In vitro metabolism of sunscreen compounds by liquid chromatography/high‐resolution tandem mass spectrometry
Bibliographic record
Abstract
RATIONALE: Exposure to UV light can induce adverse effects on human health, such as photo-aging, immunosuppression, and cancer. Sunscreens are used to prevent the absorption of UV rays, but certain UV-filtering compounds have been shown to disrupt endocrine systems or act as carcinogens. To assess the effects of the exposure to such compounds, it is important to study the pathways by which they are biotransformed in the body. METHODS: Liquid chromatography coupled to high-resolution tandem mass spectrometry (LC/HRMS/MS) was employed to evaluate the oxidative metabolism and, specifically, the formation of reactive metabolites of six active ingredients commonly used in sunscreen formulations: oxybenzone, avobenzone, homosalate, octisalate, octocrylene, and octinoxate. In vitro incubations were performed with human and rat liver microsomes in the presence of β-nicotinamide adenine dinucleotide phosphate and glutathione. An LC/HRMS/MS method was developed to identify metabolites employing a biphenyl reversed-phase column for separating parent molecules, metabolites, and glutathione (GSH) adducts. RESULTS: Each tested compound resulted in the formation of several metabolites, including at least one GSH adduct. Compounds containing ester groups were hydrolyzed, and some metabolites of the free acid forms were also detected. High-resolution MS/MS data was crucial for the structural elucidation of metabolites and GSH adducts. Fragmentation pathways were proposed for all parent compounds, as well as each described metabolite and adduct. CONCLUSIONS: The results of this study will help better understand the metabolism and detoxification pathways of these xenobiotics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".