Towards a Best Practice Approach in PBPK Modeling: Case Example of Developing a Unified Efavirenz Model Accounting for Induction of CYPs 3A4 and 2B6

A Physiologically-Based Pharmacokinetic Simulation to Evaluate Approaches to Mitigate Efavirenz-Induced Decrease in Levonorgestrel Exposure with a Contraceptive Implant

Background: Levonorgestrel implant is a highly effective hormonal contraceptive, but its efficacy may be compromised when used with cytochrome enzyme inducers such as efavirenz. The primary aim of this study was to evaluate methods of mitigating the drug interaction. Methods: Using a physiologically-based pharmacokinetic (PBPK) model for levonorgestrel that we developed within the Simcyp® program, we evaluated a higher dose of levonorgestrel implant, a lower dose of efavirenz, and the combination of both, as possible methods to mitigate the interaction. In addition, we investigated the impact on levonorgestrel total and unbound concentrations of other events likely to be associated with efavirenz coadministration: changes in plasma protein binding of levonorgestrel (as with displacement) and high variability of efavirenz exposure (as with genetic polymorphism of its metabolism). The range of fraction unbound tested was 0.6% to 2.6%, and the range of efavirenz exposure ranged from the equivalent of 200 mg to 4800 mg doses. Results: Levonorgestrel plasma concentrations at any given time with a standard 150 mg implant dose are predicted to be approximately 68% of those of control when given with efavirenz 600 mg and 72% of control with efavirenz 400 mg. With double-dose levonorgestrel, the predictions are 136% and 145% of control, respectively. A decrease in levonorgestrel plasma protein binding is predicted to primarily decrease total levonorgestrel plasma concentrations, whereas higher efavirenz exposure is predicted to decrease total and unbound concentrations. Conclusions: Simulations suggest that doubling the dose of levonorgestrel, particularly in combination with 400 mg daily efavirenz, may mitigate the drug interaction. Changes in levonorgestrel plasma protein binding and efavirenz genetic polymorphism may help explain differences between model predictions and clinical data but need to be studied further.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Physiologically Based Pharmacokinetic Modeling of the Drug-Drug Interaction Between CYP3A4 Substrate Glasdegib and Moderate CYP3A4 Inducers in Lieu of a Clinical Study

Glasdegib (DAURISMO) is a hedgehog pathway inhibitor approved for the treatment of acute myeloid leukemia (AML). Cytochrome P450 3A4 (CYP3A4) has been identified as a major metabolism and clearance pathway for glasdegib. The role of CYP3A4 in the clearance of glasdegib has been confirmed with clinical drug-drug interaction (DDI) studies following the coadministration of glasdegib with the strong CYP3A4 inhibitor ketoconazole and the strong inducer rifampin. To evaluate potential drug interactions with CYP3A4 modulators, the coadministration of glasdegib with a moderate CYP3A4 inducer, efavirenz, was evaluated using physiologically based pharmacokinetic (PBPK) modeling using the Simcyp simulator. The glasdegib compound file was developed using measured physicochemical properties, data from human intravenous and oral pharmacokinetics, absorption, distribution, metabolism, and excretion studies, and in vitro reaction phenotyping results. The modeling assumptions, model parameters, and assignments of fractional CYP3A4 metabolism were verified using results from clinical pharmacokinetics (PK) and DDI studies with ketoconazole and rifampin. The verified glasdegib and efavirenz compound files, the latter of which was available in the Simcyp simulator, were used to estimate the potential impact of efavirenz on the PK of glasdegib. PBPK modeling predicted a glasdegib area under the concentration-time curve ratio of 0.45 and maximum plasma concentration ratio of 0.75 following coadministration with efavirenz. The PBPK results, in lieu of a formal clinical study, informed the drug label, with the recommendation to double the clinical dose of glasdegib when administered in conjunction with a moderate CYP3A4 inducer, followed by a resumption of the original dose 7 days post-discontinuation.

Total and Free Blood and Plasma Concentration Changes in Pregnancy for Medicines Highly Bound to Plasma Proteins: Application of Physiologically Based Pharmacokinetic Modelling to Understand the Impact on Efficacy

Free drug concentrations are generally considered the pharmacologically active moiety and are important for cellular diffusion and distribution. Pregnancy-related changes in plasma protein binding and blood partitioning are due to decreases in plasma albumin, alpha-1-acid glycoprotein, and haematocrit; this may lead to increased free concentrations, tissue distribution, and clearance during pregnancy. In this paper we highlight the importance and challenges of considering changes in total and free concentrations during pregnancy. For medicines highly bound to plasma proteins, such as tacrolimus, efavirenz, clindamycin, phenytoin, and carbamazepine, differential changes in concentrations of free drug during pregnancy may be clinically significant and have important implications for dose adjustment. Therapeutic drug monitoring usually relies on the measurement of total concentrations; this can result in dose adjustments that are not necessary when changes in free concentrations are considered. We explore the potential of physiologically based pharmacokinetic (PBPK) models to support the understanding of the changes in plasma proteins binding, using tacrolimus and efavirenz as example drug models. The exposure to either drug was predicted to be reduced during pregnancy; however, the decrease in the exposure to the total tacrolimus and efavirenz were significantly larger than the reduction in the exposure to the free drug. These data show that PBPK modelling can support the impact of the changes in plasma protein binding and may be used for the simulation of free concentrations in pregnancy to support dosing decisions.

Potential drug-drug interaction of olverembatinib (HQP1351) using physiologically based pharmacokinetic models

Olverembatinib (HQP1351) is a third-generation BCR-ABL tyrosine kinase inhibitor for the treatment of chronic myeloid leukemia (CML) (including T315I-mutant disease), exhibits drug-drug interaction (DDI) potential through cytochrome P450 (CYP) enzymes CYP3A4, CYP2C9, CYP2C19, CYP1A2, and CYP2B6. A physiologically-based pharmacokinetic (PBPK) model was constructed based on physicochemical and in vitro parameters, as well as clinical data to predict 1) potential DDIs between olverembatinib and CYP3A4 and CYP2C9 inhibitors or inducers 2), effects of olverembatinib on the exposure of CYP1A2, CYP2B6, CYP2C9, CYP2C19, and CYP3A4 substrates, and 3) pharmacokinetics in patients with liver function injury. The PBPK model successfully described observed plasma concentrations of olverembatinib from healthy subjects and patients with CML after a single administration, and predicted olverembatinib exposure increases when co-administered with itraconazole (strong CYP3A4 inhibitor) and decreases with rifampicin (strong CYP3A4 inducer), which were validated by observed data. The predicted results suggest that 1) strong, moderate, and mild CYP3A4 inhibitors (which have some overlap with CYP2C9 inhibitors) may increase olverembatinib exposure by approximately 2.39-, 1.80- to 2.39-, and 1.08-fold, respectively; strong, and moderate CYP3A4 inducers may decrease olverembatinib exposure by approximately 0.29-, and 0.35- to 0.56-fold, respectively 2); olverembatinib, as a "perpetrator," would have no or limited impact on CYP1A2, CYP2B6, CYP2C9, CYP2C19, and CYP3A4 enzyme activity 3); systemic exposure of olverembatinib in liver function injury with Child-Pugh A, B, C may increase by 1.22-, 1.79-, and 2.13-fold, respectively. These simulations inform DDI risk for olverembatinib as either a "victim" or "perpetrator".

Development and Evaluation of a Physiologically Based Pharmacokinetic Model for Predicting Haloperidol Exposure in Healthy and Disease Populations

The physiologically based pharmacokinetic (PBPK) approach can be used to develop mathematical models for predicting the absorption, distribution, metabolism, and elimination (ADME) of administered drugs in virtual human populations. Haloperidol is a typical antipsychotic drug with a narrow therapeutic index and is commonly used in the management of several medical conditions, including psychotic disorders. Due to the large interindividual variability among patients taking haloperidol, it is very likely for them to experience either toxic or subtherapeutic effects. We intend to develop a haloperidol PBPK model for identifying the potential sources of pharmacokinetic (PK) variability after intravenous and oral administration by using the population-based simulator, PK-Sim. The model was initially developed and evaluated to predict the PK of haloperidol and its reduced metabolite in adult healthy population after intravenous and oral administration. After evaluating the developed PBPK model in healthy adults, it was used to predict haloperidol–rifampicin drug–drug interaction and was extended to tuberculosis patients. The model evaluation was performed using visual assessments, prediction error, and mean fold error of the ratio of the observed-to-predicted values of the PK parameters. The predicted PK values were in good agreement with the corresponding reported values. The effects of the pathophysiological changes and enzyme induction associated with tuberculosis and its treatment, respectively, on haloperidol PK, have been predicted precisely. For all clinical scenarios that were evaluated, the predicted values were within the acceptable two-fold error range.

Prediction of CYP‐mediated DDIs involving inhibition: Approaches to address the requirements for system qualification of the Simcyp Simulator

Physiologically‐based pharmacokinetic (PBPK) modeling is being increasingly used in drug development to avoid unnecessary clinical drug–drug interaction (DDI) studies and inform drug labels. Thus, regulatory agencies are recommending, or indeed requesting, more rigorous demonstration of the prediction accuracy of PBPK platforms in the area of their intended use. We describe a framework for qualification of the Simcyp Simulator with respect to competitive and mechanism‐based inhibition (MBI) of CYP1A2, CYP2D6, CYP2C8, CYP2C9, CYP2C19, and CYP3A4/5. Initially, a DDI matrix, consisting of a range of weak, moderate, and strong inhibitors and substrates with varying fraction metabolized by specific CYP enzymes that were susceptible to different degrees of inhibition, were identified. Simulations were run with 123 clinical DDI studies involving competitive inhibition and 78 clinical DDI studies involving MBI. For competitive inhibition, the overall prediction accuracy was good with an average fold error (AFE) of 0.91 and 0.92 for changes in the maximum plasma concentration (C_max) and area under the plasma concentration (AUC) time profile, respectively, as a consequence of the DDI. For MBI, an AFE of 1.03 was determined for both C_max and AUC. The prediction accuracy was generally comparable across all CYP enzymes, irrespective of the isozyme and mechanism of inhibition. These findings provide confidence in application of the Simcyp Simulator (V19 R1) for assessment of the DDI potential of drugs in development either as inhibitors or victim drugs of CYP‐mediated interactions. The approach described herein and the identified DDI matrix can be used to qualify subsequent versions of the platform.

Physiologically-based pharmacokinetic model predictions of inter-ethnic differences in imatinib pharmacokinetics and dosing regimens

AIMS

This study implements a physiologically-based pharmacokinetic (PBPK) modelling approach to investigate inter-ethnic differences in imatinib pharmacokinetics and dosing regimens.

METHODS

A PBPK model of imatinib was built in the Simcyp Simulator (version 17) integrating in vitro drug metabolism and clinical pharmacokinetic data. The model accounts for ethnic differences in body size and abundance of drug-metabolising enzymes and proteins involved in imatinib disposition. Utility of this model for prediction of imatinib pharmacokinetics was evaluated across different dosing regimens and ethnic groups. The impact of ethnicity on imatinib dosing was then assessed based on the established range of trough concentrations (C_ss,min ).

RESULTS

The PBPK model of imatinib demonstrated excellent predictive performance in describing pharmacokinetics and the attained C_ss,min in patients from different ethnic groups, shown by prediction differences that were within 1.25-fold of the clinically-reported values in published studies. PBPK simulation suggested a similar dose of imatinib (400-600 mg/d) to achieve the desirable range of C_ss,min (1000-3200 ng/mL) in populations of European, Japanese and Chinese ancestry. The simulation indicated that patients of African ancestry may benefit from a higher initial dose (600-800 mg/d) to achieve imatinib target concentrations, due to a higher apparent clearance (CL/F) of imatinib compared to other ethnic groups; however, the clinical data to support this are currently limited.

CONCLUSION

PBPK simulations highlighted a potential ethnic difference in the recommended initial dose of imatinib between populations of European and African ancestry, but not populations of Chinese and Japanese ancestry.

Physiologically Based Pharmacokinetic Models Are Effective Support for Pediatric Drug Development

Pediatric drug development faces many difficulties. Traditionally, pediatric drug doses are simply calculated linearly based on the body weight, age, and body surface area of adults. Due to the ontogeny of children, this simple linear scaling may lead to drug overdose in pediatric patients. The physiologically based pharmacokinetic (PBPK) model, as a mathematical model, contributes to the research and development of pediatric drugs. An example of a PBPK model guiding drug dose selection in pediatrics has emerged and has been approved by the relevant regulatory agencies. In this review, we discuss the principle of the PBPK model, emphasize the necessity of establishing a pediatric PBPK model, introduce the absorption, distribution, metabolism, and excretion of the pediatric PBPK model, and understand the various applications and related prospects of the pediatric PBPK model.

Physiologically Based Pharmacokinetic Modelling to Identify Pharmacokinetic Parameters Driving Drug Exposure Changes in the Elderly

BACKGROUND

Medication use is highly prevalent with advanced age, but clinical studies are rarely conducted in the elderly, leading to limited knowledge regarding age-related pharmacokinetic changes.

OBJECTIVE

The objective of this study was to investigate which pharmacokinetic parameters determine drug exposure changes in the elderly by conducting virtual clinical trials for ten drugs (midazolam, metoprolol, lisinopril, amlodipine, rivaroxaban, repaglinide, atorvastatin, rosuvastatin, clarithromycin and rifampicin) using our physiologically based pharmacokinetic (PBPK) framework.

METHODS

PBPK models for all ten drugs were developed in young adults (20-50 years) following the best practice approach, before predicting pharmacokinetics in the elderly (≥ 65 years) without any modification of drug parameters. A descriptive relationship between age and each investigated pharmacokinetic parameter (peak concentration [C_max], time to C_max [t_max], area under the curve [AUC], clearance, volume of distribution, elimination-half-life) was derived using the final PBPK models, and verified with independent clinically observed data from 52 drugs.

RESULTS

The age-related changes in drug exposure were successfully simulated for all ten drugs. Pharmacokinetic parameters were predicted within 1.25-fold (70%), 1.5-fold (86%) and 2-fold (100%) of clinical data. AUC increased progressively by 0.9% per year throughout adulthood from the age of 20 years, which was explained by decreased clearance, while C_max, t_max and volume of distribution were not affected by human aging. Additional clinical data of 52 drugs were contained within the estimated variability of the established age-dependent correlations for each pharmacokinetic parameter.

CONCLUSION

The progressive decrease in hepatic and renal blood flow, as well as glomerular filtration, rate led to a reduced clearance driving exposure changes in the healthy elderly, independent of the drug.

Gender-based differences in brain and plasma pharmacokinetics of letrozole in sprague-dawley rats: Application of physiologically-based pharmacokinetic modeling to gain quantitative insights

Based on the discovery that the estrogen synthase aromatase (CYP19A1) is abundantly expressed in high- grade gliomas, the aromatase inhibitor, letrozole is being investigated in pre-clinical models as a novel agent against this malignancy. Here, we investigated the systemic and brain pharmacokinetics of letrozole following single and steady state dosing in both male and female Sprague-Dawley rats. Furthermore, we employed physiologically-based pharmacokinetic (PBPK) modeling to gain quantitative insights into the blood-brain barrier penetration of this drug. Letrozole (4 mg/kg) was administered intraperitoneally daily for 5 days (for males) and 11 days (for females) and intracerebral microdialysis was performed for brain extracellular fluid (ECF) collection simultaneously with venous blood sampling. Drug levels were measured using HPLC and non-compartmental analysis was conducted employing WinNonlin®. Simcyp animal simulator was used for conducting bottom-up PBPK approach incorporating the specified multi-compartment brain model. Overall, marked gender-specific differences in the systemic and brain pharmacokinetics of letrozole were observed. Letrozole clearance was much slower in female rats resulting in markedly higher plasma and brain drug concentrations. At steady state, the plasma AUC 0-24 was 103.0 and 24.8 μg*h/ml and brain ECF AUC 0-12 was 24.0 and 4.8 μg*h/ml in female and male rats, respectively. The PBPK model simulated brain concentration profiles were in close agreement with the observed profiles. While gender-specific differences in letrozole PK are not observed in the clinical setting, these findings will guide the dose optimization during pre-clinical investigations of this compound. The PBPK model will serve as an important clinical translational tool.

Comprehensive PBPK model to predict drug interaction potential of Zanubrutinib as a victim or perpetrator

A physiologically based pharmacokinetic (PBPK) model was developed to evaluate and predict (1) the effect of concomitant cytochrome P450 3A (CYP3A) inhibitors or inducers on the exposures of zanubrutinib, (2) the effect of zanubrutinib on the exposures of CYP3A4, CYP2C8, and CYP2B6 substrates, and (3) the impact of gastric pH changes on the pharmacokinetics of zanubrutinib. The model was developed based on physicochemical and in vitro parameters, as well as clinical data, including pharmacokinetic data in patients with B-cell malignancies and in healthy volunteers from two clinical drug-drug interaction (DDI) studies of zanubrutinib as a victim of CYP modulators (itraconazole, rifampicin) or a perpetrator (midazolam). This PBPK model was successfully validated to describe the observed plasma concentrations and clinical DDIs of zanubrutinib. Model predictions were generally within 1.5-fold of the observed clinical data. The PBPK model was used to predict untested clinical scenarios; these simulations indicated that strong, moderate, and mild CYP3A inhibitors may increase zanubrutinib exposures by approximately four-fold, two- to three-fold, and <1.5-fold, respectively. Strong and moderate CYP3A inducers may decrease zanubrutinib exposures by two- to three-fold or greater. The PBPK simulations showed that clinically relevant concentrations of zanubrutinib, as a DDI perpetrator, would have no or limited impact on the enzyme activity of CYP2B6 and CYP2C8. Simulations indicated that zanubrutinib exposures are not impacted by acid-reducing agents. Development of a PBPK model for zanubrutinib as a DDI victim and perpetrator in parallel can increase confidence in PBPK models supporting zanubrutinib label dose recommendations.

Physiologically-Based Pharmacokinetic Modeling to Support the Clinical Management of Drug-Drug Interactions With Bictegravir

Bictegravir is equally metabolized by cytochrome P450 (CYP)3A and uridine diphosphate‐glucuronosyltransferase (UGT)1A1. Drug–drug interaction (DDI) studies were only conducted for strong inhibitors and inducers, leading to some uncertainty whether moderate perpetrators or multiple drug associations can be safely coadministered with bictegravir. We used physiologically‐based pharmacokinetic (PBPK) modeling to simulate DDI magnitudes of various scenarios to guide the clinical DDI management of bictegravir. Clinically observed DDI data for bictegravir coadministered with voriconazole, darunavir/cobicistat, atazanavir/cobicistat, and rifampicin were predicted within the 95% confidence interval of the PBPK model simulations. The area under the curve (AUC) ratio of the DDI divided by the control scenario was always predicted within 1.25‐fold of the clinically observed data, demonstrating the predictive capability of the used modeling approach. After the successful verification, various DDI scenarios with drug pairs and multiple concomitant drugs were simulated to analyze their effect on bictegravir exposure. Generally, our simulation results suggest that bictegravir should not be coadministered with strong CYP3A and UGT1A1 inhibitors and inducers (e.g., atazanavir, nilotinib, and rifampicin), but based on the present modeling results, bictegravir could be administered with moderate dual perpetrators (e.g., efavirenz). Importantly, the inducing effect of rifampicin on bictegravir was predicted to be reversed with the concomitant administration of a strong inhibitor such as ritonavir, resulting in a DDI magnitude within the efficacy and safety margin for bictegravir (0.5–2.4‐fold). In conclusion, the PBPK modeling strategy can effectively be used to guide the clinical management of DDIs for novel drugs with limited clinical experience, such as bictegravir.

Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling to Predict the Impact of CYP2C9 Genetic Polymorphisms, Co-Medication and Formulation on the Pharmacokinetics and Pharmacodynamics of Flurbiprofen

Physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models can serve as a powerful framework for predicting the influence as well as the interaction of formulation, genetic polymorphism and co-medication on the pharmacokinetics and pharmacodynamics of drug substances. In this study, flurbiprofen, a potent non-steroid anti-inflammatory drug, was chosen as a model drug. Flurbiprofen has absolute bioavailability of ~95% and linear pharmacokinetics in the dose range of 50–300 mg. Its absorption is considered variable and complex, often associated with double peak phenomena, and its pharmacokinetics are characterized by high inter-subject variability, mainly due to its metabolism by the polymorphic CYP2C9 (fmCYP2C9 ≥ 0.71). In this study, by leveraging in vitro, in silico and in vivo data, an integrated PBPK/PD model with mechanistic absorption was developed and evaluated against clinical data from PK, PD, drug-drug and gene-drug interaction studies. The PBPK model successfully predicted (within 2-fold) 36 out of 38 observed concentration-time profiles of flurbiprofen as well as the CYP2C9 genetic effects after administration of different intravenous and oral dosage forms over a dose range of 40–300 mg in both Caucasian and Chinese healthy volunteers. All model predictions for C_max, AUC_inf and CL/F were within two-fold of their respective mean or geometric mean values, while 90% of the predictions of C_max, 81% of the predictions of AUC_inf and 74% of the predictions of Cl/F were within 1.25 fold. In addition, the drug-drug and drug-gene interactions were predicted within 1.5-fold of the observed interaction ratios (AUC, C_max ratios). The validated PBPK model was further expanded by linking it to an inhibitory E_max model describing the analgesic efficacy of flurbiprofen and applying it to explore the effect of formulation and genetic polymorphisms on the onset and duration of pain relief. This comprehensive PBPK/PD analysis, along with a detailed translational biopharmaceutic framework including appropriately designed biorelevant in vitro experiments and in vitro-in vivo extrapolation, provided mechanistic insight on the impact of formulation and genetic variations, two major determinants of the population variability, on the PK/PD of flurbiprofen. Clinically relevant specifications and potential dose adjustments were also proposed. Overall, the present work highlights the value of a translational PBPK/PD approach, tailored to target populations and genotypes, as an approach towards achieving personalized medicine.

Physiologically based pharmacokinetic modeling and simulation to predict drug-drug interactions of ivosidenib with CYP3A perpetrators in patients with acute myeloid leukemia

PURPOSE

Develop a physiologically based pharmacokinetic (PBPK) model of ivosidenib using in vitro and clinical PK data from healthy participants (HPs), refine it with clinical data on ivosidenib co-administered with itraconazole, and develop a model for patients with acute myeloid leukemia (AML) and apply it to predict ivosidenib drug-drug interactions (DDI).

METHODS

An HP PBPK model was developed in Simcyp Population-Based Simulator (version 15.1), with the CYP3A4 component refined based on a clinical DDI study. A separate model accounting for the reduced apparent oral clearance in patients with AML was used to assess the DDI potential of ivosidenib as the victim of CYP3A perpetrators.

RESULTS

For a single 250 mg ivosidenib dose, the HP model predicted geometric mean ratios of 2.14 (plasma area under concentration-time curve, to infinity [AUC_0-∞]) and 1.04 (maximum plasma concentration [C_max]) with the strong CYP3A4 inhibitor, itraconazole, within 1.26-fold of the observed values (2.69 and 1.0, respectively). The AML model reasonably predicted the observed ivosidenib concentration-time profiles across all dose levels in patients. Predicted ivosidenib geometric mean steady-state AUC_0-∞ and C_max ratios were 3.23 and 2.26 with ketoconazole, and 1.90 and 1.52 with fluconazole, respectively. Co-administration of the strong CYP3A4 inducer, rifampin, predicted a greater DDI effect on a single dose of ivosidenib than on multiple doses (AUC ratios 0.35 and 0.67, C_max ratios 0.91 and 0.81, respectively).

CONCLUSION

Potentially clinically relevant DDI effects with CYP3A4 inducers and moderate and strong inhibitors co-administered with ivosidenib were predicted. Considering the challenges of conducting clinical DDI studies in patients, this PBPK approach is valuable in ivosidenib DDI risk assessment and management.

Predicting Drug-Drug Interactions Between Rifampicin and Long-Acting Cabotegravir and Rilpivirine Using Physiologically Based Pharmacokinetic Modeling

BACKGROUND

Cabotegravir and rilpivirine are 2 long-acting (LA) antiretrovirals that can be administered intramuscularly; their interaction with rifampicin, a first-line antituberculosis agent, has not been investigated. The aim of this study was to simulate and predict drug-drug interactions (DDIs) between these LA antiretroviral agents and rifampicin using physiologically based pharmacokinetic (PBPK) modeling.

METHODS

The designed PBPK models were qualified (according to European Medicines Agency guidelines) against observed data for oral formulations of cabotegravir, rilpivirine, and rifampicin. Induction potential of rifampicin was also qualified by comparing the DDI between oral cabotegravir and oral rilpivirine with rifampicin. Qualified PBPK models were utilized for pharmacokinetic prediction of DDIs.

RESULTS

PBPK models predicted a reduction in both area under the curve (AUC0-28 days) and trough concentration (Ctrough, 28th day) of LA cabotegravir of 41%-46% for the first maintenance dose coadministered with 600 mg once-daily oral rifampicin. Rilpivirine concentrations were predicted to decrease by 82% for both AUC0-28 days and Ctrough, 28th day following the first maintenance dose when coadministered with rifampicin.

CONCLUSIONS

The developed PBPK models predicted the theoretical effect of rifampicin on cabotegravir and rilpivirine LA intramuscular formulations. According to these simulations, it is likely that coadministration of rifampicin with these LA formulations will result in subtherapeutic concentrations of both drugs.

State-of-the-Art Review on Physiologically Based Pharmacokinetic Modeling in Pediatric Drug Development

Physiologically based pharmacokinetic modeling and simulation is an important tool for predicting the pharmacokinetics, pharmacodynamics, and safety of drugs in pediatrics. Physiologically based pharmacokinetic modeling is applied in pediatric drug development for first-time-in-pediatric dose selection, simulation-based trial design, correlation with target organ toxicities, risk assessment by investigating possible drug-drug interactions, real-time assessment of pharmacokinetic-safety relationships, and assessment of non-systemic biodistribution targets. This review summarizes the details of a physiologically based pharmacokinetic modeling approach in pediatric drug research, emphasizing reports on pediatric physiologically based pharmacokinetic models of individual drugs. We also compare and contrast the strategies employed by various researchers in pediatric physiologically based pharmacokinetic modeling and provide a comprehensive overview of physiologically based pharmacokinetic modeling strategies and approaches in pediatrics. We discuss the impact of physiologically based pharmacokinetic models on regulatory reviews and product labels in the field of pediatric pharmacotherapy. Additionally, we examine in detail the current limitations and future directions of physiologically based pharmacokinetic modeling in pediatrics with regard to the ability to predict plasma concentrations and pharmacokinetic parameters. Despite the skepticism and concern in the pediatric community about the reliability of physiologically based pharmacokinetic models, there is substantial evidence that pediatric physiologically based pharmacokinetic models have been used successfully to predict differences in pharmacokinetics between adults and children for several drugs. It is obvious that the use of physiologically based pharmacokinetic modeling to support various stages of pediatric drug development is highly attractive and will rapidly increase, provided the robustness and reliability of these techniques are well established.

Predicting Clinical Effects of CYP3A4 Modulators on Abemaciclib and Active Metabolites Exposure Using Physiologically Based Pharmacokinetic Modeling

Abemaciclib, a selective inhibitor of cyclin‐dependent kinases 4 and 6, is metabolized mainly by cytochrome P450 (CYP)3A4. Clinical studies were performed to assess the impact of strong inhibitor (clarithromycin) and inducer (rifampin) on the exposure of abemaciclib and active metabolites. A physiologically based pharmacokinetic (PBPK) model incorporating the metabolites was developed to predict the effect of other strong and moderate CYP3A4 inhibitors and inducers. Clarithromycin increased the area under the plasma concentration‐time curve (AUC) of abemaciclib and potency‐adjusted unbound active species 3.4‐fold and 2.5‐fold, respectively. Rifampin decreased corresponding exposures 95% and 77%, respectively. These changes influenced the fraction metabolized via CYP3A4 in the model. An absolute bioavailability study informed the hepatic and gastric availability. In vitro data and a human radiolabel study determined the fraction and rate of formation of the active metabolites as well as absorption‐related parameters. The predicted AUC ratios of potency‐adjusted unbound active species with rifampin and clarithromycin were within 0.7‐ and 1.25‐fold of those observed. The PBPK model predicted 3.78‐ and 7.15‐fold increases in the AUC of the potency‐adjusted unbound active species with strong CYP3A4 inhibitors itraconazole and ketoconazole, respectively; and 1.62‐ and 2.37‐fold increases with the concomitant use of moderate CYP3A4 inhibitors verapamil and diltiazem, respectively. The model predicted modafinil, bosentan, and efavirenz would decrease the AUC of the potency‐adjusted unbound active species by 29%, 42%, and 52%, respectively. The current PBPK model, which considers changes in unbound potency‐adjusted active species, can be used to inform dosing recommendations when abemaciclib is coadministered with CYP3A4 perpetrators.

Global Sensitivity Analysis of the Rodgers and Rowland Model for Prediction of Tissue: Plasma Partitioning Coefficients: Assessment of the Key Physiological and Physicochemical Factors That Determine Small-Molecule Tissue Distribution

In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fu_p) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation.

Physiologically-Based Pharmacokinetic Modeling for Predicting Drug Interactions of a Combination of Olanzapine and Samidorphan

A combination of the antipsychotic olanzapine and the opioid receptor antagonist samidorphan (OLZ/SAM) is intended to provide the antipsychotic efficacy of olanzapine while mitigating olanzapine-associated weight gain. As cytochrome P450 (CYP) 1A2 and CYP3A4 are the major enzymes involved in metabolism of olanzapine and samidorphan, respectively, physiologically-based pharmacokinetic (PBPK) modeling was applied to predict any drug-drug interaction (DDI) potential between olanzapine and samidorphan or between OLZ/SAM and CYP3A4/CYP1A2 inhibitors/inducers. A PBPK model for OLZ/SAM was developed and validated by comparing model-simulated data with observed clinical study data. Based on model-based simulations, no DDI between olanzapine and samidorphan is expected when administered as OLZ/SAM. CYP3A4 inhibition is predicted to have a weak effect on samidorphan exposure and negligible effect on olanzapine exposure. CYP3A4 induction is predicted to reduce both samidorphan and olanzapine exposure. CYP1A2 inhibition or induction is predicted to increase or decrease, respectively, olanzapine exposure only.

Predicting Efavirenz Concentrations in the Brain Tissue of HIV-Infected Individuals and Exploring their Relationship to Neurocognitive Impairment

Sparse data exist on the penetration of antiretrovirals into brain tissue. In this work, we present a framework to use efavirenz (EFV) pharmacokinetic (PK) data in plasma, cerebrospinal fluid (CSF), and brain tissue of eight rhesus macaques to predict brain tissue concentrations in HIV-infected individuals. We then perform exposure-response analysis with the model-predicted EFV area under the concentration-time curve (AUC) and neurocognitive scores collected from a group of 24 HIV-infected participants. Adult rhesus macaques were dosed daily with 200 mg EFV (as part of a four-drug regimen) for 10 days. Plasma was collected at 8 time points over 10 days and at necropsy, whereas CSF and brain tissue were collected at necropsy. In the clinical study, data were obtained from one paired plasma and CSF sample of participants prescribed EFV, and neuropsychological test evaluations were administered across 15 domains. PK modeling was performed using ADAPT version 5.0 Biomedical Simulation Resource, Los Angeles, CA) with the iterative two-stage estimation method. An eight-compartment model best described EFV distribution across the plasma, CSF, and brain tissue of rhesus macaques and humans. Model-predicted median brain tissue concentrations in humans were 31 and 8,000 ng/mL, respectively. Model-predicted brain tissue AUC was highly correlated with plasma AUC (γ = 0.99, P < 0.001) but not CSF AUC (γ = 0.34, P = 0.1) and did not show any relationship with neurocognitive scores (γ < 0.05, P > 0.05). This analysis provides an approach to estimate PK the brain tissue in order to perform PK/pharmacodynamic analyses at the target site.

Systems Biology, Systems Medicine, Systems Pharmacology: The What and The Why

Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences.

Considerations from the Innovation and Quality Induction Working Group in Response to Drug-Drug Interaction Guidances from Regulatory Agencies: Focus on CYP3A4 mRNA In Vitro Response Thresholds, Variability, and Clinical Relevance

The Innovation and Quality Induction Working Group presents an assessment of best practice for data interpretation of in vitro induction, specifically, response thresholds, variability, application of controls, and translation to clinical risk assessment with focus on CYP3A4 mRNA. Single concentration control data and Emax/EC50 data for prototypical CYP3A4 inducers were compiled from many human hepatocyte donors in different laboratories. Clinical CYP3A induction and in vitro data were gathered for 51 compounds, 16 of which were proprietary. A large degree of variability was observed in both the clinical and in vitro induction responses; however, analysis confirmed in vitro data are able to predict clinical induction risk. Following extensive examination of this large data set, the following recommendations are proposed. a) Cytochrome P450 induction should continue to be evaluated in three separate human donors in vitro. b) In light of empirically divergent responses in rifampicin control and most test inducers, normalization of data to percent positive control appears to be of limited benefit. c) With concentration dependence, 2-fold induction is an acceptable threshold for positive identification of in vitro CYP3A4 mRNA induction. d) To reduce the risk of false positives, in the absence of a concentration-dependent response, induction ≥ 2-fold should be observed in more than one donor to classify a compound as an in vitro inducer. e) If qualifying a compound as negative for CYP3A4 mRNA induction, the magnitude of maximal rifampicin response in that donor should be ≥ 10-fold. f) Inclusion of a negative control adds no value beyond that of the vehicle control.

Pharmacogenetics of Antiretroviral Drug Response and Pharmacokinetic Variations in Indigenous South African Populations

Interindividual and interethnic differences in response to antiretroviral drugs (ARVs) are influenced by genetic variation. The few genomic studies conducted among African-Americans and African ethnic groups do not reflect the extensive genetic diversity within African populations. ARVs are widely used in Africa. Therefore, genomic characterization of African populations is required before genotype-guided dosing becomes possible. The aim of this study was to determine and report on the frequency of genetic variants in genes implicated in metabolism and transport of ARVs in South African populations. The study comprised 48 self-reported South African Colored (SAC) and 296 self-reported Black African (BA) individuals. Allele and genotype frequency distributions for 93 variants contributing to metabolism and transport of ARVs were compared between groups, and other global populations. Fifty-three variants had significant differences in allele and genotype frequencies when comparing SAC and BA groups. Thirteen of these have strong clinical annotations, affecting efavirenz and tenofovir pharmacokinetics. This study provides a summary of the genetic variation within genes implicated in metabolism and transport of ARVs in indigenous South African populations. The observed differences between indigenous population groups, and between these groups and global populations, demonstrate that data generated from specific African populations cannot be used to infer genetic diversity within other populations on the continent. These results highlight the need for comprehensive characterization of genetic variation within indigenous African populations, and the clinical utility of these variants in ARV dosing for global precision medicine. Population pharmacogenetics is a nascent field of global health and warrants further research and education.

Physiologically Based Pharmacokinetic Modeling for Olaparib Dosing Recommendations: Bridging Formulations, Drug Interactions, and Patient Populations

We report physiologically based pharmacokinetic-modeling analyses to determine olaparib (tablet or capsule) drug-drug interactions (DDIs). Verified DDI simulations provided dose recommendations for olaparib coadministration with clinically relevant CYP3A4 modulators to eliminate potential risk to patient safety or olaparib efficacy. When olaparib is given with strong/moderate CYP3A inhibitors, the dose should be reduced to 100/150 mg b.i.d. (tablet), and 150/200 mg b.i.d. (capsule). Olaparib administration is not recommended with strong/moderate CYP3A inducers. No dose reductions are required with weak CYP3A inhibitors/inducers. Olaparib was shown to be a weak inhibitor of CYP3A (1.6-fold increase in exposure of a sensitive CYP3A probe) and to have no effect on P-glycoprotein or UGT1A1 substrates. Finally, this model was used to simulate exposure in scenarios where clinical data of olaparib are lacking, such as severe renal or hepatic impairment populations, and provided initial dosing recommendations in pediatric patients.

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.

Derivation of CYP3A4 and CYP2B6 degradation rate constants in primary human hepatocytes: A siRNA-silencing-based approach

The first-order degradation rate constant (k_deg) of cytochrome P450 (CYP) enzymes is a known source of uncertainty in the prediction of time-dependent drug-drug interactions (DDIs) in physiologically-based pharmacokinetic (PBPK) modelling. This study aimed to measure CYP k_deg using siRNA to suppress CYP expression in primary human hepatocytes followed by incubation over a time-course and tracking of protein expression and activity to observe degradation. The magnitude of gene knockdown was determined by qPCR and activity was measured by probe substrate metabolite formation and CYP2B6-Glo™ assay. Protein disappearance was determined by Western blotting. During a time-course of 96 and 60 h of incubation, over 60% and 76% mRNA knockdown was observed for CYP3A4 and CYP2B6, respectively. The k_deg of CYP3A4 and CYP2B6 protein was 0.0138 h^-1 (±0.0023) and 0.0375 h^-1 (±0.025), respectively. The k_deg derived from probe substrate metabolism activity was 0.0171 h^-1 (±0.0025) for CYP3A4 and 0.0258 h^-1 (±0.0093) for CYP2B6. The CYP3A4 k_deg values derived from protein disappearance and metabolic activity were in relatively good agreement with each other and similar to published values. This novel approach can now be used for other less well-characterised CYPs.

Development of Guanfacine Extended-Release Dosing Strategies in Children and Adolescents with ADHD Using a Physiologically Based Pharmacokinetic Model to Predict Drug-Drug Interactions with Moderate CYP3A4 Inhibitors or Inducers

BACKGROUND

Guanfacine extended-release (GXR) is an orally administered, non-stimulant treatment for children and adolescents with attention-deficit/hyperactivity disorder (ADHD) and is primarily metabolized by the 3A4 isozyme of cytochrome P450 (CYP3A4). The results of clinical pharmacokinetic (PK) studies indicate that guanfacine is sensitive to drug-drug interactions (DDIs) perpetrated by strong inhibitors and inducers of CYP3A4.

OBJECTIVE

The aim was to provide guidance on the possible requirement for GXR dose adjustment in children and adolescents with ADHD by predicting DDIs following co-administration with moderate CYP3A4 inhibitors and inducers.

METHODS

A physiologically based PK model for GXR orally administered to healthy adults was developed based on physicochemical, in vitro and clinical PK data. The model was validated using clinical PK data for co-administration of GXR with ketoconazole (strong CYP3A4 inhibitor) or rifampicin (strong CYP3A4 inducer).

RESULTS

Model predictions indicated that co-administration of GXR with the moderate CYP3A4 inhibitors erythromycin 500 mg three times a day or fluconazole 200 mg daily (q.d.) increased the guanfacine area under the plasma concentration-time curve (AUC) by 2.31-fold or 1.98-fold, respectively, compared with GXR monotherapy. The moderate CYP3A4 inducer efavirenz 400 mg or 600 mg q.d. was predicted to reduce guanfacine AUC to 58 or 33% of its value for GXR monotherapy, respectively.

CONCLUSION

Without the requirement for additional clinical studies, the following GXR dose recommendations were developed and approved for US labeling for use in children and adolescents with ADHD: (1) decrease GXR to 50% of the usual target dose when it is co-administered with strong or moderate CYP3A4 inhibitors; (2) consider titrating GXR up to double the usual target dose over 1-2 weeks when it is co-administered with strong or moderate CYP3A4 inducers.

Population Pharmacokinetic Model Linking Plasma and Peripheral Blood Mononuclear Cell Concentrations of Efavirenz and Its Metabolite, 8-Hydroxy-Efavirenz, in HIV Patients

The objectives of this study were to characterize the population pharmacokinetics (PK) of efavirenz (EFV) and 8-hydroxy-efavirenz (8OHEFV) in plasma and peripheral blood mononuclear cells (PBMCs) and to explore covariates affecting the PK parameters. Fifty-one patients had steady-state 0-to-24-h concentrations of EFV and 8OHEFV in plasma with corresponding concentrations in PBMCs, while 261 patients had one or two sparse concentrations at 16 ± 1 h postdose at weeks 4 and/or 16. The pharmacogenetic markers CYP2B6*6, CYP3A5*3, CYP3A5*6, UGT2B7*2, ABCB1 (3435C→T, 3842A→G), OATP1B1*1B, and OATP1B1*5, the presence of a rifampin-based antituberculosis (anti-TB) regimen, baseline body weight and organ function values, and demographic factors were explored as covariates. EFV concentration data were well described by a two-compartment model with first-order absorption (K_a ) and absorption lag time (A_lag) (K_a = 0.2 h^-1; A_lag = 0.83 h; central compartment clearance [CL_c/F] for CYP2B6*1/*1 = 18 liters/h, for CYP2B6*1/*6 = 14 liters/h, and for CYP2B6*6/*6 = 8.6 liters/h) and PBMCs as a peripheral compartment. EFV transfer from plasma to PBMCs was first order (CL_p/F = 32 liters/h), followed by capacity-limited return (V_max = 4,400 ng/ml/h; K_m = 710 ng/ml). Similarly, 8OHEFV displayed a first-order elimination and distribution to PBMCs, with a capacity-limited return to plasma. No covariate relationships resulted in a significant explanation of interindividual variability (IIV) on the estimated PK parameters of EFV and 8OHEFV, except for CYP2B6*6 genotypes, which were consistent with prior evidence. Both EFV and 8OHEFV accumulated to higher concentrations in PBMCs than in plasma and were well described by first-order input and Michaelis-Menten kinetics removal from PBMCs. CYP2B6*6 genotype polymorphisms were associated with decreased EFV and 8OHEFV clearance.

Analysis of the Change in the Blood Concentration-Time Profile Caused by Complex Drug-Drug Interactions in the Liver Considering the Enterohepatic Circulation: Examining Whether the Inhibition Constants for Uptake, Metabolism, and Biliary Excretion Can be Recovered by the Analyses Using Physiologically Based Pharmacokinetic Modeling

Hypothetical substrates undergoing transporter-mediated hepatic uptake, metabolism, and enterohepatic circulation with different rate-determining processes with a combination of inhibition constants (K_i) for hepatic uptake, metabolism, and biliary excretion processes were generated with a constant K_i for uptake and incorporated into a physiologically based pharmacokinetic model. Analyses of the kinetic model suggested that the fraction of substrates excreted in the bile to the total elimination by the liver (f_bile) can be estimated under certain conditions from kinetic analyses of their blood concentration-time profiles. Using the generated time profiles of substrates with and without coadministration of inhibitors, various pharmacokinetic parameters involving f_bile and K_i for the hepatic uptake, metabolism, and biliary excretion of drugs were back-calculated by fitting. Comparing parameters obtained with the original parameter sets by fitting, the K_i were found to be well estimated under the following conditions: the initial estimates for inhibition constants were relatively good, which corresponds to the case for obtaining reliable in vitro inhibition constants. In conclusion, the integration of top-down analyses with bottom-up estimates (experimental determination) of inhibition constants can be used to estimate in vivo inhibition constants and f_bile reliably.

A Physiologically-Based Pharmacokinetic Modeling Approach To Predict Drug-Drug Interactions of Sonidegib (LDE225) with Perpetrators of CYP3A in Cancer Patients

Sonidegib (Odomzo) is an orally available Smoothened inhibitor for the treatment of advanced basal cell carcinoma. Sonidegib was found to be metabolized primarily by cytochrome P450 (CYP)3A in vitro. The effect of multiple doses of the strong CYP3A perpetrators, ketoconazole (KTZ) and rifampin (RIF), on sonidegib pharmacokinetics (PK) after a single 800 mg dose in healthy subjects was therefore assessed. These data were used to verify a physiologically-based pharmacokinetic (PBPK) model developed to 1) bridge the clinical drug-drug interaction (DDI) study of sonidegib with KTZ and RIF in healthy subjects to the marketed dose (200 mg) in patients 2) predict acute (14 days) versus long-term dosing of the perpetrators with sonidegib at steady state and 3) predict the effect of moderate CYP3A perpetrators on sonidegib exposure in patients. Treatment of healthy subjects with KTZ resulted in an increased sonidegib exposure of 2.25- and 1.49-fold (area under the curve0-240h and maximal concentration respectively), and RIF decreased exposure by 72% and 54%, respectively. The model simulated the single- and/or multiple-dose PK of sonidegib (healthy subjects and patients) within ∼50% of observed values. The effect of KTZ and RIF on sonidegib in healthy subjects was also simulated well, and the predicted DDI in patients was slightly less and independent of sonidegib dose. At steady state, sonidegib was predicted to have a higher DDI magnitude with strong or moderate CYP3A perpetrators compared with a single dose. Different dosing regimens of sondigeb with the perpetrators were also simulated and provided guidance to the current dosing recommendations incorporated in the product label.