Current State and New Horizons in Applications of Physiologically Based Biopharmaceutics Modeling (PBBM): A Workshop Report
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide This report summarizes the proceedings for Day 3 of the workshop titled “ Physiologically Based Biopharmaceutics Modeling (PBBM) Best Practices for Drug Product Quality: Regulatory and Industry Perspectives ”. This day focused on the current and future drug product quality applications of PBBM from the innovator and generic industries as well as the regulatory agencies perspectives. The presentations, which included several case studies, covered the applications of PBBM in generic drug product development, applications of virtual bioequivalence trials to support formulation bridging and the utility of absorption modeling in clinical pharmacology assessments. In addition, recent progress in the prediction of colon absorption and in vivo performance of extended-release drug products was shared. The morning session was concluded by representatives from FDA, ANVISA, MHRA, Health Canada, EMA, and PMDA giving their perspectives on the application of PBBM in regulatory submissions. The afternoon breakout sessions focused on four parallel topics: 1) PBBM in generic drug product development; 2) virtual bioequivalence trials applications; 3) safe space and extrapolation; and 4) regional absorption and modified release PBBM applications. This allowed the participants to engage in in-depth discussions of best practices as well to identify key points of consideration to allow further progress on the applications of PBBM.
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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 | 0.028 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".