Regulatory utility of physiologically‐based pharmacokinetic modeling to support alternative bioequivalence approaches and risk assessment: A workshop summary report
Bibliographic record
Abstract
This report summarizes the proceedings for day 2 sessions 1 and 3 of the 2-day public workshop entitled "Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches," a jointly sponsored workshop by the US Food and Drug Administration (FDA) and the Center for Research on Complex Generics (CRCG). The aims of this workshop were: (1) to discuss how mechanistic modeling, including physiologically-based pharmacokinetic (PBPK) modeling and simulation, can support product development, and regulatory submissions; (2) to share the current state of mechanistic modeling for bioequivalence (BE) assessment through case studies; (3) to establish a consensus on best practices for using PBPK modeling for BE assessment to help drive further investment by the generic drug industry into mechanistic modeling and simulation; and (4) to introduce the concept of a Model Master File to improve model-sharing. The theme of day 2 covered PBPK absorption model for oral products as an alternative BE approach and a tool for supporting risk assessment and biowaiver (session 1), oral PBPK for evaluating the impact of food on BE (session 2), successful cases, and challenges for oral PBPK (session 3). This report summarizes the topics of the presentations of day 2 sessions 1 and session 3 from FDA, academia, and pharmaceutical industry, including the current status of oral PBPK, case examples as well as the challenges and opportunities in this area. In addition, panel discussions on the utility of oral PBPK in both new drugs and generic drugs from regulatory and industry perspective are also summarized.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".