A Quality by Design and green LC technique for the determination of mast cell stabilizer and histamine receptor antagonist (Olopatadine HCl) in multiple formulations
Bibliographic record
Abstract
Abstract Mast cell stabilizer and histamine receptor antagonist olopatadine hydrochloride (OPT) assay method predicated on LC have been established for the analysis in multiple formulations. The current method dealt with ophthalmic solution, nasal spray, and tablet formulation products. The isocratic chromatography method was optimized and validated with a Boston green C8 column (150 × 4.6 mm, 5 μm i.d.). Sodium dihydrogen phosphate buffer (pH 3.5) with acetonitrile in the ratio of 75:25 (v/v) was used as a mobile phase at a flow rate of 1.0 mL min −1 and at the column temperature of 30°C, and the detection was done at 299 nm. The method was validated as per International Council for Harmonisation (ICH) guidelines and United States Pharmacopoeia (USP). The accuracy results ranged from 99.9 to 100.7%, % relative standard deviation (RSD) from the precision was 0.5, and correlation coefficient from the linearity experiment was > 0.999. Solution stability was established for 24 h at room temperature and refrigerator conditions, and it was found that the solutions were stable. Using quality by design‐based experiment designs, critical quality attributes were studied and it was found that the method was robust. In all the forced degradation studies peak purity was passed, and no interference was found at the retention time of the active component. The method validation data demonstrated that the developed method is linear, precise, accurate, specific, robust, and stable for the determination of OPT from multiple formulations. Analytical eco‐scale tool, Green Analytical Procedure Index (GAPI) tool, and the National Environmental Method Index (NEMI) were used to evaluate the greenness of the method, and the analytical eco‐score of 77 for the presented method was found to be excellent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".