal and fetal drug exposure. Fostemsavir (FTR) is a prodrug of temsavir (TMR) and is approved in combination with other antiretrovirals for multi‐drug‐resistant human immunodeficiency virus (HIV) infections. This physiologically based pharmacokinetic model (PBPK) study was used to estimate TMR PK in pregnant populations during each trimester of pregnancy to inform FTR dosing. A PBPK model was developed and validated for TMR using PK data collected following intravenous TMR and oral FTR dosing (immediate‐release and extended‐release tablets) in healthy volunteers. Predicted TMR concentration–time profiles accurately predicted the reported clinical data and variability in healthy (dense data) and pregnant (sparse data) populations. Predicted versus observed TMR geometric mean (CV%) clearance following intravenous administration was 18.01 (29) versus 17 (21) (L/h). Predicted versus observed TMR AUC0–inf (ng.h/mL) in healthy volunteers following FTR administration of the extended‐release tablet were 9542 (66) versus 7339 (33). The validated TMR PBPK model was then applied to predict TMR PK in a population of pregnant individuals during each trimester. Simulations showed TMR AUC in pregnant individuals receiving FTR 600 mg twice daily was decreased by 25% and 38% in the second and third trimesters, respectively. However, TMR exposure remained within the range observed in nonpregnant adults with no need for dose adjustment. The current PBPK model can also be applied for the prediction of local tissue concentrations and drug–drug interactions in pregnancy.

Abstract Fostemsavir is an approved gp120‐directed attachment inhibitor and prodrug for the treatment of human immunodeficiency virus type 1 infection in combination with other antiretrovirals (ARVs) in heavily treatment‐experienced adults with multi‐drug resistance, intolerance, or safety concerns with their current ARV regimen. Initial in vitro studies indicated that temsavir, the active moiety of fostemsavir, and its metabolites, inhibited organic cation transporter (OCT)1, OCT2, and multidrug and toxin extrusion transporters (MATEs) at tested concentration of 100 uM, although risk assessment based on the current Food and Drug Administration in vitro drug–drug interaction (DDI) guidance using the mechanistic static model did not reveal any clinically relevant inhibition on OCTs and MATEs. However, a DDI risk was flagged with EMA static model predictions. Hence, a physiologically based pharmacokinetic (PBPK) model of fostemsavir/temsavir was developed to further assess the DDI risk potential of OCT and MATEs inhibition by temsavir and predict changes in metformin (a sensitive OCT and MATEs substrate) exposure. No clinically relevant impact on metformin concentrations across a wide range of temsavir concentrations was predicted; therefore, no dose adjustment is recommended for metformin when co‐administered with fostemsavir.

The International Consortium for Innovation and Quality in Pharmaceutical Development Transporter Working Group had a rare opportunity to analyze a crosspharma collation of in vitro data and assay methods for the evaluation of drug transporter substrate and inhibitor potential. Experiments were generally performed in accordance with regulatory guidelines. Discrepancies, such as not considering the impact of preincubation for inhibition and free or measured in vitro drug concentrations, may be due to the retrospective nature of the dataset and analysis. Lipophilicity was a frequent indicator of crosstransport inhibition (P-gp, BCRP, OATP1B, and OCT1), with high molecular weight (MW ≥500 Da) also common for OATP1B and BCRP inhibitors. A high level of overlap in in vitro inhibition across transporters was identified for BCRP, OATP1B1, and MATE1, suggesting that prediction of DDIs for these transporters will be common. In contrast, inhibition of OAT1 did not coincide with inhibition of any other transporter. Neutrals, bases, and compounds with intermediate-high lipophilicity tended to be P-gp and/or BCRP substrates, whereas compounds with MW <500 Da tended to be OAT3 substrates. Interestingly, the majority of in vitro inhibitors were not reported to be followed up with a clinical study by the submitting company, whereas those compounds identified as substrates generally were. Approaches to metabolite testing were generally found to be similar to parent testing, with metabolites generally being equally or less potent than parent compounds. However, examples where metabolites inhibited transporters in vitro were identified, supporting the regulatory requirement for in vitro testing of metabolites to enable integrated clinical DDI risk assessment. SIGNIFICANCE STATEMENT: A diverse dataset showed that transporter inhibition often correlated with lipophilicity and molecular weight (>500 Da). Overlapping transporter inhibition was identified, particularly that inhibition of BCRP, OATP1B1, and MATE1 was frequent if the compound inhibited other transporters. In contrast, inhibition of OAT1 did not correlate with the other drug transporters tested.

A recent industry perspective published in this journal describes the benefits received by drug companies from participation in the MIDD Pilot Program. Along with the primary objectives of supporting good decision-making in drug development, there were substantial savings in time and development costs. Furthermore, many sponsors reported qualitative benefits such as new learnings and clarity on MIDD strategies and methodology that could be applied to other development programs. Based on the success of the Pilot Program, the FDA recently announced the continuation of the MIDD Paired Meeting Program as part of the Prescription Drug User Fee Act (PDUFA VII). In this report, we describe the collective experiences of industry participants in the MIDD Program to date, including all aspects of the process from meeting request submission to follow-up actions. The purpose is to provide future participants with information to optimize the value of the MIDD Program.

This study was designed to assess the quantitative performance of endogenous drug-drug interaction (DDI) biomarkers (N1-methylnicotinamide (1-NMN), N1-methyladenosine (m1A), and creatinine) for the organic cation transporters, OCT2 and MATE1/2K in the kidney. Ten healthy volunteers received cimetidine (400 and 800 mg, single dose) or dolutegravir (50 mg, twice a day) together with metformin (500 mg). Cimetidine and dolutegravir were considered to act mainly as MATE1/2K and OCT2 inhibitors, respectively. The renal clearance (CLr) of metformin was decreased by 15.5% and 42.5% by cimetidine 400 and 800 mg, and by 26.8% and 56.9% by dolutegravir first and fifth doses, respectively. CLr ratio (CLrR) of 1-NMN were 0.93 and 0.64 for cimetidine 400 and 800 mg, and 0.87 and 0.47 for dolutegravir first and fifth doses, respectively. CLrR of m1A was less than that of 1-NMN: 1.0 and 0.80 for cimetidine 400 and 800 mg, and 0.77 and 0.71 for dolutegravir first and fifth doses, respectively. CLr of creatinine was significantly decreased only by cimetidine 800 mg. Individual CLrR of 1-NMN and m1A showed a positive correlation with the corresponding CLrR of metformin with r2 of 0.58 and 0.55, respectively. When evaluated individually, m1A showed a better correlation during cimetidine periods (r2 0.64) than 1-NMN (r2 0.36), but vice versa during dolutegravir periods (r2 1-NMN, 0.80; m1A, 0.32). These results suggest that 1-NMN and m1A might be more promising than creatinine as endogenous biomarkers for quantitatively assessing the DDI potential of investigational drugs for OCT2 and MATE1/2K based on their CLrR change.

Considerable interest remains across the pharmaceutical industry and regulatory landscape in capabilities to model oral contraceptives (OCs), whether combined (COCs) with ethinyl estradiol (EE) or progestin-only pill. Acceptance of COC drug-drug interaction (DDI) assessment using physiologically-based pharmacokinetic (PBPK) is often limited to the estrogen component (EE), requiring further verification, with extrapolation from EE to progestins discouraged. There is a paucity of published progestin component PBPK models to support the regulatory DDI guidance for industry to evaluate a new chemical entity's (NCE's) DDI potential with COCs. Guidance recommends a clinical interaction study to be considered if an investigational drug is a weak or moderate inducer, or a moderate/strong inhibitor, of CYP3A4. Therefore, availability of validated OC PBPK models within one software platform, will be useful in predicting the DDI potential with NCEs earlier in the clinical development. Thus, this work was focused on developing and validating PBPK models for progestins, DNG, DRSP, LNG, and NET, within Simcyp, and assessing the DDI potential with known CYP3A4 inhibitors (e.g., ketoconazole) and inducers (e.g., rifampicin) with published clinical data. In addition, this work demonstrated confidence in the Simcyp EE model for regulatory and clinical applications by extensive verification in 70+ clinical PK and CYP3A4 interaction studies. The results provide greater capability to prospectively model clinical CYP3A4 DDI with COCs using Simcyp PBPK to interrogate the regulatory decision-tree to contextualize the potential interaction by known perpetrators and NCEs, enabling model-informed decision making, clinical study designs, and delivering potential alternative COC options for women of childbearing potential.

Supplementary Data from Vorinostat Inhibits Brain Metastatic Colonization in a Model of Triple-Negative Breast Cancer and Induces DNA Double-Strand Breaks

Supplementary Figures S1-S9.

Drug-metabolizing enzymes and transporters (DMETs) are key regulators of the pharmacokinetics, efficacy, and toxicity of therapeutics. Over the past two decades, significant advancements in in vitro methodologies, targeted proteomics, in vitro to in vivo extrapolation methods, and integrated computational approaches such as physiologically based pharmacokinetic modeling have unequivocally contributed to improving our ability to quantitatively predict the role of DMETs in absorption, distribution, metabolism, and excretion and drug-drug interactions. However, the paucity of data regarding alterations in DMET activity in specific populations such as pregnant individuals, lactation, pediatrics, geriatrics, organ impairment, and disease states such as, cancer, kidney, and liver diseases and inflammation has restricted our ability to realize the full potential of these recent advancements. We envision that a series of carefully curated articles in a special supplementary issue of Drug Metabolism and Disposition will summarize the latest progress in in silico, in vitro, and in vivo approaches to characterize alteration in DMET activity and quantitatively predict drug disposition in specific populations. In addition, the supplementary issue will underscore the current scientific knowledge gaps that present formidable barriers to fully understand the clinical implications of altered DMET activity in specific populations and highlight opportunities for multistakeholder collaboration to advance our collective understanding of this rapidly emerging area. SIGNIFICANCE STATEMENT: This commentary highlights current knowledge and identifies gaps and key challenges in understanding the role of drug-metabolizing enzymes and transporters (DMETs) in drug disposition in specific populations. With this commentary for the special issue in Drug Metabolism and Disposition, the authors intend to increase interest and invite potential contributors whose research is focused or has aided in expanding the understanding around the role and impact of DMETs in drug disposition in specific populations.

Abstract In celiac disease (CeD), gastrointestinal CYP3A4 abundance and morphology is affected by the severity of disease. Therefore, exposure to CYP3A4 substrates and extent of drug interactions is altered. A physiologically‐based pharmacokinetic (PBPK) population for different severities of CeD was developed. Gastrointestinal physiology parameters, such as luminal pH, transit times, morphology, P‐gp, and CYP3A4 expression were included in development of the CeD population. Data on physiological difference between healthy and CeD subjects were incorporated into the model as the ratio of celiac to healthy. A PBPK model was developed and verified for felodipine extended‐release tablet in healthy volunteers (HVs) and then utilized to verify the CeD populations. Plasma concentration‐time profile and PK parameters were predicted and compared against those observed in both groups. Sensitivity analysis was carried out on key system parameters in CeD to understand their impact on drug exposure. For felodipine, the predicted mean concentration‐time profiles and 5th and 95th percentile intervals captured the observed profile and variability in the HV and CeD populations. Predicted and observed clearance was 56.9 versus 56.1 (L/h) in HVs. Predicted versus observed mean ± SD area under the curve for extended release felodipine in different severities of CeD were values of 14.5 ± 9.6 versus 14.4 ± 2.1, 14.6 ± 9.0 versus 17.2 ± 2.8, and 28.1 ± 13.5 versus 25.7 ± 5.0 (ng.h/mL), respectively. Accounting for physiology differences in a CeD population accurately predicted the PK of felodipine. The developed CeD population can be applied for determining the drug concentration of CYP3A substrates in the gut as well as for systemic levels, and for application in drug–drug interaction studies.

Abstract The accurate prediction of OATP1B‐mediated drug–drug interactions (DDIs) is challenging for drug development. Here, we report a physiologically‐based pharmacokinetic (PBPK) model analysis for clinical DDI data generated in heathy subjects who received oral doses of cyclosporin A (CysA; 20 and 75 mg) as an OATP1B inhibitor, and the probe drugs (pitavastatin, rosuvastatin, and valsartan). PBPK models of CysA and probe compounds were combined assuming inhibition of hepatic uptake of endogenous coproporphyrin I (CP‐I) by CysA. In vivo Ki of unbound CysA for OATP1B (Ki,OATP1B), and the overall intrinsic hepatic clearance per body weight of CP‐I (CLint,all,unit) were optimized to account for the CP‐I data (Ki,OATP1B, 0.536 ± 0.041 nM; CLint,all,unit, 41.9 ± 4.3 L/h/kg). DDI simulation using Ki,OATP1B reproduced the dose‐dependent effect of CysA (20 and 75 mg) and the dosing interval (1 and 3 h) on the time profiles of blood concentrations of pitavastatin and rosuvastatin, but DDI simulation using in vitro Ki,OATP1B failed. The Cluster Gauss–Newton method was used to conduct parameter optimization using 1000 initial parameter sets for the seven pharmacokinetic parameters of CP‐I (β, CLint, all, FaFg, Rdif, fbile, fsyn, and vsyn), and Ki,OATP1B and Ki,MRP2 of CysA. Based on the accepted 546 parameter sets, the range of CLint, all and Ki,OATP1B was narrowed, with coefficients of variation of 12.4% and 11.5%, respectively, indicating that these parameters were practically identifiable. These results suggest that PBPK model analysis of CP‐I is a promising translational approach to predict OATP1B‐mediated DDIs in drug development.