Mode of Action Frameworks in Toxicity Testing and Chemical Risk Assessment
Bibliographic record
Abstract
Recently, legislative mandates worldwide are requiring systematic consideration of much larger numbers of chemicals. This necessitates more efficient and effective toxicity testing, as a basis to be more predictive in a risk assessment context. This in turn requires much more emphasis early in the design of test strategies on both potential exposure and mechanism or modes of toxicity and a resulting shift based on the latter, from hazard identification to hazard characterization in order to group substances and additionally inform development of predictive computational tools. It also requires a much better common understanding in the regulatory risk assessment community of the nature of appropriate information to inform consideration of mode of action and resulting implications for dose-response and ultimately, risk characterization. This requires a shift in focus from the previously principally qualitative considerations of toxicological science to the necessarily more predictive and quantitative focus of risk assessment and has implications for appropriate communication and training of risk assessors. Human relevance of mode of action frameworks continue to play a critical role in hypothesis generation and the systematic consideration of the weight of evidence supporting the use of mechanistic data in regulatory risk assessment. Framework analyses increase the transparency of delineation of the relative degrees of uncertainty associated with various options for consideration in dose-response and risk characterization for impacted populations. Framework analyses are also instrumental in acquiring transparency on critical data gaps that will further reduce uncertainty. As such, they force distinction of choices made on the basis of science policy versus those that are science judgment related, including reliance on default, based on erroneous premise that it is always health protective. The potential of these frameworks to increase consistency and transparency in decision making contributes to increase common understanding among communities and jurisdictions. They are an important tool for coordination and communication between the research and regulatory risk assessment communities. They are also an essential “bridge” in the evolution of toxicity testing to be more predictive, relevant and risk-based, through relation of early perturbations to apical endpoints in a context relevant to current application in regulatory risk assessment. As we move forward to develop more integrative test strategies to meet evolving and demanding regulatory mandates to deal efficiently with significantly larger numbers of chemicals including groups and combined exposures, early assimilation of the information in a mode of action context as envisaged by application of these frameworks is essential.
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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 | 0.044 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".