Development of liquid chromatography‐tandem mass spectrometry‐based analytical assays for the determination of HIF stabilizers in preventive doping research
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Bibliographic record
Abstract
Hypoxia-inducible factor (HIF) stabilizers increase blood haemoglobin levels after oral administration and their use in sports was recently banned by the World Anti-Doping Agency. For the support of analytical assay development, the metabolic fate of two model HIF stabilizers, based on the isoquinoline-3-carboxamide scaffold of the lead drug candidate FG-2216, was assessed by in vitro methods. The analytes were identified and characterized by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in positive and negative ionization mode using an API 4000 Qtrap as well as an exactive high resolution-high accuracy MS. The model HIF stabilizer N-[(1-chloro-4-hydroxy-7-isopropoxy-isoquinolin-3-carbonyl)-amino]-acetic acid (1), was converted into 3 major phase I metabolites by hydroxylation, dealkylation, and dehydrogenation. The structures of the hydroxylated and the dealkylated metabolites were confirmed by LC-coupled nuclear magnetic resonance spectroscopy. Moreover, glucuronic acid conjugates of the active drug and one of the dealkylated phase I metabolite were identified. Hydroxylation of model compound 2 (N-[(1-chloro-4-hydroxy-isoquinolin-3-carbonyl)-amino]-acetic acid) yielded two metabolites, regioisomeric to the dealkylated product of 1. Mass spectral data of compounds 1 and 2, as well as a structure-related analogue were included into a multi-target analytical assay based on direct injection and LC-MS/MS analysis of human urine. The method was validated for quantitative purposes. In an approach of preventive doping research, more comprehensive screening methods applying precursor ion (m/z 166) and neutral loss (-10 Da) scans were developed, allowing for the detection of unknown metabolites and structurally analogous HIF stabilizers emerging from ongoing lead structure developments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it