Eco-friendly analytical methods for the determination of compounds with disparate spectral overlapping: application to antiviral formulation of sofosbuvir and velpatasvir
Bibliographic record
Abstract
Abstract Green analytical chemistry is one of the newest trends in analytical chemistry nowadays targeting the concept of green laboratory practices on chemists and environment. In this text, green practices are proposed in this work for the determination of sofosbuvir (SF) and velpatasvir (VP) in their pharmaceutical formulation. The analysis of SF in a binary mixture with VP represents an analytical challenge due to the complete overlapping of the UV spectrum of SF by that of VP. Therefore, the direct absorbance and derivative measurements cannot resolve such interference and failed to determine SF. In this paper, three direct and simple methods were developed for the analysis of SF without any interference from VP without sample pre-treatment. The proposed methods include measuring the second derivative amplitude of the ratio spectrum of the mixture using VP as a divisor, measuring the absorbance difference of the mixture in NaOH solution against its HCl solution, and using the derivative compensation technique. On the other hand, VP was determined specifically in presence of SF by two methods. Firstly, by its reaction with 4-chloro-7-nitrobenzofurazan (NBD-Cl) where the reaction product was measured spectrophotometrically and spectrofluorometrically and secondly through the reaction of VP with 3-methyl-2-benzothiazolinone hydrazone hydrochloride (MBTH). The calibration curves showed good correlation coefficient ( r 2 > 0.999). The developed methods were highly precise with RSD% values less than 2%. The method greenness profile was compared with other published methods by applying the eco-scale protocol. Assessment results proved that our analytical procedure is greener than other reported methods. Moreover, upon comparison with other methods, the proposed methods showed better or comparable sensitivity in addition to being inexpensive and ecofriendly. Accordingly, these methods could be readily applied for quality control purposes as an eco-friendly, simple and efficient analytical tool.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".