Determination of antihistaminic drugs alcaftadine and olopatadine hydrochloride via ion-pairing with eosin Y as a spectrofluorimetric and spectrophotometric probe: application to dosage forms
Bibliographic record
Abstract
Abstract Four sensitive and fast analytical approaches relied on ion pairing with eosin Y were built up and evaluated using spectroscopy for determination of Alcaftadine and Olopatadine hydrochloride with high sensitivity and selectivity. Two spectrofluorimetric techniques were employed to observe the quenching effect of Alcaftadine or Olopatadine hydrochloride on the intrinsic fluorescence of eosin Y in a 0.1 M acetate buffer solution at pH 3.8 and 3.3 for Alcaftadine and Olopatadine hydrochloride, respectively. Those methods are considered the first spectrofluorimetric methods for Alcaftadine and Olopatadine hydrochloride assay. The fluorescence quenching effect was linear with concentration ranging from 150 to 2000 and 200 to 2000 ng mL −1 for Alcaftadine and Olopatadine hydrochloride, respectively. In the two spectrophotometric techniques, the absorbance of the produced ion-pair was monitored at 548 and 547 nm in aqueous buffered solution at pH 3.8 and 3.3 for Alcaftadine and Olopatadine hydrochloride, respectively. Beer's law was obeyed in the concentrations range of 0.8–8.0 and 1.0–10.0 µg mL −1 . The four techniques were evaluated in accordance with ICH requirements and were effectively used to analyze dosage forms with a high percent recovery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".