Guiding principles for mixture threshold derivation from effect biomarkers
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,006 | 0,006 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,010 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».