Introducing the Molecular Pharmaceutics Special Issue on the 2023 PBBM Workshop for Drug Product Quality
Bibliographic record
Abstract
RecommendationsT his Special Issue is dedicated to PBBM (Physiologically based biopharmaceutics modeling), its use in drug product quality and underlines the important strategic collaboration among academics, software companies, industry, and regulators in designing and delivering a 3-day workshop during August, 2023.The Special Issue brings together the combined workshop output including the summary, 1 details of all discussions and best practices on model parametrization, model verification, validation and application, current and future drug product quality applications of PBBM from the industry as well as the regulatory agencies perspectives, and to close, the development of a PBBM Report Template, which considers how to improve PBBM quality, with potential to promote increased utility of PBBM to support product understanding and life cycle management.Physiology Based Biopharmaceutics Modeling (PBBM) is a subset of PBPK and involves the application of PBPK for biopharmaceutics applications.PBBM is an evolving tool used in drug product development (Model Informed Drug Development), regulatory approval, and life cycle management.PBBMs are used to elevate drug product quality by providing a more accurate and holistic understanding of how drugs interact with the human body.These models are based on the integration of physiological, pharmacological and pharmaceutical data to simulate and predict drug behavior in vivo.Effective utilization of PBBM requires a consistent approach to model development, verification, validation and application.Currently, only one country has a draft guidance for PBBM whereas other major regulatory authorities have had limited experience with review of PBBMs.To address this gap, industry submitted confidential PBBM case studies for collaborative review by the regulatory agencies.Successful bioequivalence "safe space" industry case examples were also presented.Overall, six regulatory agencies were involved in the case study exercises, including ANVISA, FDA, Health Canada, MHRA, PMDA and EMA (experts from Belgium, Germany, Norway, Portugal, Spain, and Sweden), and we believe this is the first time such a collaboration has taken place.The outcomes were presented, together with a participant survey on the utility and experience with PBBM submissions, to discuss the best scientific practices for developing, validating and applying PBBM.The PBBM case studies enabled industry to receive constructive feedback from cross agency regulators and highlighted clear direction for future PBBM submissions for regulatory consideration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".