Parameterization of Physiologically Based Biopharmaceutics Models: Workshop Summary Report
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".