Mass spectrometric characterization of the hypoxia‐inducible factor (HIF) stabilizer drug candidate BAY 85‐3934 (molidustat) and its glucuronidated metabolite BAY‐348, and their implementation into routine doping controls
Bibliographic record
Abstract
The development of new therapeutics potentially exhibiting performance‐enhancing properties implicates the risk of their misuse by athletes in amateur and elite sports. Such drugs necessitate preventive anti‐doping research for consideration in sports drug testing programmes. Hypoxia‐inducible factor (HIF) stabilizers represent an emerging class of therapeutics that allows for increasing erythropoiesis in patients. BAY 85‐3934 is a novel HIF stabilizer, which is currently undergoing phase‐2 clinical trials. Consequently, the comprehensive characterization of BAY 85‐3934 and human urinary metabolites as well as the implementation of these analytes into routine doping controls is of great importance. The mass spectrometric behaviour of the HIF stabilizer drug candidate BAY 85‐3934 and a glucuronidated metabolite (BAY‐348) were characterized by electrospray ionization‐(tandem) mass spectrometry (ESI‐MS(/MS)) and multiple‐stage mass spectrometry (MS n ). Subsequently, two different laboratories established different analytical approaches (one each) enabling urine sample analyses by employing either direct urine injection or solid‐phase extraction. The methods were cross‐validated for the metabolite BAY‐348 that is expected to represent an appropriate target analyte for human urine analysis. Two test methods allowing for the detection of BAY‐348 in human urine were applied and cross‐validated concerning the validation parameters specificity, linearity, lower limit of detection (LLOD; 1–5 ng/mL), ion suppression/enhancement (up to 78%), intra‐ and inter‐day precision (3–21%), recovery (29–48%), and carryover. By means of ten spiked test urine samples sent blinded to one of the participating laboratories, the fitness‐for‐purpose of both assays was provided as all specimens were correctly identified applying both testing methods. As no post‐administration study samples were available, analyses of authentic urine specimens remain desirable. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".