Derivation of point of departure (PoD) estimates in genetic toxicology studies and their potential applications in risk assessment
Bibliographic record
Abstract
Genetic toxicology data have traditionally been employed for qualitative, rather than quantitative evaluations of hazard. As a continuation of our earlier report that analyzed ethyl methanesulfonate (EMS) and methyl methanesulfonate (MMS) dose-response data (Gollapudi et al., 2013), here we present analyses of 1-ethyl-1-nitrosourea (ENU) and 1-methyl-1-nitrosourea (MNU) dose-response data and additional approaches for the determination of genetic toxicity point-of-departure (PoD) metrics. We previously described methods to determine the no-observed-genotoxic-effect-level (NOGEL), the breakpoint-dose (BPD; previously named Td), and the benchmark dose (BMD10 ) for genetic toxicity endpoints. In this study we employed those methods, along with a new approach, to determine the non-linear slope-transition-dose (STD), and alternative methods to determine the BPD and BMD, for the analyses of nine ENU and 22 MNU datasets across a range of in vitro and in vivo endpoints. The NOGEL, BMDL10 and BMDL1SD PoD metrics could be readily calculated for most gene mutation and chromosomal damage studies; however, BPDs and STDs could not always be derived due to data limitations and constraints of the underlying statistical methods. The BMDL10 values were often lower than the other PoDs, and the distribution of BMDL10 values produced the lowest median PoD. Our observations indicate that, among the methods investigated in this study, the BMD approach is the preferred PoD for quantitatively describing genetic toxicology data. Once genetic toxicology PoDs are calculated via this approach, they can be used to derive reference doses and margin of exposure values that may be useful for evaluating human risk and regulatory decision making.
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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 | 0.021 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".