Evolution of chemical-specific adjustment factors (CSAF) based on recent international experience; increasing utility and facilitating regulatory acceptance
Bibliographic record
Abstract
The application of chemical-specific toxicokinetic or toxicodynamic data to address interspecies differences and human variability in the quantification of hazard has potential to reduce uncertainty and better characterize variability compared with the use of traditional default or categorically-based uncertainty factors. The present review summarizes the state-of-the-science since the introduction of the World Health Organization/International Programme on Chemical Safety (WHO/IPCS) guidance on chemical-specific adjustment factors (CSAF) in 2005 and the availability of recent applicable guidance including the WHO/IPCS guidance on physiologically-based pharmacokinetic (PBPK) modeling in 2010 as well as the U.S. EPA guidance on data-derived extrapolation factors in 2014. A summary of lessons learned from an analysis of more than 100 case studies from global regulators or published literature illustrates the utility and evolution of CSAF in regulatory decisions. Challenges in CSAF development related to the adequacy of, or confidence in, the supporting data, including verification or validation of PBPK models. The analysis also identified issues related to adequacy of CSAF documentation, such as inconsistent terminology and often limited and/or inconsistent reporting, of both supporting data and/or risk assessment context. Based on this analysis, recommendations for standardized terminology, documentation and relevant interdisciplinary research and engagement are included to facilitate the continuing evolution of CSAF development and guidance.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".