Development of a green synchronous spectrofluorimetric technique for simultaneous determination of Montelukast sodium and Bilastine in pharmaceutical formulations
Bibliographic record
Abstract
Abstract For the treatment of rhinitis and asthma, a combination of Montelukast sodium and Bilastine has just been approved. Based on the first derivative of synchronous fluorescence, the current work developed a green, highly accurate, sensitive, and selective spectroscopic approach for estimating Montelukast sodium and Bilastine in pharmaceutical dosage form without previous separation. The selected technique focuses on measuring the synchronized fluorescence of the studied medications at a fixed wavelength range (Δλ) = 110 nm, and using the amplitude of the first derivative's peak at 381 and 324 nm, for quantitative estimation of Montelukast sodium and Bilastine, respectively. The impacts of different factors on the referred drugs' synchronized fluorescence intensity were investigated and adjusted. The calibration plots for were found to be linear over concentration ranges of 50–2000 ng mL −1 for Montelukast sodium and 50–1000 ng mL −1 for Bilastine. Montelukast sodium and Bilastine have LODs of 16.5 and 10.9 ng mL −1 , respectively. In addition, LOQs were: 49.9 and 33.0 ng mL −1 , for both drugs, respectively. The developed method was successfully employed to quantify the two drugs in synthetic tablets mixture and in laboratory prepared mixtures containing varied Montelukast and Bilastine ratios. To compare the results with the published analytical approach, a variance ratio F-test and a student t-test were used, which revealed no significant differences.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".