Models Used to Predict Chemical Bioaccumulation in Fish from in Vitro Biotransformation Rates Require Accurate Estimates of Blood–Water Partitioning and Chemical Volume of Distribution
Bibliographic record
Abstract
Abstract Methods for extrapolating measured in vitro intrinsic clearance to a whole-body biotransformation rate constant (kB) have been developed to support modeled bioaccumulation assessments for fish. The inclusion of extrapolated kB values into existing bioaccumulation models improves the prediction of chemical bioconcentration factors (BCFs), but there remains a tendency for these methods to overestimate BCFs relative to measured values. Therefore, a need exists to evaluate the extrapolation procedure to assess potential sources of error in predicted kB values. We examined how three different approaches (empirically based, composition based, and polyparameter linear free energy relationships [ppLFERs]) used to predict chemical partitioning in vitro (liver S9 system; KS9W), in blood (KBW), and in whole fish tissues (KFW) impact the prediction of a chemical's hepatic clearance binding term (fU) and apparent volume of distribution (VD), both of which factor into the calculation of kB and the BCF. Each approach yielded different KS9W, KBW, and KFW values, but resulted in fU values that were of similar magnitude and remained relatively constant at log octanol–water partition ratios (KOW) greater than 4. This is because KBW and KS9W values predicted by any given approach exhibit a similar dependence on log KOW (i.e., regression slope), which results in a cancelation of “errors” when fU is calculated. In contrast, differences in KBW values predicted by the three approaches translate to differences in VD, and by extension kB and the BCF, which become most apparent at log KOW greater than 6. There is a need to collect KBW and VD data for hydrophobic chemicals in fish that can be used to evaluate and improve existing partitioning prediction approaches in extrapolation models for fish. Environ Toxicol Chem 2023;42:33–45. © 2022 SETAC Abstract Errors in the specification of volume of distribution (VD), rather than the hepatic clearance binding term (fU), may contribute to inaccuracies in bioconcentration factors (BCFs) predicted from in vitro–in vivo extrapolation (IVIVE) methods. CLINT, intrinsic clearance rate; CLH, hepatic clearance rate; kB, biotransformation rate constant.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".