Rapid Quantitative Determination Control Male Sexual Dysfunction Drug Content of Sildenafil and Tramadol Mixture by Spectrophotometry Along With Smart Chemometrics Approaches Compared to the HPLC Reference Method.

BACKGROUND: There are reports for the simultaneous estimation of sildenafil (SIL) and tramadol (TM) using chromatographic techniques. Some limitations of these methods are high cost and time, more usage of pollutant solvents, and complicated instruments. OBJECTIVE: Simultaneous determination of SIL and TM in the anti-sexual dysfunction pharmaceutical formulation using rapid and smart chemometrics approaches along with spectrophotometry. METHOD: In this study, a spectrophotometric analysis method based on continuous wavelet transform (CWT), partial least-squares (PLS), and radial basis function neural network (RBF-NN) was presented. RESULTS: In the CWT method, the Symlet (Sym2) and Daubechies (db2) wavelet families with wavelengths of 268 and 221 nm were considered the best families for SIL and TM, respectively. The limit of detection (LOD) and limit of quantitation (LOQ) values were found to be 0.0185, 0.0054 µg/mL and 0.0458, 0.3092 µg/mL for SIL and TM, respectively. The mean recovery values were 101.06 and 101.49% for SIL and TM, respectively. The results of PLS revealed that the root-mean-square error (RMSE) of SIL and TM was 0.0286 and 0.0635, respectively. Also, the mean recovery of both drugs was obtained over 99%. The mean square error (MSE) of RBF-NN was 5.88 × 10-29 and 4.61 × 10-27 for SIL and TM, respectively. CONCLUSIONS: A statistical comparative study conducted for the proposed methods with the high-performance liquid chromatography (HPLC) technique demonstrated no significant difference between these methods. The suggested approaches were simple, fast, accurate, and affordable. HIGHLIGHTS: Three smart UV spectrophotometric methods based on chemometrics were presented to determine SIL and TM simultaneously. The efficient chemometrics methods, including CWT, PLS, and RBF-NN, were used. The mentioned methods are rapid, simple, inexpensive, and accurate. The comparison was performed between the proposed models and HPLC technique.

A Novel High Performance Liquid Chromatographic Method for Determination of Nystatin in Pharmaceutical Formulations by Box-Behnken Statistical Experiment Design.

In this study a novel High Performance Liquid Chromatography for the assay of nystatin in oral and vaginal tablets were optimized and validated using Box-Behnken experimental design. The method was performed in the isocratic mode on a RP-18 column (30 °C) using a mobile phase consisting of ammonium acetate 0.05 M buffer/ Methanol mixture (30:70) and a flow-rate of 1.0 mL/min. The specificity, linearity, precision, accuracy, LOD and LOQ of the method were validated. The method was linear over the range of 5-500 µg/mL with an acceptable correlation coefficient (r(2) = 0.9996). The method's limit of detection (LOD) and quantification (LOQ) were 0.01 and 0.025 µg/mL respectively. The results indicate that this validated method can be used as an alternative method for assay of nystatin.

Diagnostic prediction of renal failure from blood serum analysis by FTIR spectrometry and chemometrics.

A new diagnostic approach based on Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectrometry and classification algorithm has been introduced which provides a rapid, reliable, and easy way to perform blood test for the diagnosis of renal failure. Blood serum samples from 35 renal failure patients and 40 healthy persons were analyzed by ATR-FTIR spectrometry. The resulting data was processed by Quadratic Discriminant Analysis (QDA) and QDA combined with simple filtered method. Spectroscopic studies were performed in 900-2000cm(-)(1) spectral region with 3.85cm(-1) data space. Results showed 93.33% and 100% of accuracy for QDA and filter-QDA models, respectively. In the first step, 30 samples were applied to construct the model. In order to modify the capability of QDA in prediction of test samples, filter-based feature selection methods were applied. It was found that the filtered spectra coupled with QDA could correctly predict the test samples in most of the cases.