A Rapid and Sensitive Stability-Indicating Eco-Friendly HPTLC Assay for Fluorescence Detection of Ergotamine

Eco-friendly liquid chromatographic methods for measuring ergotamine (EGT) are scant in the published database. Accordingly, the goal of the current study was to develop a high-performance thin-layer chromatography (HPTLC) method for fluorescence detection of EGT in commercially available tablets. This approach was based on the application of ethyl alcohol-water (80:20 v/v) as the eco-friendly eluent mixture. The fluorescence detection of EGT was carried out at 322 nm. The greenness score of the present approach was evaluated by "Analytical GREENness (AGREE)" technology. The present approach for measuring EGT in the 25-1000 ng band^-1 range was linear. The present assay for fluorescence detection of EGT was validated successfully by ICH guidelines for various parameters. The method was found to be rapid, sensitive, eco-friendly, and stability-indicating. The computed AGREE index for the current strategy was 0.84, displaying outstanding greenness features. The present methodology successfully separated the EGT degradation products under forced-degradation circumstances, exhibiting its stability-indicating qualities and selectivity. An amount of 99.33% of EGT was found in commercial formulations, indicating the validity of the current method for pharmaceutical analysis of EGT in commercial products. The results showed that EGT in commercial products might be regularly measured by the existing method.

Quantitative Determination of Anti-Migraine Quaternary Mixture in Presence of p-Aminophenol and 4-Chloroacetanilide

A validated RP-HPLC method was developed for the estimation of paracetamol, caffeine, metoclopramide and ergotamine simultaneously in bulk and pharmaceutical formulation. The method was extended for the determination of two paracetamol genotoxic and nephrotoxic impurities and degradation products namely p-aminophenol and 4-chloroacetanilide. Separation was done on octadecyl column (15 cm × 2.1 mm, 5 μm). Gradient elution was performed using mobile phase consisting of acetonitrile and water (pH 3) in ratio of (20: 80, v/v) for the first 3 min, (50: 50, v/v) for the next 4.5 min, then (20: 80, v/v) for the final 2.5 min. The flow rate was 0.7 mL/min throughout the run which took 10 min. UV detection was done at 230 nm. The proposed method agreed with ICH guidelines of method validation. The suggested method was applied for the estimation of the proposed drugs in their dosage form. Statistical comparison was done between the results obtained from the suggested method and those obtained from reported HPLC method. The proposed method is more beneficial than the published one because of having higher sensitivity and selectivity as it allows the detection of the proposed drugs in lower concentrations in the presence of paracetamol toxic impurities, p-aminophenol and 4-chloroacetanilide.

Greenness profile assessment of selective liquid chromatographic methods for determination of a quaternary antimigraine combination along with three of their related official impurities

Two selective, sensitive and environmentally safe LC methods were developed and validated for determination of paracetamol, caffeine, ergotamine tartrate and metoclopramide in coformulated antimigraine tablets along with p-aminophenol, p-nitrophenol and theophylline as officially specified impurities. The first is based on high-performance thin-layer chromatography (HPTLC) coupled with densitometric quantitation. Separation was achieved on HPTLC silica gel 60 F₂₅₄ plates as stationary phase using ethyl acetate:aqueous ammonium hydroxide solution:glacial acetic acid (10.0:0.4:0.1, by volume) as a developing system followed by scanning of the separated bands at 210.0 nm. The subsequent method depends on HPLC with diode array detection. The LC separation was accomplished on a Scharlau C₁₈ (250 × 4.6 mm, 5 μm) column using a mixture of 20.0 mm sodium dihydrogen phosphate, pH 3.0, adjusted with o-phosphoric acid and methanol, at a flow rate of 1.3 mL/min in a gradient elution program. The separated peaks were detected at 210.0 nm. The proposed methods have been validated and proven to meet the requirements outlined in the International Council for Harmonisation (ICH) guidelines. The greenness profile evaluation was carried out using three tools, namely, the National Environmental Method Index, the Analytical EcoScale and the Green Analytical Procedure Index tool, and a comparative study was then conducted. Successful application of the developed methods for determination of the cited quaternary mixture in Metograine tablets confirms their suitability regarding the analytical performance and ecological impact in quality control assay and impurity profiling purposes.

Application of novel nanocomposite-modified electrodes for identifying rice wines of different brands

In this paper, poly(acid chrome blue K) (PACBK)/AuNP/glassy carbon electrode (GCE), polysulfanilic acid (PABSA)/AuNP/GCE and polyglutamic acid (PGA)/CuNP/GCE were self-fabricated for the identification of rice wines of different brands. The physical and chemical characterization of the modified electrodes were obtained using scanning electron microscopy and cyclic voltammetry, respectively. The rice wine samples were detected by the modified electrodes based on multi-frequency large amplitude pulse voltammetry. Chronoamperometry was applied to record the response values, and the feature data correlating with wine brands were extracted from the original responses using the 'area method'. Principal component analysis, locality preserving projections and linear discriminant analysis were applied for the classification of different wines, and all three methods presented similarly good results. Extreme learning machine (ELM), the library for support vector machines (LIB-SVM) and the backpropagation neural network (BPNN) were applied for predicting wine brands, and BPNN worked best for prediction based on the testing dataset (R 2 = 0.9737 and MSE = 0.2673). The fabricated modified electrodes can therefore be applied to identify rice wines of different brands with pattern recognition methods, and the application also showed potential for the detection aspects of food quality analysis.