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?

In-Depth Analysis of the Selection of PBPK Modeling Tools: Bibliometric and Social Network Analysis of the Open Systems Pharmacology Community

Since the Open Source Initiative laid the foundation for the open source software environment in 1998, the popularity of free and open source software has been steadily increasing. Model-informed drug discovery and development (MID3), a key component of pharmaceutical research and development, heavily makes use of computational models which can be developed using various software including the Open Systems Pharmacology (OSP) software (PK-Sim/MoBi), a free and open source software tool for physiologically based pharmacokinetic (PBPK) modeling. In this study, we aimed to investigate the impact, application areas, and reach of the OSP software as well as the relationships and collaboration patterns between organizations having published OSP-related articles between 2017 and 2023. Therefore, we conducted a bibliometric analysis of OSP-related publications and a social network analysis of the organizations with which authors of OSP-related publications were affiliated. On several levels, we found evidence for a significant growth in the size of the OSP community as well as its visibility in the MID3 community since OSP's establishment in 2017. Specifically, the annual publication rate of PubMed-indexed PBPK-related articles using the OSP software outpaced that of PBPK-related articles using any software. Our bibliometric analysis and network analysis demonstrated that the expansion of the OSP community was predominantly driven by new authors and organizations without prior connections to the community involving the generation of research clusters de novo and an overall diversification of the network. These findings suggest an ongoing evolution of the OSP community toward a more segmented, diverse, and inclusive network.

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.

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.

The Role of Model Master Files for Sharing, Acceptance, and Communication with FDA

With the evolving role of Model Integrated Evidence (MIE) in generic drug development and regulatory applications, the need for improving Model Sharing, Acceptance, and Communication with the FDA is warranted. Model Master File (MMF) refers to a quantitative model or a modeling platform that has undergone sufficient model Verification & Validation to be recognized as sharable intellectual property that is acceptable for regulatory purposes. MMF provides a framework for regulatorily acceptable modeling practice, which can be used with confidence to support MIE by both the industry and the U.S. Food and Drug Administration (FDA). In 2022, the FDA and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop to discuss the best practices for utilizing modeling approaches to support generic product development. This report summarizes the presentations and panel discussions of the workshop symposium entitled "Model Sharing, Acceptance, and Communication with the FDA". The symposium and this report serve as a kick-off discussion for further utilities of MMF and best practices of utilizing MMF in drug development and regulatory submissions. The potential advantages of MMFs have garnered acknowledgment from model developers, industries, and the FDA throughout the workshop. To foster a unified comprehension of MMFs and establish best practices for their application, further dialogue and cooperation among stakeholders are imperative. To this end, a subsequent workshop is scheduled for May 2-3, 2024, in Rockville, Maryland, aiming to delve into the practical facets and best practices of MMFs pertinent to regulatory submissions involving modeling and simulation methodologies.

Human biomonitoring and toxicokinetics as key building blocks for next generation risk assessment

Human health risk assessment is historically built upon animal testing, often following Organisation for Economic Co-operation and Development (OECD) test guidelines and exposure assessments. Using combinations of human relevant in vitro models, chemical analysis and computational (in silico) approaches bring advantages compared to animal studies. These include a greater focus on the human species and on molecular mechanisms and kinetics, identification of Adverse Outcome Pathways and downstream Key Events as well as the possibility of addressing susceptible populations and additional endpoints. Much of the advancement and progress made in the Next Generation Risk Assessment (NGRA) have been primarily focused on new approach methodologies (NAMs) and physiologically based kinetic (PBK) modelling without incorporating human biomonitoring (HBM). The integration of toxicokinetics (TK) and PBK modelling is an essential component of NGRA. PBK models are essential for describing in quantitative terms the TK processes with a focus on the effective dose at the expected target site. Furthermore, the need for PBK models is amplified by the increasing scientific and regulatory interest in aggregate and cumulative exposure as well as interactions of chemicals in mixtures. Since incorporating HBM data strengthens approaches and reduces uncertainties in risk assessment, here we elaborate on the integrated use of TK, PBK modelling and HBM in chemical risk assessment highlighting opportunities as well as challenges and limitations. Examples are provided where HBM and TK/PBK modelling can be used in both exposure assessment and hazard characterization shifting from external exposure and animal dose/response assays to animal-free, internal exposure-based NGRA.

A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis

Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework. Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b. Results: The model has been successfully tested and validated against 11 anti-TB compounds-rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices. Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.

Phys-DAT: A physiologically-based pharmacokinetic model for unraveling the dissolution, transit and absorption processes using PhysPK®

BACKGROUND AND OBJECTIVE

In silico methods have become the key for efficiently testing and qualifying drug properties. Due to the complexity of the LADME processes and drug characteristics associated to oral drug absorption, there is a growing demand in the development of Physiologically-based Pharmacokinetic (PBPK) software with greater flexibility. Thus, the aims of this work are (i) to develop a mechanistic-based modeling framework of dissolution, transit and absorption (Phys-DAT) processes in the PhysPK platform and (ii) to assess the predictive power of the acausal MOOM methodology embedded in Phys-DAT versus reference ODE-based PBPK software.

METHODS

A PBPK model was developed including unreleased, undissolved and dissolved thermodynamic states of the drug. The gastrointestinal tract (GI) was represented by nine compartments and first-order transit kinetics was assumed for the drug fractions. Dissolution processes were described using solubility-independent or solubility-dependent mechanisms and pH effects. Linear transit and linear absorption mechanisms including gradual decrease absorption rate were considered to represent the passive diffusion process. Internal validation of the Phys-DAT model was performed through simulation-based analysis, considering different theoretical scenarios. External validation was carried out using in silico and in vivo data of GI segments and plasma concentrations. Both BCS I and II class drugs were included.

RESULTS

The model predicts plasma-concentration profiles of each compartment for undissolved, dissolved, and absorbed fractions using PhysPK® v.2.4.1. Internal and external validations demonstrate that the model aligned with the theoretical assumptions and accurately predicted Cmax, Tmax, and AUC 0-t for both BCS I and II drugs. Average Fold Error (AFE), Absolute Average Fold Error (AAFE), and Percent Prediction Error (PPE) calculations indicate good predictive performance, with predicted/observed ratios falling within the acceptable range.

CONCLUSIONS

Phys-DAT represents a mechanistic model for predicting oral absorption, including the dissolution, pH effect, transit, and absorption processes. PhysPK has shown to be a tool with strong prediction accuracy, similar to the obtained by ODE-based PBPK reference software, and the results obtained with the Phys-DAT model for oral administered drugs showed predictive reliability in healthy volunteers, setting the basis to determine the interchangeability of the acausal MOOM methodology with other modeling approaches.

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.

Physiologically-Based Pharmacokinetic Modeling for Drug Dosing in Pediatric Patients: A Tutorial for a Pragmatic Approach in Clinical Care

It is well-accepted that off-label drug dosing recommendations for pediatric patients should be based on the best available evidence. However, the available traditional evidence is often low. To bridge this gap, physiologically-based pharmacokinetic (PBPK) modeling is a scientifically well-founded tool that can be used to enable model-informed dosing (MID) recommendations in children in clinical practice. In this tutorial, we provide a pragmatic, PBPK-based pediatric modeling workflow. For this approach to be successfully implemented in pediatric clinical practice, a thorough understanding of the model assumptions and limitations is required. More importantly, careful evaluation of an MID approach within the context of overall benefits and the potential risks is crucial. The tutorial is aimed to help modelers, researchers, and clinicians, to effectively use PBPK simulations to support pediatric drug dosing.

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

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

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

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

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.

Quantitative Prediction of Drug Interactions Caused by Cytochrome P450 2B6 Inhibition or Induction

BACKGROUND

Numerous drugs have the potential to be affected by cytochrome P450 (CYP) 2B6-mediated drug-drug interactions (DDIs).

OBJECTIVES

In this work, we extend a static approach to the prediction of the extent of pharmacokinetics DDIs between substrates and inhibitors or inducers of CYP2B6.

METHODS

This approach is based on the calculation of two parameters (the contribution ratio [CR], representing the fraction of dose of the substrate metabolized via this pathway and the inhibitory or inducing potency of the perpetrator [IR or IC, respectively]) calculated from the area under the concentration-time curve (AUC) ratios obtained in in-vivo DDI studies.

RESULTS

Forty-eight studies involving 5 substrates, 11 inhibitors and 18 inducers of CYP2B6 (overall 15 inhibition and 33 induction studies) were divided into test and validation sets and considered for estimation of the parameters. The proposed approach demonstrated a fair accuracy for predicting the extent of DDI related to CYP2B6 inhibition and induction, all predictions related to the validation test (N = 18) being 50-200% of the observed ratios.

CONCLUSIONS

This methodology can be used for proposing initial dose adaptations to be adopted, for example in clinical use or for designing DDI studies involving this enzyme.