Guiding principles for mixture threshold derivation from effect biomarkers
Bibliographic record
Abstract
Foreword: Currently available assessment approaches for cumulative risk of chemical mixtures can only be applicable to a small number of substances present at workplaces and in the environment. We cannot anticipate a significant change of this situation in the near future due to extensive data need for cumulative risk assessments. Presently, effect biomarkers are the most direct option to address the risk of known and unknown mixtures in an integrative way. Traditional occupational health risk assessments often rely on external exposure measurements, such as air monitoring, which may not fully capture the complexities of workplace exposures. Human biomonitoring is used to measure internal exposures or effects in exposed individuals or groups from all potential routes of exposure (i.e., inhalation, oral, and dermal). Exposure to mixtures in the workplace and environment is the most common chemical exposure scenario in our daily lives. However, methods for assessing the risks and for setting mixture threshold limits to avoid adverse effects lack global harmonization. Monitoring of effect biomarkers can support regulatory risk assessment in multiple ways. An effect biomarker indicates a stressor-induced biological effect which can be associated with a disease and can be interpreted as a potential predictor of a downstream effect i.e. measuring a key event in a Mode of Action (MoA) or Adverse Outcome Pathway (AOP). Thus, biomarkers can provide an integrated measure of the response to relevant stressors by all routes of known and unknown exposures. However, effect biomarker responses are usually not straightforward to interpret regarding their predictive value to indicate adverse effects. A systematic understanding of the relevance of effect biomarker data will enhance the protection of workers and/or ecosystems, if used under appropriate ethical and regulatory frameworks. Therefore, harmonized guidance for assessing effect biomarkers and their application to risk assessments are needed. The guiding principles proposed in this document describe the key concepts for the derivation and interpretation of mixture thresholds* for selected effect biomarkers for use in occupational or ecological risk assessments. The aim of these guiding principles is to present a harmonized assessment approach which will save resources and promote consistency across regulatory agencies at national and international levels. The development of this document was a joint activity of the Organisation for Economic Co-operation and Development (OECD) Working Party on Exposure Assessment & Working Party on Hazard Assessment (WPEA & WPHA) in collaboration with more than 90 experts from 25 countries and other stakeholders (see chapter 8 project participation). The activity was started in October 2022 and the development of this guiding principles document was co-led by Robert Pasanen-Kase (SECO*, CH) as coordinator, Maryam Zare-Jeddi (BIAC*), Nancy B. Hopf (Unisanté, CH), Susana Viegas (ENSP*/UNL, PT), Dan Villeneuve (US-EPA*, US), Martin Wilks and Rex FitzGerald (University of Basel, CH), Radu Corneliu Duca (LNS*, LU) and the OECD Secretariat. The document was drafted in close collaboration with experts providing input on different aspects of human and environmental effect-biomonitoring including Bernice Scholten, (TNO* , NL), Eszter Simon (FOEN* , CH), Devika Poddalgoda (Health Canada, CAN), Anna Bal Price (JRC*, EU); Vicente Mustieles, Antonio Hernandez-Jerez (University of Granada, ES), Christoph van Thriel (IFADO* , DE), Stefano Bonassi (IRCCS*, San Raffaele Roma, IT), Michael Fenech (University of South Australia, AUS), Sophie Ndaw (INRS*, FR), Christina Pieper (German Federal Institute for Risk Assessment, DE), Lucian Farcal, Alicia Paini (EFSA*, EU). The initial draft guidance document was reviewed in 2025 by expert group (see chapter 8) and WPEA & WPHA members and was commented by eleven experts from six different organisations / institutes / companies and was finalized. This adopted biomonitoring guiding principles document is published under the responsibility of the Chemical and Biotechnology Committee of the OECD.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".