Development of liquid chromatography‐tandem mass spectrometry‐based analytical assays for the determination of HIF stabilizers in preventive doping research
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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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".