Increasing impact of quantitative methods and modeling in establishment of bioequivalence and characterization of drug delivery.

Abstract

Quantitative methods and modeling (QMM) are rapidly demonstrating their potential to support generic drug development and assessment. This commentary is based on the public input from the Generic Drug User Fee Amendments (GDUFA) Science and Research Initiative Public Workshop held in May 2022, which recognized the implementation of model-integrated evidence (MIE) as a high research priority for the next 5 years. On May 9 and 10, 2022, the US Food and Drug Administration (FDA) hosted the Fiscal Year (FY) 2022 Generic Drug Science and Research Initiative Public Workshop as a commitment under the Generic Drug User Fee Amendments of 2017 (GDUFA II). The workshop provided updates on current science and research initiatives and solicited public input on the prioritization of scientific issues impacting generic product development and approval that will shape the next 5 years of the GDUFA science and research program (known as GDUFA III, 2022–2027). This commentary will focus on QMM, which was highlighted as one of the key research priorities in this workshop. GDUFA science and research have focused on QMM as one of the two key drivers of innovation for generic drugs along with in vitro product characterization.1 The annual science and research initiative public workshops, including this year's, have included a session dedicated to discussing research needs and priorities for QMM. Quantitative models can support product development and regulatory decisions where bioequivalence (BE) is challenging to demonstrate or assess because the empirical evidence for BE may be impractical to sufficiently demonstrate BE. This integration of evidence from empirical tests or studies and computational models has been referred to as model-integrated evidence (MIE).2 At the workshop, MIE was one of key topics of the opening session titled “The Next Five Years of the Generic Product Science and Research Program.” The session explored generic industry perspectives about the challenges impacting generic product development that are the most critical to address by GDUFA research initiatives during the next 5 years. Some of the key product development challenges and BE issues were associated with complex products, such as orally inhaled drug products (OIDPs) and long-acting injectable, implantable, and insertable products (collectively, LAI products). Industry representatives' feedback also highlighted complex BE issues in other product areas, including noncomplex generics. In general, for the GDUFA science and research program, industry has utmost interest in prioritizing research that helps address the current challenges generic drug developers are facing. For MIE, industry is particularly interested in MIE implementation, precedents set through access to MIE case studies for regulatory purposes, and detailed guidance (e.g., recommendations in the product-specific guidances) for those challenging areas. As indicated by Figure 1, articles included in this special issue contain the summaries of the two most recent modeling focused public workshops organized by the FDA in collaboration with the Center for Research on Complex Generics (CRCG), engaging multiple stakeholders, including generic industry, academia, and government,3, 4 as well as this FDA public workshop. We believe the progress we have seen in MIE is attributable to the FDA's continuous investment in research and dissemination of outcomes advancing QMM in collaboration with experts in academia and other stakeholders as well as industry's efforts to leverage and adopt innovative QMM approaches in their development programs. The significant shift in the scope of industry's MIE feedback from development to implementation is consistent with the recent increase in submission of modeling and simulation-based alternative BE approaches to the FDA in abbreviated new drug applications (ANDAs) and pre-ANDA interactions. The increase is particularly notable for pre-ANDA interactions during the past 3 years mirroring industry's strong interest and recognition of the potential MIE can bring to generic drug development programs and regulation submissions (Figure 2). The interactions were mainly for complex products, such as LAIs and OIDPs, whereas some noncomplex drug products were also included, such as certain oral drug products for oncology/rare disease drug products. These submissions generally leveraged GDUFA research outcomes, both the FDA internal and external research progresses and outcomes. Notably, the FDA and industry are collaboratively advancing the field and establishing the ecosystem for the application of QMM to aid the development of generic drug products. According to the interviews the CRCG conducted with the generic drug industry, industry is strongly interested in applying MIE approaches as an alternative to conventional end point BE studies for inhalation and LAI products to their product development programs. Industry is looking for guidance in the implementation and validation of MIE in their submissions to the FDA and indicates the most effective way may be through publications of case studies of MIE for complex generics approvals. Such cases are expected to spur the adoption of modeling into practice in product development and regulatory submissions. The perspectives shared by multiple industry representatives were consistent with the CRCG interview. Another feedback was on enhancing communications. Specifically, detailed MIE case studies representing the FDA's current thinking and expectations on MIE for regulatory applications, can be effectively disseminated through organizing workshops and other communication means, in addition to scientific publications. We note that the FDA is dedicated to actively publishing the progresses and case examples of MIE from the GDUFA research and there are several publications in the complex products including orally inhaled, long-acting injectable/implant, and dermal products.5-8 In addition, we believe that this special issue will provide a useful summary of the most recent development and discussion around the application of MIE to generic drug product development and assessment. The annual GDUFA science and research reports are another resource to check out the progress in MIE, which include a summary of research activities, research highlights, comprehensive lists of new, ongoing, and completed grants and contracts, and outcomes generated from the GDUFA-funded science and research program in each fiscal year. In-depth industry feedback on implementing QMM approaches for specific products including OIDPS, LAIs, and orally administered products can be found in the Supplementary Material. Overall, industry recognizes: (i) the benefits that standardizing MIE approaches for both complex and noncomplex generics would bring them; (ii) the value of using population pharmacokinetic (PK) and physiologically-based pharmacokinetic (PBPK) modeling to replace or reduce in vivo BE studies; and (iii) the need for model validation and verification for regulatory assurance in such MIE applications. With regard to industry's desire for guidance/criteria for model validation and verification, the two specific FDA workshops discussed best practices and practical implementations of MIE (see detailed summaries in this special issue). In the modeling focused FDA/CRCG and FDA GDUFAII workshops, the concept of model-master file and its application to facilitate generic drug development, regulatory submission, and assessment, was introduced by the Office of Generic Drugs at the FDA. The aim of this concept is to provide a framework for communication of regulatory acceptable modeling practices allowing for increased confidence in industry use of QMM. Harmonization with other agencies, such as the European Medical Agency, for modeling and simulation for BE were also commented to be helpful. Other noteworthy comments include that as generic drug field is maturing, MIE may become a routine alternative to BE studies in support of product life cycle management such as post-approval changes. In general, PBPK modeling has already been recommended for biopharmaceutical applications and been increasingly applied for supporting bioequivalence assessment.9 In addition, industry suggested that the innovative approaches that the Agency used specifically to address the unexpected challenges in BE during coronavirus disease 2019 (COVID-19) may be applied broadly with further research. Finally, the value of artificial intelligence (AI) and machine learning (ML), potentially combined with real-world evidence/data, in advancing the development of complex generics has been well-recognized by industry, regulatory agency, and other stakeholders, thus the need for further research has been emphasized. Specifically, AI and ML may demonstrate their utility in optimizing the program design and reduce study duration; in utilizing public domain information (e.g., drug labeling) to facilitate unique study designs for complex generic drug product or exploratory studies on related compounds in support of generic development. In addition, Industry's feedback was focused on research that can help establish the framework and potential regulatory pathway of translating such data-driven technologies into regulatory submissions. We also note that ML/AI technologies have been applied as part of MIE, for example, these technologies can support the model building process contributing to standardization of MIE processes for BE.10 The challenges shared by Industry are consistent with what the FDA has regarded as opportunities for MIE. FDA's GDUFA research programs in QMM have made significant investment and generated useful outcomes over the past few years. Industry recognizes the value of MIE to address challenges in generic drug development and assessment and strongly supports the FDA's investment on research that can facilitate and accelerate MIE implementation. The FDA is committed to continue further advancing this area and included facilitating the utility of MIE to support demonstrations of BE as one of the priority areas for the next 5 years of the GDUFA Science and Research Program. Overall, it is evident that both the FDA and industry are collaboratively establishing an MIE ecosystem in generic drug development and assessment. No funding was received for this work. The opinions expressed in this paper are those of the authors and should not be interpreted as the position of the US Food and Drug Administration. The authors declared no competing interests for this work. Appendix S1. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.