A Novel Multispecies Toxicokinetic Modeling Approach in Support of Chemical Risk Assessment
Bibliographic record
Abstract
Standardized laboratory tests with a limited number of model species are a key component of chemical risk assessments. These surrogate species cannot represent the entire diversity of native species, but there are practical and ethical objections against testing chemicals in a large variety of species. In previous research, we have developed a multispecies toxicokinetic model to extrapolate chemical bioconcentration across species by combining single-species physiologically based toxicokinetic (PBTK) models. This "top-down" approach was limited, however, by the availability of fully parameterized single-species models. Here, we present a "bottom-up" multispecies PBTK model based on available data from 69 freshwater fishes found in Canada. Monte Carlo-like simulations were performed using statistical distributions of model parameters derived from these data to predict steady-state bioconcentration factors (BCFs) for a set of well-studied chemicals. The distributions of predicted BCFs for 1,4-dichlorobenzene and dichlorodiphenyltrichloroethane largely overlapped those of empirical data, although a tendency existed toward overestimation of measured values. When expressed as means, predicted BCFs for 26 of 34 chemicals (82%) deviated by less than 10-fold from measured data, indicating an accuracy similar to that of previously published single-species models. This new model potentially enables more environmentally relevant predictions of bioconcentration in support of chemical risk assessments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".