RESUMEN
Bacterial resistance towards antibiotics is a significant challenge for public health, and surface-enhanced Raman spectroscopy (SERS) has great potential to be a promising technique to provide detailed information about the effect of antibiotics against biofilms. SERS is employed to check the antibacterial potential of a lab synthesized drug ([bis(1,3-dipentyl-1H-imidazol-2(3H)-ylidene)silver(i)] bromide) against Bacillus subtilis and to analyze various SERS spectral features of unexposed and exposed Bacillus strains by observing biochemical changes in DNA, protein, lipid and carbohydrate contents induced by the lab synthesized imidazole derivative. Further, PCA and PLS-DA are employed to differentiate the SERS features. PCA was employed to differentiate the biochemical contents of unexposed and exposed Bacillus strains in the form of clusters of their representative SERS spectra and is also helpful in the pairwise comparison of two spectral data sets. PLS-DA provides authentic information to discriminate different unexposed and exposed Bacillus strains with 91% specificity, 93% sensitivity and 97% accuracy. SERS can be employed to characterize the complex and heterogeneous system of biofilms and to check the changes in spectral features of Bacillus strains by exposure to the lab synthesized imidazole derivative.
RESUMEN
OBJECTIVE: To use Raman Spectroscopy for qualitative and quantitative evaluation of pharmaceutical formulations of active pharmaceutical ingredient (API) of Cephalexin. SIGNIFICANCE: Raman Spectroscopy is a noninvasive, nondestructive, reliable and rapid detection technique used for various pharmaceutical drugs quantification. The present study explores the potential of Raman Spectroscopy for quantitative analysis of pharmaceutical drugs. METHOD: For qualitative and quantitative analysis of Cephalexin API, various standard samples containing less and more concentration of API than commercial tablet was prepared. To study spectral differences, the mean plot of all the samples was prepared. For qualitative analysis, Principal Component Analysis (PCA) and for quantitative analysis Partial Least Square Regression analysis (PLSR) was used. Both of these are Multivariate data analysis techniques and give reliable results as published in previous literature. RESULTS: PCA model distinguished all the Raman Spectral data related to the various Cephalexin solid dosage formulations whereas the PLSR model was used to calculate the concentration of different unknown formulations. For the PLSR model, RMSEC and RMSEP were determined to be 3.3953 and 3.8972, respectively. The prediction efficiency of this built PLSR model was found to be very good with a goodness of the model value (R2) of 0.98. The PLSR model also predicted the concentrations of Cephalexin formulations in the blind or unknown sample. CONCLUSION: These findings demonstrate that the Raman spectroscopy coupled to PLSR analysis could be regarded as a fast and effectively reliable tool for quantitative analysis of pharmaceutical drugs.
Asunto(s)
Cefalexina , Espectrometría Raman , Espectrometría Raman/métodos , Quimiometría , Composición de Medicamentos , Comprimidos/química , Análisis de los Mínimos CuadradosRESUMEN
BACKGROUND: Insulin storage above the temperature recommended by food and drug administration (FDA) causes decrease in its functional efficacy due to degradation and aggregation of its protein based active pharmaceutical ingredient (API) that results poor glycemic control in diabetic patients. The aggregation of protein causes serious neurodegenerative diseases such as type-2 diabetes, Huntington disease, Parkinson's disease, and Alzheimer's disease. Surface-enhanced Raman spectroscopy (SERS) has been employed for the denaturation study of many proteins at the temperature above the recommendations of food and drug administration (FDA) (above 30 °C) which indicates potential of technique for such studies. OBJECTIVE: SERS along with multivariate discriminating analysis techniques-based analysis of degradation of liquid pharmaceutical insulin protein after regular intervals of time at room temperature to analyze the structural changes in this protein during the storage of insulin pharmaceutical at room temperature. METHODS: Silver nanoparticles (Ag-NPs) prepared by chemical reduction method are used as SERS active substrate for the surface enhancement of the insulin spectral signal. SERS spectral measurements of insulin were collected from eight different samples of insulin in the time range of 7 pm to 7 am first at fridge temperature (5 °C), second after half hour and next six with the time difference of 2 h each time at room temperature. The acquired SERS spectral data was preprocessed and analyzed. SERS structural transformations detection and discrimination potential in insulin was further confirmed by applying multivariate discriminating analysis techniques including principal component analysis (PCA) and Partial least square regression analysis (PLSR). RESULTS: SERS significantly detects the structural changes produced in insulin even after 2 h of insulin placement at room temperature. PCA successfully differentiates the insulin spectral data obtained after regular intervals of time according to PC-1 (77 %) explained variance. Application of PLSR model provides quantitative confirmation of SERS efficiency, by providing insulin data regression coefficients plot, efficient prediction of time with calibration data set having 0.77 mean square absolute error of calibration (RMSAEC), validation data set with 0.80 mean square absolute error of prediction (RMSAEP) and 0.98 coefficient of determination (R2) for both calibration and validation data set. CONCLUSION: SERS is proved as a highly sensitive and discriminating technique to detect and discriminate insulin structural changes after regular intervals of time at room temperature.