Constant Ion Loss Method for the Untargeted Detection of Bis-sulfate Metabolites
Bibliographic record
Abstract
The untargeted detection of phase II metabolites is a key issue for the study of drug metabolism in biological systems. Sensitive and selective mass spectrometric (MS) techniques coupled to ultrahigh performance liquid chromatographic (UHPLC) systems are the most effective for this purpose. In this study, we evaluate different MS approaches with a triple quadrupole instrument for the untargeted detection of bis-sulfate metabolites. Bis-sulfates of 23 steroid metabolites were synthesized and their MS behavior was comprehensively studied. Bis-sulfates ionized preferentially as the dianion ([M – 2H] 2– ) with a small contribution of the monoanion ([M – H] − ). Product ion spectra generated from the [M – 2H] 2– precursor ions were dominated by the loss of HSO 4 – to generate two product ions, that is, the ion at m / z 97 (HSO 4 – ) and the ion corresponding to the remaining monosulfate fragment. Other product ions were found to be specific for some structures. As an example, the loss of [CH 3 + SO 3 ] − was found to be important for several compounds with unsaturation adjacent to the sulfate. On the basis of the common behavior of the bis-sulfate metabolites two alternatives were evaluated for the untargeted detection of bis-sulfate metabolites (i) a precursor ion scan method using the ion at m / z 97 and (ii) a constant ion loss (CIL) method using the loss of HSO 4 – . Both methods allowed for the untargeted detection of the model compounds. Eight steroid bis-sulfates were synthesized in high purity in order to quantitatively evaluate the developed strategies. Lower limits of detection (2–20 ng/mL) were obtained using the CIL method. Additionally, the CIL method was found to be more specific in the detection of urinary bis-sulfates. The applicability of the CIL approach was demonstrated by determining progestogens altered during pregnancy and by detecting the bis-sulfate metabolites of tibolone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".