Unique green chromatography method for the determination of serotonin receptor antagonist (Ondansetron hydrochloride) related substances in a liquid formulation, robustness by quality by design‐based design of experiments approach
Bibliographic record
Abstract
Serotonin receptor antagonist drug Ondansetron hydrochloride injectable formulation containing all related substances was identified and quantified by a single, simple, sensitive, eco-friendly, and green high-performance liquid chromatography method. The disseverment of all impurities was achieved with the Discovery Cyano (250 × 4.6) mm, 5 μm column. The gradient program was composed of pH 5.7 phosphate buffer as mobile phase A and acetonitrile as mobile phase B. The flow rate, column compartment temperature, and detection wavelengths were 0.9 mL/min, 30°C, and 216 nm, respectively. The method was validated as per current regulatory guidelines. The obtained %relative standard deviation for the precision results was between 0.55 and 2.72% for all impurities. The correlation coefficient values from the linearity experiment for impurities and analyte were more than 0.995. The accuracy results were obtained between 88.4 and 113.0% for all impurities. Both sample and standard solutions showed 24 h stability at benchtop and refrigerator conditions. All impurities and analytes met the specificity and mass balance for all forced degradation conditions. Quality-by-design-based design of experiments was utilized to establish the method's robustness. Method greenness was assessed by using the current advanced tool green analytical procedure index, National Environmental Methods Index, and analytical eco-scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".