Identification of Metabolites for the Novel 5α‐Reductase Inhibitor Epristeride In Vitro and Its Potential Impact on Doping Testing
Bibliographic record
Abstract
Epristeride, a novel noncompetitive inhibitor of Type II 5α-reductase, has emerged as a potential therapeutic alternative for benign prostatic hyperplasia (BPH). Given that other 5α-reductase inhibitors, such as finasteride and dutasteride, are already monitored for their potential impact on doping control, comprehensive metabolic studies of epristeride are crucial for antidoping. This study investigates the metabolic pathways and metabolites of epristeride using in vitro microsome models, offering preliminary insights into the pharmacokinetics of this drug. Metabolite profiling was performed using liquid chromatography-high resolution mass spectrometry (LC-HRMS), with data acquisition facilitated by Xcalibur 4.2 software and metabolite identification facilitated by Compound Discoverer 3.3. By employing network pharmacology, the potential targets of epristeride are predicted. The binding energy is calculated using AutoDock Vina software to predict its impact on steroid metabolism. The study proposed three primary metabolites of epristeride: two Phase I oxidation products (M1 and M2) and one Phase II glucuronidation product (M3). Pathway analysis revealed that among the five CYP450 isoforms examined, CYP3A4 played a dominant role. The docking results tentatively elucidated five key target proteins (ESR1, CYP19A1, STAT3, AKR1C3, and CYP17A1) with low binding energies, indicating stable interactions. Notably, Phase I metabolites (M1 and M2) showed significant binding potential with these targets, whereas the Phase II metabolite (M3) exhibited lower binding stability. These findings provide a detailed understanding of epristeride's metabolic pathways and its potential biological impacts, offering valuable insights for monitoring its presence as a confounding factor in doping control.
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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 | 0.001 | 0.002 |
| 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".