Physiologically based pharmacokinetic modeling in regulatory decision-making at the European Medicines Agency

Dose Optimization Informed by PBPK Modeling: State-of-the Art and Future

Model-informed drug development (MIDD) is a powerful quantitative approach that plays an integral role in drug development and regulatory review. While applied throughout the life cycle of the development of new drugs, a key application of MIDD is to inform clinical trial design including dose selection and optimization. To date, physiologically-based pharmacokinetic (PBPK) modeling, an established component of the MIDD toolkit, has mainly been used for assessment of drug-drug interactions (DDIs) and consequential dose adjustments in regulatory submissions. As a result of recent scientific advances and growing confidence in the utility of the approach, PBPK models are being increasingly applied to provide dose recommendations for subjects with differing ages, genetics, and disease states. In this review, we present our perspective on the current landscape of regulatory acceptance of PBPK applications supported by relevant case studies. We also discuss the recent progress and future challenges associated with expanding the utility of PBPK models into emerging areas for regulatory decision making, especially dose optimization in highly vulnerable and understudied populations and facilitating diversity in clinical trials.

Effect of Obesity on the Exposure of Long-acting Cabotegravir and Rilpivirine: A Modeling Study

BACKGROUND

Obesity is increasingly prevalent among people with human immunodeficiency virus (HIV, PWH). Obesity can reduce drug exposure; however, limited data are available for long-acting (LA) antiretrovirals. We performed in silico trials using physiologically based pharmacokinetic (PBPK) modeling to determine the effect of obesity on the exposure of LA cabotegravir and rilpivirine after the initial injection and after multiple injections.

METHODS

Our PBPK model was verified against available clinical data for LA cabotegravir and rilpivirine in normal weight/ overweight (body mass index [BMI] <30 kg/m2) and in obese (BMI >30 kg/m2). Cohorts of virtual individuals were generated to simulate the exposure of LA cabotegravir/rilpivirine up to a BMI of 60 kg/m2. The fold change in LA cabotegravir and rilpivirine exposures (area under the curve [AUC]) and trough concentrations (Cmin) for monthly and bimonthly administration were calculated for various BMI categories relative to normal weight (18.5-25 kg/m2).

RESULTS

Obesity was predicted to impact more cabotegravir than rilpivirine with a decrease in cabotegravir AUC and Cmin of >35% for BMI >35 kg/m2 and in rilpivirine AUC and Cmin of >18% for BMI >40 kg/m2 at steady-state. A significant proportion of morbidly obese individuals were predicted to have both cabotegravir and rilpivirine Cmin below the target concentration at steady-state with the bimonthly administration, but this was less frequent with the monthly administration.

CONCLUSIONS

Morbidly obese PWH are at risk of presenting suboptimal Cmin for cabotegravir/rilpivirine after the first injection but also at steady-state particularly with the bimonthly administration. Therapeutic drug monitoring is advised to guide dosing interval adjustment.

Physiologically based pharmacokinetic modeling to predict the clinical effect of azole antifungal agents as CYP3A inhibitors on azelnidipine pharmacokinetics

In this study, a physiologically based pharmacokinetic (PBPK) model of the cytochrome P450 3A (CYP3A) substrate azelnidipine was developed using in vitro and clinical data to predict the effects of azole antifungals on azelnidipine pharmacokinetics. Modeling and simulations were conducted using the Simcyp™ PBPK simulator. The azelnidipine model consisted of a full PBPK model and a first-order absorption model. CYP3A was assumed as the only azelnidipine elimination route, and CYP3A clearance was optimized using the pharmacokinetic profile of single-dose 5-mg azelnidipine in healthy participants. The model reproduced the results of a clinical drug-drug interaction study and met validation criteria. PBPK model simulations using azole antifungals (itraconazole, voriconazole, posaconazole, fluconazole, fosfluconazole) and azelnidipine or midazolam (CYP3A index substrate) were performed. Increases in the simulated area under the plasma concentration-time curve from time zero extrapolated to infinity with inhibitors were comparable between azelnidipine (range, 2.11-6.47) and midazolam (range, 2.26-9.22), demonstrating that azelnidipine is a sensitive CYP3A substrate. Increased azelnidipine plasma concentrations are expected when co-administered with azole antifungals, potentially affecting azelnidipine safety. These findings support the avoidance of azole antifungals in patients taking azelnidipine and demonstrate the utility of PBPK modeling to inform appropriate drug use.

Drug Exposure of Long-Acting Cabotegravir and Rilpivirine in Older People With Human Immunodeficiency Virus: A Pharmacokinetic Modeling Study

Background

The life expectancy of people with human immunodeficiency virus (PWH) has significantly increased, thanks to combined antiretrovirals with improved potency and tolerability. One further step has been achieved with the development of long-acting (LA) injectable antiretrovirals, which allow for infrequent dosing. However, the pharmacokinetics of LA antiretrovirals has been poorly characterized in older PWH, as they are generally excluded from trials. We performed virtual studies using physiologically based pharmacokinetic (PBPK) modeling to determine the anticipated exposure of LA cabotegravir/rilpivirine in older individuals.

Methods

Our PBPK model was verified against available observed data for LA cabotegravir and rilpivirine. Cohorts of virtual individuals aged 20-50, 50-65, or 65-85 years were generated to simulate the exposure of LA cabotegravir/rilpivirine for each age group. The fold changes in trough concentration (Cmin) and in drug exposure (area under the time-concentration curve [AUC]) were determined for older relative to young individuals.

Results

The verified PBPK models predicted an increase in exposure within the 0.8-1.25 fold range for monthly LA cabotegravir/rilpivirine. The Cmin and AUC were predicted to be 29% and 26% higher in older compared with young adults for LA cabotegravir administered bimonthly (every 2 months) and 46% and 41% higher for LA rilpivirine bimonthly. The Cmin and AUC of LA cabotegravir and rilpivirine were predicted to be modestly increased in female compared with male individuals for all age groups.

Conclusions

LA cabotegravir/rilpivirine exposure and trough concentrations are predicted to be higher in older than in young PWH; thus, older adults could have a lower risk to present suboptimal concentrations during the dosing interval.

Evaluation of the drug-drug interaction potential of brigatinib using a physiologically-based pharmacokinetic modeling approach

Brigatinib is an oral anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK-positive metastatic non-small cell lung cancer. In vitro studies indicated that brigatinib is primarily metabolized by CYP2C8 and CYP3A4 and inhibits P-gp, BCRP, OCT1, MATE1, and MATE2K. Clinical drug-drug interaction (DDI) studies with the strong CYP3A inhibitor itraconazole or the strong CYP3A inducer rifampin demonstrated that CYP3A-mediated metabolism was the primary contributor to overall brigatinib clearance in humans. A physiologically-based pharmacokinetic (PBPK) model for brigatinib was developed to predict potential DDIs, including the effect of moderate CYP3A inhibitors or inducers on brigatinib pharmacokinetics (PK) and the effect of brigatinib on the PK of transporter substrates. The developed model was able to predict clinical DDIs with itraconazole (area under the plasma concentration-time curve from time 0 to infinity [AUC∞] ratio [with/without itraconazole]: predicted 1.86; observed 2.01) and rifampin (AUC∞ ratio [with/without rifampin]: predicted 0.16; observed 0.20). Simulations using the developed model predicted that moderate CYP3A inhibitors (e.g., verapamil and diltiazem) may increase brigatinib AUC∞ by ~40%, whereas moderate CYP3A inducers (e.g., efavirenz) may decrease brigatinib AUC∞ by ~50%. Simulations of potential transporter-mediated DDIs predicted that brigatinib may increase systemic exposures (AUC∞) of P-gp substrates (e.g., digoxin and dabigatran) by 15%-43% and MATE1 substrates (e.g., metformin) by up to 29%; however, negligible effects were predicted on BCRP-mediated efflux and OCT1-mediated uptake. The PBPK analysis results informed dosing recommendations for patients receiving moderate CYP3A inhibitors (40% brigatinib dose reduction) or inducers (up to 100% increase in brigatinib dose) during treatment, as reflected in the brigatinib prescribing information.

Towards More Robust Evaluation of the Predictive Performance of Physiologically Based Pharmacokinetic Models: Using Confidence Intervals to Support Use of Model-Informed Dosing in Clinical Care

BACKGROUND AND OBJECTIVE

With the rise in the use of physiologically based pharmacokinetic (PBPK) modeling over the past decade, the use of PBPK modeling to underpin drug dosing for off-label use in clinical care has become an attractive option. In order to use PBPK models for high-impact decisions, thorough qualification and validation of the model is essential to gain enough confidence in model performance. Currently, there is no agreed method for model acceptance, while clinicians demand a clear measure of model performance before considering implementing PBPK model-informed dosing. We aim to bridge this gap and propose the use of a confidence interval for the predicted-to-observed geometric mean ratio with predefined boundaries. This approach is similar to currently accepted bioequivalence testing procedures and can aid in improved model credibility and acceptance.

METHODS

Two different methods to construct a confidence interval are outlined, depending on whether individual observations or aggregate data are available from the clinical comparator data sets. The two testing procedures are demonstrated for an example evaluation of a midazolam PBPK model. In addition, a simulation study is performed to demonstrate the difference between the twofold criterion and our proposed method.

RESULTS

Using midazolam adult pharmacokinetic data, we demonstrated that creating a confidence interval yields more robust evaluation of the model than a point estimate, such as the commonly used twofold acceptance criterion. Additionally, we showed that the use of individual predictions can reduce the number of required test subjects. Furthermore, an easy-to-implement software tool was developed and is provided to make our proposed method more accessible.

CONCLUSIONS

With this method, we aim to provide a tool to further increase confidence in PBPK model performance and facilitate its use for directly informing drug dosing in clinical care.

Physiologically-based pharmacokinetic model of sparsentan to evaluate drug-drug interaction potential

Sparsentan is a dual endothelin/angiotensin II receptor antagonist indicated to reduce proteinuria in patients with primary IgA nephropathy at high risk of disease progression. In vitro data indicate that sparsentan is likely to inhibit or induce various CYP enzymes at therapeutic concentrations. Sparsentan as a victim and perpetrator of CYP3A4 mediated drug-drug interactions (DDIs) has been assessed clinically. A mechanistic, bottom-up, physiologically-based pharmacokinetic (PK) model for sparsentan was developed based on in vitro data of drug solubility, formulation dissolution and particle size, drug permeability, inhibition and induction of metabolic enzymes, and P-glycoprotein (P-gp) driven efflux. The model was verified using clinical PK data from healthy adult volunteers administered single and multiple doses in the fasted and fed states for a wide range of sparsentan doses. The model was also verified by simulation of clinically observed DDIs. The verified model was then used to test various DDI simulations of sparsentan as a perpetrator and victim of CYP3A4 using an expanded set of inducers and inhibitors with varying potency. Additional perpetrator and victim DDI simulations were performed using probes for CYP2C9 and CYP2C19. Simulations were conducted to predict the effect of complete inhibition of P-gp inhibition on sparsentan absorption and clearance. The predictive simulations indicated that exposure of sparsentan could increase greater than two-fold if co-administered with a strong CYP3A4 inhibitor, such as itraconazole. Other potential DDI interactions as victim or perpetrator were all within two-fold of control. The effect of complete P-gp inhibition on sparsentan PK was negligible.

Role of Physiologically Based Biopharmaceutics Modeling (PBBM) in Fed Bioequivalence Study Waivers: Regulatory Outlook, Case Studies and Future Perspectives

Over the past few decades, physiologically based biopharmaceutics modeling (PBBM) has demonstrated its utility in both new drug and generic product development. Applications of PBBM for fed bioequivalence study waivers is an upcoming area. Recently Innovation & Quality (IQ) consortium demonstrated utility of PBBM to avoid repeat food effect studies for new drugs. In the similar lines, the current manuscript aims to discuss role of PBBM in generic fed bioequivalence study waivers. Generic industry practices related to PBBM model development to predict fed bioequivalence was portrayed with special emphasis on fed bio-predictive media. Media that can simulate fed bioequivalence study outcome were discussed from practical perspective. In-depth analysis, collating the data from 36 products was performed to understand predictability of PBBM for fed bioequivalence. Cases where PBBM was successful to predict fed bioequivalence was correlated with BCS class, formulation category and type of food effect. Further, two case studies were presented wherein fed bioequivalence study waiver obtained with PBBM approach. Lastly, future direction in terms of fed bioequivalence study waivers, regulatory perspectives and best practices for PBBM were portrayed. Overall, this article paves a way to utilize PBBM for generic fed bioequivalence study waivers.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.

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.

In Silico Clinical Trials: Is It Possible?

Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.

The Interplay of Permeability, Metabolism, Transporters, and Dosing in Determining the Dynamics of the Tissue/Plasma Partition Coefficient and Volume of Distribution-A Theoretical Investigation Using Permeability-Limited, Physiologically Based Pharmacokinetic Modeling

A permeability-limited physiologically based pharmacokinetic (PBPK) model featuring four subcompartments (corresponding to the intracellular and extracellular water of the tissue, the residual plasma, and blood cells) for each tissue has been developed in MATLAB/SimBiology and applied to various what-if scenario simulations. This model allowed us to explore the complex interplay of passive permeability, metabolism in tissue or residual blood, active uptake or efflux transporters, and different dosing routes (intravenous (IV) or oral (PO)) in determining the dynamics of the tissue/plasma partition coefficient (Kp) and volume of distribution (Vd) within a realistic pseudo-steady state. Based on the modeling exercise, the permeability, metabolism, and transporters demonstrated significant effects on the dynamics of the Kp and Vd for IV bolus administration and PO fast absorption, but these effects were not as pronounced for IV infusion or PO slow absorption. Especially for low-permeability compounds, uptake transporters were found to increase both the Kp and Vd at the pseudo-steady state (Vdss), while efflux transporters had the opposite effect of decreasing the Kp and Vdss. For IV bolus administration and PO fast absorption, increasing tissue metabolism was predicted to elevate the Kp and Vdss, which contrasted with the traditional derivation from the steady-state perfusion-limited PBPK model. Moreover, metabolism in the residual blood had more impact on the Kp and Vdss compared to metabolism in tissue. Due to its ability to offer a more realistic description of tissue dynamics, the permeability-limited PBPK model is expected to gain broader acceptance in describing clinical PK and observed Kp and Vdss, even for certain small molecules like cyclosporine, which are currently treated as perfusion-limited in commercial PBPK platforms.

Development and application of a PBPK modeling strategy to support antimalarial drug development

As part of a collaboration between Medicines for Malaria Venture (MMV), Certara UK and Monash University, physiologically-based pharmacokinetic (PBPK) models were developed for 20 antimalarials, using data obtained from standardized in vitro assays and clinical studies within the literature. The models have been applied within antimalarial drug development at MMV for more than 5 years. During this time, a strategy for their impactful use has evolved. All models are described in the supplementary material and are available to researchers. Case studies are also presented, demonstrating real-world development and clinical applications, including the assessment of the drug-drug interaction liability between combination partners or with co-administered drugs. This work emphasizes the benefit of PBPK modeling for antimalarial drug development and decision making, and presents a strategy to integrate it into the research and development process. It also provides a repository of shared information to benefit the global health research community.

Physiologically-based pharmacokinetic modeling for single and multiple dosing regimens of ceftriaxone in healthy and chronic kidney disease populations: a tool for model-informed precision dosing

Introduction: Ceftriaxone is one of commonly prescribed beta-lactam antibiotics with several label and off-label clinical indications. A high fraction of administered dose of ceftriaxone is excreted renally in an unchanged form, and it may accumulate significantly in patients with impaired renal functions, which may lead to toxicity. Methods: In this study, we employed a physiologically-based pharmacokinetic (PBPK) modeling, as a tool for precision dosing, to predict the biological exposure of ceftriaxone in a virtually-constructed healthy and chronic kidney disease patient populations, with subsequent dosing optimizations. We started developing the model by integrating the physicochemical properties of the drug with biological system information in a PBPK software platform. A PBPK model in an adult healthy population was developed and evaluated visually and numerically with respect to experimental pharmacokinetic data. The model performance was evaluated based on the fold error criteria of the predicted and reported values for different pharmacokinetic parameters. Then, the model was applied to predict drug exposure in CKD patient populations with various degrees of severity. Results: The developed PBPK model was able to precisely describe the pharmacokinetic behavior of ceftriaxone in adult healthy population and in mild, moderate, and severe CKD patient populations. Decreasing the dose by approximately 25% in mild and 50% in moderate to severe renal disease provided a comparable exposure to the healthy population. Based on the simulation of multiple dosing regimens in severe CKD population, it has been found that accumulation of 2 g every 24 h is lower than the accumulation of 1 g every 12 h dosing regimen. Discussion: In this study, the observed concentration time profiles and pharmacokinetic parameters for ceftriaxone were successfully reproduced by the developed PBPK model and it has been shown that PBPK modeling can be used as a tool for precision dosing to suggest treatment regimens in population with renal impairment.

Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions between Elexacaftor/Tezacaftor/Ivacaftor and Tacrolimus in Lung Transplant Recipients

Elexacaftor/tezacaftor/ivacaftor (ETI) treatment has potential benefits in lung transplant recipients, including improvements in extrapulmonary manifestations, such as gastrointestinal and sinus disease; however, ivacaftor is an inhibitor of cytochrome P450 3A (CYP3A) and may, therefore, pose a risk for elevated systemic exposure to tacrolimus. The aim of this investigation is to determine the impact of ETI on tacrolimus exposure and devise an appropriate dosing regimen to manage the risk of this drug-drug interaction (DDI). The CYP3A-mediated DDI of ivacaftor-tacrolimus was evaluated using a physiologically based pharmacokinetic (PBPK) modeling approach, incorporating CYP3A4 inhibition parameters of ivacaftor and in vitro enzyme kinetic parameters of tacrolimus. To further support the findings in PBPK modeling, we present a case series of lung transplant patients who received both ETI and tacrolimus. We predicted a 2.36-fold increase in tacrolimus exposure when co-administered with ivacaftor, which would require a 50% dose reduction of tacrolimus upon initiation of ETI treatment to avoid the risk of elevated systemic exposure. Clinical cases (N = 13) indicate a median 32% (IQR: -14.30, 63.80) increase in the dose-normalized tacrolimus trough level (trough concentration/weight-normalized daily dose) after starting ETI. These results indicate that the concomitant administration of tacrolimus and ETI may lead to a clinically significant DDI, requiring the dose adjustment of tacrolimus.

Virtual patients, digital twins and causal disease models: paving the ground for in silico clinical trials

Computational models are being explored to simulate in silico the efficacy and safety of drug candidates and medical devices. Disease models that are based on patients' profiling data are being produced to represent interactomes of genes or proteins and to infer causality in the pathophysiology {AuQ: Edit OK?}, which makes it possible to mimic the impact of drugs on relevant targets. Virtual patients designed from medical records as well as digital twins were generated to simulate specific organs and to predict treatment efficacy at the individual patient level {AuQ: Edit OK?}. As the acceptance of digital evidence by regulators grows, predictive artificial intelligence (AI)-based models will support the design of confirmatory trials in humans and will accelerate the development of efficient drugs and medical devices.

A "middle-out approach" for the prediction of human drug disposition from preclinical data using simplified physiologically based pharmacokinetic (PBPK) models

Simplified physiologically based pharmacokinetic (PBPK) models using estimated tissue-to-unbound plasma partition coefficients (Kpus) were previously investigated by fitting them to in vivo pharmacokinetic (PK) data. After optimization with preclinical data, the performance of these models for extrapolation of distribution kinetics to human were evaluated to determine the best approach for the prediction of human drug disposition and volume of distribution (Vss) using PBPK modeling. Three lipophilic bases were tested (diazepam, midazolam, and basmisanil) for which intravenous PK data were available in rat, monkey, and human. The models with Kpu scalars using k-means clustering were generally the best for fitting data in the preclinical species and gave plausible Kpu values. Extrapolations of plasma concentrations for diazepam and midazolam using these models and parameters obtained were consistent with the observed clinical data. For diazepam and midazolam, the human predictions of Vss after optimization in rats and monkeys were better compared with the Vss estimated from the traditional PBPK modeling approach (varying from 1.1 to 3.1 vs. 3.7-fold error). For basmisanil, the sparse preclinical data available could have affected the model performance for fitting and the subsequent extrapolation to human. Overall, this work provides a rational strategy to predict human drug distribution using preclinical PK data within the PBPK modeling strategy.

Quantitative Cytochrome P450 3A4 Induction Risk Assessment Using Human Hepatocytes Complemented with Pregnane X Receptor-Activating Profiles

Reliable in vitro to in vivo translation of cytochrome P450 (CYP) 3A4 induction potential is essential to support risk mitigation for compounds during pharmaceutical discovery and development. In this study, a linear correlation of CYP3A4 mRNA induction potential in human hepatocytes with the respective pregnane-X receptor (PXR) activation in a reporter gene assay using DPX2 cells was successfully demonstrated for 13 clinically used drugs. Based on this correlation, using rifampicin as a positive control, the magnitude of CYP3A4 mRNA induction for 71 internal compounds at several concentrations up to 10 µM (n = 90) was predicted within 2-fold error for 64% of cases with only a few false positives (19%). Furthermore, the in vivo area under the curve reduction of probe CYP substrates was reasonably predicted for eight marketed drugs (carbamazepine, dexamethasone, enzalutamide, nevirapine, phenobarbital, phenytoin, rifampicin, and rufinamide) using the static net effect model using both the PXR activation and CYP3A4 mRNA induction data. The liver exit concentrations were used for the model in place of the inlet concentrations to avoid false positive predictions and the concentration achieving twofold induction (F2) was used to compensate for the lack of full induction kinetics due to cytotoxicity and solubility limitations in vitro. These findings can complement the currently available induction risk mitigation strategy and potentially influence the drug interaction modeling work conducted at clinical stages. SIGNIFICANCE STATEMENT: The established correlation of CYP3A4 mRNA in human hepatocytes to PXR activation provides a clear cut-off to identify a compound showing an in vitro induction risk, complementing current regulatory guidance. Also, the demonstrated in vitro-in vivo translation of induction data strongly supports a clinical development program although limitations remain for drug candidates showing complex disposition pathways, such as involvement of auto-inhibition/induction, active transport and high protein binding.

Prediction of the Drug–Drug Interaction Potential between Tegoprazan and Amoxicillin/Clarithromycin Using the Physiologically Based Pharmacokinetic and Pharmacodynamic Model

Tegoprazan is a novel potassium-competitive acid blocker. This study investigated the effect of drug–drug interaction on the pharmacokinetics and pharmacodynamics of tegoprazan co-administered with amoxicillin and clarithromycin, the first-line therapy for the eradication of Helicobacter pylori, using physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) modeling. The previously reported tegoprazan PBPK/PD model was modified and applied. The clarithromycin PBPK model was developed based on the model provided by the SimCYP® compound library. The amoxicillin model was constructed using the middle-out approach. All of the observed concentration–time profiles were covered well by the predicted profiles with the 5th and 95th percentiles. The mean ratios of predicted to observed PK parameters, including the area under the curve (AUC), maximum plasma drug concentration (Cmax), and clearance, were within the 30% intervals for the developed models. Two-fold ratios of predicted fold-changes of Cmax and AUC from time 0 to 24 h to observed data were satisfied. The predicted PD endpoints, including median intragastric pH and percentage holding rate at pH above 4 or 6 on day 1 and day 7, were close to the corresponding observed data. This investigation allows evaluation of the effects of CYP3A4 perpetrators on tegoprazan PK and PD changes, thus providing clinicians with the rationale for co-administration dosing adjustment.

Development and verification of a physiologically based pharmacokinetic model of dronedarone and its active metabolite N-desbutyldronedarone: Application to prospective simulation of complex drug-drug interaction with rivaroxaban

AIMS

Despite potential enzyme- and transporter-mediated drug-drug interactions (DDIs) between dronedarone and rivaroxaban in atrial fibrillation (AF) patients, pharmacokinetic/pharmacodynamic data remain limited to guide clinical practice. We aimed to develop, verify and validate a physiologically based pharmacokinetic (PBPK) model of dronedarone and its major metabolite, N-desbutyldronedarone (NDBD), to prospectively interrogate this clinically relevant DDI in healthy and mild renal impairment populations.

METHODS

The middle-out development of our PBPK model combined literature-derived or in-house in vitro data, predicted in silico data and in vivo clinical data. Model verification was performed for intravenous and oral (single and multiple) dosing regimens. Model validation for the accurate prediction of cytochrome P450 (CYP)3A4- and P-glycoprotein-mediated DDI utilized simvastatin and digoxin as respective victim drugs. Rivaroxaban-specific inhibitory parameters of dronedarone and/or NDBD against CYP3A4, CYP2J2, OAT3 and P-glycoprotein were incorporated into the PBPK-DDI model for prospective dronedarone-rivaroxaban DDI simulation.

RESULTS

Dronedarone and NDBD PK following clinically relevant doses of 400 mg dronedarone across single and multiple oral dosing were accurately simulated by incorporating effect of auto-inactivation on dose nonlinearities. Following successful model validation, nondose-adjusted rivaroxaban-dronedarone DDI in healthy and mild renal impairment populations revealed simulated rivaroxaban area under the plasma concentration-time curve up to 24 h fold change greater than dose exposure equivalence (0.70-1.43) at 1.65 and 1.84, respectively. Correspondingly, respective major bleeding risk was 4.24 and 4.70% compared with threshold of 4.5% representing contraindicated rivaroxaban-ketoconazole DDI.

CONCLUSION

Our PBPK-DDI model predicted clinically significant dronedarone-rivaroxaban DDI in both healthy and mild renal impairment subjects. Greater benefit vs. risk could be achieved with rivaroxaban dose reductions to at least 15 mg in mild renal impairment subjects on concomitant dronedarone and rivaroxaban.

Using mechanistic models to support development of complex generic drug products: European Medicines Agency perspective

Model-informed drug development (MIDD) approaches receive wide regulatory acceptance in the European Medicines Agency (EMA) to support new drug development. For generic drugs, the European regulators have not reached a common position on how to use these methods. This commentary expands on the existing EMA regulatory framework for bioequivalence and physiological based pharmacokinetic (PBPK) modeling to propose conditions where mechanistic models could support or potentially waive clinical bioequivalence (BE)/bioavailability (BA) studies.

Physiologically Based Pharmacokinetic (PBPK) Model-Informed Dosing Guidelines for Pediatric Clinical Care: A Pragmatic Approach for a Special Population

Physiologically based pharmacokinetic (PBPK) modeling can be an attractive tool to increase the evidence base of pediatric drug dosing recommendations by making optimal use of existing pharmacokinetic (PK) data. A pragmatic approach of combining available compound models with a virtual pediatric physiology model can be a rational solution to predict PK and hence support dosing guidelines for children in real-life clinical care, when it can also be employed by individuals with little experience in PBPK modeling. This comes within reach as user-friendly PBPK modeling platforms exist and, for many drugs and populations, models are ready for use. We have identified a list of drugs that can serve as a starting point for pragmatic PBPK modeling to address current clinical dosing needs.

Therapeutic Protein Drug Interactions: A White Paper From the International Consortium for Innovation and Quality in Pharmaceutical Development

Typically, therapeutic proteins (TPs) have a low risk for eliciting meaningful drug interactions (DIs). However, there are select instances where TP drug interactions (TP-DIs) of clinical concern can occur. This white paper discusses the various types of TP-DIs involving mechanisms such as changes in disease state, target-mediated drug disposition, neonatal Fc receptor (FcRn), or antidrug antibodies formation. The nature of TP drug interaction being investigated should determine whether the examination is conducted as a standalone TP-DI study in healthy participants, in patients, or assessed via population pharmacokinetic analysis. DIs involving antibody-drug conjugates are discussed briefly, but the primary focus here will be DIs involving cytokine modulation. Cytokine modulation can occur directly by certain TPs, or indirectly due to moderate to severe inflammation, infection, or injury. Disease states that have been shown to result in indirect disease-DIs that are clinically meaningful have been listed (i.e., typically a twofold change in the systemic exposure of a coadministered sensitive cytochrome P450 substrate drug). Type of disease and severity of inflammation should be the primary drivers for risk assessment for disease-DIs. While more clinical inflammatory marker data needs to be collected, the use of two or more clinical inflammatory markers (such as C-reactive protein, albumin, or interleukin 6) may help broadly categorize whether the predicted magnitude of inflammatory disease-DI risk is negligible, weak, or moderate to strong. Based on current knowledge, clinical DI studies are not necessary for all TPs, and should no longer be conducted in certain disease patient populations such as psoriasis, which do not have sufficient systemic inflammation to cause a meaningful indirect disease-DI.

A Physiologically-Based Pharmacokinetic Model of Ruxolitinib and Posaconazole to Predict CYP3A4-Mediated Drug-Drug Interaction Frequently Observed in Graft versus Host Disease Patients

Ruxolitinib (RUX) is approved for the treatment of steroid-refractory acute and chronic graft versus host disease (GvHD). It is predominantly metabolized via cytochrome P450 (CYP) 3A4. As patients with GvHD have an increased risk of invasive fungal infections, RUX is frequently combined with posaconazole (POS), a strong CYP3A4 inhibitor. Knowledge of RUX exposure under concomitant POS treatment is scarce and recommendations on dose modifications are inconsistent. A physiologically based pharmacokinetic (PBPK) model was developed to investigate the drug-drug interaction (DDI) between POS and RUX. The predicted RUX exposure was compared to observed concentrations in patients with GvHD in the clinical routine. PBPK models for RUX and POS were independently set up using PK-Sim^® Version 11. Plasma concentration-time profiles were described successfully and all predicted area under the curve (AUC) values were within 2-fold of the observed values. The increase in RUX exposure was predicted with a DDI ratio of 1.21 (C_max) and 1.59 (AUC). Standard dosing in patients with GvHD led to higher RUX exposure than expected, suggesting further dose reduction if combined with POS. The developed model can serve as a starting point for further simulations of the implemented DDI and can be extended to further perpetrators of CYP-mediated PK-DDIs or disease-specific physiological changes.

Development of a pediatric physiologically-based pharmacokinetic model to support recommended dosing of atezolizumab in children with solid tumors

Background: Atezolizumab has been studied in multiple indications for both pediatric and adult patient populations. Generally, clinical studies enrolling pediatric patients may not collect sufficient pharmacokinetic data to characterize the drug exposure and disposition because of operational, ethical, and logistical challenges including burden to children and blood sample volume limitations. Therefore, mechanistic modeling and simulation may serve as a tool to predict and understand the drug exposure in pediatric patients.

Objective: To use mechanistic physiologically-based pharmacokinetic (PBPK) modeling to predict atezolizumab exposure at a dose of 15 mg/kg (max 1,200 mg) in pediatric patients to support dose rationalization and label recommendations.

Methods: A minimal mechanistic PBPK model was used which incorporated age-dependent changes in physiology and biochemistry that are related to atezolizumab disposition such as endogenous IgG concentration and lymph flow. The PBPK model was developed using both in vitro data and clinically observed data in adults and was verified across dose levels obtained from a phase I and multiple phase III studies in both pediatric patients and adults. The verified model was then used to generate PK predictions for pediatric and adult subjects ranging from 2- to 29-year-old.

Results: Individualized verification in children and in adults showed that the simulated concentrations of atezolizumab were comparable (76% within two-fold and 90% within three-fold, respectively) to the observed data with no bias for either over- or under-prediction. Applying the verified model, the predicted exposure metrics including C_min, C_max, and AUC_tau were consistent between pediatric and adult patients with a geometric mean of pediatric exposure metrics between 0.8- to 1.25-fold of the values in adults.

Conclusion: The results show that a 15 mg/kg (max 1,200 mg) atezolizumab dose administered intravenously in pediatric patients provides comparable atezolizumab exposure to a dose of 1,200 mg in adults. This suggests that a dose of 15 mg/kg will provide adequate and effective atezolizumab exposure in pediatric patients from 2- to 18-year-old.

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.

Understanding Inter-individual Variability in the Drug Interaction of a Highly Extracted CYP1A2 Substrate Tizanidine: Application of a Permeability-limited Multi-compartment Liver Model in a Population Based PBPK Framework

Tizanidine, a centrally acting skeletal muscle relaxant, is predominantly metabolised by CYP1A2 and undergoes extensive hepatic first-pass metabolism following oral administration. As a highly extracted drug, the systemic exposure to tizanidine exhibits considerable inter-individual variability and is altered substantially when co-administered with CYP1A2 inhibitors or inducers. The aim of the current study was to compare the performance of a permeability-limited multi-compartment liver (PerMCL) model, which operates as an approximation of the dispersion model (DM), and the well-stirred model (WSM) for predicting tizanidine DDIs. Physiologically-based pharmacokinetic (PBPK) models were developed for tizanidine, incorporating the PerMCL model and the WSM, respectively, to simulate the interaction of tizanidine with a range of CYP1A2 inhibitors and inducers. While the WSM showed a tendency to under-predict the fold change of tizanidine AUC (AUC ratio) in the presence of perpetrators, the use of PerMCL model increased precision (absolute average-fold error: 1.32 - 1.42 versus 1.58) and decreased bias (average-fold error: 0.97 - 1.25 versus 0.63) for the predictions of mean AUC ratios as compared to the WSM. The PerMCL model captured the observed range of individual AUC ratios of tizanidine as well as the correlation between individual AUC ratios and CYP1A2 activities without interactions, whereas the WSM was not able to capture these. The results demonstrate the advantage of using the PerMCL model over the WSM in predicting the magnitude and inter-individual variability of DDIs for a highly extracted sensitive substrate tizanidine. Significance Statement This study demonstrates the advantages of the permeability-limited multi-compartment liver (PerMCL) model, which operates as an approximation of the dispersion model (DM), in mitigating the tendency of the well-stirred model (WSM) to under-predict the magnitude and variability of DDIs of a highly extracted CYP1A2 substrate tizanidine when it is administered with CYP1A2 inhibitors or inducers. The PBPK modelling approach described herein is valuable to the understanding of drug interactions of highly extracted substrates and the source of its inter-individual variability.

Exploration and application of a liver-on-a-chip device in combination with modelling and simulation for quantitative drug metabolism studies

Microphysiological systems (MPS) are complex and more physiologically realistic cellular in vitro tools that aim to provide more relevant human in vitro data for quantitative prediction of clinical pharmacokinetics while also reducing the need for animal testing. The PhysioMimix liver-on-a-chip integrates medium flow with hepatocyte culture and has the potential to be adopted for in vitro studies investigating the hepatic disposition characteristics of drug candidates. The current study focusses on liver-on-a-chip system exploration for multiple drug metabolism applications. Characterization of cytochrome P450 (CYP), UDP-glucuronosyl transferase (UGT) and aldehyde oxidase (AO) activities was performed using 15 drugs and in vitro to in vivo extrapolation (IVIVE) was assessed for 12 of them. Next, the utility of the liver-on-a-chip for estimation of the fraction metabolized (f_m) via specific biotransformation pathways of quinidine and diclofenac was established. Finally, the metabolite identification opportunities were also explored using efavirenz as an example drug with complex primary and secondary metabolism involving a combination of CYP, UGT and sulfotransferase enzymes. A key aspect of these investigations was the application of mathematical modelling for improved parameter calculation. Such approaches will be required for quantitative assessment of metabolism and/or transporter processes in systems where medium flow and system compartments result in non-homogeneous drug concentrations. In particular, modelling was used to explore the effect of evaporation from the medium and it was found that the intrinsic clearance (CL_int) might be underestimated by up to 40% for low clearance compounds if evaporation is not accounted for. Modelling of liver-on-a-chip in vitro data also enhanced the approach to f_m estimation allowing objective assessment of metabolism models of different complexity. The resultant diclofenac f_m,UGT of 0.64 was highly comparable with values reported previously in the literature. The current study demonstrates the integration of mathematical modelling with experimental liver-on-a-chip studies and illustrates how this approach supports generation of high quality of data from complex in vitro cellular systems.

Frequency of Urinary Tract Infections in Type 2 Diabetic Patients Taking Dapagliflozin

Background

Urinary tract infections (UTIs) are common in patients with diabetes. The use of sodium-glucose cotransporter-2 inhibitors (SGLT2i) to achieve good glycemic control increases glucose levels in urine. This glycosuria further enhances the risk of UTIs. This study aimed to evaluate the frequency of UTIs in patients with type 2 diabetes receiving the SGLT2i dapagliflozin as an add-on therapy.

Methods

We conducted this cross-sectional study at the Endocrinology Department of Hayatabad Medical Complex in Peshawar from April 2020 to September 2020. A total of 400 patients with diabetes receiving either 5 mg or 10 mg of dapagliflozin as an add-on therapy for the treatment of type 2 diabetes were included in this study. We collected blood and urine samples from participants and measured glycosylated hemoglobin levels. Urine samples were cultured on cysteine lactose electrolyte deficient agar. We used IBM SPSS Statistics for Windows, version 25.0 (IBM Corp., Armonk, NY) to analyze our data.

Results

The prevalence of UTIs in diabetic patients receiving 5 mg or 10 mg of dapagliflozin was 5.3%. Women were more affected (76.2%) than men (p < 0.05). UTIs were more prevalent in patients older than 50 years (85.7%) than in any other age group. The dose strength of dapagliflozin was not associated with UTIs (p > 0.05).

Conclusion

This study examined UTIs in patients taking dapagliflozin for the treatment of type 2 diabetes. These infections were mild to moderate and were treated easily. None of these infections caused the patient to discontinue the treatment. Dapagliflozin is well-tolerated in patients with diabetes but should be used with appropriate caution and monitoring.

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.

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

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

Physiologically based pharmacokinetic (PBPK) modelling of tamsulosin related to CYP2D6*10 allele

Tamsulosin, a selective [Formula: see text]-adrenoceptor blocker, is commonly used for alleviation of lower urinary tract symptoms related to benign prostatic hyperplasia. Tamsulosin is predominantly metabolized by CYP3A4 and CYP2D6 enzymes, and several studies reported the effects of CYP2D6 genetic polymorphism on the pharmacokinetics of tamsulosin. This study aims to develop and validate the physiologically based pharmacokinetic (PBPK) model of tamsulosin in CYP2D6*wt/*wt, CYP2D6*wt/*10, and CYP2D6*10/*10 genotypes, using Simcyp® simulator. Physicochemical, and formulation properties and data for absorption, distribution, metabolism and excretion were collected from previous publications, predicted in the simulator, or optimized in different CYP2D6 genotypes. The tamsulosin PBPK model in CYP2D6*wt/*wt and CYP2D6*wt/*10 genotypes were developed based on the clinical pharmacokinetic study where a single oral dose of 0.2 mg tamsulosin was administered to 25 healthy Korean male volunteers with CYP2D6*wt/*wt and CYP2D6*wt/*10 genotypes. A previous pharmacokinetic study was used to develop the model in CYP2D6*10/*10 genotype. The developed model was validated using other clinical pharmacokinetic studies not used in development. The predicted exposures via the PBPK model in CYP2D6*wt/*10 and CYP2D6*10/*10 genotype was 1.23- and 1.76-fold higher than CYP2D6*wt/*wt genotype, respectively. The simulation profiles were visually similar to the observed profiles, and fold errors of all development and validation datasets were included within the criteria. Therefore, the tamsulosin PBPK model in different CYP2D6 genotypes with regards to CYP2D6*10 alleles was appropriately established. Our model can contribute to the implementation of personalized pharmacotherapy of patients, appropriately predicting the pharmacokinetics of tamsulosin reflecting their demographic and CYP2D6 genotype characteristics without unnecessary drug exposure.

Prediction of Drug-Drug Interaction Potential of Tegoprazan Using Physiologically Based Pharmacokinetic Modeling and Simulation

This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of tegoprazan and to predict the drug-drug interaction (DDI) potential between tegoprazan and cytochrome P450 (CYP) 3A4 perpetrators. The PBPK model of tegoprazan was developed using SimCYP Simulator^® and verified by comparing the model-predicted pharmacokinetics (PKs) of tegoprazan with the observed data from phase 1 clinical studies, including DDI studies. DDIs between tegoprazan and three CYP3A4 perpetrators were predicted by simulating the difference in tegoprazan exposure with and without perpetrators, after multiple dosing for a clinically used dose range. The final PBPK model adequately predicted the biphasic distribution profiles of tegoprazan and DDI between tegoprazan and clarithromycin. All ratios of the predicted-to-observed PK parameters were between 0.5 and 2.0. In DDI simulation, systemic exposure to tegoprazan was expected to increase about threefold when co-administered with the maximum recommended dose of clarithromycin or ketoconazole. Meanwhile, tegoprazan exposure was expected to decrease to ~30% when rifampicin was co-administered. Based on the simulation by the PBPK model, it is suggested that the DDI potential be considered when tegoprazan is used with CYP3A4 perpetrator, as the acid suppression effect of tegoprazan is known to be associated with systemic exposure.

Novel Bruton's Tyrosine Kinase inhibitor remibrutinib: Drug-drug interaction potential as a victim of CYP3A4 inhibitors based on clinical data and PBPK modeling

Remibrutinib, a novel oral Bruton’s Tyrosine Kinase inhibitor (BTKi) is highly selective for BTK, potentially mitigating the side effects of other BTKis. Enzyme phenotyping identified CYP3A4 to be the predominant elimination pathway of remibrutinib. The impact of concomitant treatment with CYP3A4 inhibitors, grapefruit juice and ritonavir (RTV), was investigated in this study in combination with an intravenous microtracer approach. Pharmacokinetic (PK) parameters, including the fraction absorbed, the fractions escaping intestinal and hepatic first‐pass metabolism, the absolute bioavailability, systemic clearance, volume of distribution at steady‐state, and the fraction metabolized via CYP3A4 were evaluated. Oral remibrutinib exposure increased in the presence of RTV 4.27‐fold, suggesting that remibrutinib is not a sensitive CYP3A4 substrate. The rich PK dataset supported the building of a robust physiologically‐based pharmacokinetic (PBPK) model, which well‐described the therapeutic dose range of 25–100 mg. Simulations of untested scenarios revealed an absence of drug‐drug interaction (DDI) risk between remibrutinib and the weak CYP3A4 inhibitor fluvoxamine (area under the concentration‐time curve ratio [AUCR] <1.25), and a moderate effect with the CYP3A4 inhibitor erythromycin (AUCR: 2.71). Predictions with the moderate and strong CYP3A4 inducers efavirenz and rifampicin, suggested a distinct remibrutinib exposure decrease of 64% and 89%. Oral bioavailability of remibrutinib was 34%. The inclusion of an intravenous microtracer allowed the determination of all relevant remibrutinib PK parameters, which facilitated construction of the PBPK model. This will provide guidance on the selection or restriction of comedications and prediction of DDI risks.

Physiologically based pharmacokinetic modelling to predict the clinical effect of CYP3A inhibitors/inducers on esaxerenone pharmacokinetics in healthy subjects and subjects with hepatic impairment

Purpose

Esaxerenone is a novel, oral, nonsteroidal treatment for hypertension. Physiologically based pharmacokinetic (PBPK) modelling was performed to predict the drug–drug interaction (DDI) effect of cytochrome P450 (CYP)3A modulators on esaxerenone pharmacokinetics in healthy subjects and subjects with hepatic impairment.

Methods

In our PBPK model, the fraction of esaxerenone metabolised by CYP3A was estimated from mass-balance data and verified and optimised by clinical DDI study results with strong CYP3A modulators. The model was also verified by the observed pharmacokinetics after multiple oral dosing and by the effect of hepatic impairment on esaxerenone pharmacokinetics. The model was applied to predict the DDI effects on esaxerenone pharmacokinetics with untested CYP3A modulators in healthy subjects and with strong CYP3A modulators in subjects with hepatic impairment.

Results

The PBPK model well described esaxerenone pharmacokinetics after multiple oral dosing. The predicted fold changes in esaxerenone plasma exposure after coadministration with strong CYP3A modulators were comparable with the observed data (1.53-fold with itraconazole and 0.31-fold with rifampicin). Predicted DDIs with untested moderate CYP3A modulators were less than the observed DDI with strong CYP3A modulators. The PBPK model also described the effect of hepatic impairment on esaxerenone plasma exposure. The predicted DDI results with strong CYP3A modulators in subjects with hepatic impairment indicate that, for concomitant use of CYP3A modulators, caution is advised for subjects with hepatic impairment, as is for healthy subjects.

Conclusion

The PBPK model developed predicted esaxerenone pharmacokinetics and DDIs and informed concurrent use of esaxerenone with CYP3A modulators.

Supplementary information

The online version contains supplementary material available at 10.1007/s00228-021-03194-x.

Model-Based Drug-Drug Interaction Extrapolation Strategy From Adults to Children: Risdiplam in Pediatric Patients With Spinal Muscular Atrophy

Risdiplam (Evrysdi) improves motor neuron function in patients with spinal muscular atrophy (SMA) and has been approved for the treatment of patients ≥2 months old. Risdiplam exhibits time‐dependent inhibition of cytochrome P450 (CYP) 3A in vitro. While many pediatric patients receive risdiplam, a drug–drug interaction (DDI) study in pediatric patients with SMA was not feasible. Therefore, a novel physiologically‐based pharmacokinetic (PBPK) model‐based strategy was proposed to extrapolate DDI risk from healthy adults to children with SMA in an iterative manner. A clinical DDI study was performed in healthy adults at relevant risdiplam exposures observed in children. Risdiplam caused an 1.11‐fold increase in the ratio of midazolam area under the curve with and without risdiplam (AUCR)), suggesting an 18‐fold lower in vivo CYP3A inactivation constant compared with the in vitro value. A pediatric PBPK model for risdiplam was validated with independent data and combined with a validated midazolam pediatric PBPK model to extrapolate DDI from adults to pediatric patients with SMA. The impact of selected intestinal and hepatic CYP3A ontogenies on the DDI susceptibility in children relative to adults was investigated. The PBPK analysis suggests that primary CYP3A inhibition by risdiplam occurs in the intestine rather than the liver. The PBPK‐predicted risdiplam CYP3A inhibition risk in pediatric patients with SMA aged 2 months–18 years was negligible (midazolam AUCR of 1.09–1.18) and included in the US prescribing information of risdiplam. Comprehensive evaluation of the sensitivity of predicted CYP3A DDI on selected intestinal and hepatic CYP3A ontogeny functions, together with PBPK model‐based strategy proposed here, aim to guide and facilitate DDI extrapolations in pediatric populations.

Comparison between physiologically based pharmacokinetic and population pharmacokinetic modelling to select paediatric doses of gepotidacin in plague

Aims

To develop physiologically based pharmacokinetic (PBPK) and population pharmacokinetic (PopPK) models to predict effective doses of gepotidacin in paediatrics for the treatment of pneumonic plague (Yersinia pestis).

Methods

A gepotidacin PBPK model was constructed using a population‐based absorption, distribution, metabolism and excretion simulator, Simcyp®, with physicochemical and in vitro data, optimized with clinical data from a dose‐escalation intravenous (IV) study and a human mass balance study. A PopPK model was developed with pooled PK data from phase 1 studies with IV gepotidacin in healthy adults.

Results

For both the PopPK and PBPK models, body weight was found to be a key covariate affecting gepotidacin clearance. With PBPK, ~90% of the predicted PK for paediatrics fell between the 5th and 95th percentiles of adult values except for subjects weighing ≤5 kg. PopPK‐simulated paediatric means for C_max and AUC_(0‐τ) were similar to adult exposures across various weight brackets. The proposed dosing regimens were weight‐based for subjects ≤40 kg and fixed‐dose for subjects >40 kg. Comparison of observed and predicted exposures in adults indicated that both PBPK and PopPK models achieved similar AUC and C_max for a given dose, but the C_max predictions with PopPK were slightly higher than with PBPK. The two models differed on dose predictions in children <3 months old. The PopPK model may be suboptimal for low age groups due to the absence of maturation characterization of drug‐metabolizing enzymes involved with clearance in adults.

Conclusions

Both PBPK and PopPK approaches can reasonably predict gepotidacin exposures in children.

Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility

The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for their evaluation, accepted by all stakeholders involved, are still to be established. In this white paper, we propose a risk‐informed evaluation framework for mechanistic model credibility evaluation. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk‐based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper. To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick‐start tool by regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts.

Physiologically Based Pharmacokinetic Modeling and Allometric Scaling in Pediatric Drug Development: Where Do We Draw the Line?

Developing medicines for children is now established in legislation in both the United States and Europe; new drugs require pediatric study or investigation plans as part of their development. Particularly in early age groups, many developmental processes are not reflected by simple scalars such as body weight or body surface area, and even projecting doses based on simple allometric scaling can lead to significant overdoses in certain age groups. Modeling and simulation methodology, including physiologically based modeling, has evolved as part of the drug development toolkit and is being increasingly applied to various aspects of pediatric drug development. Pediatric physiologically based pharmacokinetic (PBPK) models account for the development of organs and the ontogeny of specific enzymes and transporters that determine the age-related pharmacokinetic profiles. However, when should this approach be used, and when will simpler methods such as allometric scaling suffice in answering specific problems? The aim of this review article is to illustrate the application of allometric scaling and PBPK in pediatric drug development and explore the optimal application of the latter approach with reference to case examples. In reality, allometric scaling included as part of population pharmacokinetic and PBPK approaches are all part of a model-informed drug development toolkit helping with decision making during the process of drug discovery and development; to that end, they should be viewed as complementary.

In vitro and In silico biopharmaceutic regulatory guidelines for generic bioequivalence for oral products: Comparison among various regulatory agencies

Generic drug development is a complex process that involves development of formulation similar to reference product. Because of the complexity associated with generic drug development, many regulatory agencies have come up with various guidelines. Out of many guidelines, the biopharmaceutics classification system that was introduced in 1995 based on aqueous solubility and permeability helped many pharmaceutical scientists across the globe to utilize the tool for formulation development, waiver of in vivo studies. Later on in vitro guidelines based on dissolution and in vitro in vivo correlation were introduced by many regulatory agencies with an intent to reduce number of in vivo human testing thereby facilitating shorter development time and faster approvals and launch. Most recently, understanding the importance in silico approaches such as physiologically based pharmacokinetic modelling, regulatory agencies such as United States Food and Drug Administration (USFDA) and European Middle East and Africa (EMA) came up with modelling guidance documents. Even though consensus exists between guidance documents from various regulatory agencies, still there are many minor to major differences exists between these guidance documents that needs to be considered while submitting a generic drug application. This review aims to compare all the in vitro and in silico guidance documents from major regulatory agencies with emphasis on latest trends and technologies combined with regulatory acceptability with an intention to harmonize regulations. Guidance documents from major regulatory agencies such as USFDA, EMA, World Health Organization, International Council for Harmonization and other emerging markets were compared. Similarities &differences among these guidance documents are critically reviewed to provide the reader a detailed overview of these guidance documents at one place.

Model-based assessments of CYP3A-mediated drug-drug interaction risk of milademetan

Milademetan is a small‐molecule inhibitor of murine double minute 2 (MDM2) that is in clinical development for advanced solid tumors and hematological cancers, including liposarcoma and acute myeloid leukemia. Milademetan is a CYP3A and P‐glycoprotein substrate and moderate CYP3A inhibitor. The current study aims to understand the drug‐drug interaction (DDI) risk of milademetan as a CYP3A substrate during its early clinical development. A clinical DDI study of milademetan (NCT03614455) showed that concomitant administration of single‐dose milademetan with the strong CYP3A inhibitor itraconazole or posaconazole increased milademetan mean area under the curve from zero to infinity (AUC_inf) by 2.15‐fold (90% confidence interval [CI], 1.98–2.34) and 2.49‐fold (90% CI, 2.26–2.74), respectively, supporting that the milademetan dose should be reduced by 50% when concomitantly administered with strong CYP3A inhibitors. A physiologically‐based pharmacokinetic (PBPK) model of milademetan was subsequently developed to predict the magnitude of CYP3A‐mediated DDI potential of milademetan with moderate CYP3A inhibitors. The PBPK model predicted an increase in milademetan exposure of 1.72‐fold (90% CI, 1.69–1.76) with fluconazole, 1.91‐fold (90% CI, 1.83–1.99) with erythromycin, and 2.02‐fold (90% CI, 1.93–2.11) with verapamil. In addition, it estimated that milademetan’s original dose (160 mg once daily) could be resumed from its half‐reduced dose 3 days after discontinuation of concomitant strong CYP3A inhibitors. The established PBPK model of milademetan was qualified and considered to be robust enough to support continued development of milademetan.

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

The success of applications of physiologically‐based pharmacokinetic (PBPK) modeling in drug development and drug labeling has triggered regulatory agencies to demand rigorous demonstration of the predictive capability of the specific PBPK platform for a particular intended application purpose. The effort needed to comply with such qualification requirements exceeds the costs for any individual PBPK application. Because changes or updates of a PBPK platform would require (re‐)qualification, a reliable and efficient generic qualification framework is needed. We describe the development and implementation of an agile and sustainable technical framework for automatic PBPK platform (re‐)qualification of PK‐Sim^® embedded in the open source and open science GitHub landscape of Open Systems Pharmacology. The qualification approach enables the efficient assessment of all aspects relevant to the qualification of a particular purpose and provides transparency and traceability for all stakeholders. As a showcase example for the power and versatility of the qualification framework, we present the qualification of PK‐Sim^® for the intended purpose of predicting cytochrome P450 3A4 (CYP3A4)–mediated drug–drug interactions (DDIs). Several perpetrator PBPK models featuring various degrees of CYP3A4 modulation and different types of mechanisms (competitive inhibition, mechanism‐based inactivation, and induction) were coupled with a set of PBPK models of sensitive CYP3A4 victim drugs. Simulations were compared to a comprehensive data set of 135 observations from published clinical DDI studies. The platform's overall predictive performance showed reasonable accuracy and precision (geometric mean fold error of 1.4 for both area under the plasma concentration‐time curve ratios and peak plasma concentration ratios with/without perpetrator) and suggests that PK‐Sim^® can be applied to quantitatively assess CYP3A4‐mediated DDI in clinically untested scenarios.

A Preterm Physiologically Based Pharmacokinetic Model. Part I: Physiological Parameters and Model Building

BACKGROUND

Developmental physiology can alter pharmacotherapy in preterm populations. Because of ethical and clinical constraints in studying this vulnerable age group, physiologically based pharmacokinetic models offer a viable alternative approach to predicting drug pharmacokinetics and pharmacodynamics in this population. However, such models require comprehensive information on the changes of anatomical, physiological and biochemical variables, where such data are not available in a single source.

OBJECTIVE

The objective of this study was to integrate the relevant physiological parameters required to build a physiologically based pharmacokinetic model for the preterm population.

METHODS

Published information on developmental preterm physiology and some drug-metabolising enzymes were collated and analysed. Equations were generated to describe the changes in parameter values during growth.

RESULTS

Data on organ size show different growth patterns that were quantified as functions of bodyweight to retain physiological variability and correlation. Protein binding data were quantified as functions of age as the body weight was not reported in the original articles. Ontogeny functions were derived for cytochrome P450 1A2, 3A4 and 2C9. Tissue composition values and how they change with age are limited.

CONCLUSIONS

Despite the limitations identified in the availability of some tissue composition values, the data presented in this article provide an integrated resource of system parameters needed for building a preterm physiologically based pharmacokinetic model.

Preterm Physiologically Based Pharmacokinetic Model. Part II: Applications of the Model to Predict Drug Pharmacokinetics in the Preterm Population

BACKGROUND

Preterm neonates are usually not part of a traditional drug development programme, however they are frequently administered medicines. Developing modelling and simulation tools, such as physiologically based pharmacokinetic (PBPK) models that incorporate developmental physiology and maturation of drug metabolism, can be used to predict drug exposure in this group of patients, and may help to optimize drug dose adjustment.

OBJECTIVE

The aim of this study was to assess and verify the predictability of a preterm PBPK model using compounds that undergo diverse renal and/or hepatic clearance based on the knowledge of their disposition in adults.

METHODS

A PBPK model was developed in the Simcyp Simulator V17 to predict the pharmacokinetics (PK) of drugs in preterm neonates. Drug parameters for alfentanil, midazolam, caffeine, ibuprofen, gentamicin and vancomycin were collated from the literature. Predicted PK parameters and profiles were compared against the observed data.

RESULTS

The preterm PBPK model predicted the PK changes of the six compounds using ontogeny functions for cytochrome P450 (CYP) 1A2, CYP2C9 and CYP3A4 after oral and intravenous administrations. For gentamicin and vancomycin, the maturation of renal function was able to predict the exposure of these two compounds after intravenous administration. All PK parameter predictions were within a twofold error criteria.

CONCLUSION

While the developed preterm model for the prediction of PK behaviour in preterm patients is not intended to replace clinical studies, it can potentially help with deciding on first-time dosing in this population and study design in the absence of clinical data.

End-to-end application of model-informed drug development for ertugliflozin, a novel sodium-glucose cotransporter 2 inhibitor

Model-informed drug development (MIDD) is critical in all stages of the drug-development process and almost all regulatory submissions for new agents incorporate some form of modeling and simulation. This review describes the MIDD approaches used in the end-to-end development of ertugliflozin, a sodium-glucose cotransporter 2 inhibitor approved for the treatment of adults with type 2 diabetes mellitus. Approaches included (1) quantitative systems pharmacology modeling to predict dose-response relationships, (2) dose-response modeling and model-based meta-analysis for dose selection and efficacy comparisons, (3) population pharmacokinetics (PKs) modeling to characterize PKs and quantify population variability in PK parameters, (4) regression modeling to evaluate ertugliflozin dose-proportionality and the impact of uridine 5'-diphospho-glucuronosyltransferase (UGT) 1A9 genotype on ertugliflozin PKs, and (5) physiologically-based PK modeling to assess the risk of UGT-mediated drug-drug interactions. These end-to-end MIDD approaches for ertugliflozin facilitated decision making, resulted in time/cost savings, and supported registration and labeling.

PBPK modeling to predict drug-drug interactions of ivosidenib as a perpetrator in cancer patients and qualification of the Simcyp platform for CYP3A4 induction

Ivosidenib is a potent, targeted, orally active, small-molecule inhibitor of mutant isocitrate dehydrogenase 1 (IDH1) that has been approved in the United States for the treatment of adults with newly diagnosed acute myeloid leukemia (AML) who are greater than or equal to 75 years of age or ineligible for intensive chemotherapy, and those with relapsed or refractory AML, with a susceptible IDH1 mutation. Ivosidenib is an inducer of the CYP2B6, CYP2C8, CYP2C9, and CYP3A4 and an inhibitor of P-glycoprotein (P-gp), organic anion transporting polypeptide-1B1/1B3 (OATP1B1/1B3), and organic anion transporter-3 (OAT3) in vitro. A physiologically-based pharmacokinetic (PK) model was developed to predict drug-drug interactions (DDIs) of ivosidenib in patients with AML. The in vivo CYP3A4 induction effect of ivosidenib was quantified using 4β-hydroxycholesterol and was subsequently verified with the PK data from an ivosidenib and venetoclax combination study. The verified model was prospectively applied to assess the effect of multiple doses of ivosidenib on a sensitive CYP3A4 substrate, midazolam. The simulated midazolam geometric mean area under the curve (AUC) and maximum plasma concentration (C_max ) ratios were 0.18 and 0.27, respectively, suggesting ivosidenib is a strong inducer. The model was also used to predict the DDIs of ivosidenib with CYP2B6, CYP2C8, CYP2C9, P-gp, OATP1B1/1B3, and OAT3 substrates. The AUC ratios following multiple doses of ivosidenib and a single dose of CYP2B6 (bupropion), CYP2C8 (repaglinide), CYP2C9 (warfarin), P-gp (digoxin), OATP1B1/1B3 (rosuvastatin), and OAT3 (methotrexate) substrates were 0.90, 0.52, 0.84, 1.01, 1.02, and 1.27, respectively. Finally, in accordance with regulatory guidelines, the Simcyp modeling platform was qualified to predict CYP3A4 induction using known inducers and sensitive substrates.