Mini Review

Role of Physiologically Based Modeling and Simulations in Clinical Pharmacokinetic Study Waivers for Orally Administered Drug Products

Mahendra Chougule, Sivacharan Kollipara, Paramita Saha and Tausif Ahmed*

Biopharmaceutics Group, Global Clinical Management, Dr. Reddy’s Laboratories Ltd., Integrated Product Development Organization (IPDO), India

Submission: September 19, 2024; Published: September 30, 2024

*Corresponding author: Tausif Ahmed, Biopharmaceutics Group, Global Clinical Management, Dr. Reddy’s Laboratories Ltd., Integrated Product Development Organization (IPDO), Bachupally, Medchal Malkajgiri District, Hyderabad-500 090, Telangana, India

How to cite this article: Mahendra C, Sivacharan K, Paramita S, Tausif A. Role of Physiologically Based Modeling and Simulations in Clinical Pharmacokinetic Study Waivers for Orally Administered Drug Products. JOJ Case Stud. 2024; 14(5): 555899. DOI: 10.19080/JOJCS.2024.14.555899.

Abstract

The pharmaceutical industry’s drug development process, from discovery to post-marketing surveillance, has evolved significantly with the advent of Model-Informed Drug Development. These models, which simulate drug behavior in the body using physiological parameters, are crucial for predicting drug interactions, optimizing dosing, and assessing various factors impacting drug exposure. Physiologically based pharmacokinetic models streamline drug development by reducing the need for extensive clinical trials, thus accelerating the approval process. In the generic drug sector, these models are instrumental in achieving bioequivalence and expediting market access. By offering a predictive framework, physiologically based pharmacokinetic modeling ensures that generic drugs meet regulatory standards efficiently, ultimately enhancing the affordability and accessibility of essential medicines. These models support biowaiver justification, scale-up and post-approval changes, and evaluation of critical bioavailability attributes. This review summarizes physiologically based pharmacokinetic models applications in obtaining clinical pharmacokinetic study waivers, emphasizing dissolution data incorporation and detailing a workflow for model development, validation, and application. Additionally, it highlights literature reports and case studies where physiologically based pharmacokinetic models and physiologically based biopharmaceutics models were used for biowaivers, dissolution mismatch, formulation impact, and safety evaluations, showcasing their broad utility in drug development.

Keywords: PBBM; Clinical study; Biowaiver; Safe space; Bioequivalence

Abbreviations: ADMET: Absorption, Distribution, Metabolism, Excretion, Toxicity; API: Active Pharmaceutical Ingredient; B/P: Blood-to-Plasma Ratio; BCS: Biopharmaceutics Classification System; BE: Bioequivalence; CBA: Critical Bioavailability Attribute; CES-1: Carboxyl Esterase 1; CFV: Critical Formulation Variable; CMA: Critical Material Attribute; CPP: Critical Process Parameter; CR: Controlled Release; CRDS: Clinically Relevant Dissolution Specifications; DDI: Drug-Drug Interactions; DLM: Diffusion Layer Model; DR: Delayed Release; EMA: European Medicines Agency; f2: Dissolution Similarity Factor; FDC: Fixed Drug Combinations; Fup: Fraction Unbound in Plasma; IR: Immediate Release; IV: Intravenous; IVIVC: In-vitro In vivo Correlation; IVIVR: In-vitro In vivo Relationship; MIDD: Model-Informed Drug Development; MR: Modified Release; NTI: Narrow Therapeutic Index; OC: Oseltamivir Carboxylate; OP: Oseltamivir Phosphate; PBBM: Physiologically Based Biopharmaceutics Model; PBPK: Physiologically Based Pharmacokinetic Model; US FDA: United States Food and Drug Administration; USP: United State Pharmacopeia; VBE: Virtual Bioequivalence

Introduction

Drug development process in the pharmaceutical industry is a complex and multifaceted journey that spans from initial discovery to final approval and post-marketing surveillance. Traditionally, this process relied heavily on empirical testing and trial-and-error approaches to gather the necessary data [1]. However, as computational power increased and our understanding of biological systems deepened, the industry has increasingly turned to more predictive and efficient methods. This shift has led to the integration of these approaches under the paradigm of Model-Informed Drug Development (MIDD), which enhances decision-making across various stages of drug development, from preclinical research to clinical trials [2-4]. A significant advancement in this area is physiologically based pharmacokinetic (PBPK) models or physiologically based biopharmaceutics model (PBBM) that have shown plethora of applications in clinical drug discovery and development. These models are mathematical representations of drug behavior within the body, incorporating physiological parameters such as organ sizes, blood flow rates, and tissue composition. PBPK models provide a detailed understanding of how a drug interacts with the human body, making them invaluable tools not only in new chemical entity development but also in optimizing drug formulations and predicting clinical outcomes during later phases of clinical development and during generic formulation optimization [5]. In the innovator industry, PBPK models play a critical role in various stages of drug development. They are employed to predict drug-drug interactions (DDIs), optimize dosing regimens, and assess the impact of intrinsic and extrinsic factors on drug exposure. Additionally, PBPK models are increasingly recognized for their ability to evaluate pharmacokinetics in special populations, such as pediatrics and geriatrics, and to support regulatory submissions. It can also be applied for evaluating API properties, selection of suitable salt or polymorphic form, selection of appropriate formulation that can aid in sufficient exposures in phase-1 study, formulation bridging across the clinical phases of drug development [6]. By reducing the need for extensive clinical trials, PBPK modeling saves time and resources, ultimately accelerating the development and approval of innovative drugs.

As the pharmaceutical industry has adopted MIDD, the generic drug sector has reaped substantial benefits from these advancements. The generic industry plays a crucial role in controlling the costs of essential medicines, thereby enhancing their affordability and accessibility without compromising quality [7]. In this evolving landscape, bioequivalence studies are fundamental, ensuring that generic products are therapeutically equivalent to their innovator counterparts. However, the complexity of certain products—such as those with intricate active pharmaceutical ingredients (APIs), complex dosage forms, or novel routes of delivery—can lead to increased development timelines and costs, delaying market entry. The intricate interplay of physiological variables, pharmacokinetics, and formulation characteristics presents significant challenges in achieving bioequivalence [8]. Regulatory agencies worldwide are therefore encouraging innovative clinical development approaches to expedite the process and facilitate earlier market access for generics. In this context, PBPK modeling has emerged as a transformative tool. Regulatory agencies such as the United States Food and Drug Administration (USFDA) and European Medicines Agency (EMA) have recognized the value of PBPK modeling in drug development and have provided detailed guidance’s to support its use and implementation in regulatory context [9-11]. These guidelines outline the requirements for development of such models, importance of model input parameters, sensitivity analyses, strategies for verification and validation followed by specific application in the context-of-use (e.g. assessing impact of food, gender impact, establishing dissolution specifications, linking formulation attributes to in vivo performance). It also underscores the importance of PBPK modeling by providing structured formats for regulatory submissions, reflecting its growing significance in generic drug development. PBPK modeling is pivotal in several key areas, including biowaiver justification, scale-up and post-approval changes, dissolution specification, evaluation of gender and food effects, and f2 mismatch justifications [12-17] (Figure 1). Additionally, it helps assess critical bioavailability attributes, such as the critical material attribute (CMA), critical formulation variable (CFV), and critical process parameter (CPP) on in vivo performance [18]. By offering a predictive framework for demonstrating bioequivalence, PBPK models can reduce or even eliminate the need for extensive clinical studies, thus streamlining the development process and ensuring that generics meet regulatory standards efficiently [19-22].

Despite the significant benefits of PBPK modeling in facilitating bioequivalence study waivers, there remains a notable gap in the literature with respect to specific emphasis in generic product development. There is a need for comprehensive review that provides specific details of PBBM in obtaining clinical pharmacokinetic study waivers. Hence in this context, our objective of this review article is to summarize applications of PBBM in obtaining clinical pharmacokinetic study waivers (Figure 1) for various applications pertaining to dissolution dissimilarity, specific study waivers and formulation modifications. We also have performed through literature review in order to unleash the potential of PBBM in obtaining clinical study waivers. A special emphasis is also made with respect to dissolution data incorporation into such models for simulation of in vivo behavior. Additionally, a detailed workflow for development, validation of these models is portrayed from practical perspective. Overall, this review aims to summarize applications of PBBM in clinical pharmacokinetic study waivers at one place.

Modeling and Simulations Methodology

A general workflow used for PBPK simulations consists of three steps namely: model development, model validation, and model application as portrayed in Figure 2. Details of each step is provided in below mentioned sections.

Model development

The development of PBPK models is a systematic process that integrates data from various sources to accurately represent the pharmacokinetics of a drug. The foundation of a robust PBPK model lies in the quality and comprehensiveness of its input data. Data are sourced from the scientific literature, experimental studies, and in silico tools such as ADMET predictor. The data inputs in PBBM can be divided into three categories namely: drug related, formulation related and system related. Drug specific parameters include solubility, permeability, pKa and LogP whereas physiological parameters include systemic models with specific inputs related to organs (e.g. size, blood flow, enzymes, transporter expression, age, gender etc). To simulate formulation behavior, specific input related to dissolution can be incorporated. For this purpose, typically various dissolution models are employed, including Johnson Model, Z-Factor Model, P-PSD Model, weibull model, diffusion layer model (DLM) (Figure 3), depending on specific formulation principle [23].

To characterize the disposition of a drug, an intravenous (IV) bolus or infusion is typically used as a starting point. During this phase, the model is refined to accurately fit and describe the drug’s disposition parameters. This refinement process may involve adjusting parameters such as clearance, volume of distribution, or physicochemical properties like log P, fraction unbound in plasma (fup), or the blood-to-plasma concentration ratio (B/P ratio). Once the IV model is developed, it undergoes validation using either literature data or in-house clinical data. Validation is essential to ensure that the model accurately predicts drug behavior. Once the IV model is validated, the next step involves developing the oral model. This step incorporates data on drug permeability and solubility. The oral model is then refined by adjusting physicochemical parameters, such as permeability or enzyme/transporter kinetics, to achieve a good fit with observed data.

Model validation

Validation step of the model is crucial to confirm the reliability and accuracy of the model’s predictions. Once the model is developed against the literature data, the model validation can follow a multi-step approach. In generic development, all the available pilot study clinical data serve as the initial benchmark for comparing model predictions, and if required, further refinement of the model can be taken place. In order to further understand the governing factor for model predictions, sensitivity analysis can be performed. This analysis helps assess the robustness of the model and ensures the reliability of its predictions across various conditions.

In addition to single simulations, approaches such as virtual bioequivalence (VBE) can be performed to predict drug product exposure in multiple subjects. For this purpose, a virtual population that is representive of actual study population can be simulated. Variability can be incorporated into virtual population in a systemic manner to have the same variability as that of observed in clinic. Further, VBE can be conducted to assess the model’s ability to predict clinical outcome and variability accurately. In this way, both single and population simulations help in demonstrating the credibility of the model before using it for intended purpose.

Model application

Once validated, the PBPK model is applied to a range of scenarios relevant to bioequivalence and generic drug development as indicated in Table 1. Depending on the purpose and type of model application, further steps to be incorporated into the model as specified in Table 1. These applications can range from establishing dissolution safe space, biowaivers due to f2 mismatch, waiver of additional BE study (e.g. fed bioequivalence), safety studies etc. Once the model is utilized for specific application, the model outcome can be discussed with respect to internal decision making or for regulatory submissions.

Literature Review

Literature review indicated that PBPK/PBBM approach has been used widely to support waiver of clinical studies. A summary of selected literature reports that employed modeling approaches for biowaivers in various scenario’s is presented in Table 2. This table summarizes objectives, methodologies employed to obtain biowaivers. Further, specific case studies where PBPK and PBBM was successfully utilized for various applications ranging from dissolution mismatch, dissolution safe space establishment, formulation impact, specific study waivers, and safety evaluations is provided below.

Dissolution dissimilarity (f2 mismatch)

Dissolution similarity analysis is essential part of product development as it has implications on biowaivers (BCS based, lower strength waivers). Typically, the f2 factor is used for similarity analysis and value below 50 indicates dissolution dissimilarity. In cases where f2 mismatch is seen, potential additional BE study is warranted. However, using approaches such as PBBM and PBPK, absence of impact of f2 mismatch on in vivo performance can be established thereby avoiding potential BE study. Few notable examples from literature for this application is presented below.

Zhang et al. [24] has attempted the use of PBPK model for BE study waiver of metronidazole 500 mg tablets. Metronidazole, a BCS class I drug, is eligible for a BE study waiver based on BCS principles, however due to potential dissolution dissimilarity (f2 mismatch), waiver was not possible. In order to justify biowaiver, a multi-step approach was used that include in vitro dissolution testing, in silico PBPK simulations using GastroPlus™, and in vivo bioequivalence studies in Beagle dogs. Dissolution tests were conducted using the USP type II dissolution apparatus at 50rpm in pH media of 1.2, 4.0, and 6.8, with similarity factors (f2) calculated to compare the test and reference formulations. In this study, PBPK model for immediate release metronidazole tablets was successfully developed to evaluate performance of reference and two test formulations (A, B). In vitro dissolution indicated that both formulations A and B are similar to reference product in pH 1.2. However, formulation A and B had significantly different dissolution profiles in pH 4.0 and 6.8 media against reference (f2 < 50). Despite these differences, the in vivo bioequivalence study in dogs demonstrated that the test products were bioequivalent to the reference formulation (the T/R and 90% confidence intervals were within the range of 80-125%). Further, incorporation of multimedia dissolution into PBPK model indicated that despite the dissolution differences, all the formulations were bioequivalent to the reference product. This work underscore the utility of PBPK modeling in supporting BE study waivers for metronidazole tablets and aiding in risk assessment during formulation development.

Dissolution safe space & specifications justification

PBPK modeling offers mechanistic insights into in vivo drug release and its physiological interactions and can lead to in vitro-in vivo relationships (IVIVRs) and potentially greater regulatory flexibility, especially for immediate-release (IR) products. By integrating dissolution and permeability models, PBPK modeling can demonstrate bioequivalence despite differences in dissolution profiles, thereby broadening approval possibilities for Biopharmaceutics Classification System (BCS) and lower strength biowaivers. Additionally, the concept of a “safe space” in drug product quality and biopharmaceutics defines boundaries— typically based on in vitro dissolution and other quality attributes—within which different drug product variants are expected to be bioequivalent. This approach can also supersede the f2 dissimilarity factor. Further, PBBM and PBPK models offer advantages in terms of justification of dissolution specifications. Virtual dissolution profiles can be generated at both lower and upper specifications and together with modeling approach, BE can be shown between various specifications. This approach can be efficiently utilized to justify the dissolution specifications. Few case studies describing these applications is presented below:

Miao et al. [25] established dissolution safe space of Oseltamivir in adults and pediatrics using PBPK modeling and simulation. The study aimed to establish the dissolution boundaries required to maintain BE between test and reference Oseltamivir phosphate (OP) drug products using PBPK absorption modeling in both adult and pediatric populations. Oseltamivir, a neuraminidase inhibitor classified as BCS I or III, is commonly used for the prophylaxis and treatment of influenza A and B infections. OP, an oral prodrug, is absorbed from the gastrointestinal tract and extensively metabolized in the liver to its active metabolite, Oseltamivir carboxylate (OC), primarily by carboxyl esterase 1 (CES1). In this study, adult PBPK for OP and OC were developed and validated using intravenous and oral data from multiple generic OP products, while the pediatric PBPK was extrapolated from the adult model by adjusting physiological parameters according to age-related changes using the PEAR™ and ACAT™ modules. Virtual BE studies were conducted using simulated PK profiles from reference and generic products, with theoretical dissolution profiles as inputs. The results revealed that generic products with a 10% slower dissolution rate than the pivotal reference bio-batch could maintain BE in adults. However, a more stringent dissolution boundary was required for pediatric populations, with only a 6% slower rate permissible for adolescents and 4% slower for neonates aged 0-2 months to maintain BE. This study highlights the critical role of PBPK in assessing BE across different age groups, reducing the risk associated with formulation or batch changes, and providing a quantitative foundation for setting clinically relevant dissolution specifications for OP and OC in both adults and pediatrics.

Laisney et al. [26] demonstrated virtual bioequivalence and established dissolution safe space of ribociclib with the help of in-silico approach. In this study, a PBBM was developed to support the formulation development of ribociclib, an orally bioavailable selective CDK4/6 inhibitor. ribociclib, characterized as a weak base with moderate permeability, demonstrated complete in vitro dissolution under stomach pH conditions. Using GastroPlus™, PK simulations were conducted in healthy volunteers following capsule dosing. These simulations revealed rapid and complete dissolution in the human stomach without intestinal precipitation, with absorption being controlled by permeability. The PBBM successfully predicted BE between the capsule and tablet formulations in healthy volunteers, despite the non-similarity of in vitro dissolution kinetics (f2<50). This prediction was subsequently verified in a clinical study. Further VBE simulations were performed to predict comparable PK profiles in cancer patients between the capsule and the tablet formulations of the commercial batch, which was also confirmed in clinical trials. To explore the safe-space for in vitro dissolution that would ensure BE, virtual trial simulations were conducted using virtual batches with progressively slower dissolution profiles. In vitro dissolution testing of each dosage form was conducted using USP 1 basket (for capsule and tablet) and USP 2 paddle apparatus (for the tablet). Simulated crossover studies (10 trials of 25 virtual subjects) compared the tablet BE batch (reference) against a virtual tablet batch with slower dissolution (80% dissolved after 45 minutes), which was found to be bioequivalent to the reference tablet BE batch thereby extending the dissolution safe space by a greater margin.

Kollipara et al. [27] has reviewed current practices of performing VBE and provided detailed workflow on best practices for running population simulations using two model case studies. In one of case study, PBBM was utilized to justify the dissolution specifications of extended release formulations. At specific time point, proposed dissolution specification range was ±25%, which is beyond acceptable range of ±20%. In order to justify this specification, PBBM approach was employed. The model was developed and validated using clinical BE study data in fasting and fed conditions. VBE was performed using workflow described by the authors and to justify the dissolution specifications, virtual dissolution profiles were generated at lower and upper dissolution specifications. Integration of these virtual dissolution profiles into the model indicated that bioequivalence is achieved between lower or upper specifications profiles against pivotal reference formulation and thus justifying the wider specification of ±25% at specific time point. The authors further provided deep insight into effective ways of performing VBE trials, incorporation of variability, considerations for API and formulation variability together with regulatory perspective.

In the literature example by Bhattiprolu AK et al. [19] the concept of dissolution safe space establishment and superseding f2 dissimilarity is demonstrated. The formulation in question was an immediate-release (IR) tablet containing a BCS class III API. A pivotal fasting bioequivalence study was conducted for Market-A. When leveraging the same study for Market-B, f2 dissimilarity was observed between the generic product and the Market-B reference product. To address this dissimilarity, a PBBM approach was utilized. Dissolution data, including multiple z-factor vs pH, was inputted into the model. The results indicated that the f2 dissimilarity had no impact on bioequivalence, thus establishing bioequivalence between the pivotal test product and the Market-B reference product using the PBBM approach. Furthermore, a dissolution safe space was established, indicating that the release of 85% up to 60 minutes can be considered bioequivalent to the reference product due to permeability-driven absorption. This establishment of a dissolution safe space not only enabled biowaivers in this case but also helped to broaden the traditional dissolution criteria for BCS-based biowaivers. As a result, more candidates could potentially be eligible for BCS-based biowaivers.

Formulation variables impact

During the product development, impact of API and formulation variables is impacted only on the dissolution. However, extrapolation of in vitro performance to in vivo performance can help in establishing suitable specifications for various formulation variables such as particle size, crystallinity etc. PBBM and PBPK models can be utilized in such cases and few literatures reported examples are presented below:

Purohit et al. [28] investigated impact of drug crystallinity on BE of tacrolimus 1mg capsules using PBPK modeling and simulation. In this study, the dissolution performance of commercial tacrolimus capsules, which are formulated to contain amorphous drug, was evaluated under different conditions to assess the impact of varying degrees of crystallinity on oral absorption. Fresh capsules and those exposed to stressed storage conditions (40°C, 75% RH) that induced tacrolimus crystallization were tested using both USP and non-compendial dissolution methods with different media and volumes. The study aimed to explore how intrinsically derived crystallinity affects dissolution performance and to develop a PBPK absorption model to predict the impact of crystallinity on oral absorption. VBE simulations were conducted using the PBPK model and in vitro dissolution data from both fresh and partially crystallized capsules under various dissolution conditions. The PBPK model, verified using clinical literature data, was used to predict plasma concentrations and perform virtual BE trials in a two-way crossover study design. The findings indicated that traditional compendial dissolution tests might not be sufficiently sensitive to detect the presence of crystallinity in amorphous formulations. Non-compendial tests with lower dissolution volumes provided more discriminatory profiles, predicting different pharmacokinetics for tacrolimus capsules with varying crystallinity levels. PBPK modeling coupled with dissolution data indicated that the generic formulation with crystallinity greater than 20% may have the chance of failed BE and thus indicating the potential cut-off for crystallanity. This study concluded that PBPK modeling is a valuable tool for assessing the impact of partial drug crystallinity in formulated products and can guide the development of more appropriate dissolution methods. Simulations performed under fasted conditions with a 5mg dose, representing a worst-case scenario, further emphasized the importance of these findings in evaluating the virtual BE between the reference Prograf and generic tacrolimus capsules.

Pepin et al. [29] utilized PBPK model to justify Lesinurad immediate release tablet dissolution and particle size specifications. In this study, in silico absorption modeling was performed to evaluate the impact of in vitro dissolution on the in vivo performance of ZURAMPIC™ (lesinurad) tablets. The study aimed to predict human exposure using dissolution profiles generated through the quality control method and inputting these profiles into a GastroPlus™ model to estimate in vivo dissolution across different parts of the gastrointestinal tract. The model’s predictive ability was validated by confirming its capability to reproduce the Cmax observed in independent clinical trials. Furthermore, the model demonstrated that drug product batches meeting the proposed dissolution specification of Q=80% in 30 minutes would likely be bioequivalent to the clinical reference batch. To explore the dissolution space further, simulations with theoretical dissolution profiles below the proposed specification were conducted. These simulations indicated that even batches failing the dissolution specification could still be bioequivalent to standard clinical batches, providing additional confidence in the proposed specifications. Additionally, the model was used to simulate a virtual drug substance batch with a particle size distribution at the limit of the proposed specification, demonstrating that such a batch would also be bioequivalent to the clinical reference. The study involved fitting the in vitro dissolution rates of selected batches to an apparent particle size distribution using Excel based algorithm (P-PSD), which was then used as input in GastroPlus. Various methodologies were tested for integrating dissolution data into the PBPK model, including fitting in vitro dissolution data to a particle size distribution, Weibull function, and Z-factor, each tailored to different formulation characteristics and dissolution scenarios. The study highlighted the robustness of the proposed dissolution and particle size specifications, ensuring that the final product would remain bioequivalent to pivotal clinical batches.

Specific study waivers

Potential of PBBM and PBPK approaches relies in utilizing them for waiver of specialized BE studies such as drug-drug interaction and fed BE studies. Such approaches are now increasingly being utilized during product development for the BE risk assessment as well as for waiving such specialized BE studies. Examples from literature where modeling approaches were utilized for DDI and fed BE study waivers is presented below:

Doki et al. [30] utilized PBPK modeling in demonstrating VBE for achlorhydric subjects and also to assess formulation dependent effect of achlorhydria. In this study, the focus was on evaluating the impact of achlorhydria on the bioequivalence of levothyroxine and nifedipine formulations using a PBPK modeling approach. The researchers incorporated in vitro dissolution profiles at neutral pH into the PBPK models to simulate achlorhydria conditions and established an in vitro–in vivo relationship with bio-relevant pH media. The PBPK models effectively reproduced the outcomes of bioequivalence studies conducted under normal conditions and in scenarios with proton pump inhibitor-induced achlorhydria. For levothyroxine, the geometric mean test/reference ratios for Cmax and AUC in patients with achlorhydria were 1.21 (90% CI, 1.13–1.29) and 1.09 (90% CI, 1.02–1.17), respectively, indicating that the formulations were bioequivalent. However, when extending the analysis to Japanese elderly populations—who have a higher incidence of achlorhydria—the study found significant bio-inequivalence for nifedipine. The ratios for Cmax and AUC between the control-released reference and test formulations were 3.08 (90% CI, 2.81–3.38) and 1.57 (90% CI, 1.43–1.74), respectively. This discrepancy underscores the need for targeted bioequivalence studies in elderly populations with specific pH-sensitivity issues, highlighting the value of PBPK modeling in identifying such needs.

Fasting and fed bioequivalence studies, conducted as either single or multiple doses, are typically required depending on product-specific bioequivalence guidelines. To increase confidence in bioequivalence results, in silico methods such as PBBM modeling, combined with biopredictive dissolution media, are becoming increasingly popular. These approaches are valuable not only in new drug development—where they can prevent the need for repeat food effect studies due to formulation changes— but also in generic drug development, where they can potentially eliminate the need for fed bioequivalence studies. Once the model is developed and validated, PBBM can be used to support a waiver for the fed bioequivalence study, as demonstrated in the following example.

Kollipara, et al. [16] demonstrated the utility of PBBM from fed bioequivalence studies perspective. Before development of PBBM, it is essential that a biopredictive media has been identified that can provide insight into in vivo performance. Various factors such as formulation, administration condition (fasting/fed), BCS class, pH vs solubility profile can be considered for development of biopredictive media. For media simulating fed condition, biorelevant media’s such as FeSSIF, or fat containing media’s such as Ensure-Plus, peanut oil, milk based media’s or aqueous media’s with surfactants such as pH 4 with sodium lauryl sulfate can be utilized. Once the predictive ability of such media’s is demonstrated with pilot studies, further data generated in these media’s can be incorporated into PBBM to understand in vivo behavior and to estimate bioequivalence using virtual bioequivalence approach. This example demonstrated two case studies wherein fed bioequivalence studies waiver has been obtained with PBBM approach. In one of the example, the reference formulation consisted of BCS class II API formulated with micronized API in tablet dosage form in order to enhance dissolution rate. The generic product consisted of solid dispersion approach to enhance solubility and dissolution rate. While pivotal fasting bioequivalence study was conducted for Market-A, when the same study was leveraged to Market-B, the agency asked to perform fed bioequivalence study. To support waiver of fed bioequivalence study, PBBM approach was utilized wherein the solubility, dissolution rate and particle size were incorporated into the model. Upon validating the model against pivotal fasting study, virtual bioequivalence approach was utilized wherein fed bioequivalence was demonstrated successfully. Together with modeling, biopharmaceutics risk assessment has been made and considering totality of evidence, agency accepted the approach and granted waiver for fed bioequivalence study. This work signifies the impact of PBBM approach in obtaining complex study waivers for fed bioequivalence and highlights the importance of identification of biopredictive media mimicking fed condition. Together with modeling approach, biopharmaceutics risk assessment can be appropriately considered in order to establish study waiver justification based on “totality of evidence”.

Safety evaluations

In new drug development, both efficacy and safety are rigorously examined during clinical trials. For generic products, bioequivalence studies traditionally focus on ensuring the generic matches the reference product in terms of efficacy. However, safety assessments are also crucial in generic development, especially when faster dissolution profiles or differences in formulation composition could affect the product’s safety and labeling. The PBBM approach is a valuable tool for assessing these safety concerns. Following example demonstrates utilization of PBBM modeling and simulation in assessing the safety.

The work performed by Boddu et al. [17] elucidates the importance of PBBM in evaluating safety aspects. The drug product in question contains a BCS Class I API formulated as an extended-release (ER) product using release-controlling excipients. During stability testing, the product exhibited faster dissolution profiles, raising concerns from the agency due to the API’s classification as a narrow therapeutic index (NTI) drug. The agency requested an evaluation of the potential safety implications of these dissolution profiles. To address this, a PBBM model was developed, incorporating physicochemical properties, physiological parameters, and dissolution data fitted using the Weibull model. The model was rigorously validated against literature-reported IVIVC data and population simulations from pivotal fasting and fed studies. When the faster dissolution profiles were inputted into the model, the results showed that the maximum concentration (Cmax) remained below the safety threshold, indicating no safety concerns. This case underscores the broader applications of PBBM modeling in establishing drug safety, demonstrating how such approaches can preempt the need for additional safety studies and expedite the product’s market entry.

Conclusion

PBBM modeling provides a comprehensive framework for tackling the complexities of generic drug development, including considerations like food effects, gender variations, dissolution safe space specifications, biowaivers, safety evaluations, and the impact of formulation variables on in vivo performance. By utilizing PBPK modeling in their development processes, generic manufacturers can better meet regulatory requirements, ensuring the delivery of safe, effective, and affordable medications to patients globally. The growing body of literature on modeling and simulation underscores the vast applications of these approaches, which can be employed early in the development process to rationalize formulation design and ensure consistent in vivo performance. Furthermore, recent initiatives like the Model Master File are paving the way for the sharing of modeling practices across sponsor companies, thereby facilitating more generic submissions and approvals. With the spread of these advancements through regulatory and academic channels, there’s a strong potential to inspire more widespread adoption of these approaches by pharmaceutical and biopharmaceutics scientists, ultimately driving both innovation and greater accessibility in drug development.

Acknowledgement

The authors thank Dr. Reddy’s Laboratories Ltd. for providing assistance and support to publish this work.

Conflict of interest

All the authors are employees of Dr Reddy’s Laboratories Ltd. and report no conflict of interest.

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