Development of a quantitative structure-response relationships to estimate concentrations of plasticizer metabolites in urine without reference standards using non-targeted analysis with liquid chromatography high-resolution mass spectrometry
Bibliographic record
Abstract
BACKGROUND: Plasticizers are characterized as endocrine disruptors, and human exposure to these chemicals can adversely affect the reproductive system. Non-targeted analysis (NTA) using liquid chromatography - high resolution mass spectrometry (LC-HRMS) is a discovery-based approach for screening and identifying hundreds of compounds and metabolites in human samples without a priori knowledge of the chemical species that would be present in the sample. However, while NTA methods need to be standardized in order to provide reliable and reproducible qualitative results, there is also an urgent need to develop NTA methods that can provide quantitative information for identified compounds. RESULTS: We describe a quantitative structure-response relationship (QSRR) in NTA using LC-electrospray ionization (ESI)-HRMS that was established by combining retention behavior and structural descriptors. This relationship can be used to estimate the signal response factors of chemicals and we can utilize the estimated response factors to approximate the concentrations of plasticizer metabolites without reference standards. The model consists of 12 variables, being retention time and 11 molecular descriptors, that can estimate response factors of eleven testing chemicals with an error less than 30 %. The study demonstrated that the model can be updated with routine (e.g. daily) calibration curves and estimate concentrations of unknown metabolites in urine with an error less than 50 % for most compounds. The model can be applied to two different ionization systems without having a significant impact on its prediction accuracy. SIGNIFICANCE: While NTA provides the important information on identification of unknown and new chemicals/metabolites, it is crucial to provide quantitative concentrations for these compounds identified in NTA to support exposure assessment. Our approach enables a fast and simple estimation of the concentration of unknown metabolites of phthalates and their alternative chemicals without the need for respective reference standards. More importantly, the method is applicable to routine NTA studies and can be adapted to different LC-MS systems by simply updating the model with daily calibration data.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".