Applications of PBPK/PBBM modeling in generic product development: An industry perspective

Physiologically Based Biopharmaceutics Modeling Coupled with Biopredictive Dissolution in Development of Bioequivalent Formulation for Mesalamine Enteric Coated Tablet: A Tough Nut to Crack

Mesalamine is a locally acting anti-inflammatory drug used to treat mild to moderate ulcerative colitis. Because of complex formulation principle and high in vivo variability, development of bioequivalent formulation for mesalamine is challenging. Further, fed state possess significant challenges for bioequivalence (BE) due to interplay of multiple factors. In the work, we have developed a novel biopredictive media for mesalamine enteric coated tablets and integrated into physiologically based biopharmaceutics model (PBBM) to predict in vivo fed behavior. USP III based gradient media was developed to mimic in vivo fed condition. The developed PBBM was initially validated with literature data and subsequently re-optimized with pilot BE study data. Further, virtual bioequivalence (VBE) was performed to evaluate model predictability for pilot BE data. Later, the model was applied for prospective BE predictions with increased subjects and parametric sensitivity analysis was performed to identify physiological factors that can impact in vivo performance. Further, the model was used to predict luminal and enterocyte concentrations in colon to demonstrate equivalent efficacy. Additionally, a novel dissolution/permeation tool (Dissoflux) was employed to compare permeability behavior of formulations. Overall, this work enabled BE prediction for complex mesalamine enteric coated tablets and helped to understand parameters that can impact in vivo performance.

A critical review on approaches to generate and validate virtual population for physiologically based pharmacokinetic models: Methodologies, case studies and way forward

PURPOSE

In silico modeling and simulation techniques such as physiologically based pharmacokinetic (PBPK) and physiologically based biopharmaceutics modeling (PBBM) have demonstrated various applications in drug discovery and development. Virtual bioequivalence leverages these computation tools to predict bioequivalence between reference and test formulations thereby demonstrating possibilities to reduce human studies. A pre-requisite for virtual bioequivalence is development of validated virtual population that depicts the same variability as that of observed in clinic. This development, validation and optimization of virtual population is a key attribute of virtual bioequivalence based on which conclusion of bioequivalence is made.

METHODS

Various strategies for optimization of virtual population based on appropriate considerations of physicochemical, physiological and disposition aspects are demonstrated with the help of six diverse case studies of immediate and modified release formulations. Once the virtual population is optimized to match in vivo variability, it can be used for various applications such as biowaivers, dissolution specification justification, f2 mismatch, establishing dissolution safe space, etc. In this review article, we attempted to describe various methodologies and approaches for optimization of virtual population using Gastroplus.

RESULTS

Strategies based on optimization of virtual population with emphasis on specific and sensitive parameters were portrayed. We have further elucidated considerations related to study design, in vivo variability, sample size for optimization of virtual population from Gastroplus perspective.

CONCLUSION

We believe that this review article provides a step-by-step process for virtual population optimization for interest of biopharmaceutics modeling scientists in order to ensure reliable and credible physiological models.

Justification of widened dissolution specifications of an extended-release product using physiologically based biopharmaceutics modeling

Drug products meeting the dissolution specifications is crucial in order to ensure consistent clinical performance. However, in certain cases, wider dissolution specifications may be required based on product behaviour. While the justification of such wider specifications may be challenging from a regulatory context, approaches such as physiological-based biopharmaceutics modeling (PBBM) can be utilised for this purpose.Product DRL is a fixed-dose combination product consisting of immediate release (IR) and extended-release (ER) portions. For the ER portion, the dissolution specifications consisted of four time points, and a proposal was made to relax the specification at the 2h time point (from 50-70% to 45-67%) to reduce the batch failures at the commercial scale.To support the wider specification, a PBBM was developed and extensively validated with literature & in-house studies. Virtual bioequivalence was performed using the pivotal clinical study data.Virtual dissolution profiles for proposed wider specifications were generated using three different approaches. The incorporation of lower and upper dissolution profiles into the model indicated the absence of impact on in vivo performance thereby justifying the specifications.Regulatory acceptance of proposed specifications with PBBM indicated the significance of using modeling approaches to reduce repeated testing thereby facilitating faster approvals.

A Semi-Mechanistic Physiologically Based Biopharmaceutics Model to Describe Complex and Saturable Absorption of Metformin: Justification of Dissolution Specifications for Extended Release Formulation

Physiologically based pharmacokinetic (PBPK) or physiologically based biopharmaceutics models (PBBM) demonstrated plethora of applications in both new drugs and generic product development. Justification of dissolution specifications and establishment of dissolution safe space is an important application of such modeling approaches. In case of molecules exhibiting saturable absorption behavior, justification of dissolution specifications requires development of a model that incorporates effects of transporters is critical to simulate in vivo scenario. In the present case, we have developed a semi-mechanistic PBBM to describe the non-linearity of BCS class III molecule metformin for justification of dissolution specifications of extended release formulation at strengths 500 mg and 1000 mg. Semi-mechanistic PBBM was built using physicochemical properties, dissolution and non-linearity was accounted through incorporation of multiple transporter kinetics at absorption level. The model was extensively validated using literature reported intravenous, oral (immediate & extended release) formulations and further validated using in-house bioequivalence data in fasting and fed conditions. Virtual dissolution profiles at lower and upper specifications were generated to justify the dissolution specifications. The model predicted literature as well as in-house clinical study data with acceptable prediction errors. Further, virtual bioequivalence trials predicted the bioequivalence outcome that matched with clinical study data. The model predicted bioequivalence when lower and upper specifications were compared against pivotal test formulations thereby justifying dissolution specifications. Overall, complex and saturable absorption pathway of metformin was successfully simulated and this work resulted in regulatory acceptance of dissolution specifications which has ability to reduce multiple dissolution testing.

Physiologically Based Biopharmaceutics Modeling for Gefapixant IR Formulation Development and Defining the Bioequivalence Dissolution Safe Space

Gefapixant is a weakly basic drug which has been formulated as an immediate release tablet for oral administration. A physiologically based biopharmaceutics model (PBBM) was developed based on gefapixant physicochemical properties and clinical pharmacokinetics to aid formulation selection, bioequivalence safe space assessment and dissolution specification settings. In vitro dissolution profiles of different free base and citrate salt formulations were used as an input to the model. The model was validated against the results of independent studies, which included a bioequivalence and a relative bioavailability study, as well as a human ADME study, all meeting acceptance criteria of prediction errors ≤ 20% for both Cmax and AUC.  PBBM was also applied to evaluate gastric pH-mediated drug-drug-interaction potential with co-administration of a proton pump inhibitor (PPI), omeprazole. Model results showed good agreement with clinical data in which omeprazole lowered gefapixant exposure for the free base formulation but did not significantly alter gefapixant pharmacokinetics for the citrate based commercial drug product. An extended virtual dissolution bioequivalence safe space was established.  Gefapixant drug product batches are anticipated to be bioequivalent with the clinical reference batch when their dissolution is > 80% in 60 minutes. PBBM established a wide dissolution bioequivalence space as part of assuring product quality.

Conventional vs Mechanistic IVIVC: A Comparative Study in Establishing Dissolution Safe Space for Extended Release Formulations

The use of in vitro-in vivo correlation (IVIVC) for extended release oral dosage forms is an important technique that can avoid potential clinical studies. IVIVC has been a topic of discussion over the past two decades since the inception of USFDA guidance. It has been routinely used for biowaivers, establishment of dissolution safe space and clinically relevant dissolution specifications, for supporting site transfers, scale-up and post approval changes. Although conventional or mathematical IVIVC is routinely used, other approach such as mechanistic IVIVC can be of attractive choice as it integrates all the physiological aspects. In the present study, we have performed comparative evaluation of mechanistic and conventional IVIVC for establishment of dissolution safe space using divalproex sodium and tofacitinib extended release formulations as case examples. Conventional IVIVC was established using Phoenix and mechanistic IVIVC was set up using Gastroplus physiologically based biopharmaceutics model (PBBM). Virtual dissolution profiles with varying release rates were constructed around target dissolution profile using Weibull function. After internal and external validation, the virtual dissolution profiles were integrated into mechanistic and conventional IVIVC and safe space was established by absolute error and T/R ratio's methods. The results suggest that mechanistic IVIVC yielded wider safe space as compared to conventional IVIVC. The results suggest that a mechanistic approach of establishing IVIVC may be a flexible approach as it integrates physiological aspects. These findings suggest that mechanistic IVIVC has wider potential as compared to conventional IVIVC to gain wider dissolution safe space and thus can avoid potential clinical studies.

Physiologically Based Biopharmaceutics Modeling (PBBM): Best Practices for Drug Product Quality, Regulatory and Industry Perspectives: 2023 Workshop Summary Report

Physiologically based biopharmaceutics modeling (PBBM) is used to elevate drug product quality by providing a more accurate and holistic understanding of how drugs interact with the human body. These models are based on the integration of physiological, pharmacological, and pharmaceutical data to simulate and predict drug behavior in vivo. Effective utilization of PBBM requires a consistent approach to model development, verification, validation, and application. Currently, only one country has a draft guidance document for PBBM, whereas other major regulatory authorities have had limited experience with the review of PBBM. To address this gap, industry submitted confidential PBBM case studies to be reviewed by the regulatory agencies; software companies committed to training. PBBM cases were independently and collaboratively discussed by regulators, and academic colleagues participated in some of the discussions. Successful bioequivalence "safe space" industry case examples are also presented. Overall, six regulatory agencies were involved in the case study exercises, including ANVISA, FDA, Health Canada, MHRA, PMDA, and EMA (experts from Belgium, Germany, Norway, Portugal, Spain, and Sweden), and we believe this is the first time such a collaboration has taken place. The outcomes were presented at this workshop, together with a participant survey on the utility and experience with PBBM submissions, to discuss the best scientific practices for developing, validating, and applying PBBMs. The PBBM case studies enabled industry to receive constructive feedback from global regulators and highlighted clear direction for future PBBM submissions for regulatory consideration.

Virtual Bioequivalence Assessment of Ritlecitinib Capsules with Incorporation of Observed Clinical Variability Using a Physiologically Based Pharmacokinetic Model

Ritlecitinib, an orally available Janus kinase 3 and tyrosine kinase inhibitor being developed for the treatment of alopecia areata (AA), is highly soluble across the physiological pH range at the therapeutic dose. As such, it is expected to dissolve rapidly in any in vitro dissolution conditions. However, in vitro dissolution data showed slower dissolution for 100-mg capsules, used for the clinical bioequivalence (BE) study, compared with proposed commercial 50-mg capsules. Hence, a biowaiver for the lower 50-mg strength using comparable multimedia dissolution based on the f2 similarity factor was not possible. The in vivo relevance of this observed in vitro dissolution profile was evaluated with a physiologically based pharmacokinetic (PBPK) model. This report describes the development, verification, and application of the ritlecitinib PBPK model to translate observed in vitro dissolution data to an in vivo PK profile for ritlecitinib capsule formulations. Virtual BE (VBE) trials were conducted using the Simcyp VBE module, including the model-predicted within-subject variability or intra-subject coefficient of variation (ICV). The results showed the predicted ICV was predicted to be smaller than observed clinical ICV, resulting in a more optimistic BE risk assessment. Additional VBE assessment was conducted by incorporating clinically observed ICV. The VBE trial results including clinically observed ICV demonstrated that proposed commercial 50-mg capsules vs clinical 100-mg capsules were bioequivalent, with > 90% probability of success. This study demonstrates a PBPK model-based biowaiver for a clinical BE study while introducing a novel method to integrate clinically observed ICV into VBE trials with PBPK models. Trial registration: NCT02309827, NCT02684760, NCT04004663, NCT04390776, NCT05040295, NCT05128058.

Experience Learned and Perspectives on Using Model-Integrated Evidence in the Regulatory Context for Generic Drug Products-a Meeting Report

This report summarizes relevant insights and discussions from a 2022 FDA public workshop titled Best Practices for Utilizing Modeling Approaches to Support Generic Product Development which illustrated how model-integrated evidence has been used and can be leveraged further to inform generic drug product development and regulatory decisions during the assessment of generic drug applications submitted to the FDA. The workshop attendees discussed that model-integrated evidence (MIE) approaches for generics are being applied in the space of long-acting injectable (LAI) products to develop shorter and more cost-effective alternative study designs for LAI products. Modeling and simulation approaches are utilized to support virtual BE assessments at the site of action for locally acting drug products and to assess the impact of food on BE assessments for oral dosage forms. The factors contributing to the success of the model-informed drug development program under PDUFA VI were discussed. The generic drug industry shared that decisions on formulation candidate/formulation variant selection, on pilot in vivo bioavailability studies, and on alternative study designs for BE assessment are informed by modeling and simulation approaches. There was agreement that interactions between the regulatory agencies and the industry are desirable because they improve the industry's understanding of scientific and other regulatory considerations on implementing modeling and simulation approaches in drug development and regulatory submissions.

In Silico Clinical Trials: Is It Possible?

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

Dissolution Profiles Comparison Using Conventional and Bias Corrected and Accelerated f2 Bootstrap Approaches with Different Software's: Impact of Variability, Sample Size and Number of Bootstraps

Dissolution profiles comparison is an important element in order to support biowaivers, scale-up and post approval changes and site transfers. Highly variable dissolution can possess significant challenges for comparison and f2 bootstrap approach can be utilized in such cases. However, availability of different types of f2 and confidence intervals (CI) methods indicates necessity to understand each type of calculation thoroughly. Among all approaches, bias corrected and accelerated (BCa) can be an attractive choice as it corrects the bias and skewness of the distribution. In this manuscript, we have performed comparison of highly variable dissolution data using various software's by adopting percentile and BCa CI approaches. Diverse data with different variability's, number of samples and bootstraps were evaluated with JMP, DDSolver, R-software, SAS and PhEq. While all software's yielded similar observed f2 values, differences in lower percentile CI was observed. BCa with R-software and JMP provided superior lower percentile as compared to other computations. Expected f2 recommended by EMA has resulted as stringent criteria as compared to estimated f2. No impact of number of bootstraps on similarity analysis was observed whereas number of samples increased chance of acceptance. Variability has impacted similarity outcome with estimated f2 but chance of acceptance enhanced with BCa approach. Further, freely available R-software can be of attractive choice due to computation of various types of f2, percentile and BCa intervals. Overall, this work can enable regulatory submissions to enhance probability of similarity through appropriate selection of number of samples, technique based on variability of dissolution data.

Approaches to Account for Colon Absorption in Physiologically Based Biopharmaceutics Modeling of Extended-Release Drug Products

The rate and extent of colon absorption are important determinants of the in vivo performance of extended-release (ER) drug products. The ability to appropriately predict this at different stages of development using mechanistic physiologically based biopharmaceutic modeling (PBBM) is highly desirable. This investigation aimed to evaluate the prediction performance of three different approaches to account for colon absorption in predictions of the in vivo performance of ER drug product variants with different in vitro release profiles. This was done by mechanistic predictions of the absorption and plasma exposure of the ER drug products using GastroPlus and GI-Sim for five drugs with different degrees of colon absorption limitations in humans. Colon absorption was accounted for in the predictions using three different approaches: (1) by an a priori approach using the default colon models, (2) by fitting the colon absorption scaling factors to the observed plasma concentration-time profiles after direct administration to the colon in humans, or (3) from the ER drug product variant with the slowest in vitro release profile. The prediction performance was evaluated based on the percentage prediction error and the average absolute prediction error (AAPE). Two levels of acceptance criteria corresponding to highly accurate (AAPE ≤ 20%) and accurate (AAPE 20-50%) predictions were defined prior to the evaluation. For the a priori approach, the relative bioavailability (Frel), AUC0-t, and Cmax of the ER drug product variants for the low to medium colon absorption limitation risk drugs was accurately predicted with an AAPE range of 11-53 and 8-59% for GastroPlus and GI-Sim, respectively. However, the prediction performance was poor for the high colon absorption limitation risk drugs. Moreover, accounting for the human regional colon absorption data in the models did not improve the prediction performance. In contrast, using the colon absorption scaling factors derived from the slowest ER variant significantly improved the prediction performance regardless of colon absorption limitation, with a majority of the predictions meeting the high accuracy criteria. For the slowest ER approach, the AAPE ranges were 5-24 and 5-32% for GastroPlus and GI-Sim, respectively, excluding the low permeability drug. In conclusion, the a priori PBBM can be used during candidate selection and early product design to predict the in vivo performance of ER drug products for low to medium colon absorption limitation risk drugs with sufficient accuracy. The results also indicate a limited value in performing human regional absorption studies in which the drug is administered to the colon as a bolus to support PBBM development for ER drug products. Instead, by performing an early streamlined relative bioavailability study with the slowest relevant ER in vitro release profile, a highly accurate PBBM suitable for ER predictions for commercial and regulatory applications can be developed, except for permeability-limited drugs.

Applications of Modeling and Simulation Approaches in Support of Drug Product Development of Oral Dosage Forms and Locally Acting Drug Products: a Symposium Summary

The number of modeling and simulation applications, including physiologically based pharmacokinetic (PBPK) models, physiologically based biopharmaceutics modeling (PBBM), and empirical models, has been constantly increasing along with the regulatory acceptance of these methodologies. While aiming at minimizing unnecessary human testing, these methodologies are used today to support the development and approval of novel drug products and generics. Modeling approaches are leveraged today for assessing drug-drug interaction, informing dose adjustments in renally or hepatically impaired patients, perform dose selection in pediatrics and pregnant women and diseased populations, and conduct biopharmaceutics-related assessments such as establish clinically relevant specifications for drug products and achieve quality assurance throughout the product life cycle. In the generics space, PBPK analyses are utilized toward virtual bioequivalence assessments within the scope of alternative bioequivalence approaches, product-specific guidance development, and food effect assessments among others. Case studies highlighting the evolving and expanding role of modeling and simulation approaches within the biopharmaceutics space were presented at the symposium titled "Model Informed Drug Development (MIDD): Role in Dose Selection, Vulnerable Populations, and Biowaivers - Chemical Entities" and Prologue "PBPK/PBBM to inform the Bioequivalence Safe Space, Food Effects, and pH-mediated DDIs" at the American Association of Pharmaceutical Scientists (AAPS) PharmSci 360 Annual Meeting in Boston, MA, on October 16-19, 2022, and are summarized here.

Novel application of PBBM to justify impact of faster dissolution on safety and pharmacokinetics - a case study and utility in regulatory justifications

Physiologically based biopharmaceutics modelling (PBBM) was recognised as potential approach for biopharmaceutics applications. However, PBBM to justify safety is an unexplored area.In this manuscript, we elucidated PBBM application for safety justification. Product DRL is a generic extended release tablet containing an anti-epileptic narrow therapeutic index (NTI) drug. During dossier review, regulatory agency requested to evaluate the impact of faster dissolution profiles observed during stability on safety aspects. In order to justify, PBBMbased strategy was adapted.Model was validated and population simulations were performed for reference and test formulations and the predictions matched with clinical outcome. The model was found to be sensitive to dissolution changes and hence applied for the prediction of stability batches exhibiting faster dissolution profiles, virtually generated profiles at lower and upper specifications. The maximum predicted plasma levels were well below the reported safety levels, thereby demonstrating safety of the product.Overall, a novel application of PBBM to justify safety was demonstrated. Similar justifications using PBBM and linking with safety can be adopted where safety can be impacted due to aggravated dissolution profiles. Such justifications have potential to avoid clinical safety studies and helps in faster approval of drug product.

Biopharmaceutics Risk Assessment-Connecting Critical Bioavailability Attributes with In Vitro, In Vivo Properties and Physiologically Based Biopharmaceutics Modeling to Enable Generic Regulatory Submissions

Quality risk assessment following ICH Q9 principles is an important activity to ensure optimal clinical efficacy and safety of a drug product. Typically, risk assessment is focused on product performance wherein critical material attributes, formulation variables, and process parameters are evaluated from a manufacturing perspective. Extending ICH Q9 principles to biopharmaceutics risk assessment to identify factors that can impact in vivo performance is an upcoming area. This is evident by recent regulatory trends wherein a new term critical bioavailability attributes (CBA) has been coined to identify such factors. Although significant work has been performed for biopharmaceutics risk assessment for new molecules, there is a need for harmonized biopharmaceutics risk assessment workflow for generic submissions. In this manuscript, we attempted to provide a framework for performing biopharmaceutics risk assessment for generic regulatory submissions. A detailed workflow for performing biopharmaceutics risk assessment includes identification of initial CBA (iCBA), their confirmatory evaluation followed by definition of the control strategy. Tools for biopharmaceutics risk assessment, i.e., bio-discriminatory dissolution method and physiologically based biopharmaceutics modeling (PBBM) were discussed from a practical perspective. Furthermore, a case study for CBA evaluation using PBBM modeling for an extended-release product for regulatory submission has been described using the proposed workflow. Finally, future directions of integrating CBA evaluation, biopharmaceutics risk assessment to the FDA Knowledge Aided Structured Assessment (KASA) initiative, the necessity of risk assessment templates, and knowledge sharing between industry and academia are discussed. Overall, the work described in this manuscript can facilitate and provide guidance for biopharmaceutics risk assessment for generic submissions.

Power of Integrating PBPK with PBBM (PBPK-BM): A Single Model Predicting Food Effect, Gender Impact, Drug-Drug Interactions and Bioequivalence in Fasting & Fed Conditions

Over the past few years, PBPK and PBBM modeling have proven their significance in drug development. PBPK modeling is traditionally used to predict drug-drug interactions, exposures in special populations whereas PBBM modeling is a part of PBPK modeling that is used for a range of biopharmaceutics applications.Because of these differences in utilities, often PBPK and PBBM models are developed separately. When both models are combined, they serve multiple purposes through unified model. In the present case, an integrated PBPK-PBBM model for an IR product has been utilized for bioequivalence prediction in fasting & fed conditions, evaluating gender impact and food effect, prediction of drug-drug interactions.Model was built using physicochemical properties, enzymes and transporter kinetics, bio-predictive dissolution and has been validated with passing and failed pilot BE studies. The validated model predicted pivotal bioequivalence outcome in fasting & fed conditions accurately, predicted gender impact and food effect in line with literature. Drug-drug interactions arising from transporter and metabolizing enzymes were predicted accurately.Overall, this work demonstrates utility of combining PBPK and PBBM model that can yield a single model which can be used for multiple purposes, regulatory justifications and can reduce regulatory review timelines.

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.

Best Practices for Integration of Dissolution Data into Physiologically Based Biopharmaceutics Models (PBBM): A Biopharmaceutics Modeling Scientist Perspective

Dissolution is considered as a critical input into physiologically based biopharmaceutics models (PBBM) as it governs in vivo exposure. Despite many workshops, initiatives by academia, industry, and regulatory, wider practices are followed for dissolution data input into PBBM models. Due to variety of options available for dissolution data input into PBBM models, it is important to understand pros, cons, and best practices while using specific dissolution model. This present article attempts to summarize current understanding of various dissolution models and data inputs in PBBM software's and aims to discuss practical challenges and ways to overcome such scenarios. Different approaches to incorporate dissolution data for immediate, modified, and delayed release formulations are discussed in detail. Common challenges faced during fitting of z-factor are discussed along with novel approach of dissolution data incorporation using P-PSD model. Ways to incorporate dissolution data for MR formulations using Weibull and IVIVR approaches were portrayed with examples. Strategies to incorporate dissolution data for DR formulations was depicted along with practical aspects. Approaches to generate virtual dissolution profiles, using Weibull function, DDDPlus, and time scaling for defining dissolution safe space, and strategies to generate virtual dissolution profiles for justifying single and multiple dissolution specifications were discussed. Finally, novel ways to integrate dissolution data for complex products such as liposomes, data from complex dissolution systems, importance of precipitation, and bio-predictive ability of QC media for evaluation of CBA's impact were discussed. Overall, this article aims to provide an easy guide for biopharmaceutics modeling scientist to integrate dissolution data effectively into PBBM models.

Current State and Challenges of Physiologically Based Biopharmaceutics Modeling (PBBM) in Oral Drug Product Development

Physiologically based biopharmaceutics modeling (PBBM) emphasizes the integration of physicochemical properties of drug substance and formulation characteristics with system physiological parameters to predict the absorption and pharmacokinetics (PK) of a drug product. PBBM has been successfully utilized in drug development from discovery to postapproval stages and covers a variety of applications. The use of PBBM facilitates drug development and can reduce the number of preclinical and clinical studies. In this review, we summarized the major applications of PBBM, which are classified into six categories: formulation selection and development, biopredictive dissolution method development, biopharmaceutics risk assessment, clinically relevant specification settings, food effect evaluation and pH-dependent drug-drug-interaction risk assessment. The current state of PBBM applications is illustrated with examples from published studies for each category of application. Despite the variety of PBBM applications, there are still many hurdles limiting the use of PBBM in drug development, that are associated with the complexity of gastrointestinal and human physiology, the knowledge gap between the in vitro and the in vivo behavior of drug products, the limitations of model interfaces, and the lack of agreed model validation criteria, among other issues. The challenges and essential considerations related to the use of PBBM are discussed in a question-based format along with the scientific thinking on future research directions. We hope this review can foster open discussions between the pharmaceutical industry and regulatory agencies and encourage collaborative research to fill the gaps, with the ultimate goal to maximize the applications of PBBM in oral drug product development.

Physiologically Based Pharmacokinetics Modeling in Biopharmaceutics: Case Studies for Establishing the Bioequivalence Safe Space for Innovator and Generic Drugs

For successful oral drug development, defining a bioequivalence (BE) safe space is critical for the identification of newer bioequivalent formulations or for setting of clinically relevant in vitro specifications to ensure drug product quality. By definition, the safe space delineates the dissolution profile boundaries or other drug product quality attributes, within which the drug product variants are anticipated to be bioequivalent. Defining a BE safe space with physiologically based biopharmaceutics model (PBBM) allows the establishment of mechanistic in vitro and in vivo relationships (IVIVR) to better understand absorption mechanism and critical bioavailability attributes (CBA). Detailed case studies on how to use PBBM to establish a BE safe space for both innovator and generic drugs are described. New case studies and literature examples demonstrate BE safe space applications such as how to set in vitro dissolution/particle size distribution (PSD) specifications, widen dissolution specification to supersede f2 tests, or application toward a scale-up and post-approval changes (SUPAC) biowaiver. A workflow for detailed PBBM set-up and common clinical study data requirements to establish the safe space and knowledge space are discussed. Approaches to model in vitro dissolution profiles i.e. the diffusion layer model (DLM), Takano and Johnson models or the fitted PSD and Weibull function are described with a decision tree. The conduct of parameter sensitivity analyses on kinetic dissolution parameters for safe space and virtual bioequivalence (VBE) modeling for innovator and generic drugs are shared. The necessity for biopredictive dissolution method development and challenges with PBBM development and acceptance criteria are described.

Clinical Ocular Exposure Extrapolation for Ophthalmic Solutions Using PBPK Modeling and Simulation

BACKGROUND

The development of generic ophthalmic drug products is challenging due to the complexity of the ocular system, and a lack of sensitive testing to evaluate the interplay of physiology with ophthalmic formulations. While measurements of drug concentration at the site of action in humans are typically sparse, these measurements are more easily obtained in rabbits. The purpose of this study is to demonstrate the utility of an ocular physiologically based pharmacokinetic (PBPK) model for translation of ocular exposure from rabbit to human.

METHOD

The Ocular Compartmental Absorption and Transit (OCAT™) model within GastroPlus® v9.8.2 was used to build PBPK models for levofloxacin (Lev), moxifloxacin (Mox), and gatifloxacin (Gat) ophthalmic solutions. in the rabbit eye. The models were subsequently used to predict Lev, Mox, and Gat exposure after ocular solution administrations in humans. Drug-specific parameters were used as fitted and validated in the rabbit OCAT model. The physiological parameters were scaled to match human ocular physiology.

RESULTS

OCAT model simulations for rabbit well described the observed concentrations in the eye compartments following Lev, Mox, and Gat solution administrations of different doses and various administration schedules. The clinical ocular exposure following ocular administration of Lev, Mox, and Gat solutions at different doses and various administration schedules was well predicted.

CONCLUSION

Even though additional case studies for different types of active pharmaceutical ingredients (APIs) and formulations will be needed, the current study represents an important step in the validation of the extrapolation method to predict human ocular exposure for ophthalmic drug products using PBPK models.