A database of fish biotransformation rates for organic chemicals
Bibliographic record
Abstract
Biotransformation is a key process that can mitigate the bioaccumulation potential of organic substances and is an important parameter for exposure assessments. A recently published method for estimating whole-body in vivo metabolic biotransformation rate constants (kM) is applied to a database of measured laboratory bioconcentration factors and total elimination rate constants for fish. The method uses a kinetic mass balance model to estimate rates of chemical uptake and elimination when measured values are not reported. More than 5400 measurements for more than 1000 organic chemicals were critically reviewed to compile a database of 1535 kM estimates for 702 organic chemicals. Biotransformation rates range over six orders of magnitude across a diverse domain of chemical classes and structures. Screening-level uncertainty analyses provide guidance for the selection and interpretation of kM values. In general, variation in kM estimates from different routes of exposure (water vs diet) and between fish species is approximately equal to the calculation uncertainty in kM values. Examples are presented of structure-biotransformation relationships. Biotransformation rate estimates in the database are compared with estimates of biodegradation rates from existing quantitative structure-activity relationship models. Modest correlations are found, suggesting some consistency in biotransformation capabilities between fish and microorganisms. Additional analyses to further explore possible quantitative structure-biotransformation relationships for estimating kM from chemical structure are encouraged, and recommendations for improving the database are provided.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".