Physiologically Based Pharmacokinetic Modeling in Regulatory Science: An Update From the U.S. Food and Drug Administration's Office of Clinical Pharmacology

Addressing Drug-Drug Interaction Knowledge Gaps at the Time of Approval: An Analysis of FDA Postmarketing Requirements and Commitments from 2009 to 2023

It has become increasingly common for patients to rely on the use of multiple prescription medications. The management of polypharmacy requires careful consideration for how drugs are metabolized and their potential for interaction with other drugs. Drug-drug interaction (DDI) assessments are typically performed in a stepwise manner during drug development, though knowledge gaps can exist at the time of approval. The US Food and Drug Administration can establish postmarketing requirements (PMRs) or postmarketing commitments (PMCs) to address these knowledge gaps. In this study, we systematically evaluated PMRs and PMCs established to new molecular entities (NMEs) at the time of initial approval between 2009 and 2023, for the assessment of pharmacokinetics-based DDIs (i.e., drug metabolizing enzyme- and transporter-related DDIs). We found that 22% of NMEs had at least one DDI-related PMR or PMC, with a total of 263 that were pharmacokinetic based. Of these, 67% were for the assessment of drug metabolizing enzymes, which were established most frequently for their evaluation as a substrate, and 28% for transporters, which were established most frequently for their evaluation as an inhibitor. The 89% of PMRs and PMCs that were considered fulfilled had a revision to the United States prescribing information, the majority of which resulted in updated new instructions for use. This study highlights the value in conducting DDI-related studies early in the drug development process allowing broad patient inclusion at the time of initial drug approval. [Correction added on October 28, 2024, after first online publication: The last sentence of the abstract has been updated in this version.].

Clinical and Physiologically Based Pharmacokinetic Model Evaluations of Adagrasib Drug-Drug Interactions

Adagrasib is a potent, highly selective, orally available, small molecule, covalent inhibitor of G12C mutated KRAS. As both a substrate and strong inhibitor of cytochrome P450 (CYP) 3A4, adagrasib inhibits its own CYP3A4-mediated metabolism following multiple dosing, resulting in time-dependent drug-drug interaction (DDI) liabilities. A physiologically-based pharmacokinetic (PBPK) model was developed and verified using a combination of physicochemical, in vitro and clinical pharmacokinetic (PK) data from healthy volunteers and cancer patients. The PBPK model well-described the single and multiple-dose adagrasib PK data as well as DDI data with itraconazole, rifampin, midazolam, warfarin, dextromethorphan, and digoxin, with model predictions within 1.5-fold of the observed clinical data. The PBPK model was used to predict untested scenarios including the clinical victim and perpetrator DDI liabilities at the approved dosing regimen of 600 mg twice daily (b.i.d.) in cancer patients. Strong, moderate, and weak inhibitors of CYP3A4 are predicted to have a negligible effect on the steady-state exposure of adagrasib 600 mg b.i.d. resulting from the significant inactivation of CYP3A4 by adagrasib. Additionally, strong and moderate inducers of CYP3A4 are predicted to decrease adagrasib exposure by 68% and 22%, respectively. As a perpetrator, adagrasib 600 mg b.i.d. is predicted to be a strong inhibitor of CYP3A4, a moderate inhibitor of CYP2C9 and CYP2D6, and an inhibitor of P-glycoprotein (P-gp). These results successfully supported regulatory interactions with the United States Food and Drug Administration regarding dosing recommendations for when adagrasib is used concomitantly with other medications, supporting a range of label claims in lieu of clinical trials.

Prediction of surfactant-mediated dissolution of poorly soluble drugs from drug powder

Beyond rough "what if" estimation, in vitro dissolution is infrequently predicted. The objective was to assess the predictability of a powder dissolution model with a single diffusion layer thickness model, where dissolution of various drugs was facilitated by several surfactant micelles. Powder dissolution of three poorly water soluble drugs (i.e., posaconazole, ritonavir, and griseofulvin) was measured into buffer, as well as four surfactant solutions [i.e., sodium lauryl sulfate (SLS), polysorbate 80 (PS80), polyoxyethylene (10) lauryl ether (POE10), and cetyltrimethylammonium bromide (CTAB)]. Drug solubility, micelle sizing, and powder sizing were also performed. Prediction of drug dissolution employed the film dissolution model, applied to spherical drug particle fractions of the percent weight particle size distribution, and with a surfactant-mediated dissolution component. There were two competing models for diffusion layer thickness: fixed thickness (i.e., hfixed) and radius-dependent thickness (i.e., hmax) models. SLS, PS80, POE10, and CTAB increased the dissolution of posaconazole, ritonavir, and griseofulvin, compared to no-surfactant buffer. Results show that in vitro drug dissolution from various polydisperse powders into several surfactant solutions was successfully predicted using a surfactant-mediated dissolution model. The best diffusion layer thickness for the fixed thickness model and the radius-dependent model were separately found to be hfixed = 12 µm and hmax = 12 µm, respectively, with hfixed = 12 µm being the more preferred. Also, the powder dissolution model where powder was parameterized in terms of its entire particle size distribution was successful in predicting observed dissolution profiles using each hfixed = 12 µm and hmax = 12 µm; model use of a mean particle size was also successful in prediction using hfixed = 12 µm. Credibility assessment of the in vitro dissolution model was performed, including model verification and validation considerations in light of the question of interest, the context of use, and model risk.

A physiologically based biopharmaceutics modeling (PBBM) framework for characterizing formulation-dependent food effects: paving the road towards fed virtual BE studies for itraconazole amorphous solid dispersions

This study leverages physiologically based biopharmaceutics modeling (PBBM) to predict the clinical performance of two itraconazole (ITRA) amorphous solid dispersions (ASDs), Sempera® and Tolsura®, under fasted and fed state conditions, exploring the potential of PBBM in predicting formulation-specific food interactions. The ITRA formulations were subjected to extensive in vitro biopharmaceutical testing, including solubility studies and dissolution tests under fasted and fed state conditions, revealing significant differences in dissolution behaviors between Sempera® and Tolsura®. The impact of food and hypochlorhydria on drug absorption was evaluated using a stepwise mechanistic deconvolution-reconvolution PBBM approach, integrating fundamental parameters based on the in vitro data into the final model. Our model not only successfully predicted the effects of acid reducing agents (ARA) and food on the oral absorption of ITRA, but also captured the between-subject variability, demonstrating the utility of this approach in understanding the complex interplay between drug, formulation, and gastrointestinal environment. Most importantly, the PBBM was able to accurately predict the positive impact of food on the absorption of Sempera® and the negative food effect of Tolsura®. The findings highlight the importance of considering formulation characteristics and gastrointestinal physiology, underscoring the potential of PBBM in bioequivalence (BE) assessment of generic formulations under varying physiological conditions, including in the fed state and in hypochlorhydric patients. The successful application of this stepwise and mechanistic PBBM approach suggests a potential pathway for streamlining drug development and may contribute to more informed decision-making for BE assessment.

Insights into preclinical evaluation of dissolvable microarray patches

Drug-loaded dissolvable microarray patches (MAP) have gained significant attention due to their patient-friendly, economical, and environmentally beneficial attributes. Despite extensive research and advancements, only a limited number of MAP have progressed to clinical trials. While existing literature predominantly covers the initial stages of MAP development (e.g., manufacturing techniques, materials, design), there remains a notable gap in examining an experimental design during preclinical evaluation phase undertaken to inform progression to clinical studies. To address this gap, we present a comprehensive review of the experimental factors influencing MAP performance in preclinical research. Our in-depth analysis of the skin environment and its implications to in vitro MAP performance revealed that skin insertion methodology, media used for release and permeation testing, skin models for permeation studies, and skin metabolism are key factors that need to be considered. We critically assess current research trends and propose potential optimisations to enhance efficacy and biorelevance of in vitro methods for MAP. Additionally, we review factors influencing in vivo and in silico performance, underscoring the promising potential of in silico approaches. This article aims to provide insights that will facilitate the development and standardisation of reliable methodologies in preclinical studies of drug-loaded MAP, ultimately advancing their clinical translation.

Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective

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

Optimization of the betamethasone and dexamethasone dosing regimen during pregnancy: a combined placenta perfusion and pregnancy physiologically based pharmacokinetic modeling approach

BACKGROUND

Antenatal betamethasone and dexamethasone are prescribed to women who are at high risk of premature birth to prevent neonatal respiratory distress syndrome (RDS). The current treatment regimens, effective to prevent neonatal RDS, may be suboptimal. Recently, concerns have been raised regarding possible adverse long-term neurological outcomes due to high fetal drug exposures. Data from nonhuman primates and sheep suggest maintaining a fetal plasma concentration above 1 ng/mL for 48 hours to retain efficacy, while avoiding undesirable high fetal plasma levels.

OBJECTIVE

We aimed to re-evaluate the current betamethasone and dexamethasone dosing strategies to assess estimated fetal exposure and provide new dosing proposals that meet the efficacy target but avoid excessive peak exposures.

STUDY DESIGN

A pregnancy physiologically based pharmacokinetic (PBPK) model was used to predict fetal drug exposures. To allow prediction of the extent of betamethasone and dexamethasone exposure in the fetus, placenta perfusion experiments were conducted to determine placental transfer. Placental transfer rates were integrated in the PBPK model to predict fetal exposure and model performance was verified using published maternal and fetal pharmacokinetic data. The verified pregnancy PBPK models were then used to simulate alternative dosing regimens to establish a model-informed dose.

RESULTS

Ex vivo data showed that both drugs extensively cross the placenta. For betamethasone 15.7±1.7% and for dexamethasone 14.4±1.5%, the initial maternal perfusate concentration reached the fetal circulations at the end of the 3-hour perfusion period. Pregnancy PBPK models that include these ex vivo-derived placental transfer rates accurately predicted maternal and fetal exposures resulting from current dosing regimens. The dose simulations suggest that for betamethasone intramuscular, a dose reduction from 2 dosages 11.4 mg, 24 hours apart, to 4 dosages 1.425 mg, 12 hours apart would avoid excessive peak exposures and still meet the fetal response threshold. For dexamethasone, the dose may be reduced from 4 times 6 mg every 12 hours to 8 times 1.5 mg every 6 hours.

CONCLUSION

A combined placenta perfusion and pregnancy PBPK modeling approach adequately predicted both maternal and fetal drug exposures of 2 antenatal corticosteroids (ACSs). Strikingly, our PBPK simulations suggest that drug doses might be reduced drastically to still meet earlier proposed efficacy targets and minimize peak exposures. We propose the provided model-informed dosing regimens are used to support further discussion on an updated ACS scheme and design of clinical trials to confirm the effectiveness and safety of lower doses.

Physiologically Based Pharmacokinetic Modeling to Refine Dosing of Posaconazole in Young Children

PURPOSE

Posaconazole is a broad-spectrum antifungal for treating and preventing invasive fungal infections (IFIs) in immunocompromised individuals, including children as young as 2 years. Available in delayed-release (DR) oral suspension, intravenous formulation, and older immediate-release (IR) formulation (off-label in younger children), dosing harmonization across age groups and formulations remains inconsistent. This inconsistency arises from the unique physiology of young children and posaconazole's pH-dependent absorption. Limited pharmacokinetic (PK) data for children under 2 years complicates dosing, as absorption, distribution, metabolism, and excretion processes are underdeveloped and age-dependent. This work aims to harmonize pediatric dosing for children aged 2 to 7 years and extend dosing guidance for those aged 6 months to 2 years using physiologically-based PK (PBPK) modeling.

METHODS

An adult PBPK model was created using posaconazole's physicochemical properties and ADME characteristics with virtual populations from PK-Sim. Calibrated with single-dose data from healthy subjects, the model was verified by predicting PK following multiple doses in adults at risk for IFIs. The model was then scaled to children, accounting for developmental anatomy and physiology, including UGT1A4 ontogeny. The pediatric model was evaluated against observed data from children aged 2 to 7 years. Simulations were conducted to harmonize dosing across formulations and extend dosing to children as young as 6 months, acknowledging standard plasma concentration targets for treatment of IFIs (1000 ng/mL) as well as prophylaxis (700 ng/mL).

FINDINGS

The pediatric model adequately captured observed PK data from literature following all three formulations. The IR oral suspension is impractical and likely subtherapeutic for most children under 7 years due to solubility limits. Intravenous doses of 11-13 mg/kg once daily (QD) may be optimal for treatment, and 8 to 9 mg/kg QD for prophylaxis, varying by age. Oral DR suspension doses of 12 to 14 mg/kg QD for treatment and 8.5 to 10 mg/kg QD for prophylaxis may be optimal, also age-dependent. Dividing the total daily dose by a factor of 0.7 and administering twice daily can achieve similar trough levels.

IMPLICATIONS

PBPK modeling for posaconazole bridges the gap between PK principles and clinical practice, potentially improving therapeutic outcomes and minimizing risks associated with inadequate dosing in pediatric patients.

Physiologically based pharmacokinetic modeling of small molecules: How much progress have we made?

Physiologically based pharmacokinetic (PBPK) models of small molecules have become mainstream in drug development and in academic research. The use of PBPK models is continuously expanding, with the majority of work now focusing on predictions of drug-drug interactions, drug-disease interactions, and changes in drug disposition across lifespan. Recently, publications that use PBPK modeling to predict drug disposition during pregnancy and in organ impairment have increased reflecting the advances in incorporating diverse physiologic changes into the models. Because of the expanding computational power and diversity of modeling platforms available, the complexity of PBPK models has also increased. Academic efforts have provided clear advances in better capturing human physiology in PBPK models and incorporating more complex mathematical concepts into PBPK models. Examples of such advances include the segregated gut model with a series of gut compartments allowing modeling of physiologic blood flow distribution within an organ and zonation of metabolic enzymes and series compartment liver models allowing simulations of hepatic clearance for high extraction drugs. Despite these advances in academic research, the progress in assessing model quality and defining model acceptance criteria based on the intended use of the models has not kept pace. This Minireview suggests that awareness of the need for predefined criteria for model acceptance has increased, but many manuscripts still lack description of scientific justification and/or rationale for chosen acceptance criteria. As artificial intelligence and machine learning approaches become more broadly accepted, these tools offer promise for development of comprehensive assessment for existing observed data and analysis of model performance. SIGNIFICANCE STATEMENT: Physiologically based pharmacokinetic (PBPK) modeling has become a mainstream application in academic literature and is broadly used for predictions, analysis, and evaluation of pharmacokinetic data. Significant progress has been made in developing advanced PBPK models that better capture human physiology, but oftentimes sufficient justification for the chosen model acceptance criterion and model structure is still missing. This Minireview provides a summary of the current landscape of PBPK applications used and highlights the need for advancing PBPK modeling science and training in academia.

Evaluation of the Drug-Drug Interaction Potential of Cannabidiol Against UGT2B7-Mediated Morphine Metabolism Using Physiologically Based Pharmacokinetic Modeling

Background: Morphine is a commonly prescribed opioid analgesic used to treat chronic pain. Morphine undergoes glucuronidation by UDP-glucuronosyltransferase (UGT) 2B7 to form morphine-3-glucuronide and morphine-6-glucuronide. Morphine is the gold standard for chronic pain management and has a narrow therapeutic index. Reports have shown that chronic pain patients have increasingly used other supplements to treat their chronic pain, including cannabidiol (CBD). Up to 50% of chronic pain patients report that they co-use cannabis with their prescribed opioid for pain management, including morphine. Previous work has shown that cannabidiol is a potent inhibitor of UGT2B7, including morphine-mediated metabolism. Co-use of morphine and CBD may result in unwanted drug-drug interactions (DDIs). Methods: Using available physiochemical and clinical parameters, morphine and CBD physiologically based pharmacokinetic (PBPK) models were developed and validated in both healthy and cirrhotic populations. Models for the two populations were then combined to predict the severity and clinical relevance of the potential DDIs during coadministration of both morphine and CBD in both healthy and hepatic-impaired virtual populations. Results: The predictive DDI model suggests that a ~5% increase in morphine exposure is to be expected in healthy populations. A similar increase in exposure of morphine is predicted in severe hepatic-impaired populations with an increase of ~10. Conclusions: While these predicted increases in morphine exposure are below the Food and Drug Administration's cutoff (1.25-fold increase), morphine has a narrow therapeutic index and a 5-10% increase in exposure may be clinically relevant. Future clinical studies are needed to fully characterize the clinical relevance of morphine-related DDIs.

The emerging challenge of Enterococcus faecalis endocarditis after transcatheter aortic valve implantation: time for innovative treatment approaches

SUMMARYInfective endocarditis (IE) is a life-threatening infection that has nearly doubled in prevalence over the last two decades due to the increase in implantable cardiac devices. Transcatheter aortic valve implantation (TAVI) is currently one of the most common cardiac procedures. TAVI usage continues to exponentially rise, inevitability increasing TAVI-IE. Patients with TAVI are frequently nonsurgical candidates, and TAVI-IE 1-year mortality rates can be as high as 74% without valve or bacterial biofilm removal. Enterococcus faecalis, a historically less common IE pathogen, is the primary cause of TAVI-IE. Treatment options are limited due to enterococcal intrinsic resistance and biofilm formation. Novel approaches are warranted to tackle current therapeutic gaps. We describe the existing challenges in treating TAVI-IE and how available treatment discovery approaches can be combined with an in silico "Living Heart" model to create solutions for the future.

A quantification of gastric and duodenal fluid volumes in older adults using MRI

Older adults are an inherently heterogeneous population with various underlying pathologies, medication use, and habits. In this study, the variability of this population was studied for the gastric and duodenal fluid volumes, as the amount of gastrointestinal volumes could play an essential role in the dissolution of drugs. The fluid volumes were retrospectively quantified by using magnetic resonance imaging (MRI). In 265 included fasted older individuals, the gastric fluid volume was 28.9 ± 21.1 mL (arithmetic mean ± standard deviation). No significant covariate-effect on stomach fluid volume was observed for various medication use, pathologies, and habits (e.g. hypertension, smoking, proton-pump inhibitors (PPIs), and aspirin). The gastric fluid volume remained constant with increasing age and had a high variability. The volumes and the variability were, however, not higher than the gastric values reported in healthy younger adults. The duodenal fluid volume was 16.6 ± 10.0 mL and a slight but statistically significant decrease with age was seen. In addition, cystic pancreas, obesity, diuretics, and PPI use demonstrated a moderate but significant correlation with the duodenal fluid volume. The findings of this study could be considered when developing and testing new drug candidates for the older adult population. For example, the volumes including their variability could be used as an input in physiologically based pharmacokinetic (PBPK) modelling approaches to predict drug exposure in this population.

Physiologically based pharmacokinetic model of brivaracetam to predict the exposure and dose exploration in hepatic impairment and elderly populations

Brivaracetam (BRV) is a new third-generation antiseizure medication for the treatment of focal epileptic seizures. Its use has been increasing among epileptic populations in recent years, but pharmacokinetic (PK) behavior may change in hepatic impairment and the elderly populations. Due to ethical constraints, clinical trials are difficult to conduct and data are limited. This study used PK-Sim® to develop a physiologically based pharmacokinetic (PBPK) model for adults and extrapolate it to hepatic impairment and the elderly populations. The model was evaluated with clinical PK data, and dosage explorations were conducted. For the adult population with mild hepatic impairment, the dose is recommended to be adjusted to 70 % of the recommended dose, and to 60 % for moderate and severe hepatic impairment. For the elderly population with mild hepatic impairment under 80 years old, it is recommended that the dose be adjusted to 60 % of the recommended dose and to 50 % for moderate and severe conditions. The elderly population with hepatic impairment over 80 years old is adjusted to 50 % of the recommended dose for all stages. Healthy elderly do not need to adjust. The BRV PBPK model was successfully developed, studying exposure in hepatic impairment and elderly populations and optimizing dosing regimens.

Prediction of Pharmacokinetic Drug-Drug Interactions Involving Anlotinib as a Victim by Using Physiologically Based Pharmacokinetic Modeling

Background

Anlotinib was approved as a third line therapy for advanced non-small cell lung cancer in China. However, the impact of concurrent administration of various clinical drugs on the drug-drug interaction (DDI) potential of anlotinib remains undetermined. As such, this study aims to evaluate the DDI of anlotinib as a victim by establishing a physiologically based pharmacokinetic (PBPK) model.

Methods

The PBPK model of anlotinib as a victim drug was constructed and validated in the Simcyp® incorporating parameters derived from in vitro studies, pre-clinical investigations, and clinical research encompassing patients with cancer. Subsequently, plasma exposure of anlotinib in cancer patients was predicted for single- and multi-dose co-administration with typical perpetrators mentioned in Food and Drug Administration (FDA) industrial guidance.

Results

Based on predictions, the CYP3A potent inhibitor ketoconazole demonstrated the most significant DDI with anlotinib, regardless of whether anlotinib is administered as a single dose or multiple doses. Ketoconazole increased the area under the concentration-time curve (AUC) and maximum concentration (Cmax) of single-dose anlotinib to 1.41-fold and 1.08-fold, respectively. In contrast, rifampicin, a potent inducer of CYP3A enzymes, exhibited a relatively higher level of DDI, with AUCR and CmaxR values of 0.44 and 0.79, respectively.

Conclusion

Based on the PBPK modeling, there is a low risk of DDI between anlotinib and potent CYP3A/1A2 inhibitors, but caution and enhanced monitoring for adverse reactions are advised. To mitigate the risk of anti-tumor treatment failure, it is recommended to avoid concurrent use of strong CYP3A inducers. In conclusion, our study enhances understanding of anlotinib's interaction with medications, aiding scientists, prescribers, and drug labels in gauging the expected impact of CYP3A/1A2 modulators on anlotinib's pharmacokinetics.

Key Factors for Improving Predictive Accuracy and Avoiding Overparameterization of the PBPK Absorption Model in Food Effect Studies of Weakly Basic Water-Insoluble Compounds in Immediate Release Formulations

Background/Objectives: Physiologically based pharmacokinetic (PBPK) absorption models are instrumental for assessing drug absorption prior to clinical food effect studies, though discrepancies in predictive and actual outcomes are observed. This study focused on immediate release formulations of weakly basic water-insoluble compounds, namely rivaroxaban, ticagrelor, and PB-201, to investigate factors that could improve the predictive accuracy of PBPK models regarding food effects. Methods: Comprehensive in vitro experimental results provided the basis for the development of mechanistic absorption models, which were then combined with mechanistic disposition models to predict the systemic exposure of the model drugs in both fasted and fed states. Results: The developed PBPK models showed moderate to high predictive accuracy for food effects in Caucasian populations. For the Chinese population, the ticagrelor model's initial overestimation of fed-state absorption was addressed by updating the permeability parameters from Caco-2 cell assays to those derived from parallel artificial membrane permeability assays in FaSSIF and FeSSIF media. This refinement was also applied to the rivaroxaban and ticagrelor models, leading to a more accurate representation of absorption in Caucasians. Conclusions: This study highlights the importance of apparent permeability in enhancing the predictive accuracy of PBPK absorption models for weakly basic water-insoluble compounds. Furthermore, the precipitation of PB-201 in the two-stage transfer experiments suggests that precipitation may not be a universal phenomenon for such compounds in vivo. Consequently, the precipitation rate constant, a theoretically essential parameter, should be determined based on experimental evidence to avoid overparameterization and ensure robust predictive accuracy of PBPK models.

Development and Evaluation of Ontogeny Functions of the Major UDP-Glucuronosyltransferase Enzymes to Underwrite Physiologically Based Pharmacokinetic Modeling in Pediatric Populations

Uridine 5'-diphospho-glucuronosyltransferases (UGTs) demonstrate variable expression in the pediatric population. Thus, understanding of age-dependent maturation of UGTs is critical for accurate pediatric pharmacokinetics (PK) prediction of drugs that are susceptible for glucuronidation. Ontogeny functions of major UGTs have been previously developed and reported. However, those ontogeny functions are based on in vitro data (i.e., enzyme abundance, in vitro substrate activity, and so on) and therefore, may not translate to in vivo maturation of UGTs in the clinical setting. This report describes meta-analysis of the literature to develop and compare ontogeny functions for 8 primary UGTs (UGT1A1, UGT1A4, UGT1A6, UGT1A9, UGT2B7, UGT2B10, UGT2B15, and UGT2B17) based on published in vitro and in vivo studies. Once integrated with physiologically based pharmacokinetics modeling models, in vivo activity-based ontogeny functions demonstrated somewhat greater prediction accuracy (mean squared error, MSE: 0.05) compared to in vitro activity (MSE: 0.104) and in vitro abundance-based ontogeny functions (MSE: 0.129).

A guide to developing population files for physiologically-based pharmacokinetic modeling in the Simcyp Simulator

The Simcyp Simulator is a software platform widely used in the pharmaceutical industry to conduct stochastic physiologically-based pharmacokinetic (PBPK) modeling. This approach has the advantage of combining routinely generated in vitro data on drugs and drug products with knowledge of biology and physiology parameters to predict a priori potential pharmacokinetic changes in absorption, distribution, metabolism, and excretion for populations of interest. Combining such information with pharmacodynamic knowledge of drugs enables planning for potential dosage adjustment when clinical studies are feasible. Although the conduct of dedicated clinical studies in some patient groups (e.g., with hepatic or renal diseases) is part of the regulatory path for drug development, clinical studies for all permutations of covariates potentially affecting pharmacokinetics are impossible to perform. The role of PBPK in filling the latter gap is becoming more appreciated. This tutorial describes the different input parameters required for the creation of a virtual population giving robust predictions of likely changes in pharmacokinetics. It also highlights the considerations needed to qualify the models for such contexts of use. Two case studies showing the step-by-step development and application of population files for obese or morbidly obese patients and individuals with Crohn's disease are provided as the backbone of our tutorial to give some hands-on and real-world examples.

Physiologically based pharmacokinetic models for predicting lamotrigine exposure and dose optimization in pediatric patients receiving combination therapy with carbamazepine or valproic acid

INTRODUCTION

Lamotrigine (LTG) is an antiepileptic drug that has been used in pediatric epilepsy as a combination therapy or monotherapy after stabilization in recent years. However, there are significant drug-drug interactions (DDI) between LTG and combined drugs such as carbamazepine (CBZ) and valproic acid (VPA). It is particularly important to consider the risk of DDI in combination therapy for intractable epilepsy in pediatric patients. Therefore, it is necessary to adjust the dosage of LTG accordingly. The aim of this study was to establish and validate a pediatric physiologically based pharmacokinetic (PBPK) model for predicting LTG exposure. The model is designed to explore the potential for quantifying pharmacokinetic (PK) DDI of LTG when administered concurrently with CBZ or VPA in pediatric patients.

METHOD

Adult and pediatric PBPK models for LTG and VPA were developed using PK-Sim® software in combination with physiological information and drug-specific parameters, and a DDI model was developed in combination with the published CBZ model. The models were validated against available PK data.

RESULTS

Predictive and observational results in adults, children, and the DDI model were in good agreement. The recommended doses of LTG for preschool children (2-6 years) and school-aged children (6-12 years) in the absence of drug interactions were 1.47 and 1.2 times higher than those for adults, respectively; 3.1 and 2.6 times higher than those for adults in combination with CBZ; and 0.67 and 0.57 times lower than those for adults in combination with VPA. In addition, plasma exposures in adolescents (12-18 years) were similar to those in adults at the same doses.

CONCLUSION

We have successfully developed PBPK models and DDI models for LTG in adults and children, which provide a reference for rational drug use in the pediatric population.

Mechanism-based inactivation of cytochromes P450: implications in drug interactions and pharmacotherapy

Cytochrome P40 (CYP) enzymes dominate the metabolism of numerous endogenous and xenobiotic substances. While it is commonly believed that CYP-catalysed reactions result in the detoxication of foreign substances, these reactions can also yield reactive intermediates that can bind to cellular macromolecules to cause cytotoxicity or irreversibly inactivate CYPs that create them.Mechanism-based inactivation (MBI) produces either irreversible or quasi-irreversible inactivation and is commonly caused by CYP metabolic bioactivation to an electrophilic reactive intermediate. Many drugs that have been known to cause MBI in CYPs have been discovered as perpetrators in drug-drug interactions throughout the last 20-30 years.This review will highlight the key findings from the recent literature about the mechanisms of CYP enzyme inhibition, with a focus on the broad mechanistic elements of MBI for widely used drugs linked to the phenomenon. There will also be a brief discussion of the clinical or pharmacokinetic consequences of CYP inactivation with regard to drug interaction and toxicity risk.Gaining knowledge about the selective inactivation of CYPs by common therapeutic drugs helps with the assessment of factors that affect the systemic clearance of co-administered drugs and improves comprehension of anticipated interactions with other drugs or xenobiotics.

Application of physiologically based pharmacokinetic modeling of novel drugs approved by the U.S. food and drug administration

Physiologically based pharmacokinetic (PBPK) models which can leverage preclinical data to predict the pharmacokinetic properties of drugs rapidly became an essential tool to improve the efficiency and quality of novel drug development. In this review, by searching the Application Review Files in Drugs@FDA, we analyzed the current application of PBPK models in novel drugs approved by the U.S. Food and Drug Administration (FDA) in the past five years. According to the results, 243 novel drugs were approved by the FDA from 2019 to 2023. During this period, 74 Application Review Files of novel drugs approved by the FDA that used PBPK models. PBPK models were used in various areas, including drug-drug interactions (DDI), organ impairment (OI) patients, pediatrics, drug-gene interaction (DGI), disease impact, and food effects. DDI was the most widely used area of PBPK models for novel drugs, accounting for 74.2 % of the total. Software platforms with graphical user interfaces (GUI) have reduced the difficulty of PBPK modeling, and Simcyp was the most popular software platform among applicants, with a usage rate of 80.5 %. Despite its challenges, PBPK has demonstrated its potential in novel drug development, and a growing number of successful cases provide experience learned for researchers in the industry.

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.

Physiologically-based pharmacokinetic/pharmacodynamic modeling of meropenem in critically ill patients

This study aimed to develop a physiologically based pharmacokinetic/pharmacodynamic model (PBPK/PD) of meropenem for critically ill patients. A PBPK model of meropenem in healthy adults was established using PK-Sim software and subsequently extrapolated to critically ill patients based on anatomic and physiological parameters. The mean fold error (MFE) and geometric mean fold error (GMFE) methods were used to compare the differences between predicted and observed values of pharmacokinetic parameters Cmax, AUC0-∞, and CL to evaluate the accuracy of the PBPK model. The model was verified using meropenem plasma samples obtained from Intensive Care Unit (ICU) patients, which were determined by HPLC-MS/MS. After that, the PBPK model was combined with a PKPD model, which was developed based on f%T > MIC. Monte Carlo simulation was utilized to calculate the probability of target attainment (PTA) in patients. The developed PBPK model successfully predicted the meropenem disposition in critically ill patients, wherein the MFE average and GMFE of all predicted PK parameters were within the 1.25-fold error range. The therapeutic drug monitoring (TDM) of meropenem was conducted with 92 blood samples from 31 ICU patients, of which 71 (77.17%) blood samples were consistent with the simulated value. The TDM results showed that meropenem PBPK modeling is well simulated in critically ill patients. Monte Carlo simulations showed that extended infusion and frequent administration were necessary to achieve curative effect for critically ill patients, whereas excessive infusion time (> 4 h) was unnecessary. The PBPK/PD modeling incorporating literature and prospective study data can predict meropenem pharmacokinetics in critically ill patients correctly. Our study provides a reference for dose adjustment in critically ill patients.

Drug-Drug Interactions between COVID-19 and Tuberculosis Medications: A Comprehensive Review of CYP450 and Transporter-Mediated Effects

Most medications undergo metabolism and elimination via CYP450 enzymes, while uptake and efflux transporters play vital roles in drug elimination from various organs. Interactions often occur when multiple drugs share CYP450-transporter-mediated metabolic pathways, necessitating a unique clinical care strategy to address the diverse types of CYP450 and transporter-mediated drug-drug interactions (DDI). The primary focus of this review is to record relevant mechanisms regarding DDI between COVID-19 and tuberculosis (TB) treatments, specifically through the influence of CYP450 enzymes and transporters on drug absorption, distribution, metabolism, elimination, and pharmacokinetics. This understanding empowers clinicians to prevent subtherapeutic and supratherapeutic drug levels of COVID medications when co-administered with TB drugs, thereby mitigating potential challenges and ensuring optimal treatment outcomes. A comprehensive analysis is presented, encompassing various illustrative instances of TB drugs that may impact COVID-19 clinical behavior, and vice versa. This review aims to provide valuable insights to healthcare providers, facilitating informed decision-making and enhancing patient safety while managing co-infections. Ultimately, this study contributes to the body of knowledge necessary to optimize therapeutic approaches and improve patient outcomes in the face of the growing challenges posed by infectious diseases.

Considerations from an International Regulatory and Pharmaceutical Industry (IQ MPS Affiliate) Workshop on the Standardization of Complex In Vitro Models in Drug Development

In May 2022, there is an International Regulatory and Pharmaceutical Industry (Innovation and Quality [IQ] Microphysiological Systems [MPS] Affiliate) Workshop on the standardization of complex in vitro models (CIVMs) in drug development. This manuscript summarizes the discussions and conclusions of this joint workshop organized and executed by the IQ MPS Affiliate and the United States Food and Drug Administration (FDA). A key objective of the workshop is to facilitate discussions around opportunities and/or needs for standardization of MPS and chart potential pathways to increase model utilization in the context of regulatory decision making. Participation in the workshop included 200 attendees from the FDA, IQ MPS Affiliate, and 26 global regulatory organizations and affiliated parties representing Europe, Japan, and Canada. It is agreed that understanding global perspectives regarding the readiness of CIVM/MPS models for regulatory decision making and potential pathways to gaining acceptance is useful to align on globally. The obstacles are currently too great to develop standards for every context of use (COU). Instead, it is suggested that a more tractable approach may be to think of broadly applicable standards that can be applied regardless of COU and/or organ system. Considerations and next steps for this effort are described.

Docetaxel, cyclophosphamide, and epirubicin: application of PBPK modeling to gain new insights for drug-drug interactions

The new adjuvant chemotherapy of docetaxel, epirubicin, and cyclophosphamide has been recommended for treating breast cancer. It is necessary to investigate the potential drug-drug Interactions (DDIs) since they have a narrow therapeutic window in which slight differences in exposure might result in significant differences in treatment efficacy and tolerability. To guide clinical rational drug use, this study aimed to evaluate the DDI potentials of docetaxel, cyclophosphamide, and epirubicin in cancer patients using physiologically based pharmacokinetic (PBPK) models. The GastroPlus™ was used to develop the PBPK models, which were refined and validated with observed data. The established PBPK models accurately described the pharmacokinetics (PKs) of three drugs in cancer patients, and the predicted-to-observed ratios of all the PK parameters met the acceptance criterion. The PBPK model predicted no significant changes in plasma concentrations of these drugs during co-administration, which was consistent with the observed clinical phenomenon. Besides, the verified PBPK models were then used to predict the effect of other Cytochrome P450 3A4 (CYP3A4) inhibitors/inducers on these drug exposures. In the DDI simulation, strong CYP3A4 modulators changed the exposure of three drugs by 0.71-1.61 fold. Therefore, patients receiving these drugs in combination with strong CYP3A4 inhibitors should be monitored regularly to prevent adverse reactions. Furthermore, co-administration of docetaxel, cyclophosphamide, or epirubicin with strong CYP3A4 inducers should be avoided. In conclusion, the PBPK models can be used to further investigate the DDI potential of each drug and to develop dosage recommendations for concurrent usage by additional perpetrators or victims.

Clinical Trial Data-Driven Risk Assessment of Drug-Drug Interactions: A Rapid and Accurate Decision-Making Tool

BACKGROUND

In clinical practice, the vast array of potential drug combinations necessitates swift and accurate assessments of pharmacokinetic drug-drug interactions (DDIs), along with recommendations for adjustments. Current methodologies for clinical DDI evaluations primarily rely on basic extrapolations from clinical trial data. However, these methods are limited in accuracy owing to their lack of a comprehensive consideration of various critical factors, including the inhibitory potency, dosage, and type of the inhibitor, as well as the metabolic fraction and intestinal availability of the substrate.

OBJECTIVE

This study aims to propose an efficient and accurate clinical pharmacokinetic-mediated DDI assessment tool, which comprehensively considers the effects of inhibitory potency and dosage of inhibitors, intestinal availability and fraction metabolized of substrates on DDI outcomes.

METHODS

This study focuses on DDIs caused by cytochrome P450 3A4 enzyme inhibition, utilizing extensive clinical trial data to establish a methodology to calculate the metabolic fraction and intestinal availability for substrates, as well as the concentration and inhibitory potency for inhibitors ( K i ork inact / K I ). These parameters were then used to predict the outcomes of DDIs involving 33 substrates and 20 inhibitors. We also defined the risk index for substrates and the potency index for inhibitors to establish a clinical DDI risk scale. The training set for parameter calculation consisted of 73 clinical trials. The validation set comprised 89 clinical DDI trials involving 53 drugs. which was used to evaluate the reliability of in vivo values of K i andk inact / K I , the accuracy of DDI predictions, and the false-negative rate of risk scale.

RESULTS

First, the reliability of the in vivo K i andk inact / K I values calculated in this study was assessed using a basic static model. Compared with values obtained from other methods, this study values showed a lower geometric mean fold error and root mean square error. Additionally, incorporating these values into the physiologically based pharmacokinetic-DDI model facilitated a good fitting of the C-t curves when the substrate's metabolic enzymes are inhibited. Second, area under the curve ratio predictions of studied drugs were within a 1.5 × margin of error in 81% of cases compared with clinical observations in the validation set. Last, the clinical DDI risk scale developed in this study predicted the actual risks in the validation set with only a 5.6% incidence of serious false negatives.

CONCLUSIONS

This study offers a rapid and accurate approach for assessing the risk of pharmacokinetic-mediated DDIs in clinical practice, providing a foundation for rational combination drug use and dosage adjustments.

Physiologically based pharmacokinetic modeling of CYP2C8 substrate rosiglitazone and its metabolite to predict metabolic drug-drug interaction

Rosiglitazone is an activator of nuclear peroxisome proliferator-activated (PPAR) receptor gamma used in the treatment of type 2 diabetes mellitus. The elimination of rosiglitazone occurs mainly via metabolism, with major contribution by enzyme cytochrome P450 (CYP) 2C8. Primary routes of rosiglitazone metabolism are N-demethylation and hydroxylation. Modulation of CYP2C8 activity by co-administered drugs lead to prominent changes in the exposure of rosiglitazone and its metabolites. Here, we attempt to develop mechanistic parent-metabolite physiologically based pharmacokinetic (PBPK) model for rosiglitazone. Our goal is to predict potential drug-drug interaction (DDI) and consequent changes in metabolite N-desmethyl rosiglitazone exposure. The PBPK modeling was performed in the PKSim® software using clinical pharmacokinetics data from literature. The contribution to N-desmethyl rosiglitazone formation by CYP2C8 was delineated using vitro metabolite formation rates from recombinant enzyme system. Developed model was verified for prediction of rosiglitazone DDI potential and its metabolite exposure based on observed clinical DDI studies. Developed model exhibited good predictive performance both for rosiglitazone and N-desmethyl rosiglitazone respectively, evaluated based on commonly acceptable criteria. In conclusion, developed model helps with prediction of CYP2C8 DDI using rosiglitazone as a substrate, as well as changes in metabolite exposure. In vitro data for metabolite formation can be successfully utilized to translate to in vivo conditions.

Transporter-mediated drug-drug interactions: regulatory guidelines, in vitro and in vivo methodologies and translation, special populations, and the blood-brain barrier

This review, part of a special issue on drug-drug interactions (DDIs) spearheaded by the International Society for the Study of Xenobiotics (ISSX) New Investigators, explores the critical role of drug transporters in absorption, disposition, and clearance in the context of DDIs. Over the past two decades, significant advances have been made in understanding the clinical relevance of these transporters. Current knowledge on key uptake and efflux transporters that affect drug disposition and development is summarized. Regulatory guidelines from the FDA, EMA, and PMDA that inform the evaluation of potential transporter-mediated DDIs are discussed in detail. Methodologies for preclinical and clinical testing to assess potential DDIs are reviewed, with an emphasis on the utility of physiologically based pharmacokinetic (PBPK) modeling. This includes the application of relative abundance and expression factors to predict human pharmacokinetics (PK) using preclinical data, integrating the latest regulatory guidelines. Considerations for assessing transporter-mediated DDIs in special populations, including pediatric, hepatic, and renal impairment groups, are provided. Additionally, the impact of transporters at the blood-brain barrier (BBB) on the disposition of CNS-related drugs is explored. Enhancing the understanding of drug transporters and their role in drug disposition and toxicity can improve efficacy and reduce adverse effects. Continued research is essential to bridge remaining gaps in knowledge, particularly in comparison with cytochrome P450 (CYP) enzymes.

A physiologically-based pharmacokinetic precision dosing approach to manage dasatinib drug-drug interactions

Dasatinib, a second-generation tyrosine kinase inhibitor, is approved for treating chronic myeloid and acute lymphoblastic leukemia. As a sensitive cytochrome P450 (CYP) 3A4 substrate and weak base with strong pH-sensitive solubility, dasatinib is susceptible to enzyme-mediated drug-drug interactions (DDIs) with CYP3A4 perpetrators and pH-dependent DDIs with acid-reducing agents. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model of dasatinib to describe and predict enzyme-mediated and pH-dependent DDIs, to evaluate the impact of strong and moderate CYP3A4 inhibitors and inducers on dasatinib exposure and to support optimized dasatinib dosing. Overall, 63 plasma profiles from perorally administered dasatinib in healthy volunteers and cancer patients were used for model development. The model accurately described and predicted plasma profiles with geometric mean fold errors (GMFEs) for area under the concentration-time curve from the first to the last timepoint of measurement (AUClast) and maximum plasma concentration (Cmax) of 1.27 and 1.29, respectively. Regarding the DDI studies used for model development, all (8/8) predicted AUClast and Cmax ratios were within twofold of observed ratios. Application of the PBPK model for dose adaptations within various DDIs revealed dasatinib dose reductions of 50%-80% for strong and 0%-70% for moderate CYP3A4 inhibitors and a 2.3-3.1-fold increase of the daily dasatinib dose for CYP3A4 inducers to match the exposure of dasatinib administered alone. The developed model can be further employed to personalize dasatinib therapy, thereby help coping with clinical challenges resulting from DDIs and patient-related factors, such as elevated gastric pH.

Physiologically based pharmacokinetic modeling of imatinib and N-desmethyl imatinib for drug-drug interaction predictions

The first-generation tyrosine kinase inhibitor imatinib has revolutionized the development of targeted cancer therapy and remains among the frontline treatments, for example, against chronic myeloid leukemia. As a substrate of cytochrome P450 (CYP) 2C8, CYP3A4, and various transporters, imatinib is highly susceptible to drug-drug interactions (DDIs) when co-administered with corresponding perpetrator drugs. Additionally, imatinib and its main metabolite N-desmethyl imatinib (NDMI) act as inhibitors of CYP2C8, CYP2D6, and CYP3A4 affecting their own metabolism as well as the exposure of co-medications. This work presents the development of a parent-metabolite whole-body physiologically based pharmacokinetic (PBPK) model for imatinib and NDMI used for the investigation and prediction of different DDI scenarios centered around imatinib as both a victim and perpetrator drug. Model development was performed in PK-Sim® using a total of 60 plasma concentration-time profiles of imatinib and NDMI in healthy subjects and cancer patients. Metabolism of both compounds was integrated via CYP2C8 and CYP3A4, with imatinib additionally transported via P-glycoprotein. The subsequently developed DDI network demonstrated good predictive performance. DDIs involving imatinib and NDMI were simulated with perpetrator drugs rifampicin, ketoconazole, and gemfibrozil as well as victim drugs simvastatin and metoprolol. Overall, 12/12 predicted DDI area under the curve determined between first and last plasma concentration measurements (AUClast) ratios and 12/12 predicted DDI maximum plasma concentration (Cmax) ratios were within twofold of the respective observed ratios. Potential applications of the final model include model-informed drug development or the support of model-informed precision dosing.

Physiologically based pharmacokinetic modeling to predict the pharmacokinetics of codeine in different CYP2D6 phenotypes

Objectives

Codeine, a prodrug used as an opioid agonist, is metabolized to the active product morphine by CYP2D6. This study aimed to establish physiologically based pharmacokinetic (PBPK) models of codeine and morphine and explore the influence of CYP2D6 genetic polymorphisms on the pharmacokinetics of codeine and morphine.

Methods

An initial PBPK modeling of codeine in healthy adults was established using PK-Sim® software and subsequently extrapolated to CYP2D6 phenotype-related PBPK modeling based on the turnover frequency (Kcat) of CYP2D6 for different phenotype populations (UM, EM, IM, and PM). The mean fold error (MFE) and geometric mean fold error (GMFE) methods were used to compare the differences between the predicted and observed values of the pharmacokinetic parameters to evaluate the accuracy of PBPK modeling. The validated models were then used to support dose safety for different CYP2D6 phenotypes.

Results

The developed and validated CYP2D6 phenotype-related PBPK model successfully predicted codeine and morphine dispositions in different CYP2D6 phenotypes. Compared with EMs, the predicted AUC0-∞ value of morphine was 98.6% lower in PMs, 60.84% lower in IMs, and 73.43% higher in UMs. Morphine plasma exposure in IMs administered 80 mg of codeine was roughly comparable to that in EMs administered 30 mg of codeine. CYP2D6 UMs may start dose titration to achieve an optimal individual regimen and avoid a single dose of over 20 mg. Codeine should not be used in PMs for pain relief, considering its insufficient efficacy.

Conclusion

PBPK modeling can be applied to explore the dosing safety of codeine and can be helpful in predicting the effect of CYP2D6 genetic polymorphisms on drug-drug interactions (DDIs) with codeine in the future.

A physiologically-based pharmacokinetic/pharmacodynamic modeling approach for drug-drug-gene interaction evaluation of S-warfarin with fluconazole

Warfarin is a widely used anticoagulant, and its S-enantiomer has higher potency compared to the R-enantiomer. S-warfarin is mainly metabolized by cytochrome P450 (CYP) 2C9, and its pharmacological target is vitamin K epoxide reductase complex subunit 1 (VKORC1). Both CYP2C9 and VKORC1 have genetic polymorphisms, leading to large variations in the pharmacokinetics (PKs) and pharmacodynamics (PDs) of warfarin in the population. This makes dosage management of warfarin difficult, especially in the case of drug-drug interactions (DDIs). This study provides a whole-body physiologically-based pharmacokinetic/PD (PBPK/PD) model of S-warfarin for predicting the effects of drug-drug-gene interactions on S-warfarin PKs and PDs. The PBPK/PD model of S-warfarin was developed in PK-Sim and MoBi. Drug-dependent parameters were obtained from the literature or optimized. Of the 34 S-warfarin plasma concentration-time profiles used, 96% predicted plasma concentrations within twofold range compared to observed data. For S-warfarin plasma concentration-time profiles with CYP2C9 genotype, 364 of 386 predicted plasma concentration values (~94%) fell within the twofold of the observed values. This model was tested in DDI predictions with fluconazole as CYP2C9 perpetrators, with all predicted DDI area under the plasma concentration-time curve to the last measurable timepoint (AUClast) ratio within twofold of the observed values. The anticoagulant effect of S-warfarin was described using an indirect response model, with all predicted international normalized ratio (INR) within twofold of the observed values. This model also incorporates a dose-adjustment method that can be used for dose adjustment and predict INR when warfarin is used in combination with CYP2C9 perpetrators.

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.

Understanding CYP3A4 and P-gp mediated drug-drug interactions through PBPK modeling - Case example of pralsetinib

Pralsetinib, a potent and selective inhibitor of oncogenic RET fusion and RET mutant proteins, is a substrate of the drug metabolizing enzyme CYP3A4 and a substrate of the efflux transporter P-gp based on in vitro data. Therefore, its pharmacokinetics (PKs) may be affected by co-administration of potent CYP3A4 inhibitors and inducers, P-gp inhibitors, and combined CYP3A4 and P-gp inhibitors. With the frequent overlap between CYP3A4 and P-gp substrates/inhibitors, pralsetinib is a challenging and representative example of the need to more quantitatively characterize transporter-enzyme interplay. A physiologically-based PK (PBPK) model for pralsetinib was developed to understand the victim drug-drug interaction (DDI) risk for pralsetinib. The key parameters driving the magnitude of pralsetinib DDIs, the P-gp intrinsic clearance and the fraction metabolized by CYP3A4, were determined from PBPK simulations that best captured observed DDIs from three clinical studies. Sensitivity analyses and scenario simulations were also conducted to ensure these key parameters were determined with sound mechanistic rationale based on current knowledge, including the worst-case scenarios. The verified pralsetinib PBPK model was then applied to predict the effect of other inhibitors and inducers on the PKs of pralsetinib. This work highlights the challenges in understanding DDIs when enzyme-transporter interplay occurs, and demonstrates an important strategy for differentiating enzyme/transporter contributions to enable PBPK predictions for untested scenarios and to inform labeling.

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.

Research Landscape of Physiologically Based Pharmacokinetic Model Utilization in Different Fields: A Bibliometric Analysis (1999-2023)

PURPOSE

The physiologically based pharmacokinetic (PBPK) modeling has received increasing attention owing to its excellent predictive abilities. However, there has been no bibliometric analysis about PBPK modeling. This research aimed to summarize the research development and hot points in PBPK model utilization overall through bibliometric analysis.

METHODS

We searched for publications related to the PBPK modeling from 1999 to 2023 in the Web of Science Core Collection (WoSCC) database. The Microsoft Office Excel, CiteSpace and VOSviewers were used to perform the analyses.

RESULTS

A total of 4,649 records from 1999 to 2023 were identified, and the largest number of publications focused in the period 2018-2023. The United States was the leading country, and the Environmental Protection Agency (EPA) was the leading institution. The journal Drug Metabolism and Disposition published and co-cited the most articles. Drug-drug interactions, special populations, and new drug development are the main topics in this research field.

CONCLUSION

We first visualize the research landscape and hotspots of the PBPK modeling through bibliometric methods. Our study provides a better understanding for researchers, especially beginners about the dynamization of PBPK modeling and presents the relevant trend in the future.

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.

Membrane transporters in drug development and as determinants of precision medicine

The effect of membrane transporters on drug disposition, efficacy and safety is now well recognized. Since the initial publication from the International Transporter Consortium, significant progress has been made in understanding the roles and functions of transporters, as well as in the development of tools and models to assess and predict transporter-mediated activity, toxicity and drug-drug interactions (DDIs). Notable advances include an increased understanding of the effects of intrinsic and extrinsic factors on transporter activity, the application of physiologically based pharmacokinetic modelling in predicting transporter-mediated drug disposition, the identification of endogenous biomarkers to assess transporter-mediated DDIs and the determination of the cryogenic electron microscopy structures of SLC and ABC transporters. This article provides an overview of these key developments, highlighting unanswered questions, regulatory considerations and future directions.

Virus-Induced Epilepsy vs. Epilepsy Patients Acquiring Viral Infection: Unravelling the Complex Relationship for Precision Treatment

The intricate relationship between viruses and epilepsy involves a bidirectional interaction. Certain viruses can induce epilepsy by infecting the brain, leading to inflammation, damage, or abnormal electrical activity. Conversely, epilepsy patients may be more susceptible to viral infections due to factors, such as compromised immune systems, anticonvulsant drugs, or surgical interventions. Neuroinflammation, a common factor in both scenarios, exhibits onset, duration, intensity, and consequence variations. It can modulate epileptogenesis, increase seizure susceptibility, and impact anticonvulsant drug pharmacokinetics, immune system function, and brain physiology. Viral infections significantly impact the clinical management of epilepsy patients, necessitating a multidisciplinary approach encompassing diagnosis, prevention, and treatment of both conditions. We delved into the dual dynamics of viruses inducing epilepsy and epilepsy patients acquiring viruses, examining the unique features of each case. For virus-induced epilepsy, we specify virus types, elucidate mechanisms of epilepsy induction, emphasize neuroinflammation's impact, and analyze its effects on anticonvulsant drug pharmacokinetics. Conversely, in epilepsy patients acquiring viruses, we detail the acquired virus, its interaction with existing epilepsy, neuroinflammation effects, and changes in anticonvulsant drug pharmacokinetics. Understanding this interplay advances precision therapies for epilepsy during viral infections, providing mechanistic insights, identifying biomarkers and therapeutic targets, and supporting optimized dosing regimens. However, further studies are crucial to validate tools, discover new biomarkers and therapeutic targets, and evaluate targeted therapy safety and efficacy in diverse epilepsy and viral infection scenarios.

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.

Evaluation of the potential impact on pharmacokinetics of various cytochrome P450 substrates of increasing IL-6 levels following administration of the T-cell bispecific engager glofitamab

Glofitamab is a novel T cell bispecific antibody developed for treatment of relapsed-refractory diffuse large B cell lymphoma and other non-Hodgkin's lymphoma indications. By simultaneously binding human CD20-expressing tumor cells and CD3 on T cells, glofitamab induces tumor cell lysis, in addition to T-cell activation, proliferation, and cytokine release. Here, we describe physiologically-based pharmacokinetic (PBPK) modeling performed to assess the impact of glofitamab-associated transient increases in interleukin 6 (IL-6) on the pharmacokinetics of several cytochrome P450 (CYP) substrates. By refinement of a previously described IL-6 model and inclusion of in vitro CYP suppression data for CYP3A4, CYP1A2, and 2C9, a PBPK model was established in Simcyp to capture the induced IL-6 levels seen when glofitamab is administered at the intended dose and dosing regimen. Following model qualification, the PBPK model was used to predict the potential impact of CYP suppression on exposures of various CYP probe substrates. PBPK analysis predicted that, in the worst-case, the transient elevation of IL-6 would increase exposures of CYP3A4, CYP2C9, and CYP1A2 substrates by less than or equal to twofold. Increases for CYP3A4, CYP2C9, and CYP1A2 substrates were projected to be 1.75, 1.19, and 1.09-fold following the first administration and 2.08, 1.28, and 1.49-fold following repeated administrations. It is recommended that there are no restrictions on concomitant treatment with any other drugs. Consideration may be given for potential drug-drug interaction during the first cycle in patients who are receiving concomitant CYP substrates with a narrow therapeutic index via monitoring for toxicity or for drug concentrations.

A Physiologically Based Pharmacokinetic Modeling Approach to Assess the Potential for Drug Interactions Between Trofinetide and CYP3A4-Metabolized Drugs

PURPOSE

Trofinetide is the first drug to be approved by the US Food and Drug Administration for use in the treatment of patients with Rett syndrome, a multisystem disorder requiring multimodal therapies. Cytochrome P450 (CYP) 3A4 metabolizes >50% of therapeutic drugs and is the CYP isozyme most commonly expressed in the liver and intestines. In vitro studies suggest the concentration of trofinetide producing 50% inhibition (IC50) of CYP3A4 is >15 mmol/L; that concentration was much greater than the target clinical concentration associated with the maximal intended therapeutic dose (12 g). Thus, trofinetide has a low potential for drug-drug interactions in the liver. However, there is potential for drug-drug interactions in the intestines given the oral route of administration and expected relatively high concentration in the gastrointestinal tract after dose administration.

METHODS

Using a validated physiologically based pharmacokinetic (PBPK) model, deterministic and stochastic simulations were used for assessing the PK properties related to exposure and bioavailability of midazolam (sensitive index substrate for CYP3A4) following an oral (15 mg) or intravenous (2 mg) dose, with and without single-dose and steady-state (12 g) coadministration of oral trofinetide.

FINDINGS

Following coadministration of intravenous midazolam and oral trofinetide, the PK properties of midazolam were unchanged. The trofinetide concentration in the gut wall was >15 mmol/L during the first 1.5 hours after dosing. With the coadministration of oral midazolam and trofinetide, the model predicted increases in fraction of dose reaching the portal vein, bioavailability, Cmax, and AUCinf of 30%, 30%, 18%, and 30%, respectively.

IMPLICATIONS

In this study that used a PBPK modeling approach, it was shown that CYP3A4 enzyme activity in the liver was not affected by trofinetide coadministration, but trofinetide was predicted to be a weak inhibitor of intestinal CYP3A4 metabolism after oral administration at therapeutic doses.

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.

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.

Digital patient twins for personalized therapeutics and pharmaceutical manufacturing

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

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.

Exploring the impact of ethnicity on drug pharmacokinetics using PBPK models: A case study with lansoprazole in Japanese subjects

AIMS

The aim of this study is to demonstrate the use of PBPK modelling to explore the impact of ethnic differences on drug PK.

METHODS

A PBPK model developed for lansoprazole was used to predict the clinical PK of lansoprazole in Japanese subjects by incorporating the physiological parameters of a Japanese population into the model. Further verification of the developed Japanese population with clinical studies involving eight other CYP substrates-omeprazole, ticlopidine, alprazolam, midazolam, nifedipine, cinacalcet, paroxetine and dextromethorphan-was also carried out.

RESULTS

The PK of lansoprazole in both Caucasian and Japanese subjects was well predicted by the model as the observed data were within the 5th and 95th percentiles across all the clinical studies. In age- and sex-matched simulations in both the Caucasian and Japanese populations, the predicted PK (mean ± SD) of a single oral dose of 30-mg lansoprazole was higher in the Japanese population in all cases, with more than twofold higher AUC of 5.98 ± 6.43 mg/L.h (95% CI: 4.72, 7.24) vs. 2.46 ± 2.45 mg/L.h (95% CI: 1.98, 2.94) in one scenario. In addition, in two out of the nine clinical DDIs of lansoprazole and the additional CYP substrates simulated using the Japanese population, the predicted DDI in Japanese was more than 1.25-fold that in Caucasians, indicating an increased DDI liability.

CONCLUSIONS

By accounting for various physiological parameters that characterize a population in a PBPK model, the impact of the different identified interethnic differences on the drug's PK can be explored, which can inform the adoption of drugs from one region to another.

Incorporation and Performance Verification of Hepatic Portal Blood Flow Shunting in Minimal and Full PBPK Models of Liver Cirrhosis

Patho-physiological changes in liver cirrhosis create portacaval shunts that allow blood flow to bypass the hepatic portal vein into the systemic circulation affecting drug pharmacokinetics (PKs). The objectives of this work were to implement a physiologically-based pharmacokinetic (PBPK) framework describing shunted blood flows in virtual patients with differing degrees of liver cirrhosis; and to assess the minimal and full PBPK model's performance using drugs with intermediate to high hepatic extraction. Single dose concentration-time profiles and PK parameters for oral ibrutinib, midazolam, propranolol, and buspirone were simulated in healthy volunteers (HVs) and subjects with cirrhosis (Child-Pugh severity score (CP-A, CP-B, or CP-C)). Model performance was verified by comparing predicted to observed fold-changes in PK parameters between HVs and cirrhotic subjects. The verified model was used to simulate the PK changes for simvastatin in patients with cirrhosis. The predicted area under the curve ratios (AUCCirr :AUCHV ) for ibrutinib were 3.38, 6.87, and 11.46 using the minimal PBPK model with shunt and 1.61, 2.58, and 4.33 without the shunt, these compared with observed values of 4.33, 8.14, and 9.04, respectively. For ibrutinib, propranolol, and buspirone, including a shunt in the PBPK model improved the prediction of the AUCCirr :AUCHV and maximum plasma concentration ratios (CmaxCirr :CmaxHV ). For midazolam, an intermediate extraction drug, the differences were less clear. Simulated simvastatin dose adjustments in cirrhosis suggested that 20 mg in CP-A and 10 mg in CP-B could be used clinically. A mechanistic model-informed understanding of the anatomic and pathophysiology of cirrhosis will facilitate improved dose prediction and adjustment in this vulnerable population.