Stability Indicating HPLC Method Development for a Marketed Retinol Acetate
Bibliographic record
Abstract
A rapid, sensitive, and accurate stability indicating high performance liquid chromatography for the determination of retinol acetate from the marketed sample was developed. Retinol acetate is a vitamin A acetate, useful in normal eye vision, also acts as a supportive anticancer, which works by binding to glycocalyx of colon slowing or stopping the growth of cancer cells. The chromatographic separation was performed on a HPLC system consisted of Plus intelligent LC pump® PU-2080 from Agilent, Germany equipped with a Agilent®UV-2075 Intelligent UV-Visible detector, an injector Rheodyne®7725 (Rheodyne, Cotati, CA, USA), along with Agilent chromapass chromatography data system software (Version 1.8.6.1). Column Purospher Star 5μm Agilent® RP C18 XDB (4.6 mm × 150 mm) using a sonicated, degassed mobile phase containing solvent A: acetonitrile and solvent B: methanol in ratio (89:11v/v) having pH 3.5 with a flow rate of1 mL/min. The elution was detected by a uv-visible detector at 360 nm. The total chromatographic runtime is 20.0 min with a retention time for sample BASF stabilized sample and standard Sigma Aldrirch internal standard was of 8.05 and 8.2 min, respectively. The method was validated over a dynamic linear range of 10-50µg/mL for retinol acetate with a correlation coefficient (r2) 0.999. The forced degradation study also revealed the susceptibility (sensitivity) of a drug towards heat, acid, base, hydrogen peroxide, light and photolytic degradation. This indicated photosensitivity and temperature sensitivity of the drug substance.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".