Mathematical modeling in autoimmune diseases: from theory to clinical application

The research & development (R&D) of novel therapeutic agents for the treatment of autoimmune diseases is challenged by highly complex pathogenesis and multiple etiologies of these conditions. The number of targeted therapies available on the market is limited, whereas the prevalence of autoimmune conditions in the global population continues to rise. Mathematical modeling of biological systems is an essential tool which may be applied in support of decision-making across R&D drug programs to improve the probability of success in the development of novel medicines. Over the past decades, multiple models of autoimmune diseases have been developed. Models differ in the spectra of quantitative data used in their development and mathematical methods, as well as in the level of "mechanistic granularity" chosen to describe the underlying biology. Yet, all models strive towards the same goal: to quantitatively describe various aspects of the immune response. The aim of this review was to conduct a systematic review and analysis of mathematical models of autoimmune diseases focused on the mechanistic description of the immune system, to consolidate existing quantitative knowledge on autoimmune processes, and to outline potential directions of interest for future model-based analyses. Following a systematic literature review, 38 models describing the onset, progression, and/or the effect of treatment in 13 systemic and organ-specific autoimmune conditions were identified, most models developed for inflammatory bowel disease, multiple sclerosis, and lupus (5 models each). ≥70% of the models were developed as nonlinear systems of ordinary differential equations, others - as partial differential equations, integro-differential equations, Boolean networks, or probabilistic models. Despite covering a relatively wide range of diseases, most models described the same components of the immune system, such as T-cell response, cytokine influence, or the involvement of macrophages in autoimmune processes. All models were thoroughly analyzed with an emphasis on assumptions, limitations, and their potential applications in the development of novel medicines.

Quantitative systems pharmacology modeling of HER2-positive metastatic breast cancer for translational efficacy evaluation and combination assessment across therapeutic modalities

HER2-positive (HER2+) metastatic breast cancer (mBC) is highly aggressive and a major threat to human health. Despite the significant improvement in patients' prognosis given the drug development efforts during the past several decades, many clinical questions still remain to be addressed such as efficacy when combining different therapeutic modalities, best treatment sequences, interindividual variability as well as resistance and potential coping strategies. To better answer these questions, we developed a mechanistic quantitative systems pharmacology model of the pathophysiology of HER2+ mBC that was extensively calibrated and validated against multiscale data to quantitatively predict and characterize the signal transduction and preclinical tumor growth kinetics under different therapeutic interventions. Focusing on the second-line treatment for HER2+ mBC, e.g., antibody-drug conjugates (ADC), small molecule inhibitors/TKI and chemotherapy, the model accurately predicted the efficacy of various drug combinations and dosing regimens at the in vitro and in vivo levels. Sensitivity analyses and subsequent heterogeneous phenotype simulations revealed important insights into the design of new drug combinations to effectively overcome various resistance scenarios in HER2+ mBC treatments. In addition, the model predicted a better efficacy of the new TKI plus ADC combination which can potentially reduce drug dosage and toxicity, while it also shed light on the optimal treatment ordering of ADC versus TKI plus capecitabine regimens, and these findings were validated by new in vivo experiments. Our model is the first that mechanistically integrates multiple key drug modalities in HER2+ mBC research and it can serve as a high-throughput computational platform to guide future model-informed drug development and clinical translation.

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.

Model-Informed Approaches to Support Drug Development for Patients With Obesity: A Regulatory Perspective

Obesity, which is defined as having a body mass index of 30 kg/m2 or greater, has been recognized as a serious health problem that increases the risk of many comorbidities (eg, heart disease, stroke, and diabetes) and mortality. The high prevalence of individuals who are classified as obese calls for additional considerations in clinical trial design. Nevertheless, gaining a comprehensive understanding of how obesity affects the pharmacokinetics (PK), pharmacodynamics (PD), and efficacy of drugs proves challenging, primarily as obese patients are seldom selected for enrollment at the early stages of drug development. Over the past decade, model-informed drug development (MIDD) approaches have been increasingly used in drug development programs for obesity and its related diseases as they use and integrate all available sources and knowledge to inform and facilitate clinical drug development. This review summarizes the impact of obesity on PK, PD, and the efficacy of drugs and, more importantly, provides an overview of the use of MIDD approaches in drug development and regulatory decision making for patients with obesity: estimating PK, PD, and efficacy in specific dosing scenarios, optimizing dose regimen, and providing evidence for seeking new indication(s). Recent review cases using MIDD approaches to support dose selection and provide confirmatory evidence for effectiveness for patients with obesity, including pediatric patients, are discussed. These examples demonstrate the promise of MIDD as a valuable tool in supporting clinical trial design during drug development and facilitating regulatory decision-making processes for the benefit of patients with obesity.

Mechanism-based pharmacokinetic and pharmacodynamic modeling for bispecific antibodies: challenges and opportunities

INTRODUCTION

Unlike conventional antibodies, bispecific antibodies (bsAbs) are engineered antibody- or antibody fragment-based molecules that can simultaneously recognize two different epitopes or antigens. Over the past decade, there has been an explosion of bsAbs being developed across therapeutic areas. Development of bsAbs presents unique challenges and mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) modeling has served as a powerful tool to optimize their development and realize their clinical utility.

AREAS COVERED

In this review, the guiding principles and case examples of how fit-for-purpose, mechanism-based PK/PD models have been applied to answer questions commonly encountered in bsAb development are presented. Such models characterize the key pharmacological elements of bsAbs, and they can be utilized for model-informed drug development. We also include the discussion of challenges, knowledge gaps and future direction for such models.

EXPERT OPINION

Mechanistic PK/PD modeling is a powerful tool to support the development of bsAbs. These models can be extrapolated to predict treatment outcomes based on mechanisms of action (MoA) and clinical observations to form positive learn-and-confirm cycles during drug development, due to their abilities to differentiate system- and drug-specific parameters. Meanwhile, the models should keep being adapted according to novel drug design and MoA, providing continuous opportunities for model-informed drug development.

Advances in pharmacokinetic-pharmacodynamic modelling for pediatric drug development: extrapolations and exposure-response analyses

INTRODUCTION

Pharmacokinetic (PK)-Pharmacodynamic (PD) and exposure-response (E-R) modeling are critical parts of pediatric drug development. By integrating available knowledge and supportive data to support the design of future studies and pediatric dose selection, these techniques increase the efficiency of pediatric drug development and lowers the risk of exposing pediatric study participants to suboptimal or unsafe dose regimens.

AREAS COVERED

The role of PK, PK-PD and E-R modeling within pediatric drug development and pediatric dose selection is discussed. These models allow investigation of the impact of age and bodyweight on PK and PD in children, despite the often sparse data on the pediatric population. Also discussed is how E-R analyses strengthen the evidence basis to support (full or partial) extrapolation of drug efficacy from adults to children, and between different pediatric age groups.

EXPERT OPINION

Accelerated pediatric drug development and optimized pediatric dosing guidelines are expected from three future developments: (1) Increased focus on E-R modeling of currently approved drugs in children resulting in (novel) E-R modeling techniques and best practices, (2) increased use of real-world data for E-R (3) increased implementation of available population PK and E-R information in pediatric drug dosing guidelines.

Model-informed drug development: The mechanistic HSK3486 physiologically based pharmacokinetic model informing dose decisions in clinical trials of specific populations

HSK3486, a central nervous system inhibitor, has demonstrated superior anesthetic properties in comparison with propofol. Owing to the high liver extraction ratio of HSK3486 and the limited susceptibility to the multi-enzyme inducer, rifampicin, the indicated population of HSK3486 is substantial. Nevertheless, in order to expand the population with indications, it is crucial to assess the systemic exposure of HSK3486 in specific populations. Moreover, the main metabolic enzyme of HSK3486 is UGT1A9, which shows a gene polymorphism in the population. Thus, to scientifically design the dose regimen for clinical trials in specific populations, a HSK3486 physiologically based pharmacokinetic model was developed in 2019 to support model-informed drug development (MIDD). Several untested scenarios of HSK3486 administration in specific populations, and the effect of the UGT1A9 gene polymorphism on HSK3486 exposure were estimated as well. The predicted systemic exposure was increased slightly in patients with hepatic impairment and in the elderly, consistent with later clinical trial data. Meanwhile, there was no change in the systemic exposure of patients with severe renal impairment and in neonates. However, under the same dose, the predicted exposure of pediatric patients aged 1 month to 17 years was decreased significantly (about 21%-39%). Although these predicted results in children have not been validated by clinical data, they are comparable to clinical findings for propofol in children. The dose of HSK3486 in pediatrics may need to be increased and can be adjusted according to the predicted results. Moreover, the predicted HSK3486 systemic exposure in the obese population was increased by 28%, and in poor metabolizers of UGT1A9 might increase by about 16%-31% compared with UGT1A9 extensive metabolizers. Combined with the relatively flat exposure-response relationship for efficacy and safety (unpublished), obesity and genetic polymorphisms are unlikely to result in clinically significant changes in the anesthetic effect at the 0.4 mg/kg dose in adults. Therefore, MIDD can indeed provide supportive information for dosing decisions and facilitate the efficient and effective development of HSK3486.

Model-Informed Approach Supporting Approval of Nexviazyme (Avalglucosidase Alfa-ngpt) in Pediatric Patients with Late-Onset Pompe Disease

In August 2021, the US Food and Drug Administration approved Nexviazyme (avalglucosidase alfa-ngpt) for intravenous infusion to treat patients 1 year of age and older with late-onset Pompe disease (LOPD). The effectiveness and safety were studied in patients with LOPD and patients with infantile-onset Pompe disease (IOPD). The dosage(s) tested in clinical trials was 20 mg/kg every other week (qow) in patients with LOPD and 20 mg/kg and 40 mg/kg qow in patients with IOPD. While patients 3 years old and greater with LOPD were eligible for participation in the pivotal trial, the youngest patient enrolled was 16 years old. Therefore, pediatric patients with LOPD were not well represented in the clinical trial. The prevalence of LOPD in pediatrics is extremely low. Thus, conducting a clinical trial in pediatric patients with LOPD would be challenging. Given the similar pathophysiology, mechanism of action, and disease manifestations across the age spectrum of patients with LOPD, the approved dosages for pediatric patients younger than 16 years old with LOPD were based on extrapolation of efficacy using a model-informed exposure bridging strategy, leveraging the safety data from pediatric patients with IOPD. Specifically, the exposure associated with 20 mg/kg qow in adult patients with LOPD was the target exposure for bridging of efficacy. The safety data obtained with 40 mg/kg qow in patients with IOPD was leveraged to support approval in pediatric patients with LOPD aged 1 year and older. This article illustrates a regulatory use of model-informed extrapolation approach for dose selection in pediatric patients with a rare disease.

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

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

Review: Role of Model-Informed Drug Development Approaches in the Lifecycle of Drug Development and Regulatory Decision-Making

Model-informed drug development (MIDD) is a powerful approach to support drug development and regulatory review. There is a rich history of MIDD applications at the U.S. Food and Drug Administration (FDA). MIDD applications span across the life cycle of the development of new drugs, generics, and biologic products. In new drug development, MIDD approaches are often applied to inform clinical trial design including dose selection/optimization, aid in the evaluation of critical regulatory review questions such as evidence of effectiveness, and development of policy. In the biopharmaceutics space, we see a trend for increasing role of computational modeling to inform formulation development and help strategize future in vivo studies or lifecycle plans in the post approval setting. As more information and knowledge becomes available pre-approval, quantitative mathematical models are becoming indispensable in supporting generic drug development and approval including complex generic drug products and are expected to help reduce overall time and cost. While the application of MIDD to inform the development of cell and gene therapy products is at an early stage, the potential for future application of MIDD include understanding and quantitative evaluation of information related to biological activity/pharmacodynamics, cell expansion/persistence, transgene expression, immune response, safety, and efficacy. With exciting innovations on the horizon, broader adoption of MIDD is poised to revolutionize drug development for greater patient and societal benefit.

Huntington's Disease Regulatory Science Consortium: Accelerating Medical Product Development

Huntington's disease (HD) is a devastating neurodegenerative disorder that urgently needs disease-modifying therapeutics. To this end, collaboration to standardize clinical research practices in the field and drive progress in addressing drug development challenges is paramount. At a meeting in 2017 organized by CHDI Foundation and the Critical Path Institute, stakeholders across the pharmaceutical industry, academia, regulatory agencies, and patient advocacy groups discussed the need for and potential impact of a consortium dedicated to HD regulatory science. Consequently, the Huntington's Disease Regulatory Science Consortium (HD-RSC) was formed, a precompetitive consortium that is dedicated to building a regulatory strategy to expedite the approval of HD therapeutics.

Caplacizumab Model-Based Dosing Recommendations in Pediatric Patients With Acquired Thrombotic Thrombocytopenic Purpura

Acquired thrombotic thrombocytopenic purpura (aTTP) is a rare and life‐threatening autoimmune thrombotic microangiopathy. Caplacizumab, evaluated in phase II and III studies in adults, shortens the time to platelet count response and reduces aTTP exacerbations, has a favorable safety profile, and can potentially reduce refractoriness and mortality associated with aTTP. Since no children with aTTP were enrolled in these clinical trials, caplacizumab has been initially approved for use only in adult patients with aTTP (10 mg). Pediatric dosing recommendations were developed using model‐based simulations. A semimechanistic pharmacokinetic/pharmacodynamic population model has been developed describing the interaction between caplacizumab and von Willebrand factor antigen (vWF:Ag) following intravenous and subcutaneous administration of caplacizumab in different adult populations, at various dose levels, using nonlinear mixed‐effects modeling. Based on the allometrically scaled pharmacokinetic/pharmacodynamic model, different dosing regimens were simulated in 8000 children (aged 2‐18 years). Simulated caplacizumab exposures and vWF:Ag levels across different age categories were compared to an adult reference group. A simulated daily dose of 5 mg in children weighing <40 kg and of 10 mg in children weighing ≥40 kg resulted in similar exposures and vWF:Ag suppression across age and weight groups. Despite the lack of pediatric clinical data, the results of this modeling and simulation analysis constituted the basis for the European extension of indication for caplacizumab (10 mg) to adolescents aged >12 years and with a body weight ≥40 kg. This represents a rare case in which regulatory authorities have deemed a modeling and simulation study robust enough to approve a variation of indication.

Is It Time to Use Modeling of Cellular Transporter Homeostasis to Inform Drug-Drug Interaction Studies: Theoretical Considerations

Mathematical modeling has been an important tool in pharmaceutical research for 50 + years and there is increased emphasis over the last decade on using modeling to improve the efficiency and effectiveness of drug development. In an earlier commentary, we applied a multiscale model linking 6 scales (whole body, tumor, vasculature, cell, spatial location, time), together with literature data on nanoparticle and tumor properties, to demonstrate the effects of nanoparticle particles on systemic disposition. The current commentary used a 4-scale model (cell membrane, intracellular organelles, spatial location, time) together with literature data on the intracellular processing of membrane receptors and transporters to demonstrate disruption of transporter homeostasis can lead to drug-drug interaction (DDI) between victim drug (VD) and perpetrator drug (PD), including changes in the area-under-concentration-time-curve of VD in cells that are considered significant by the US Food and Drug Administration (FDA). The model comprised 3 computational components: (a) intracellular transporter homeostasis, (b) pharmacokinetics of extracellular and intracellular VD/PD concentrations, and (c) pharmacodynamics of PD-induced stimulation or inhibition of an intracellular kinetic process. Model-based simulations showed that (a) among the five major endocytic processes, perturbation of transporter internalization or recycling led to the highest incidence and most extensive DDI, with minor DDI for perturbing transporter synthesis and early-to-late endosome and no DDI for perturbing transporter degradation and (b) three experimental conditions (spatial transporter distribution in cells, VD/PD co-incubation time, extracellular PD concentrations) were determinants of DDI detection. We propose modeling is a useful tool for hypothesis generation and for designing experiments to identify potential DDI; its application further aligns with the model-informed drug development paradigm advocated by FDA.

Model-Informed Repurposing of Medicines for SARS-CoV-2: Extrapolation of Antiviral Activity and Dose Rationale for Paediatric Patients

Repurposing of remdesivir and other drugs with potential antiviral activity has been the basis of numerous clinical trials aimed at SARS-CoV-2 infection in adults. However, expeditiously designed trials without careful consideration of dose rationale have often resulted in treatment failure and toxicity in the target patient population, which includes not only adults but also children. Here we show how paediatric regimens can be identified using pharmacokinetic-pharmacodynamic (PKPD) principles to establish the target exposure and evaluate the implications of dose selection for early and late intervention. Using in vitro data describing the antiviral activity and published pharmacokinetic data for the agents of interest, we apply a model-based approach to assess the exposure range required for adequate viral clearance and eradication. Pharmacokinetic parameter estimates were subsequently used with clinical trial simulations to characterise the probability target attainment (PTA) associated with enhanced antiviral activity in the lungs. Our analysis shows that neither remdesivir, nor anti-malarial drugs can achieve the desirable target exposure range based on a mg/kg dosing regimen, due to a limited safety margin and high concentrations needed to ensure the required PTA. To date, there has been limited focus on suitable interventions for children affected by COVID-19. Most clinical trials have defined doses selection criteria empirically, without thorough evaluation of the PTA. The current results illustrate how model-based approaches can be used for the integration of clinical and nonclinical data, providing a robust framework for assessing the probability of pharmacological success and consequently the dose rationale for antiviral drugs for the treatment of SARS-CoV-2 infection in children.

Pharmacometrics meets statistics-A synergy for modern drug development

Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology-based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.

Model-Informed Drug Development in Pediatric Dose Selection

Model-informed drug development (MIDD) has been a powerful and efficient tool applied widely in pediatric drug development due to its ability to integrate and leverage existing knowledge from different sources to narrow knowledge gaps. The dose selection is the most common MIDD application in regulatory submission related to pediatric drug development. This article aims to give an overview of the 3 broad categories of use of MIDD in pediatric dose selection: leveraging from adults to pediatric patients, leveraging from animals to pediatric patients, and integrating mechanism in infants and neonates. Population pharmacokinetic analyses with allometric scaling can reasonably predict the clearance in pediatric patients aged >5 years. A mechanistic-based approach, such as physiologically based pharmacokinetic accounting for ontogeny, or an allometric model with age-dependent exponent, can be applied to select the dose in pediatric patients aged ≤2 years. The exposure-response relationship from adults or from other drugs in the same class may be useful in aiding the pediatric dose selection and benefit-risk assessment. Increasing application and understanding of use of MIDD have contributed greatly to several policy developments in the pediatric field. With the increasing efforts of MIDD under the Prescription Drug User Fee Act VI, bigger impacts of MIDD approaches in pediatric dose selection can be expected. Due to the complexity of model-based analyses, early engagement between drug developers and regulatory agencies to discuss MIDD issues is highly encouraged, as it is expected to increase the efficiency and reduce the uncertainty.

Systematic in silico analysis of clinically tested drugs for reducing amyloid-beta plaque accumulation in Alzheimer's disease

Introduction

Despite strong evidence linking amyloid beta (Aβ) to Alzheimer's disease, most clinical trials have shown no clinical efficacy for reasons that remain unclear. To understand why, we developed a quantitative systems pharmacology (QSP) model for seven therapeutics: aducanumab, crenezumab, solanezumab, bapineuzumab, elenbecestat, verubecestat, and semagacestat.

Methods

Ordinary differential equations were used to model the production, transport, and aggregation of Aβ; pharmacology of the drugs; and their impact on plaque.

Results

The calibrated model predicts that endogenous plaque turnover is slow, with an estimated half‐life of 2.75 years. This is likely why beta‐secretase inhibitors have a smaller effect on plaque reduction. Of the mechanisms tested, the model predicts binding to plaque and inducing antibody‐dependent cellular phagocytosis is the best approach for plaque reduction.

Discussion

A QSP model can provide novel insights to clinical results. Our model explains the results of clinical trials and provides guidance for future therapeutic development.

Model-informed drug repurposing: A pharmacometric approach to novel pathogen preparedness, response and retrospection

During a pandemic caused by a novel pathogen (NP), drug repurposing offers the potential of a rapid treatment response via a repurposed drug (RD) while more targeted treatments are developed. Five steps of model‐informed drug repurposing (MIDR) are discussed: (i) utilize RD product label and in vitro NP data to determine initial proof of potential, (ii) optimize potential posology using clinical pharmacokinetics (PK) considering both efficacy and safety, (iii) link events in the viral life cycle to RD PK, (iv) link RD PK to clinical and virologic outcomes, and optimize clinical trial design, and (v) assess RD treatment effects from trials using model‐based meta‐analysis. Activities which fall under these five steps are categorized into three stages: what can be accomplished prior to an NP emergence (preparatory stage), during the NP pandemic (responsive stage) and once the crisis has subsided (retrospective stage). MIDR allows for extraction of a greater amount of information from emerging data and integration of disparate data into actionable insight.

Regulatory Considerations for the Mother, Fetus and Neonate in Fetal Pharmacology Modeling

The regulatory framework for considering the fetal effects of new drugs is limited. This is partially due to the fact that pediatric regulations (21 CFR subpart D) do not apply to the fetus, and only US Health and Human Service (HHS) regulations apply to the fetus. The HHS regulation 45 CFR Part 46 Subpart B limits research approvable by an institutional review board to research where the risk to the fetus is minimal unless the research holds out the prospect of a direct benefit to the fetus or the pregnant woman (45 CFR 46.204). Research that does not meet these requirements, but presents an opportunity to understand, prevent, or alleviate a serious problem affecting the health of pregnant women, fetuses, or neonates, may be permitted by the Secretary of the HHS after expert panel consultation and opportunity for public review and comment (45 CFR 46.407). If the product is regulated by the US Food and Drug Administration (FDA), FDA may get involved in the review process. The FDA does however have a Reviewer Guidance on Evaluating the Risks of Drug Exposure in Human Pregnancies from 2005 and this guidance does discuss the intensity of drug exposure. Estimation of that exposure using physiologically based pharmacokinetic (PBPK) modeling has been suggested by some investigators. Given that drug exposure during pregnancy will impact the fetus, a number of new guidances in the last 2 years also address inclusion of pregnant women in clinical drug trials. Therefore, the drug-specific information on fetal pharmacology will increase dramatically in the next decade due to interest in drugs administered in pregnancy and with the assistance of model-informed drug development.

Physiologically Based Pharmacokinetic Model-Informed Drug Development for Polatuzumab Vedotin: Label for Drug-Drug Interactions Without Dedicated Clinical Trials

Model-informed drug development (MIDD) has become an important approach to improving clinical trial efficiency, optimizing drug dosing, and proposing drug labeling in the absence of dedicated clinical trials. For the first time, we developed a physiologically based pharmacokinetic (PBPK) model-based approach to assess CYP3A-mediated drug-drug interaction (DDI) risk for polatuzumab vedotin (Polivy), an anti-CD79b-vc-monomethyl auristatin E (MMAE) antibody-drug conjugate (ADC). The model was developed and verified using data from the existing clinical DDI study for brentuximab vedotin, a similar vc-MMAE ADC. Analogous to the brentuximab vedotin clinical study, polatuzumab vedotin at the proposed labeled dose was predicted to have a limited drug interaction potential with strong CYP3A inhibitor and inducer. Polatuzumab vedotin was also predicted to neither inhibit nor induce CYP3A. The present work demonstrated a high-impact application using a PBPK MIDD approach to predict the CYP3A-mediated DDI to enable drug labeling in the absence of any dedicated clinical DDI study. The key considerations for the PBPK report included in the Biologics License Application/Marketing Authorization Application submission, as well as the strategy and responses to address some of the critical and challenging questions from the health authorities following the submission are also discussed. Our experience and associated perspective using a PBPK approach to ultimately enable a drug interaction label claim for polatuzumab vedotin in lieu of a dedicated clinical DDI study, as well as the interactions with the regulatory agencies, further provides confidence in applying MIDD to accelerate the registration and approval of new drug therapies.

Incorporating Ontogeny in Physiologically Based Pharmacokinetic Modeling to Improve Pediatric Drug Development: What We Know About Developmental Changes in Membrane Transporters

Developmental changes in the biological processes involved in the disposition of drugs, such as membrane transporter expression and activity, may alter the drug exposure and clearance in pediatric patients. Physiologically based pharmacokinetic (PBPK) models take these age-dependent changes into account and may be used to predict drug exposure in children. As a result, this mechanistic-based tool has increasingly been applied to improve pediatric drug development. Under the Prescription Drug User Fee Act VI, the US Food and Drug Administration has committed to facilitate the advancement of PBPK modeling in the drug application review process. Yet, significant knowledge gaps on developmental biology still exist, which must be addressed to increase the confidence of prediction. Recently, more data on ontogeny of transporters have emerged and supplied a missing piece of the puzzle. This article highlights the recent findings on the ontogeny of transporters specifically in the intestine, liver, and kidney. It also provides a case study that illustrates the utility of incorporating this information in predicting drug exposure in children using a PBPK approach. Collaborative work has greatly improved the understanding of the interplay between developmental physiology and drug disposition. Such efforts will continue to be needed to address the remaining knowledge gaps to enhance the application of PBPK modeling in drug development for children.

Role of Model-Informed Drug Development in Pediatric Drug Development, Regulatory Evaluation, and Labeling

The unique challenges in pediatric drug development require efficient and innovative tools. Model-informed drug development (MIDD) offers many powerful tools that have been frequently applied in pediatric drug development. MIDD refers to the application of quantitative models to integrate and leverage existing knowledge to bridge knowledge gaps and facilitate development and decision-making processes. This article discusses the current practices and visions of applying MIDD in pediatric drug development, regulatory evaluation, and labeling, with detailed examples. The application of MIDD in pediatric drug development can be broadly classified into 3 categories: leveraging knowledge for bridging the gap, dose selection and optimization, and informing clinical trial design. In particular, MIDD can provide evidence for the assumption of exposure-response similarity in bridging existing knowledge from reference to target population, support the dose selection and optimization based on the "exposure-matching" principle in the pediatric population, and increase the efficiency and success rate of pediatric trials. In addition, the role of physiologically based pharmacokinetics in drug-drug interaction in children and adolescents and in utilizing ontogeny data to predict pharmacokinetics in neonates and infants has also been illustrated. Moving forward, MIDD should be incorporated into all pediatric drug development programs at every stage to inform clinical trial design and dose selection, with both its strengths and limitations clearly laid out. The accumulated experience and knowledge of MIDD has and will continue to drive regulatory policy development and refinement, which will ultimately improve the consistency and efficiency of pediatric drug development.

Model-Informed Drug Development, Pharmacokinetic/Pharmacodynamic Cutoff Value Determination, and Antibacterial Efficacy of Benapenem against Enterobacteriaceae

Benapenem is a novel carbapenem. The objective of this study was to determine the pharmacokinetic (PK)/pharmacodynamic (PD) cutoff values and evaluate the optimal administration regimens of benapenem for the treatment of bacterial infections via PK/PD modeling and simulation. Ertapenem was used as a control. Infected mice received an intravenous (i.v.) injection of benapenem or ertapenem of 14.6, 58.4, or 233.6 mg/kg of body weight, and the PK/PD profiles were evaluated.

Benapenem is a novel carbapenem. The objective of this study was to determine the pharmacokinetic (PK)/pharmacodynamic (PD) cutoff values and evaluate the optimal administration regimens of benapenem for the treatment of bacterial infections via PK/PD modeling and simulation. Ertapenem was used as a control. Infected mice received an intravenous (i.v.) injection of benapenem or ertapenem of 14.6, 58.4, or 233.6 mg/kg of body weight, and the PK/PD profiles were evaluated. The MICs were determined by using a 2-fold agar dilution method. Mathematical models were developed to characterize the pharmacokinetic profile of benapenem in humans and mice. Monte Carlo simulations were employed to determine the cutoff values and the appropriate benapenem dosing regimens for the treatment of infections caused by clinical isolates of Enterobacteriaceae. Two 2-compartment models were developed to describe the PK profiles of benapenem in humans and mice. A two-site binding model was applied to fit the protein binding in mouse plasma. Through correlation analysis, the percentage of the time that the free drug concentration remains above the MIC (%fT_>MIC) was determined to be the indicator of efficacy. Results from the simulation showed that the probability of target attainment (PTA) against the tested isolates was over 90% with the dosing regimens studied. The PK/PD cutoff value of benapenem was 1 mg/liter at a %fT_>MIC of 60% when given at a dose of 1,000 mg/day by i.v. drip for 0.5 h. The established model provides a better understanding of the pharmacological properties of benapenem for the treatment of Enterobacteriaceae infections. The proposed PK/PD cutoff value suggests that benapenem is a promising antibacterial against the Enterobacteriaceae. The cutoff value of 1 mg/liter may be a useful guide for the clinical use of benapenem and for surveillance for benapenem resistance.

Model-based evaluation of the efficacy and safety of nivolumab once every 4 weeks across multiple tumor types

BACKGROUND

Nivolumab 480 mg every 4 weeks (Q4W) is approved in the European Union, United States, and several other markets across multiple tumor types. Its approval was supported by quantitative efficacy/safety analyses bridging to 3 mg/kg every 2 weeks (Q2W).

PATIENTS AND METHODS

The benefit-risk profile of nivolumab 480 mg Q4W relative to 3 mg/kg Q2W was evaluated using population pharmacokinetic modeling and exposure-response (E-R) analyses for safety and efficacy. Pharmacokinetic exposures were predicted for 3203 patients with melanoma, non-small-cell lung cancer (NSCLC), renal cell carcinoma (RCC), squamous cell carcinoma of the head and neck, urothelial carcinoma, or classical Hodgkin lymphoma. Quantitative models analyzed E-R to predict 480-mg Q4W safety across all indications and efficacy for melanoma, NSCLC, and RCC. Intratumoral receptor occupancy (RO) was predicted for parameters representing different tumor types.

RESULTS

Time-averaged concentrations for 480 mg Q4W versus 3 mg/kg Q2W were higher during the first 28 days (26.8%) and similar at steady state (5.2%). The maximum concentration (C_max) after the first dose was higher (110.4%), and the trough concentration at day 28 was lower (-22.1%) with 480 mg Q4W versus 3 mg/kg Q2W. The C_max achieved with 480 mg Q4W was lower than the previously established safe dose of 10 mg/kg Q2W. The probability of adverse events for key safety end points was similar for 480 mg Q4W and 3 mg/kg Q2W. The predicted overall survival and objective response rates with 480 mg Q4W were comparable to 3 mg/kg Q2W. The predicted high intratumoral RO provided additional evidence to support 480 mg Q4W across tumor types.

CONCLUSIONS

The benefit-risk profile for nivolumab 480 mg Q4W was predicted to be similar to that of 3 mg/kg Q2W across tumor types while providing a convenient and flexible option for patients and their caregivers.

Model-Informed Drug Development Approach Supporting Approval of Adalimumab (HUMIRA) in Adolescent Patients with Hidradenitis Suppurativa: a Regulatory Perspective

On October 16, 2018, FDA expanded the adalimumab dosing regimen to adolescent hidradenitis suppurativa (HS) patients 12 years and older, weighing at least 30 kg without new clinical data. This approval was mainly supported by the model-informed drug development approach. Population pharmacokinetic simulations suggest body weight-tiered dosing regimens in adolescent patients will achieve similar exposure to adult patients across all weight range. Adalimumab has a well-established, 16-year long-term safety profile in various diseases in adult and pediatric populations. Current data of adalimumab in the pediatric population demonstrate no exposure-response relationship for adverse events. The effectiveness in adolescent patients was extrapolated from two adequate and well-controlled phase 3 studies in adult patients, assuming similar positive exposure-efficacy relationships in adults and adolescents. This article provides the insight of the application of MIDD on the adalimumab approval in adolescent HS patients and its implication on drug development and regulatory decision especially for pediatrics or rare diseases.

Model-informed drug development approach supporting approval of the 4-week (Q4W) dosing schedule for nivolumab (Opdivo) across multiple indications: a regulatory perspective

BACKGROUND

A nivolumab dosage regimen of 480 mg intravenously (i.v.) every 4 weeks (Q4W) was approved by FDA for the majority of the approved indications for nivolumab.

METHODS

The proposed new dosage regimen was supported by pharmacokinetic modeling and simulation, dose/exposure-response relationships for efficacy and safety in the indicated patient populations, and the clinical safety data with the 480 mg Q4W dosage regimen. Pharmacokinetic exposures achieved with 480 mg Q4W were predicted for 4166 patients in 21 clinical studies with various types of solid and hematological tumors. Exposure-response analyses were conducted to predict 480 mg Q4W safety and efficacy across all FDA-approved indications for nivolumab.

RESULTS

For the overall population, the geometric mean exposure achieved with 480 mg i.v. Q4W was 5.2% higher for steady state Cavg and 15.6% lower for Ctrough than those with 3 mg/kg i.v. Q2W, the approved dosage regimen. The simulated concentration-time course achieved with 480 mg Q4W regimen was below the median concentration achieved with 10 mg/kg i.v. Q2W that was also studied in clinical trials. The predicted probability of adverse events was similar between 480 mg Q4W and that observed with the 3 mg/kg Q2W regimen. Efficacy results were found to be similar between Q2W and Q3W dosage regimens in patients with renal cell carcinoma. The predicted efficacy for each indication suggested that the efficacy with 480 mg Q4W is unlikely to be compromised compared with that observed with 3 mg/kg Q2W.

CONCLUSIONS

The model-informed analyses of predicted exposure, efficacy and safety based on data from extensive clinical experience with nivolumab suggest that the benefit-risk profile of 480 mg Q4W regimen is comparable to the approved 3 mg/kg Q2W regimen, thus providing the regulatory basis for the approval of 480 mg Q4W regimen in the absence of clinical efficacy data with this new dosage regimen.

Model-Informed Drug Development for Malaria Therapeutics

Malaria is a critical public health problem resulting in substantial morbidity and mortality, particularly in developing countries. Owing to the development of resistance toward current therapies, novel approaches to accelerate the development efforts of new malaria therapeutics are urgently needed. There have been significant advancements in the development of in vitro and in vivo experiments that generate data used to inform decisions about the potential merit of new compounds. A comprehensive disease-drug model capable of integrating discrete data from different preclinical and clinical components would be a valuable tool across all stages of drug development. This could have an enormous impact on the otherwise slow and resource-intensive process of traditional clinical drug development.