Overview on legislation and scientific approaches for risk assessment of combined exposure to multiple chemicals: the potential EuroMix contribution
Bibliographic record
Abstract
This article reviews the current legislative requirements for risk assessment of combined exposure to multiple chemicals via multiple exposure routes, focusing on human health and particularly on food-related chemicals. The aim is to identify regulatory needs and current approaches for this type of risk assessment as well as challenges of the implementation of appropriate and harmonized guidance at international level. It provides an overview of the current legal requirements in the European Union (EU), the United States and Canada. Substantial differences were identified in the legal requirements for risk assessment of combined exposure to multiple chemicals and its implementation between EU and non-EU countries and across several regulatory sectors. Frameworks currently proposed and in use for assessing risks from combined exposure to multiple chemicals via multiple routes and different durations of exposure are summarized. In order to avoid significant discrepancies between regulatory sectors or countries, the approach for assessing risks of combined exposure should be based on similar principles for all types of chemicals. OECD and EFSA identified the development of harmonized methodologies for combined exposure to multiple chemicals as a key priority area. The Horizon 2020 project "EuroMix" aims to contribute to the further development of internationally harmonized approaches for such risk assessments by the development of an integrated test strategy using in vitro and in silico tests verified for chemical mixtures based on more appropriate data on potential combined effects. These approaches and testing strategies should be integrated in a scientifically based weight of evidence approach to account for complexity and uncertainty, to improve risk assessment.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".