Quantitative in vitro-in vivo extrapolation of biotransformation rates for bioaccumulation assessment: Focus on organic sunscreen agents in rainbow trout
Bibliographic record
Abstract
An improved understanding of chemical biotransformation has been identified as a key requirement for the bioaccumulation assessment of commercial chemicals. In vitro biotransformation assays, in combination with in vitro-in vivo extrapolation (IVIVE) represents one initiative to generate chemical biotransformation rates for use in bioaccumulation modeling efforts. However, rigorous evaluation of the IVIVE approach requires studies with well-matched animals to ensure in vitro tests adequately predict in vivo biotransformation potential. Therefore, the overarching objective of this thesis was to evaluate factors that may influence the extrapolation of hepatic in vitro biotransformation rate constants (kdep) using well-matched studies with rainbow trout. Hydrophobic organic ultraviolet filters (UVFs) 4-methylbenzylidene camphor, 2-ethylhexyl-4-methoxycinnamate (EHMC), and octocrylene (OCT) represented model chemicals in this investigation. The first study showed that measured kdep values for UVFs were highly dependent on the selected assay concentration. Modeled bioconcentration factors (BCF) derived from kdep measured at concentrations well below corresponding Michaelis-Menten constants (KM) were closer to empirical BCFs than those calculated from kdep measured at higher test concentrations. A corresponding in vivo study demonstrated that during standardized dietary exposures that measured UVF concentrations in trout were well below the previously derived KM values This demonstrated that biotransformation pathways in trout operate under first-order conditions and that working at an appropriate concentration range in in vitro assays (i.e., C0 << KM) can be expected to improve estimates of in vivo biotransformation potential. In a final study, an existing IVIVE model was expanded to consider biotransformation in both the intestinal epithelia and liver. For chemicals biotransformed at higher rates by hepatic S9 fractions (e.g., EHMC), the ‘liver only’ IVIVE model was sufficient in estimating whole-body biotransformation rate constants (kMET). For chemicals biotransformed at higher rates in intestinal S9 fractions (i.e., OCT), the inclusion of both hepatic and intestinal activities improved estimates of kMET relative to the in vivo data generated here. The results of this study indicate that current ‘liver only’ IVIVE approaches may underestimate kMET for chemicals that undergo substantial intestinal biotransformation. The presented findings suggest that the future use of quantitative IVIVE methods for bioaccumulation assessment require greater consideration of extrahepatic biotransformation.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".