Stability-Indicating TLC-Densitometric Assay for Methyltestosterone and Quantum Chemical Calculations
Bibliographic record
Abstract
Methyltestosterone is a synthetic testosterone derivative commonly used for the treatment of testosterone deficiency in males and one the anabolic steroids whose use is banned by World Anti-Doping Agency (WADA). This study presents a simple, cost-effective and rapid stability-indicating assay for densitometric quantification of methyltestosterone in pharmaceutical formulation. The developed method employed pre-coated TLC plates with mobile phase hexane:acetone (6.5:3.5 v/v). Limit of detection and limit of quantitation were found to be 2.06 and 6.24 ng/spot, respectively. Stress degradation study of methyltestosterone was conducted by applying various stress conditions such as hydrolysis under acidic, basic and neutral conditions, heating in anhydrous conditions and exposure to light. Methyltestosterone was found to be susceptible to photodegradation, acidic and basic hydrolysis. Degraded products were well resolved with significantly different Rf values. Acid degraded product was identified as 17,17-dimethyl-18-norandrosta-4,13(14)-dien-3-one through spectroscopic methods. The reactivity of methyltestosterone under applied stress conditions was also explained by quantum chemical calculations. The developed method is found to be repeatable, selective and accurate for quantification of methyltestosterone and can be employed for routine analysis.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".