Characterization of the Metabolic Interaction between Trihalomethanes and Chloroacetic Acids using Rat Liver Microsomes
Bibliographic record
Abstract
The aim of this study was to investigate the in vitro metabolism of trihalomethanes (THMs) in the presence of trichloroacetic acid (TCA), dichloracetic acid (DCA), monochloroacetic acid (MCA), and 4-methylpyrazole (4-MP) using liver microsomes from male Sprague-Dawley rats. Using the vial equilibration technique, initial experiments were carried out with starting concentrations of approximately 40 ppm THMs and 12-22 mM chloroacetic acids. The results indicated a mutual metabolic inhibition between THMs present as binary or quaternary mixtures. Although DCA and MCA had no influence on THMs, TCA produced a marked inhibition of the metabolism of all THMs: chloroform (CHCl3) (55%), bromodichloromethane (BDCM) (34%), dibromochloromethane (DBCM) (30%), and bromoform (TBM) (23%). The presence of 4-MP also reduced THM metabolism, the importance of which decreased in the following order: CHCl3 > BDCM > DBCM = TBM. In further vial equilibration experiments, using 9-140 ppm as starting concentrations of THMs, enzyme kinetic parameters (i.e., Michaelis constant, K(m), and maximum velocity, V(max)) were determined both in the absence and in the presence of TCA (12.2 mM). Results are consistent with a competitive inhibition between TCA and CHCl3, whereas the metabolic inhibition of BDCM and TMB by TCA was non-competitive. As for DBCM, results suggest a more complex pattern of inhibition. These results suggest that CYP2E1 is involved in the metabolism of THMs as well as in the metabolic interaction between THMs and TCA.
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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 | 0.000 | 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 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".