Towards a holistic mechanistic link between source and toxicological effects using aggregate exposure pathways
Bibliographic record
Abstract
The aggregate exposure pathway (AEP) framework, designed to map changes in chemical exposures from source to target site, has struggled to gain significant traction since its proposal a decade ago. Despite its promise, AEPs have seen limited utility, largely due to scarcity of harmonized, interoperable, holistic, and mechanistic exposure data as well as the impracticality of gaining exposure data for all commercially available chemicals. We propose a paradigm shift to revitalize the AEP by adopting Bioanalytical Equivalence Quotients (BEQs) as a universal, holistic metric. This BEQ-centric framework quantifies Key Exposure States by their integrated toxicological potential and defines Key Translation Relationships through measurable changes in BEQ (ΔBEQ), transforming them from conceptual arrows into quantifiable parameters. We detail an operational toolbox combining passive sampling for harmonized bioavailability assessment and in vitro bioassays for BEQ generation. Furthermore, we outline a strategic path forward, leveraging lessons from the successful Adverse Outcome Pathway (AOP) community—such as centralized knowledge bases and stakeholder engagement—and harnessing artificial intelligence for predictive modeling and pathway development. This integrated approach bridges the gap between external exposure and biological effect, ultimately providing a dynamic, quantitative, and actionable framework for mechanistic mixture risk assessment in the exposome era.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".