Frederico Martins1, Tarang Vora1, Grace Fraczkiewicz1, Viera Lukacova1, Kanthikiran Varanasi2, Olga Ribot2, Luis Gomez2, Yuvaneshwari Kanagasabapathy2, Eleftheria Tsakalozou3, Fang Wu3, Maxime Le Merdy1
1Simulations Plus, Inc, 2Galenicum Health SLUFDA, 3Division of Quantitative Methods and Modeling, Office of Research and Standards (ORS), Office of Generic Drugs (OGD), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA)
PHYSIOLOGICALLY BASED BIOPHARMACEUTICS MODELING FOR VIRTUAL BIOEQUIVALENCE ASSESSMENT OF METFORMIN IMMEDIATE AND EXTENDED-RELEASE FORMULATIONS Frederico S. Martins1, Tarang Vora1, Grace Fraczkiewicz1, Viera Lukacova1, Kanthikiran Varanasi2, Olga Ribot2, Luis Gomez2, Yuvaneshwari Kanagasabapathy2, Khondoker Alam3, Eleftheria Tsakalozou3, Fang Wu 3, Maxime Le Merdy1 (1)Simulations Plus, Inc., Research Triangle Park, NC, USA (2)Galenicum Health SLUFDA (3)Division of Quantitative Methods and Modeling, Office of Research and Standards (ORS), Office of Generic Drugs (OGD), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, MD, USA. Objectives: Metformin (MTF) is a medication used to treat type 2 diabetes. It works by decreasing glucose production in the liver and improving insulin sensitivity in the body’s tissues. [1] MTF is marketed as immediate release (IR) and extended release (XR) tablets; the XR tablet shows a longer time to maximum plasma concentration (Cmax) than the IR one. [1-2] The goal of this work was to develop a workflow for virtual bioequivalence (VBE) assessments using Physiologically Based Biopharmaceutics Modeling (PBBM). We conducted VBE on IR and XR MTF formulations as model products to help support the development of the workflow. Methods: An MTF PBBM model was developed using GastroPlus version 10.1 (Simulations Plus, Inc., Research Triangle Park, NC, USA), including all critical mechanisms impacting MTF absorption and systemic disposition such as contributions of intestinal transporters and paracellular permeability to absorption, transporter-mediated uptake into kidney and liver, and transporter-mediated renal secretion. Intra-subject variability was considered in gut physiology parameters. The model input parameters were a combination of literature sourced values, in silico predicted values (ADMET Predictor), and values fitted against observed clinical data. The systemic disposition model for MTF was developed using intravenous data. In vitro dissolution data and the in vivo BE study data on two IR MTF and two XR MTF formulations (Study 1 and Study 2, respectively) were provided by Galenicum Health SLU. Reference (R) and Test (T) products were assigned in accordance with Studies 1 and 2. The in vivo dissolution for each of the IR and XR tablets was parametrized as single Weibull function fitted to corresponding in vitro dissolution data measured in USP II apparatus in media with pH 6.8. The model was refined against in vivo pharmacokinetic (PK) data from Studies 1 and 2 and further validated with literature data for oral (IR) administration. The VBE evaluations were performed using the previously validated model and involved 20 trials for each for the IR and XR tablets. Moreover, the VBE study design mirrored single dose Studies 1 and 2. More specifically, Study 1 enrolled 20 subjects in a 2×2 crossover design under fed conditions. Study 2 enrolled 58 subjects in a 2×2 crossover design under fasted conditions. Results: Model performance for IR and XR tablets was satisfactory when simulated vs observed plasma MTF PK profiles were compared. The predicted-to-observed fold error for Cmax and AUC0-t ranged between 0.5-1.25 across all studies. Additionally, Tmax and the impact of inter- and intra-subject variability on Cmax and AUC across independent studies were considered in the model performance assessment. The VBE assessment results were consistent with Studies 1 and 2. Across all VBE studies, the 90% confidence intervals for the T/R geometric mean ratio ranged from 0.88–1.20 (point estimate range: 0.94–1.07) for AUC0–t and 0.89–1.14 (point estimate range: 0.98–1.09) for Cmax, meeting the BE acceptance criteria.[3] Conclusions: The developed PBBM approach effectively predicts the absorption and MTF PKs; predicted and observed data align well across multiple studies. The VBE assessments confirmed that both the IR R vs T and XR R vs T formulations were found bioequivalent, consistent with the Study 1 and 2 bioequivalence outcomes. These results suggest that PBBM, when appropriately validated, has the potential to support VBE evaluations. This work summarizes the initial efforts of the best practices for the performance of VBE assessments with considerations on model development and validation as well as VBE study design. Funding Statement: This abstract/poster was supported by the Food and Drug Administration (FDA) of the U.S. Department of Health and Human Services (HHS) as part of a financial assistance award 1U01FD007906 totaling $500,000 with 100 percent funded by FDA]/HHS. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by FDA/HHS, or the U.S. Government. Note: To enhance research collaborations with the generic industry, and maximize the predictive capabilities of these VBE models, the Center for Research on Complex Generics (CRCG) announced open invitations for generic industry stakeholders (non-federal entities) to provide in vitro and/or in vivo preclinical and/or clinical study data for specific drug products within the scope of this Grant (refer to CRCG Announcement for Data Contribution to Grant U01FD007906 awarded to Simulations Plus (Center for Research on Complex Generics (CRCG). Call for Data Contribution #2. Available at https://complexgenerics.org/research-capabilities/funding-opportunities/call-for-data-contribution-2/. Last accessed Dec. 23rd, 2024). The goal was to afford the Grant awardee access to relevant experimental data that could be leveraged when developing and validating their models. Multiple generic industry stakeholders responded to the announcement and were willing to contribute experimental data to the awardees of these research Grants. Specifically, Adium, Galenicum Health S.L.U, Libbs Farmacêutica, and Sandoz agreed to contribute to the research under Grant U01FD007906. Note that the terms of the contribution were negotiated directly between the Grant awardee and each external contributor. Each awardee and contributor understood that the contributions would not result in undue influence on FDA regulatory decisions, not result in an endorsement of the identified contributors or any of their products or activities by FDA, and not provide access to non-public product specific information from FDA. It was also agreed that the contributions may be publicly acknowledged by the identified contributors, but the contributors may not promote themselves as having a relationship with FDA.
[1] Foretz, M., Guigas, B. & Viollet, B. Metformin: update on mechanisms of action and repurposing potential. Nat Rev Endocrinol 19, 460–476 (2023). https://doi.org/10.1038/s41574-023-00833-4 [2] Orange Book: Approved Drug Products with Therapeutic Equivalence Evaluations. https://www.fda.gov/drugs/drug-approvals-and-databases/approved-drug-products-therapeutic-equivalence-evaluations-orange-book [3] Food and Drug Administration. (2021). Bioequivalence Studies With Pharmacokinetic Endpoints for Drugs Submitted Under an ANDA: Guidance for Industry. U.S. Department of Health and Human Services, Center for Drug Evaluation and Research. https://www.fda.gov/media/87219/download
Reference: PAGE 33 (2025) Abstr 11565 [www.page-meeting.org/?abstract=11565]
Poster: Drug/Disease Modelling - Absorption & PBPK