Benchmark Response (<scp>BMR</scp>) Values for In Vivo Mutagenicity Endpoints
Bibliographic record
Abstract
The benchmark dose (BMD) approach constitutes the most effective and pragmatic strategy for the derivation of a point of departure (PoD) for comparative potency analysis, risk assessment, and regulatory decision-making. There is considerable controversy regarding the most appropriate benchmark response (BMR) for genotoxicity endpoints. This work employed the Slob (2017) Effect Size (ES) theory to define robust BMR values for the in vivo transgenic rodent (TGR) and Pig-a mutagenicity endpoints. An extensive database of dose-response data was prepared and curated; BMD analyses were used to determine endpoint-specific maxima (i.e., parameter c) and within-group variance (i.e., var). Detailed analyses investigated the dependence of var on experimental factors such as tissue, administration route, treatment duration, and post-exposure tissue sampling time. The overall lack of influence of these experimental factors on var permitted the determination of typical values for the endpoints investigated. Typical var for the TGR endpoint is 0.19; the value for the Pig-a endpoint is 0.29. Endpoint-specific var values were used to calculate endpoint-specific BMR values; the values are 47% for TGR and 60% for Pig-a. Endpoint-specific BMR values were also calculated using the trimmed distribution of study-specific standard deviation (SD) values for concurrent controls. Those analyses yielded endpoint-specific BMR values for the TGR and Pig-a endpoints of 33% and 58%, respectively. Considering the results obtained, and the in vivo genetic toxicity BMR values noted in the literature, we recommend a BMR of 50% for in vivo mutagenicity endpoints. The value can be employed to interpret mutagenicity dose-response data in a risk assessment context.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".