An assessment of the impact of multi‐route co‐exposures on human variability in toxicokinetics: A case study with binary and quaternary mixtures of volatile drinking water contaminants
Bibliographic record
Abstract
Abstract This study aimed to assess the impact of multi‐route co‐exposures to chemicals on interindividual variability in toxicokinetics. Probabilistic physiologically based pharmacokinetic multi‐route interaction models were developed for adults and four younger subpopulations. Drinking water‐mediated multi‐route exposures were simulated for benzene alone or in co‐exposure with toluene, ethylbenzene and m‐xylene, for trichloroethylene or vinyl chloride (VC), alone and in mixture. These simulations were performed for “low” and “high” exposure scenarios, involving respectively the US EPA's short‐term drinking water health advisories, and 10 times these advisory values. Distributions of relevant internal dose metrics for benzene, trichloroethylene and VC were obtained using Monte Carlo simulations. Intergroup variability indexes (VI) were computed for the “low” (VI L ) and “high” (VI H ) exposure scenarios, as the ratio between the 95th percentile in each subpopulation over the median in adults. Thus, for benzene, parent compound's area under the curve‐based VI L for single exposures vs. co‐exposures correspondingly varied between 1.7 (teenagers) and 2.8 (infants) vs. 1.9 and 3.1 respectively. VI H varied between 2.5 and 3.5 vs. 2.9 and 4.1. Inversely, VI L and VI H for the amount of benzene metabolized via CYP2E1 pathway decreased in co‐exposure compared to single exposure. For VC and trichloroethylene, similar results were obtained for the “high” exposure, but “low” co‐exposures did not impact the toxicokinetics of individual substances. In conclusion, multi‐route co‐exposures can have an impact on the toxicokinetics of individual substances, but to an extent, that does not seem to challenge the default values attributed to the factors deemed at reflecting interindividual or child/adult differences in toxicokinetics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".