Physiologically Based Biopharmaceutics Model for Selumetinib Food Effect Investigation and Capsule Dissolution Safe Space - Part I: Adults

Development of a Physiologically Based Biopharmaceutics Model Report Template: Considerations for Improved Quality in View of Regulatory Submissions

Pharmaceutical innovators and generic companies use Physiologically Based Biopharmaceutics Models (PBBMs) to guide drug product development and potentially waive clinical pharmacokinetic studies for both pre- and postapproval changes. This modeling approach can assist with biopharmaceutics risk assessment and the establishment of patient centric, clinically relevant drug product specifications. However, the variability of possible model strategies and the existence of gaps in scientific knowledge associated with the lack of standardized regulatory expectations for model parametrization, data requirements for model development, and criteria for fit-for-purpose model validation leads to varied acceptance rates and frequent requests for additional information and deficiencies in PBBM submissions across regulatory agencies. During the 2023 Maryland Center of Excellence in Regulatory Science and Innovation (M-CERSI) PBBM Best Practices for Drug Product Quality: Regulatory and Industry Perspectives workshop, it was identified that a PBBM report template summarizing model considerations and proposing a structure for presenting question(s) of interest, model context, input data, a modeling plan, and validation would be beneficial for both industry and regulatory agencies. The present work is not a regulatory guideline but rather a summary of current best practices and considerations for PBBM submissions. The associated template can be downloaded directly from the Supporting Information to guide one in the preparation of PBBM reports. The current paper discusses the critical elements of the PBBM report template, which were identified during the industry-regulator scientific collaboration and interactions.

Parameterization of Physiologically Based Biopharmaceutics Models: Workshop Summary Report

This Article shares the proceedings from the August 29th, 2023 (day 1) workshop "Physiologically Based Biopharmaceutics Modeling (PBBM) Best Practices for Drug Product Quality: Regulatory and Industry Perspectives". The focus of the day was on model parametrization; regulatory authorities from Canada, the USA, Sweden, Belgium, and Norway presented their views on PBBM case studies submitted by industry members of the IQ consortium. The presentations shared key questions raised by regulators during the mock exercise, regarding the PBBM input parameters and their justification. These presentations also shed light on the regulatory assessment processes, content, and format requirements for future PBBM regulatory submissions. In addition, the day 1 breakout presentations and discussions gave the opportunity to share best practices around key questions faced by scientists when parametrizing PBBMs. Key questions included measurement and integration of drug substance solubility for crystalline vs amorphous drugs; impact of excipients on apparent drug solubility/supersaturation; modeling of acid-base reactions at the surface of the dissolving drug; choice of dissolution methods according to the formulation and drug properties with a view to predict the in vivo performance; mechanistic modeling of in vitro product dissolution data to predict in vivo dissolution for various patient populations/species; best practices for characterization of drug precipitation from simple or complex formulations and integration of the data in PBBM; incorporation of drug permeability into PBBM for various routes of uptake and prediction of permeability along the GI tract.

A Population Pharmacokinetic Assessment of the Effect of Food on Selumetinib in Patients with Neurofibromatosis Type 1-Related Plexiform Neurofibromas and Healthy Volunteers

Selumetinib is clinically used for pediatric patients with neurofibromatosis type 1 and symptomatic, inoperable plexiform neurofibromas. Until recently, selumetinib had to be taken twice daily, after 2 hours of fasting and followed by 1 hour of fasting, which could be inconvenient. This population analysis evaluated the effect of low- and high-fat meals on the pharmacokinetic (PK) parameters of selumetinib and its active metabolite N-desmethyl selumetinib. The dataset comprised 511 subjects from 15 clinical trials who received ≥1 dose of selumetinib and provided ≥1 measurable postdose concentration of selumetinib and N-desmethyl selumetinib. A 2-compartment model with sequential 0- and 1st-order delayed absorption and 1st-order elimination adequately described selumetinib PK characteristics. A 1-compartment model reasonably described N-desmethyl selumetinib PK characteristics over time simultaneously with selumetinib. Selumetinib geometric mean area under the concentration-time curve ratio (1-sided 90% confidence interval [CI] lower bound) was 76.9% (73.3%) with a low-fat meal and 79.3% (76.3%) with a high-fat meal versus fasting. The lower bound of the 1-sided 90% CI demonstrated a difference of <30% between fed and fasted states. Considering the flat exposure-response relationship within the dose range (20-30 mg/m2), the observed range of exposure, and the variability in the SPRINT trial, this was not considered clinically relevant.

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.

Role of Physiologically Based Biopharmaceutics Modeling (PBBM) in Fed Bioequivalence Study Waivers: Regulatory Outlook, Case Studies and Future Perspectives

Over the past few decades, physiologically based biopharmaceutics modeling (PBBM) has demonstrated its utility in both new drug and generic product development. Applications of PBBM for fed bioequivalence study waivers is an upcoming area. Recently Innovation & Quality (IQ) consortium demonstrated utility of PBBM to avoid repeat food effect studies for new drugs. In the similar lines, the current manuscript aims to discuss role of PBBM in generic fed bioequivalence study waivers. Generic industry practices related to PBBM model development to predict fed bioequivalence was portrayed with special emphasis on fed bio-predictive media. Media that can simulate fed bioequivalence study outcome were discussed from practical perspective. In-depth analysis, collating the data from 36 products was performed to understand predictability of PBBM for fed bioequivalence. Cases where PBBM was successful to predict fed bioequivalence was correlated with BCS class, formulation category and type of food effect. Further, two case studies were presented wherein fed bioequivalence study waiver obtained with PBBM approach. Lastly, future direction in terms of fed bioequivalence study waivers, regulatory perspectives and best practices for PBBM were portrayed. Overall, this article paves a way to utilize PBBM for generic fed bioequivalence study waivers.

Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs

INTRODUCTION

Physiologically based pharmacokinetics (PBPK) models are increasingly used in the drug research and development, especially in anti-cancer drugs. Between 2001 and 2020, a total of 89 small-molecule targeted antitumor drugs were approved in China and the United States, some of which already included PBPK modeling in their application or approval packages. This article intended to review the prevalence and application of PBPK model in these drugs.

METHOD

Article search was performed in the PubMed to collect English research articles on small-molecule targeted anti-cancer drugs using PBPK modeling. The selected articles were classified into nine categorizes according to the application areas and further analyzed.

RESULT

From 2001 to 2020, more than 60% of small-molecule targeted anti-cancer drugs (54/89) were studied using PBPK model with a wide range of application. Ninety research articles were included, of which 48 involved enzyme-mediated drug-drug interaction (DDI). Of these retrieved articles, Simcyp, GastroPlus, and PK-Sim were the most widely model building platforms, which account for 63.8%, 15.2%, and 8.6%, respectively.

CONCLUSION

PBPK modeling is commonly and widely used to research small-molecule targeted anti-cancer drugs.

Streamlining Food Effect Assessment - Are Repeated Food Effect Studies Needed? An IQ Analysis

Current regulatory guidelines on drug-food interactions recommend an early assessment of food effect to inform clinical dosing instructions, as well as a pivotal food effect study on the to-be-marketed formulation if different from that used in earlier trials. Study waivers are currently only granted for BCS class 1 drugs. Thus, repeated food effect studies are prevalent in clinical development, with the initial evaluation conducted as early as the first-in-human studies. Information on repeated food effect studies is not common in the public domain. The goal of the work presented in this manuscript from the Food Effect PBPK IQ Working Group was to compile a dataset on these studies across pharmaceutical companies and provide recommendations on their conduct. Based on 54 studies collected, we report that most of the repeat food effect studies do not result in meaningful differences in the assessment of the food effect. Seldom changes observed were more than twofold. There was no clear relationship between the change in food effect and the formulation change, indicating that in most cases, once a compound is formulated appropriately within a specific formulation technology, the food effect is primarily driven by inherent compound properties. Representative examples of PBPK models demonstrate that following appropriate validation of the model with the initial food effect study, the models can be applied to future formulations. We recommend that repeat food effect studies should be approached on a case-by-case basis taking into account the totality of the evidence including the use of PBPK modeling.

Assessment of food effects during clinical development

Food-drug interactions frequently hamper oral drug development due to various physicochemical, physiological and formulation-dependent mechanisms. This has stimulated the development of a range of promising biopharmaceutical assessment tools which, however, lack standardized settings and protocols. Hence, this manuscript aims to provide an overview of the general approach and the methodology used in food effect assessment and prediction. For in vitro dissolution-based predictions, the expected food effect mechanism should be carefully considered when selecting the level of complexity of the model, together with its drawbacks and advantages. Typically, in vitro dissolution profiles are then incorporated into physiologically based pharmacokinetic models, which can estimate the impact of food-drug interactions on bioavailability within 2-fold prediction error, at least. Positive food effects related to drug solubilization in the GI tract are easier to predict than negative food effects. Preclinical animal models also provide a good level of food effect prediction, with beagle dogs remaining the gold standard. When solubility-related food-drug interactions have large clinical impact, advanced formulation approaches can be used to improve fasted state pharmacokinetics, hence decreasing the fasted/fed difference in oral bioavailability. Finally, the knowledge from all studies should be combined to secure regulatory approval of the labelling instructions.

Acalabrutinib Maleate Tablets: The Physiologically Based Biopharmaceutics Model behind the Drug Product Dissolution Specification

Acalabrutinib maleate tablets correspond to an improved formulation compared to acalabrutinib capsules as they can be dosed with and without acid reducing agents and therefore benefit more cancer patients. The dissolution specification for the drug product was determined using all the information available on the drug safety, efficacy, and in vitro performance. In addition, a physiologically based biopharmaceutics model was developed for acalabrutinib maleate tablets on the back of a previously published model for acalabrutinib capsules to establish that the proposed drug product dissolution specification would ensure safe and effective products for all patients including those under acid reducing agent treatment. The model was built, validated, and used to predict the exposure of virtual batches where the dissolution was slower than that of the clinical target. A combination of exposure prediction and the use of a PK-PD model allowed it to be demonstrated that the proposed drug product dissolution specification was acceptable. This combination of models enabled a larger safe space than would have been granted by consideration of bioequivalence only.

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.