Enhanced Characterization of Naproxen Formulation by Near Infrared Spectroscopy

Near infrared spectroscopy in combination with appropriate chemometric methods is an effective technique for quantitative analysis of parameters of interest for the pharmaceutical industry. In this study, the artificial neural network (ANN) was applied to monitor critical parameters (compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets) in the process of naproxen pharmaceutical preparation. The performance of ANN was compared to linear methods (partial least squares regression (PLS) and synergy interval partial squares (siPLS)). The ANN models for compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets yielded the low root mean square error of prediction (RMSEP) values of 0.936 KN, 0.302 kg, 4.49 mg, and 2.14 µm, respectively. The predictive ability of the PLS model was improved by siPLS with selection of spectral regions and the best performance among all calibration methods was showed by the nonlinear method (ANN). Effective models were built by using these approaches using near infrared spectroscopy.     

Effect of expired doxofylline drug on corrosion protection of soft steel in 1 M HCl: Electrochemical,quantum chemical and synergistic effect studies

The corrosion inhibition property of expired Doxofylline (DF) was tested for soft steel in 1 M hydrochloric acid solution by adopting mass change and electrochemical measurement techniques. At 200 ppm concentration of DF, maximum of 72.84% inhibition efficiency was noticed. However with addition of 50 ppm of KI, it enhances the percentage inhibition efficiency up to 88.48%. DF resists both anodic and cathodic reactions and functioned as mixed-inhibition mechanism. At higher temperatures, electrochemical impedance response noticed that, the diameter of the semicircle decreases as solution temperature increases As a result, in both absence and presence of the inhibitor the R_p values were decreased. Quantum chemical studies revealed about structural and electronic effects in relation to the inhibition efficiencies. Surface morphology of both inhibited and corroded soft steel was assessed by means of scanning electron microscopy (SEM)) and atomic force microscope (AFM). The SEM images of soft steel reflect the inhibitive property of the DF at optimized concentration and a significant decrease in the surface roughness was observed (surface roughness was reduced from 606 nm to 294 nm as measured by AFM)). UV-Visible absorption peaks signifies that CC and CO groups from the inhibitor were interacted with iron cations, which is the evidence for the formation protective film over the soft steel surface.     

Topological indices and QSPR analysis of some chemical structures applied for the treatment of heart patients

In this study, various beta-blocker drugs used for heart disease were analyzed, and their degree-based topological indices derived from the M-polynomial were calculated. Linear and quadratic regression analysis was used to obtain quantitative structure-property relationship models between the topological indices and eight the physicochemical properties of the drugs to determine their effectiveness. The results show that the harmonic index was the best predictor for boiling point, flashpoint, and enthalpy of vaporization, while the redefined third Zagreb index was effective for polarizability, molar refractivity, and molar volume. The inverse sum indeg index was found to be effective for molar refractivity, and the second modified Zagreb index was surface tension in linear regression models. In addition, the redefined third Zagreb index was the best predictor for polarizability and molar refractivity, while the second modified Zagreb index was effective for molar volume. The SDD index was found to be effective for surface tension in quadratic regression models.