A generic framework for the physiologically-based pharmacokinetic platform qualification of PK-Sim and its application to predicting cytochrome P450 3A4-mediated drug-drug interactions

PBPK modeling: What is the role of CYP3A4 expression in the gastrointestinal tract to accurately predict first-pass metabolism?

Gastrointestinal first-pass metabolism plays an important role in bioavailability and in drug-drug interactions. Physiologically-based pharmacokinetic (PBPK) modeling is a powerful tool to integrate these processes mechanistically. However, a correct bottom-up prediction of GI first-pass metabolism is challenging and depends on various model parameters like the level of enzyme expression and the basolateral intestinal mucosa permeability (Pmucosa). This work aimed to investigate if cytochrome P450 (CYP) 3A4 expression could help predict the first-pass effect using PBPK modeling or whether additional factors like Pmucosa do play additional roles using PBPK modeling. To this end, a systematic review of the absolute CYP3A expression in the human gastrointestinal tract and liver was conducted. The resulting CYP3A4 expression profile and two previously published profiles were applied to PBPK models of seven CYP3A4 substrates (alfentanil, alprazolam, felodipine, midazolam, sildenafil, triazolam, and verapamil) built-in PK-Sim®. For each compound, it was assessed whether first-pass metabolism could be adequately predicted based on the integrated CYP3A4 expression profile alone or whether an optimization of Pmucosa was required. Evaluation criteria were the precision of the predicted interstudy bioavailabilities and area under the concentration-time curves. It was found that none of the expression profiles provided upfront an adequate description of the extent of GI metabolism and that optimization of Pmucosa as a compound-specific parameter improved the prediction of most models. Our findings indicate that a pure bottom-up prediction of gastrointestinal first-pass metabolism is currently not possible and that compound-specific features like Pmucosa must be considered as well.

Evaluation of BCRP-Related DDIs Between Methotrexate and Cyclosporin A Using Physiologically Based Pharmacokinetic Modelling

BACKGROUND AND OBJECTIVE

This study provides a physiologically based pharmacokinetic (PBPK) model-based analysis of the potential drug-drug interaction (DDI) between cyclosporin A (CsA), a breast cancer resistance protein transporter (BCRP) inhibitor, and methotrexate (MTX), a putative BCRP substrate.

METHODS

PBPK models for CsA and MTX were built using open-source tools and published data for both model building and for model verification and validation. The MTX and CsA PBPK models were evaluated for their application in simulating BCRP-related DDIs. A qualification of an introduced empirical uniform in vitro scaling factor of Ki values for transporter inhibition by CsA was conducted by using a previously developed model of rosuvastatin (sensitive index BCRP substrate), and assessing if corresponding DDI ratios were well captured.

RESULTS

Within the simulated DDI scenarios for MTX in the presence of CsA, the developed models could capture the observed changes in PK parameters as changes in the area under the curve ratios (area under the curve during DDI/area under the curve control) of 1.30 versus 1.31 observed and the DDI peak plasma concentration ratios (peak plasma concentration during DDI/peak plasma concentration control) of 1.07 versus 1.28 observed. The originally reported in vitro Ki values of CsA were scaled with the uniform qualified scaling factor for their use in the in vivo DDI simulations to correct for the low intracellular unbound fraction of the CsA effector concentration. The resulting predicted versus observed ratios of peak plasma concentration and area under the curve DDI ratios with MTX were 0.82 and 0.99, respectively, indicating adequate model accuracy and choice of a scaling factor to capture the observed DDI.

CONCLUSIONS

All models have been comprehensively documented and made publicly available as tools to support the drug development and clinical research community and further community-driven model development.

Pharmacological and biochemical insights into lead-induced hepatotoxicity: Pathway interplay and the protective effects of arbutin via the oral and intraperitoneal routes in silico and in vivo

INTRODUCTION

Lead acetate (PbAc), a hazardous heavy metal, poses significant threats to human health and the environment because of widespread industrial exposure. PbAc exposure leads to liver injury primarily through oxidative stress and the disruption of key regulatory pathways. Understanding these mechanisms and exploring protective agents are vital for mitigating PbAc-induced hepatotoxicity. Therefore, we aimed to investigate the molecular pathways implicated in PbAc-induced liver damage, focusing on Sirt-1, Nrf2 (HO-1, NQO1, and SOD), Akt-1/GSK3β, m-TOR, and P53. Additionally, we aimed to assess the hepatoprotective effects of arbutin, which is administered orally and intraperitoneally, to determine the most effective delivery method.

METHODOLOGY

In silico analyses were conducted to identify relevant protein networks associated with Sirt-1 and AKT-1/GSK-3B pathways. The pharmacodynamic properties of arbutin were examined, followed by molecular docking studies to elucidate its interactions with the selected protein network. In vivo preclinical studies were carried out on adult male rats randomly assigned to 6 different treatment groups, including PbAc exposure and PbAc exposure treated with arbutin either orally or intraperitoneally.

RESULTS

PbAc exposure led to hepatic oxidative stress, as evidenced by elevated MDA levels and SIRT-1 inhibition, disrupting antioxidant pathways and activating antiautophagic and proapoptotic pathways, ultimately resulting in hepatocyte necrosis. Both oral and intraperitoneal arbutin administration effectively modifed these effects, with intraperitoneal delivery showing superior efficacy in mitigating PbAc-induced histological, immunological, and biochemical alterations.

CONCLUSION

This study provides insights into the molecular mechanisms underlying PbAc-induced liver injury and highlights the hepatoprotective potential of arbutin. These findings suggest that arbutin, particularly when administered intraperitoneally, holds promise as a therapeutic agent for combating PbAc-induced hepatotoxicity.

Model-Based Dose Selection of a Sphingosine-1-Phosphate Modulator, Etrasimod, in Patients with Various Degrees of Hepatic Impairment

BACKGROUND/OBJECTIVES

Etrasimod is a newly FDA-approved Sphingosine-1-Phosphate modulator indicated for moderate and severe ulcerative colitis. It is extensively metabolized in the liver via the cytochrome P450 system and may accumulate markedly in patients with hepatic dysfunction, exposing them to toxicity. The aim of the current study is to utilize a physiologically-based pharmacokinetic modeling approach to evaluate the impact of hepatic impairment on the pharmacokinetic behavior of etrasimod and to appropriately select dosage regimens for patients with chronic liver disease; Methods: PK-Sim was used to develop the etrasimod PBPK model, which was verified using clinical data from healthy subjects and subsequently adapted to reflect the physiological changes associated with varying degrees of hepatic dysfunction; Results: Simulations indicated that hepatic clearance of etrasimod is clearly reduced in patients with Child-Pugh B and C liver impairment. Based on these findings, dosing adjustments were proposed to achieve therapeutic exposures equivalent to those in individuals with normal liver function. In the Child-Pugh B and C population groups, 75% and 62.5%, respectively, of the standard dose were enough to have comparable exposure to the healthy population. These adjusted dosages aim to mitigate the risk of drug toxicity while maintaining efficacy; Conclusions: The PBPK model provides a robust framework for individualizing drug therapy in patients with hepatic impairment, ensuring safer and more effective treatment outcomes. Further clinical studies are warranted to verify these dosing recommendations and to refine the model for broader clinical applications.

Assessment of the CYP3A4 Induction Potential by Carbamazepine: Insights from Two Clinical DDI Studies and PBPK Modeling

In the past, rifampicin was well-established as strong index CYP3A inducer in clinical drug-drug interaction (DDI) studies. However, due to identified potentially genotoxic nitrosamine impurities, it should not any longer be used in healthy volunteer studies. Available clinical data suggest carbamazepine as an alternative to rifampicin as strong index CYP3A4 inducer in clinical DDI studies. Further, physiologically-based pharmacokinetic (PBPK) modeling is a tool with increasing importance to support the DDI risk assessment of drugs during drug development. CYP3A4 induction properties and the safety profile of carbamazepine were investigated in two open-label, fixed sequence, crossover clinical pharmacology studies in healthy volunteers using midazolam as a sensitive index CYP3A4 substrate. Carbamazepine was up-titrated from 100 mg twice daily (b.i.d.) to 200 mg b.i.d., and to a final dose of 300 mg b.i.d. for 10 consecutive days. Mean area under plasma concentration-time curve from zero to infinity (AUC(0-∞)) of midazolam consistently decreased by 71.8% (ratio: 0.282, 90% confidence interval (CI): 0.235-0.340) and 67.7% (ratio: 0.323, 90% CI: 0.256-0.407) in study 1 and study 2, respectively. The effect was adequately described by an internally developed PBPK model for carbamazepine which has been made freely available to the scientific community. Further, carbamazepine was safe and well-tolerated in the investigated dosing regimen in healthy participants. The results demonstrated that the presented design is appropriate for the use of carbamazepine as alternative inducer to rifampicin in DDI studies acknowledging its CYP3A4 inductive potency and safety profile.

Physiologically based pharmacokinetic modeling of ritonavir-oxycodone drug interactions and its implication for dosing strategy

The concomitant administration of ritonavir and oxycodone may significantly increase the plasma concentrations of oxycodone. This study was aimed to simulate DDI between ritonavir and oxycodone, a widely used opioid, and to formulate dosing protocols for oxycodone by using physiologically based pharmacokinetic (PBPK) modeling. We developed a ritonavir PBPK model incorporating induction and competitive and time-dependent inhibition of CYP3A4 and competitive inhibition of CYP2D6. The ritonavir model was evaluated with observed clinical pharmacokinetic data and validated for its CYP3A4 inhibition potency. We then used the model to simulate drug interactions between oxycodone and ritonavir under various dosing scenarios. The developed model captured the pharmacokinetic characteristics of ritonavir from clinical studies. The model also accurately predicts exposure changes of midazolam, triazolam, and oxycodone in the presence of ritonavir. According to model simulations, the steady-state maximum, minimum and average concentrations of oxycodone increased by up to 166% after co-administration with ritonavir, and the total exposure increased by approximately 120%. To achieve similar steady-state concentrations, halving the dose with an unchanged dosing interval or doubling the dosing interval with an unaltered single dose should be practical for oxycodone, whether formulated in uncoated or controlled-release tablets during long-term co-medication with ritonavir. The results revealed exposure-related risks of oxycodone-ritonavir interactions that have not been studied clinically and emphasized PBPK as a workable method to direct judicious dosage.

Applied physiologically-based pharmacokinetic modeling to assess uridine diphosphate-glucuronosyltransferase-mediated drug-drug interactions for Vericiguat

Vericiguat (Verquvo; US: Merck, other countries: Bayer) is a novel drug for the treatment of chronic heart failure. Preclinical studies have demonstrated that the primary route of metabolism for vericiguat is glucuronidation, mainly catalyzed by uridine diphosphate-glucuronosyltransferase (UGT)1A9 and to a lesser extent UGT1A1. Whereas a drug-drug interaction (DDI) study of the UGT1A9 inhibitor mefenamic acid showed a 20% exposure increase, the effect of UGT1A1 inhibitors has not been assessed clinically. This modeling study describes a physiologically-based pharmacokinetic (PBPK) approach to complement the clinical DDI liability assessment and support prescription labeling. A PBPK model of vericiguat was developed based on in vitro and clinical data, verified against data from the mefenamic acid DDI study, and applied to assess the UGT1A1 DDI liability by running an in silico DDI study with the UGT1A1 inhibitor atazanavir. A minor effect with an area under the plasma concentration-time curve (AUC) ratio of 1.12 and a peak plasma concentration ratio of 1.04 was predicted, which indicates that there is no clinically relevant DDI interaction anticipated. Additionally, the effect of potential genetic polymorphisms of UGT1A1 and UGT1A9 was evaluated, which showed that an average modest increase of up to 1.7-fold in AUC may be expected in the case of concomitantly reduced UGT1A1 and UGT1A9 activity for subpopulations expressing non-wild-type variants for both isoforms. This study is a first cornerstone to qualify the PK-Sim platform for use of UGT-mediated DDI predictions, including PBPK models of perpetrators, such as mefenamic acid and atazanavir, and sensitive UGT substrates, such as dapagliflozin and raltegravir.

The Pharmacokinetics of the Nonsteroidal Mineralocorticoid Receptor Antagonist Finerenone

Finerenone, a selective and nonsteroidal antagonist of the mineralocorticoid receptor, has received regulatory approval with the indication of cardiorenal protection in patients with chronic kidney disease associated with type 2 diabetes. It is rapidly and completely absorbed and undergoes first-pass metabolism in the gut wall and liver resulting in a bioavailability of 43.5%. Finerenone can be taken with or without food. The pharmacokinetics of finerenone are linear and its half-life is 2 to 3 h in the dose range of up to 20 mg. Cytochrome P450 (CYP) 3A4 (90%) and CYP2C8 (10%) are involved in the extensive biotransformation of finerenone to pharmacologically inactive metabolites, which are excreted via both renal (80%) and biliary (20%) routes. Moderate or severe renal impairment, or moderate hepatic impairment result in area-under-the-curve increases of finerenone (< 40%), which do not require a dose adjustment per se, as the starting dose is based on estimated glomerular filtration rate (eGFR) and titrated according to serum potassium levels and eGFR decline. No relevant effects of age, sex, body size or ethnicity on systemic finerenone exposure were identified. Modulators of CYP3A4 activity were found to affect finerenone exposure, consistent with its classification as a sensitive CYP3A4 substrate. Serum potassium should be monitored during drug initiation or dosage adjustment of either a moderate or weak CYP3A4 inhibitor or finerenone, and the dose of finerenone should be adjusted as appropriate. Its use with strong inhibitors is contraindicated and strong or moderate inducers of CYP3A4 should be avoided. Finerenone has no potential to affect relevant CYP enzymes and drug transporters.

Physiologically Based Pharmacokinetic Modeling to Simulate CYP3A4-Mediated Drug-Drug Interactions for Pyrotinib

INTRODUCTION

Pyrotinib is a newly developed tyrosine kinase inhibitor whose in vivo clearance relies heavily on cytochrome P450 3A4 (CYP3A4) activity. Clinical trials are ongoing to explore the effects of coadministration with CYP3A4 perpetrators on pyrotinib exposure. The present study aims to utilize physiologically based pharmacokinetic (PBPK) modeling to predict CYP3A4-based drug interactions of pyrotinib.

METHODS

Pyrotinib PBPK model was developed in the PK-Sim® multicompartmental physiology structure. Physiochemical parameters were obtained from the literature, and clearance-related parameters were optimized by fitting clinical single-dose pharmacokinetic data. Pharmacokinetic parameters from the model output were compared with the observed data to validate the model predictive performance. Using validated CYP3A4 perpetrator models, we conducted PBPK simulations for drug interactions in a virtual population to explore the impacts of comedication with these perpetrators.

RESULTS

The PBPK model accurately describes pyrotinib single- and multi-dose pharmacokinetics. The model also predicts dramatic exposure change of pyrotinib in the presence of itraconazole and rifampicin, though the impact of rifampicin is somewhat underestimated. According to model predictions, coadministration with typical potent or moderate CYP3A4 perpetrators increases pyrotinib concentration by over sixfold, extinguishing the possibility of dose adjustment for pyrotinib. A weak CYP3A4 inhibitor has minimal influence on pyrotinib pharmacokinetics.

CONCLUSION

PBPK modeling provides valuable information to avoid irrational medication when receiving pyrotinib chemotherapy.

Physiologically Based Pharmacokinetic Modeling of Extracellular Vesicles

Extracellular vesicles (EVs) are lipid membrane bound-cell-derived structures that are a key player in intercellular communication and facilitate numerous cellular functions such as tumor growth, metastasis, immunosuppression, and angiogenesis. They can be used as a drug delivery platform because they can protect drugs from degradation and target specific cells or tissues. With the advancement in the technologies and methods in EV research, EV-therapeutics are one of the fast-growing domains in the human health sector. Therapeutic translation of EVs in clinics requires assessing the quality, safety, and efficacy of the EVs, in which pharmacokinetics is very crucial. We report here the application of physiologically based pharmacokinetic (PBPK) modeling as a principal tool for the prediction of absorption, distribution, metabolism, and excretion of EVs. To create a PBPK model of EVs, researchers would need to gather data on the size, shape, and composition of the EVs, as well as the physiological processes that affect their behavior in the body. The PBPK model would then be used to predict the pharmacokinetics of drugs delivered via EVs, such as the rate at which the drug is absorbed and distributed throughout the body, the rate at which it is metabolized and eliminated, and the maximum concentration of the drug in the body. This information can be used to optimize the design of EV-based drug delivery systems, including the size and composition of the EVs, the route of administration, and the dose of the drug. There has not been any dedicated review article that describes the PBPK modeling of EV. This review provides an overview of the absorption, distribution, metabolism, and excretion (ADME) phenomena of EVs. In addition, we will briefly describe the different computer-based modeling approaches that may help in the future of EV-based therapeutic research.

Modelling Based Approaches to Support Generic Drug Regulatory Submissions-Practical Considerations and Case Studies

Model informed drug development (MiDD) is useful to predict in vivo exposure of drugs during various stages of the drug development process. This approach employs a variety of quantitative tools to assess the risks during the drug development process. One important tool in the MiDD tool kit is the Physiologically Based Pharmacokinetic Modelling (PBPK). This tool is extensively used to reduce the development cost and to accelerate the access of medicines to the patients. In this work, we provide an overview of PBPK modelling approaches in the generic drug development process, with a special emphasis on the bio-waiver applications. We describe herein approaches and common pitfalls while submitting model based justifications as a response to the regulatory deficiencies during the generic drug development process. With some in-house case studies, we have attempted to provide a clear path for PBPK model based justifications for bio-waivers. With this review, the gap between theoretical knowledge and practical application of modelling and simulation tools for generic drug product development could be potentially reduced.

Another Step Toward Qualification of Pediatric Physiologically Based Pharmacokinetic Models to Facilitate Inclusivity and Diversity in Pediatric Clinical Studies

Robust prediction of pharmacokinetics (PKs) in pediatric subjects of diverse ages, ethnicities, and morbidities is critical. Qualification of pediatric physiologically-based pharmacokinetic (P-PBPK) models is an essential step toward enabling precision dosing of these vulnerable groups. Twenty-two manuscripts involving P-PBPK predictions and corresponding observed PK data (e.g., area under the curve and clearance) for 22 small-molecule compounds metabolized by CYP (3A4, 1A2, and 2C9), UGT (1A9 and 2B7), FMO3, renal, non-renal, and complex routes were identified; ratios of mean predicted/observed (P/O) PK parameters were calculated. Seventy-eight of 115 mean predicted PK parameters were within 0.8 to 1.25-fold of observed data, 98 within 0.67 to 1.5-fold, 109 within 2-fold, and only 6 P/O ratios were outside of these bounds. A set of 12 CYP3A4-metabolized compounds and a set of 6 metabolized by other enzymes, CYP1A2 (1 compound), CYP2C9 (2 compounds), UGT1A9 (1 compound) and UGT2B7 (2 compounds) had 56 of 59 and 22 of 25 mean P/O ratios, respectively, that fell within the > 0.5 and < 2.0-fold boundaries. For compounds covering renal, non-renal, complex, and FM03 routes of elimination, 29 of 31 mean P/O ratios fell within the 0.67 to 1.5-fold bounds, including 4 of 5 P/O ratios from newborns. P-PBPK modeling and simulation is a strategic component of the complement of precision dosing methods and has a vital role to play in dose adjustment in vulnerable pediatric populations, such as those with disease or in different ethnic groups. Qualification of such models is an essential step toward acceptance of this methodology by regulators.

Can Mechanistic Static Models for Drug-Drug Interactions Support Regulatory Filing for Study Waivers and Label Recommendations?

BACKGROUND AND OBJECTIVE

Mechanistic static and dynamic physiologically based pharmacokinetic models are used in clinical drug development to assess the risk of drug-drug interactions (DDIs). Currently, the use of mechanistic static models is restricted to screening DDI risk for an investigational drug, while dynamic physiologically based pharmacokinetic models are used for quantitative predictions of DDIs to support regulatory filing. As physiologically based pharmacokinetic model development by sponsors as well as a review of models by regulators require considerable resources, we explored the possibility of using mechanistic static models to support regulatory filing, using representative cases of successful physiologically based pharmacokinetic submissions to the US Food and Drug Administration under different classes of applications.

METHODS

Drug-drug interaction predictions with mechanistic static models were done for representative cases in the different classes of applications using the same data and modelling workflow as described in the Food and Drug Administration clinical pharmacology reviews. We investigated the hypothesis that the use of unbound average steady-state concentrations of modulators as driver concentrations in the mechanistic static models should lead to the same conclusions as those from physiologically based pharmacokinetic modelling for non-dynamic measures of DDI risk assessment such as the area under the plasma concentration-time curve ratio, provided the same input data are employed for the interacting drugs.

RESULTS

Drug-drug interaction predictions of area under the plasma concentration-time curve ratios using mechanistic static models were mostly comparable to those reported in the Food and Drug Administration reviews using physiologically based pharmacokinetic models for all representative cases in the different classes of applications.

CONCLUSIONS

The results reported in this study should encourage the use of models that best fit an intended purpose, limiting the use of physiologically based pharmacokinetic models to those applications that leverage its unique strengths, such as what-if scenario testing to understand the effect of dose staggering, evaluating the role of uptake and efflux transporters, extrapolating DDI effects from studied to unstudied populations, or assessing the impact of DDIs on the exposure of a victim drug with concurrent mechanisms. With this first step, we hope to trigger a scientific discussion on the value of a routine comparison of the two methods for regulatory submissions to potentially create a best practice that could help identify examples where the use of dynamic changes in modulator concentrations could make a difference to DDI risk assessment.

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.

Quality Assurance of PBPK Modeling Platforms and Guidance on Building, Evaluating, Verifying and Applying PBPK Models Prudently under the Umbrella of Qualification: Why, When, What, How and By Whom?

Modeling and simulation emerges as a fundamental asset of drug development. Mechanistic modeling builds upon its strength to integrate various data to represent a detailed structural knowledge of a physiological and biological system and is capable of informing numerous drug development and regulatory decisions via extrapolations outside clinically studied scenarios. Herein, physiologically based pharmacokinetic (PBPK) modeling is the fastest growing branch, and its use for particular applications is already expected or explicitly recommended by regulatory agencies. Therefore, appropriate applications of PBPK necessitates trust in the predictive capability of the tool, the underlying software platform, and related models. That has triggered a discussion on concepts of ensuring credibility of model-based derived conclusions. Questions like 'why', 'when', 'what', 'how' and 'by whom' remain open. We seek for harmonization of recent ideas, perceptions, and related terminology. First, we provide an overview on quality assurance of PBPK platforms with the two following concepts. Platform validation: ensuring software integrity, security, traceability, correctness of mathematical models and accuracy of algorithms. Platform qualification: demonstrating the predictive capability of a PBPK platform within a particular context of use. Second, we provide guidance on executing dedicated PBPK studies. A step-by-step framework focuses on the definition of the question of interest, the context of use, the assessment of impact and risk, the definition of the modeling strategy, the evaluation of the platform, performing model development including model building, evaluation and verification, the evaluation of applicability to address the question, and the model application under the umbrella of a qualified platform.

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.

Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective

Several regulatory guidances on the use of physiologically based pharmacokinetic (PBPK) analyses and physiologically based biopharmaceutics model(s) (PBBM(s)) have been issued. Workshops are routinely held, demonstrating substantial interest in applying these modeling approaches to address scientific questions in drug development. PBPK models and PBBMs have remarkably contributed to model-informed drug development (MIDD) such as anticipating clinical PK outcomes affected by extrinsic and intrinsic factors in general and specific populations. In this review, we proposed practical considerations for a "base" PBPK model construction and development, summarized current status, challenges including model validation and gaps in system models, and future perspectives in PBPK evaluation to assess a) drug metabolizing enzyme(s)- or drug transporter(s)- mediated drug-drug interactions b) dosing regimen prediction, sampling timepoint selection and dose validation in pediatric patients from newborns to adolescents, c) drug exposure in patients with renal and/or and hepatic organ impairment, d) maternal-fetal drug disposition during pregnancy, and e) pH-mediated drug-drug interactions in patients treated with proton pump inhibitors/acid-reducing agents (PPIs/ARAs) intended for gastric protection. Since PBPK can simulate outcomes in clinical studies with enrollment challenges or ethical issues, the impact of PBPK models on waivers and how to strengthen study waiver is discussed.

Physiologically-based pharmacokinetic modeling to predict CYP3A4-mediated drug-drug interactions of finerenone

Finerenone is a nonsteroidal, selective mineralocorticoid receptor antagonist that recently demonstrated its efficacy to delay chronic kidney disease (CKD) progression and reduce cardiovascular events in patients with CKD and type 2 diabetes. Here, we report the development of a physiologically‐based pharmacokinetic (PBPK) model for finerenone and its application as a victim drug of cytochrome P450 3A4 (CYP3A4)‐mediated drug‐drug interactions (DDIs) using the open‐source PBPK platform PK‐Sim, which has recently been qualified for this application purpose. First, the PBPK model for finerenone was developed using physicochemical, in vitro, and clinical (including mass balance) data. Subsequently, the finerenone model was validated regarding the contribution of CYP3A4 metabolism to total clearance by comparing to observed data from dedicated clinical interaction studies with erythromycin (simulated geometric mean ratios of the area under the plasma concentration‐time curve [AUCR] of 3.46 and geometric mean peak plasma concentration ratios [C_maxRs] of 2.00 vs. observed of 3.48 and 1.88, respectively) and verapamil (simulated AUCR of 2.91 and C_maxR of 1.86 vs. observed of 2.70 and 2.22, respectively). Finally, the finerenone model was applied to predict clinically untested DDI studies with various CYP3A4 modulators. An AUCR of 6.31 and a C_maxR of 2.37 was predicted with itraconazole, of 5.28 and 2.25 with clarithromycin, 1.59 and 1.40 with cimetidine, 1.57 and 1.38 with fluvoxamine, 0.19 and 0.32 with efavirenz, and 0.07 and 0.14 with rifampicin. This PBPK analysis provides a quantitative basis to guide the label and clinical use of finerenone with concomitant CYP3A4 modulators.

Novel models for the prediction of drug-gene interactions

INTRODUCTION

Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.

AREAS COVERED

Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.

EXPERT OPINION

Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.