Atrazine Metabolite Screening in Human Microsomes: Detection of Novel Reactive Metabolites and Glutathione Adducts by LC-MS
Bibliographic record
Abstract
Atrazine (ATZ), one of the most widely used herbicides worldwide, has been the subject of several scientific studies associated with its human and ecological risks. In order to study atrazine's toxicity, the formation of its metabolites and the result of their exposure must be assessed. This relies on our ability to detect and identify all of atrazine's metabolites; however, no previous untargeted screening method has reported the detection of all known metabolites and glutathione conjugates at once. In this study, a compound-specific, postacquisition metabolic screening method was employed following a generic HPLC separation coupled with high resolution time-of-flight mass spectrometry (TOF-MS) to detect Phase I metabolites and glutathione conjugates generated by in vitro human liver microsomal incubations. Our method was designed to be unbiased and applicable to a wide variety of compounds since methods that can detect a broad range of metabolites with high sensitivity are of great importance for many types of experiments requiring thorough metabolite screening. On the basis of incubations with atrazine and three closely related analogues (simazine, propazine, and cyanazine), we have proposed a new Phase I metabolism scheme. All known Phase I transformations of atrazine were successfully detected, as well as a new N-oxidation product. Novel reactive metabolites were also detected as well as their glutathione conjugates. These newly detected species were produced via imine formation on the N-ethyl group, a biotransformation not previously observed for atrazine or its analogues.
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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) | 0.001 | 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".