Physiologically-based pharmacokinetic modeling for investigating the effect of simeprevir on concomitant drugs and an endogenous biomarker of OATP1B.

The orally available anti-hepatitis C virus (HCV) drug simeprevir exhibits nonlinear pharmacokinetics at the clinical doses due to saturation of cytochrome P450 (CYP) 3A4 metabolism and organic anion transporting peptide (OATP) 1B mediated hepatic uptake. Additionally, simeprevir increases exposures of concomitant drugs by CYP3A4 and OATP1B inhibition. The objective of this study was to develop physiologically-based pharmacokinetic (PBPK) models that could describe drug-drug interactions (DDIs) of simeprevir with concomitant drugs via CYP3A4 and OATP1B inhibition, and also to capture the effects on coproporphyrin-I (CP-I), an endogenous biomarker of OATP1B. PBPK modeling estimated unbound simeprevir inhibitory constant (Ki ) of 2.89 µM against CYP3A4 in the DDI results between simeprevir and midazolam in healthy volunteers. Then, we analyzed the DDIs between simeprevir and atorvastatin, a dual substrate of CYP3A4 and OATP1B, in healthy volunteers, and unbound Ki against OATP1B was estimated to be 0.00347 µM. Finally, we analyzed the increase in the blood level of CP-I by simeprevir to verify the Ki,OATP1B . Because CP-I was measured in subjects with HCV with various hepatic fibrosis state, Monte Carlo simulation was performed to involve the decreases in expression levels of hepatic CYP3A4 and OATP1B and their interindividual variabilities. The PBPK modeling coupled with Monte Carlo simulation using the Ki,OATP1B value obtained from atorvastatin study reasonably recovered the observed relationship between CP-I and simeprevir blood levels. In conclusion, the simeprevir PBPK model developed in this study can quantitatively describe the increase in exposures of concomitant drugs and an endogenous biomarker via inhibition of CYP3A4 and OATP1B.

Discovery of a highly potent, selective, orally bioavailable inhibitor of KAT6A/B histone acetyltransferases with efficacy against KAT6A-high ER+ breast cancer.

KAT6A, and its paralog KAT6B, are histone lysine acetyltransferases (HAT) that acetylate histone H3K23 and exert an oncogenic role in several tumor types including breast cancer where KAT6A is frequently amplified/overexpressed. However, pharmacologic targeting of KAT6A to achieve therapeutic benefit has been a challenge. Here we describe identification of a highly potent, selective, and orally bioavailable KAT6A/KAT6B inhibitor CTx-648 (PF-9363), derived from a benzisoxazole series, which demonstrates anti-tumor activity in correlation with H3K23Ac inhibition in KAT6A over-expressing breast cancer. Transcriptional and epigenetic profiling studies show reduced RNA Pol II binding and downregulation of genes involved in estrogen signaling, cell cycle, Myc and stem cell pathways associated with CTx-648 anti-tumor activity in ER-positive (ER+) breast cancer. CTx-648 treatment leads to potent tumor growth inhibition in ER+ breast cancer in vivo models, including models refractory to endocrine therapy, highlighting the potential for targeting KAT6A in ER+ breast cancer.

Effect of Hepatic Impairment on OATP1B Activity: Quantitative Pharmacokinetic Analysis of Endogenous Biomarker and Substrate Drugs.

Hepatic impairment (HI) is known to modulate drug disposition and may lead to elevated plasma exposure. The aim of this study was to quantitate the in vivo OATP1B-mediated hepatic uptake activity in populations with varying degrees of HI. First, we measured baseline levels of plasma coproporphyrin-I, an endogenous OATP1B biomarker, in an open-label, parallel cohort study in adult subjects with normal liver function and mild, moderate, and severe HI (n = 24, 6/cohort). The geometric mean plasma concentrations of coproporphyrin-I were 1.66-fold, 2.81-fold (P < 0.05), and 7.78-fold (P < 0.0001) higher in mild, moderate, and severe impairment than those healthy controls. Second, we developed a dataset of 21 OATP1B substrate drugs with HI data extracted from literature. Median disease-to-healthy plasma area under the curve (AUC) ratios for substrate drugs were ~ 1.4, 3.0, and 6.4 for mild, moderate, and severe HI, respectively. Additionally, significant linear relationship was noted between AUC ratios of substrate drugs without and with co-administration of rifampin, a prototypic OATP1B inhibitor, and AUC ratios in moderate (P < 0.01) and severe (P < 0.001) HI. Third, a physiologically-based pharmacokinetic model analysis was conducted with 10 substrate drugs following estimation of relative OATP1B functional activity in virtual disease population models using coproporphyrin-I data and verification of substrate models with rifampin drug-drug interaction data. This approach adequately predicted plasma AUC change particularly in moderate (9 of 10 within 2-fold) and severe (5 of 5 within 2-fold) HI. Collective findings indicate progressive reduction, by as much as 90-92%, in OATP1B activity in the HI population.

SAM-Competitive PRMT5 Inhibitor PF-06939999 Demonstrates Antitumor Activity in Splicing Dysregulated NSCLC with Decreased Liability of Drug Resistance.

Protein arginine methyltransferase 5 (PRMT5) overexpression in hematologic and solid tumors methylates arginine residues on cellular proteins involved in important cancer functions including cell-cycle regulation, mRNA splicing, cell differentiation, cell signaling, and apoptosis. PRMT5 methyltransferase function has been linked with high rates of tumor cell proliferation and decreased overall survival, and PRMT5 inhibitors are currently being explored as an approach for targeting cancer-specific dependencies due to PRMT5 catalytic function. Here, we describe the discovery of potent and selective S-adenosylmethionine (SAM) competitive PRMT5 inhibitors, with in vitro and in vivo characterization of clinical candidate PF-06939999. Acquired resistance mechanisms were explored through the development of drug resistant cell lines. Our data highlight compound-specific resistance mutations in the PRMT5 enzyme that demonstrate structural constraints in the cofactor binding site that prevent emergence of complete resistance to SAM site inhibitors. PRMT5 inhibition by PF-06939999 treatment reduced proliferation of non-small cell lung cancer (NSCLC) cells, with dose-dependent decreases in symmetric dimethyl arginine (SDMA) levels and changes in alternative splicing of numerous pre-mRNAs. Drug sensitivity to PF-06939999 in NSCLC cells associates with cancer pathways including MYC, cell cycle and spliceosome, and with mutations in splicing factors such as RBM10. Translation of efficacy in mouse tumor xenograft models with splicing mutations provides rationale for therapeutic use of PF-06939999 in the treatment of splicing dysregulated NSCLC.

Physiologically-based pharmacokinetic modeling to evaluate in vitro-to-in vivo extrapolation for intestinal P-glycoprotein inhibition.

As one of the key components in model-informed drug discovery and development, physiologically-based pharmacokinetic (PBPK) modeling linked with in vitro-to-in vivo extrapolation (IVIVE) is widely applied to quantitatively predict drug-drug interactions (DDIs) on drug-metabolizing enzymes and transporters. This study aimed to investigate an IVIVE for intestinal P-glycoprotein (Pgp, ABCB1)-mediated DDIs among three Pgp substrates, digoxin, dabigatran etexilate, and quinidine, and two Pgp inhibitors, itraconazole and verapamil, via PBPK modeling. For Pgp substrates, assuming unbound Michaelis-Menten constant (Km ) to be intrinsic, in vitro-to-in vivo scaling factors for maximal Pgp-mediated efflux rate (Jmax ) were optimized based on the clinically observed results without co-administration of Pgp inhibitors. For Pgp inhibitors, PBPK models utilized the reported in vitro values of Pgp inhibition constants (Ki ), 1.0 µM for itraconazole and 2.0 µM for verapamil. Overall, the PBPK modeling sufficiently described Pgp-mediated DDIs between these substrates and inhibitors with the prediction errors of less than or equal to ±25% in most cases, suggesting a reasonable IVIVE for Pgp kinetics in the clinical DDI results. The modeling results also suggest that Pgp kinetic parameters of both the substrates (Km and Jmax ) and the inhibitors (Ki ) are sensitive to Pgp-mediated DDIs, thus being key for successful DDI prediction. It would also be critical to incorporate appropriate unbound inhibitor concentrations at the site of action into PBPK models. The present results support a quantitative prediction of Pgp-mediated DDIs using in vitro parameters, which will significantly increase the value of in vitro studies to design and run clinical DDI studies safely and effectively.

Unraveling pleiotropic effects of rifampicin by using physiologically based pharmacokinetic modeling: Assessing the induction magnitude of P-glycoprotein-cytochrome P450 3A4 dual substrates.

Rifampicin induces both P-glycoprotein (P-gp) and cytochrome P450 3A4 (CYP3A4) through regulating common nuclear receptors (e.g., pregnane X receptor). The interplay of P-gp and CYP3A4 has emerged to be an important factor in clinical drug-drug interactions (DDIs) with P-gp-CYP3A4 dual substrates and requires qualitative and quantitative understanding. Although physiologically based pharmacokinetic (PBPK) modeling has become a widely accepted approach to assess DDIs and is able to reasonably predict DDIs caused by CYP3A4 induction and P-gp induction individually, the predictability of PBPK models for the effect of simultaneous P-gp and CYP3A4 induction on P-gp-CYP3A4 dual substrates remains to be systematically evaluated. In this study, we used a PBPK modeling approach for the assessment of DDIs between rifampicin and 12 drugs: three sensitive P-gp substrates, seven P-gp-CYP3A4 dual substrates, and two P-gp-CYP3A4 dual substrates and inhibitors. A 3.5-fold increase of intestinal P-gp abundance was incorporated in the PBPK models to account for rifampicin-mediated P-gp induction at steady state. The simulation results showed that accounting for P-gp induction in addition to CYP3A4 induction improved the prediction accuracy of the area under the concentration-time curve and maximum (peak) plasma drug concentration ratios compared with considering CYP3A4 induction alone. Furthermore, the interplay of relevant drug-specific parameters and its impact on the magnitude of DDIs were evaluated using sensitivity analysis. The PBPK approach described herein, in conjunction with robust in vitro and clinical data, can help in the prospective assessment of DDIs involving other P-gp and CYP3A4 dual substrates. The database reported in the present study provides a valuable aid in understanding the combined effect of P-gp and CYP3A4 induction during drug development.

Quantitative prediction of breast cancer resistant protein mediated drug-drug interactions using physiologically-based pharmacokinetic modeling.

Quantitative assessment of drug-drug interactions (DDIs) involving breast cancer resistance protein (BCRP) inhibition is challenged by overlapping substrate/inhibitor specificity. This study used physiologically-based pharmacokinetic (PBPK) modeling to delineate the effects of inhibitor drugs on BCRP- and organic anion transporting polypeptide (OATP)1B-mediated disposition of rosuvastatin, which is a recommended BCRP clinical probe. Initial static model analysis using in vitro inhibition data suggested BCRP/OATP1B DDI risk while considering regulatory cutoff criteria for a majority of inhibitors assessed (25 of 27), which increased rosuvastatin plasma exposure to varying degree (~ 0-600%). However, rosuvastatin area under plasma concentration-time curve (AUC) was minimally impacted by BCRP inhibitors with calculated G-value (= gut concentration/inhibition potency) below 100. A comprehensive PBPK model accounting for intestinal (OATP2B1 and BCRP), hepatic (OATP1B, BCRP, and MRP4), and renal (OAT3) transport mechanisms was developed for rosuvastatin. Adopting in vitro inhibition data, rosuvastatin plasma AUC changes were predicted within 25% error for 9 of 12 inhibitors evaluated via PBPK modeling. This study illustrates the adequacy and utility of a mechanistic model-informed approach in quantitatively assessing BCRP-mediated DDIs.

A retrospective analysis of actionable pharmacogenetic/genomic biomarker language in FDA labels.

The primary goal of precision medicine is to maximize the benefit-risk relationships for individual patients by delivering the right drug to the right patients at the right dose. To achieve this goal, it has become increasingly important to assess gene-drug interactions (GDIs) in clinical settings. The US Food and Drug Administration (FDA) periodically updates the table of pharmacogenetic/genomic (PGx) biomarkers in drug labeling on their website. As described herein, an effort was made to categorize various PGx biomarkers covered by the FDA-PGx table into certain groups. There were 2 major groups, oncology molecular targets (OMT) and drug-metabolizing enzymes and transporters (DMETs), which constitute ~70% of all biomarkers (~33% and ~35%, respectively). These biomarkers were further classified whether their labeling languages could be actionable in clinical practice. For OMT biomarkers, ~70% of biomarkers are considered actionable in clinical practice as they are critical for the selection of appropriate drugs to individual patients. In contrast, ~30% of DMET biomarkers are considered actionable for the dose adjustments or alternative therapies in specific populations, such as CYP2C19 and CYP2D6 poor metabolizers. In addition, the GDI results related to some of the other OMT and DMET biomarkers are considered to provide valuable information to clinicians. However, clinical GDI results on the other DMET biomarkers can possibly be used more effectively for dose recommendation. As the labels of some drugs already recommend the precise doses in specific populations, it will be desirable to have clear language for dose recommendation of other (or new) drugs if appropriate.

Design and Characterization of a Pyridone-Containing EZH2 Inhibitor Phosphate Prodrug.

A pyridone-derived phosphate prodrug of an enhancer of zeste homolog 2 (EZH2) inhibitor was designed and synthesized to improve the inhibitor's aqueous solubility. This prodrug (compound 5) was profiled in pharmacokinetic experiments to assess its ability to deliver the corresponding parent compound (compound 2) to animals in vivo following oral administration. Results from these studies showed that the prodrug was efficiently converted to its parent compound in vivo. In separate experiments, the prodrug demonstrated impressive in vivo tumor growth inhibition in a diffuse large B-cell lymphoma Karpas-422 cell line-derived xenograft model. The described prodrug strategy is expected to be generally applicable to poorly soluble pyridone-containing EZH2 inhibitors and provides a new option to enable such compounds to achieve sufficiently high exposures in vivo.

Evaluation of Prediction Accuracy for Volume of Distribution in Rat and Human Using In Vitro, In Vivo, PBPK and QSAR Methods.

Volume of distribution at steady state (Vss) is an important pharmacokinetic parameter of a drug candidate. In this study, Vss prediction accuracy was evaluated by using: (1) seven methods for rat with 56 compounds, (2) four methods for human with 1276 compounds, and (3) four in vivo methods and three Kp (partition coefficient) scalar methods from scaling of three preclinical species with 125 compounds. The results showed that the global QSAR models outperformed the PBPK methods. Tissue fraction unbound (fu,t) method with adipose and muscle also provided high Vss prediction accuracy. Overall, the high performing methods for human Vss prediction are the global QSAR models, Øie-Tozer and equivalency methods from scaling of preclinical species, as well as PBPK methods with Kp scalar from preclinical species. Certain input parameter ranges rendered PBPK models inaccurate due to mass balance issues. These were addressed using appropriate theoretical limit checks. Prediction accuracy of tissue Kp were also examined. The fu,t method predicted Kp values more accurately than the PBPK methods for adipose, heart and muscle. All the methods overpredicted brain Kp and underpredicted liver Kp due to transporter effects. Successful Vss prediction involves strategic integration of in silico, in vitro and in vivo approaches.

Translational Pharmacokinetic-Pharmacodynamic Modeling for an Orally Available Novel Inhibitor of Epigenetic Regulator Enhancer of Zeste Homolog 2.

PF06821497 has been identified as an orally available small-molecule enhancer of zeste homolog 2 inhibitor. The objectives of the present study were to characterize pharmacokinetic-pharmacodynamic-disease relationships of PF06821497 in xenograft mouse models with diffuse large B-cell lymphoma (Karpas422). An indirect-response model reasonably fit dose-dependent pharmacodynamic responses [histone H3 on lysine 27 (H3K27) me3 inhibition] with an unbound EC 50 of 76 nM, whereas a signal-transduction model sufficiently fit dose-dependent disease responses (tumor growth inhibition) with an unbound tumor stasis concentration (T sc ) of 168 nM. Thus, effective concentration for 70% of maximal effect (EC70) for H3K27me3 inhibition was roughly comparable to T sc , suggesting that 70% H3K27me3 inhibition could be required for tumor stasis. Consistently, an integrated pharmacokinetic-pharmacodynamic-disease model adequately describing tumor growth inhibition also suggested that ∼70% H3K27me3 inhibition was associated with tumor stasis. Based on these results, we would propose that an EC70 estimate for H3K27me3 inhibition corresponding to tumor stasis could be considered a minimum target efficacious concentration of PF06821497 in cancer patients. SIGNIFICANCE STATEMENT: Using a mathematical modeling approach, the quantitative relationships of an orally available anticancer small-molecule enhancer of zeste homolog 2 inhibitor, PF06821497, were characterized among pharmacokinetics, pharmacodynamic biomarker inhibition, and disease responses in nonclinical xenograft models with diffuse large B-cell lymphoma. The modeling results suggest that >70% histone H3 on lysine 27 (H3K27) me3 inhibition would be required for tumor stasis (i.e., 100% tumor growth inhibition). Accordingly, we would propose that an effective concentration for 70% of maximal effect estimate for H3K27me3 inhibition could be considered a minimum target efficacious concentration of PF06821497 in cancer patients.

Physiologically-Based Pharmacokinetic Modeling Approach to Predict Rifampin-Mediated Intestinal P-Glycoprotein Induction.

Physiologically-based pharmacokinetic (PBPK) modeling is a powerful tool to quantitatively describe drug disposition profiles in vivo, thereby providing an alternative to predict drug-drug interactions (DDIs) that have not been tested clinically. This study aimed to predict effects of rifampin-mediated intestinal P-glycoprotein (Pgp) induction on pharmacokinetics of Pgp substrates via PBPK modeling. First, we selected four Pgp substrates (digoxin, talinolol, quinidine, and dabigatran etexilate) to derive in vitro to in vivo scaling factors for intestinal Pgp kinetics. Assuming unbound Michaelis-Menten constant (Km ) to be intrinsic, we focused on the scaling factors for maximal efflux rate (Jmax ) to adequately recover clinically observed results. Next, we predicted rifampin-mediated fold increases in intestinal Pgp abundances to reasonably recover clinically observed DDI results. The modeling results suggested that threefold to fourfold increases in intestinal Pgp abundances could sufficiently reproduce the DDI results of these Pgp substrates with rifampin. Hence, the obtained fold increases can potentially be applicable to DDI prediction with other Pgp substrates.

Application of Physiologically Based Pharmacokinetic Modeling in Understanding Bosutinib Drug-Drug Interactions: Importance of Intestinal P-Glycoprotein.

Bosutinib is an orally available Src/Abl tyrosine kinase inhibitor indicated for the treatment of patients with Ph+ chronic myelogenous leukemia at a clinically recommended dose of 500 mg once daily. Clinical results indicated that increases in bosutinib oral exposures were supraproportional at the lower doses (50-200 mg) and approximately dose-proportional at the higher doses (200-600 mg). Bosutinib is a substrate of CYP3A4 and P-glycoprotein and exhibits pH-dependent solubility with moderate intestinal permeability. These findings led us to investigate the factors influencing the underlying pharmacokinetic mechanisms of bosutinib with physiologically based pharmacokinetic (PBPK) models. Our primary objectives were to: 1) refine the previously developed bosutinib PBPK model on the basis of the latest oral bioavailability data and 2) verify the refined PBPK model with P-glycoprotein kinetics on the basis of the bosutinib drug-drug interaction (DDI) results with ketoconazole and rifampin. Additionally, the verified PBPK model was applied to predict bosutinib DDIs with dual CYP3A/P-glycoprotein inhibitors. The results indicated that 1) the refined PBPK model adequately described the observed plasma concentration-time profiles of bosutinib and 2) the verified PBPK model reasonably predicted the effects of ketoconazole and rifampin on bosutinib exposures by accounting for intestinal P-glycoprotein inhibition/induction. These results suggested that bosutinib DDI mechanism could involve not only CYP3A4-mediated metabolism but also P-glycoprotein-mediated efflux on absorption. In summary, P-glycoprotein kinetics could constitute an element in the PBPK models critical to understanding the pharmacokinetic mechanism of dual CYP3A/P-glycoprotein substrates, such as bosutinib, that exhibit nonlinear pharmacokinetics owing largely to a saturation of intestinal P-glycoprotein-mediated efflux.

Relationships of Changes in Pharmacokinetic Parameters of Substrate Drugs in Drug-Drug Interactions on Metabolizing Enzymes and Transporters.

A general objective of drug-drug interaction (DDI) studies is to determine whether potential interactions of new molecular entities with concomitantly administered other drugs exist and, if DDIs occur, whether dosage adjustments are required. A typical end point for DDI evaluations is the ratio of area under the plasma concentration-time curve (AUC) of substrate drugs (AUCR), whereas the ratios of maximal plasma concentration (Cmax ) and terminal half-life (t1/2 ) are also important to understand DDI mechanisms (Cmax R and t1/2 R, respectively). Because changes in substrate AUC by precipitant drugs ultimately result from alterations of Cmax and t1/2 , AUCR can be considered a hybrid parameter of Cmax R and t1/2 R, for example, AUCR ≈ Cmax R  ×  t1/2 R. The primary objective of this study was to investigate the relationships between AUCR, Cmax R, and t1/2 R in physiologically based pharmacokinetic model-predicted and clinically observed DDI results. First, the model-predicted results showed the excellent proportional relationship between AUCR and (Cmax R × t1/2 R) in DDI results of virtual substrates having a wide range of oral bioavailability with coadministration of ketoconazole, ritonavir, and rifampin. Second, the reasonable proportional relationships were also observed in the clinically observed DDI results of midazolam and statins (atorvastatin, cerivastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin, and simvastatin) with various inhibitors and inducers. Finally, these results suggest that utilization of the proportional relationship between AUCR and (Cmax R × t1/2 R) can provide an additional framework to further interpret DDI results reasonably and clearly. Furthermore, the proportional relationship can be purposely used to assess study design and pharmacokinetic analyses in DDI studies.

Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective.

This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.

Optimization of Orally Bioavailable Enhancer of Zeste Homolog 2 (EZH2) Inhibitors Using Ligand and Property-Based Design Strategies: Identification of Development Candidate (R)-5,8-Dichloro-7-(methoxy(oxetan-3-yl)methyl)-2-((4-methoxy-6-methyl-2-oxo-1,2-dihydropyridin-3-yl)methyl)-3,4-dihydroisoquinolin-1(2H)-one (PF-06821497).

A new series of lactam-derived EZH2 inhibitors was designed via ligand-based and physicochemical-property-based strategies to address metabolic stability and thermodynamic solubility issues associated with previous lead compound 1. The new inhibitors incorporated an sp3 hybridized carbon atom at the 7-position of the lactam moiety present in lead compound 1 as a replacement for a dimethylisoxazole group. This transformation enabled optimization of the physicochemical properties and potency compared to compound 1. Analysis of relationships between calculated log D (clogD) values and in vitro metabolic stability and permeability parameters identified a clogD range that afforded an increased probability of achieving favorable ADME data in a single molecule. Compound 23a exhibited the best overlap of potency and pharmaceutical properties as well as robust tumor growth inhibition in vivo and was therefore advanced as a development candidate (PF-06821497). A crystal structure of 23a in complex with the three-protein PRC2 complex enabled understanding of the key structural features required for optimal binding.

Application of Physiologically Based Pharmacokinetic Modeling to the Understanding of Bosutinib Pharmacokinetics: Prediction of Drug-Drug and Drug-Disease Interactions.

Bosutinib is an orally available Src/Abl tyrosine kinase inhibitor indicated for the treatment of patients with Philadelphia chromosome-positive chronic myelogenous leukemia. Bosutinib is predominantly metabolized by CYP3A4 as the primary clearance mechanism. The main objectives of this study were to 1) develop physiologically based pharmacokinetic (PBPK) models of bosutinib; 2) verify and refine the PBPK models based on clinical study results of bosutinib single-dose drug-drug interaction (DDI) with ketoconazole and rifampin, as well as single-dose drug-disease interaction (DDZI) in patients with renal and hepatic impairment; 3) apply the PBPK models to predict DDI outcomes in patients with weak and moderate CYP3A inhibitors; and 4) apply the PBPK models to predict DDZI outcomes in renally and hepatically impaired patients after multiple-dose administration. Results showed that the PBPK models adequately predicted bosutinib oral exposures in patients after single- and multiple-dose administrations. The PBPK models also reasonably predicted changes in bosutinib exposures in the single-dose DDI and DDZI results, suggesting that the PBPK models were sufficiently developed and verified based on the currently available data. Finally, the PBPK models predicted 2- to 4-fold increases in bosutinib exposures by moderate CYP3A inhibitors, as well as comparable increases in bosutinib exposures in renally and hepatically impaired patients between single- and multiple-dose administrations. Given the challenges in conducting numerous DDI and DDZI studies of anticancer drugs in patients, we believe that the PBPK models verified in our study would be valuable to reasonably predict bosutinib exposures under various scenarios that have not been tested clinically.