Envisioning an international validation process for New Approach Methodologies in chemical hazard and risk assessment
Bibliographic record
Abstract
Despite the global toxicology community discussing New Approach Methodologies (NAMs) for chemical hazard and risk assessment, such as in vitro, in silico, and ‘omics-based approaches, for some 30 years, their formal adoption by regulators remains limited. Previous research suggests that insufficient validation, complexity of interpretation, and lack of standardization are salient obstacles to adoption. In this paper we aim to better understand the policy challenges associated with adopting NAMs in chemical risk assessment; and to identify actions that could facilitate and accelerate their formal adoption internationally. We conducted a Delphi study – a group communication process that solicits expert judgments through iterative questioning and feedback – with panelists from government, industry, and non-governmental organizations in Europe and North America. Expert panelists identified two key activities to facilitate and accelerate the validation of NAMs internationally: 1) the development of common data collection, reporting and sharing procedures; and 2) the improvement of knowledge about new test methods among members of the regulatory community. Both activities suggest the need for a common regulatory science infrastructure, including international regulatory dialogues, large-scale research collaborations, and coordinated innovation in technological tools, the discourse of scientific validation, and regulatory procedures. To build trust across many sites (laboratories, regulatory agencies, contract research organizations, chemical producers, and the public), stakeholders will need to agree on validation requirements for particular uses (content in relation to context) as well as how results are to be communicated (data format), and measured (metrics). There is also a need for a global orchestrator, who can exert leadership and inspire voluntary cooperation of diverse organizations to address shared validation goals, to play a key role.
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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 | 0.669 | 0.291 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".