Towards a systematic use of effect biomarkers in population and occupational biomonitoring
Bibliographic record
Abstract
Effect biomarkers can be used to elucidate relationships between exposure to environmental chemicals and their mixtures with associated health outcomes, but they are often underused, as underlying biological mechanisms are not understood. We aim to provide an overview of available effect biomarkers for monitoring chemical exposures in the general and occupational populations, and highlight their potential in monitoring humans exposed to chemical mixtures. We also discuss the role of the adverse outcome pathway (AOP) framework and physiologically based kinetic and dynamic (PBK/D) modelling to strengthen the understanding of the biological mechanism of effect biomarkers, and in particular for use in regulatory risk assessments. An interdisciplinary network of experts from the European chapter of the International Society for Exposure Science (ISES Europe) and the Organization for Economic Co-operation and Development (OECD) Occupational Biomonitoring activity of Working Parties of Hazard and Exposure Assessment group worked together to map the conventional framework of biomarkers and provided recommendations for their systematic use. We summarized the key aspects of this work here, and discussed these in three parts. Part I, we inventory available effect biomarkers and promising new biomarkers for the general population based on the H2020 Human Biomonitoring for Europe (HBM4EU) initiative. Part II, we provide an overview AOP and PBK/D modelling use that improved the selection and interpretation of effect biomarkers. Part III, we describe the collected expertise from the OECD Occupational Biomonitoring subtask effect biomarkers in prioritizing relevant mode of actions (MoAs) and suitable effect biomarkers. Furthermore, we propose a tiered risk assessment approach for occupational biomonitoring. Several effect biomarkers, especially for use in occupational settings, are validated. They offer a direct assessment of the overall health risks associated with exposure to chemicals, chemical mixtures and their transformation products. Promising novel effect biomarkers are emerging for biomonitoring of the general population. Efforts are being dedicated to prioritizing molecular and biochemical effect biomarkers that can provide a causal link in exposure-health outcome associations. This mechanistic approach has great potential in improving human health risk assessment. New techniques such as in silico methods (e.g. QSAR, PBK/D modelling) as well as 'omics data will aid this process. Our multidisciplinary review represents a starting point for enhancing the identification of effect biomarkers and their mechanistic pathways following the AOP framework. This may help in prioritizing the effect biomarker implementation as well as defining threshold limits for chemical mixtures in a more structured way. Several ex vivo biomarkers have been proposed to evaluate combined effects including genotoxicity and xeno-estrogenicity. There is a regulatory need to derive effect-based trigger values using the increasing mechanistic knowledge coming from the AOP framework to address adverse health effects due to exposure to chemical mixtures. Such a mechanistic strategy would reduce the fragmentation observed in different regulations. It could also stimulate a harmonized use of effect biomarkers in a more comparable way, in particular for risk assessments to chemical mixtures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".