Considerations from an International Regulatory and Pharmaceutical Industry (IQ MPS Affiliate) Workshop on the Standardization of Complex In Vitro Models in Drug Development
Bibliographic record
Abstract
In May 2022, there is an International Regulatory and Pharmaceutical Industry (Innovation and Quality [IQ] Microphysiological Systems [MPS] Affiliate) Workshop on the standardization of complex in vitro models (CIVMs) in drug development. This manuscript summarizes the discussions and conclusions of this joint workshop organized and executed by the IQ MPS Affiliate and the United States Food and Drug Administration (FDA). A key objective of the workshop is to facilitate discussions around opportunities and/or needs for standardization of MPS and chart potential pathways to increase model utilization in the context of regulatory decision making. Participation in the workshop included 200 attendees from the FDA, IQ MPS Affiliate, and 26 global regulatory organizations and affiliated parties representing Europe, Japan, and Canada. It is agreed that understanding global perspectives regarding the readiness of CIVM/MPS models for regulatory decision making and potential pathways to gaining acceptance is useful to align on globally. The obstacles are currently too great to develop standards for every context of use (COU). Instead, it is suggested that a more tractable approach may be to think of broadly applicable standards that can be applied regardless of COU and/or organ system. Considerations and next steps for this effort are described.
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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 | 0.145 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".