Rapid simultaneous analysis of anti human immunodeficiency virus drugs in pharmaceutical formulation by smart spectrophotometry based on multivariate calibration and least squares support vector machine methods.

In this study, two chemometrics methods, including partial least squares regression (PLS) and least squares support vector machine (LS-SVM) were applied for the simultaneous determination of zidovudine (ZDV) and lamivudine (LMV) in synthetic mixtures and anti-HIV pharmaceutical formulation. These approaches along with the spectrophotometric method were used to solve spectral overlapping problems between mentioned components. The results of PLS showed that the number of components for ZDV and LMV were 10 and 10 with mean square prediction error (MSPE) of 0.4045 and 2.1189, respectively. This method revealed recoveries ranging from 99.48% to 100.40% and 99.55% to 101.25% for ZDV and LMV, respectively. By applying leave-one-out cross-validation (LOO-CV), γ (regularization parameter) and σ2 (width of the function) values were found to be 50, 1500 and 210, 20 with root mean square error (RMSE) of 0.6156 and 0.3163 for ZDV and LMV, respectively. The mean recoveries obtained by the LS-SVM were 100.82% and 98.93% for ZDV and LMV, respectively. A comparison between the suggested methods and high-performance liquid chromatography (HPLC) as a reference technique was implemented, which did not show a significant difference. The results obtained in this research revealed that the chemometrics approaches can be efficient, simple, inexpensive, and precise for routine analysis and quality control of the drug.

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.

The efficient removal of bisphenol A from aqueous solution using an assembled nanocomposite of zero-valent iron nanoparticles/graphene oxide/copper: Adsorption isotherms, kinetic, and thermodynamic studies.

In this study, nanoparticles of zero-valent iron (nZVI) along with graphene oxide (GO) and copper (Cu) was synthesized to apply as a promising adsorbent for the rapid removal of bisphenol A (BPA) from aqueous solution. The characteristics of nZVI-GO-Cu were analyzed by field emission scanning electron microscope (FESEM), energy-dispersive X-ray spectroscopy (EDS), Fourier-transform infrared spectroscopy (FTIR), X-Ray Diffraction (XRD), and vibrating sample magnetometer (VSM). The average particle size of nZVI-GO-Cu was found to be 20.89 nm. The effective experimental variables such as pH, adsorbent dosage, contact time, initial BPA concentration, and temperature were surveyed to assess optimum conditions. Results revealed that the maximum removal percentage was obtained at pH of 7, adsorbent dosage of 0.2 g, contact time of 10 min, the BPA concentration of 10 mg/L, and a temperature of 35 °C as optimum conditions. Experimental data were fitted to the Langmuir and pseudo second-order models with a coefficient of determination (R2) equal to 1 and 0.995, respectively. The obtained maximum adsorption capacity (qmax) of the Langmuir isotherm was 21.59 mg g-1. Thermodynamic parameters under the various temperatures confirmed that the adsorption process was endothermic (ΔH = 17,459.4 J/mol and ΔS = 61.23 J/mol/K) and spontaneous (ΔG < 0). As a conclusion, nZVI-GO-Cu can be selected as an efficient adsorbent for the treatment of aqueous media from BPA and the other pollutants, due to its low-cost, high removal efficiency (97%), and rapid adsorption with the minimum time of 10 min compared with the other adsorbents.

An intelligent method based on feed-forward artificial neural network and least square support vector machine for the simultaneous spectrophotometric estimation of anti hepatitis C virus drugs in pharmaceutical formulation and biological fluid.

This study proposed simple and reliable spectrophotometry method for simultaneous analysis of hepatitis C antiviral binary mixture containing sofosbuvir (SOF) and daclatasvir (DAC). This technique is based on the use of feed-forward artificial neural network (FF-ANN) and least square support vector machine (LS-SVM). FF-NN with Levenberg-Marquardt (LM) and Cartesian genetic programming (CGP) algorithms was trained to determine the best number of hidden layers and the number of neurons. This comparison demonstrated that the LM algorithm had the minimum mean square error (MSE) for SOF (1.59 × 10-28) and DAC (4.71 × 10-28). In LS-SVM model, the optimum regularization parameter (γ) and width of the function (σ) were achieved with root mean square error (RMSE) of 0.9355 and 0.2641 for SOF and DAC, respectively. The coefficient of determination (R2) value of mixtures containing SOF and DAC was 0.996 and 0.997, respectively. The percentage recovery values were in the range of 94.03-104.58 and 94.04-106.41 for SOF and DAC, respectively. Statistical test (ANOVA) was implemented to compare high-performance liquid chromatography (HPLC) and spectrophotometry, which showed no significant difference. These results indicate that the proposed method possesses great potential ability for prediction of concentration of components in pharmaceutical formulations.

Application of Silica Supported Calix[4]arene Derivative as Anti-reversion Agent in Tire Tread Formulation.

In this research the influence of the silica supported calix[4]arene derivative (SS-Calix) on the reversion resistance, mechanical properties and thermal behavior of NR/BR tire tread formulation was investigated by the oscillating disc rheometer, FTIR, TGA and tensile testing. The results revealed that the reversion behavior of NR/BR vulcanizate is affected by SS-Calix. The data obtained from curing characteristics and thermal stability of test pieces indicate that, SS-Calix acts as an anti-reversion for rubbery materials that are exposed to thermal shock in the early stages of temperature rise. It's predicted that these results are due to the interaction between the OH groups present in the SS-Calix surface and the carbon of the polymer chains. The broad peak observed in the IR spectrum around 1824 cm-1 which is referred to C=O bond, confirms this prediction. In addition, the presence of SS-Calix in compound causes to increase modulus and hardness but reduce elongation and resilience.

Improved spectral resolution for the rapid simultaneous spectrophotometric determination of sofosbuvir and daclatasvir as anti hepatitis C virus drugs in pharmaceutical formulation and biological fluid using continuous wavelet and derivative transform.

In this study, the simultaneous spectrophotometric estimation of Sofosbuvir (SOF) and Daclatasvir (DAC) in synthetic mixtures and tablet formulation in the presence of overlapping spectra was performed based on continuous wavelet transform (CWT) and derivative spectrophotometry (DS) methods without any separation process. The Coiflet (Coif2) and Daubechies (Db3) wavelet families with wavelength of 256 nm and 218 nm were obtained as the best families for the simultaneous determination of SOF and DAC, respectively. Also, the first derivative absorption spectra revealed the best results corresponding to the analysis of SOF and DAC at 237 nm and 291 nm, respectively. The ranges of limit of detection (LOD) and limit of quantitation (LOQ) related to the CWT and DS methods were 2.45 × 10-3 to 0.5054 and 6.91 × 10-3 to 0.6027, respectively. Mean recovery values of SOF and DAC in synthetic mixtures for CWT approach were 98.55%, 98.09% and in DS method were 98.78% and 95.83%, respectively. Real samples, including Sovodak tablet and urine was used for accurate simultaneous determination of the mentioned components. Analyzing Sovodak tablet was implemented using high-performance liquid chromatography (HPLC) as a reference method that the results were near to the CWT and DS methods. In order to investigate the existence of significant differences between the methods, analysis of variance (ANOVA) test at the 95% confidence level was performed but no significant differences were observed. In addition, the amounts of SOF and DAC in the complex matrix of biological sample were well predicted by the proposed methods.

Preparation and characterization of a new sawdust/MNP/PEI nanocomposite and its applications for removing Pb (II) ions from aqueous solution.

A new sawdust/magnetite nanoparticles/polyethyleneimine (SD/MNP/PEI) nanocomposite was synthesized by grafting polyethyleneimine (PEI) to magnetic sawdust. Features of SD/MNP/PEI were characterized using Fourier transform infrared spectroscopy (FT-IR) and X-ray diffraction (XRD), vibrating sample magnetometer (VSM) and scanning electron microscopy (SEM). SD/MNP/PEI was used as an adsorbent for the removal of lead (Pb (II)) from aqueous solution. The effects of independent variables including pH of solution, adsorbent dose and contact time were performed and adsorption isotherms were obtained. Experimental results show that priority effective variables were pH and the amount of nanocomposite, and it was found that the sorption capacity increases with the increasing phase contact time. The adsorption process followed the Langmuir adsorption isotherm. Although SD and SD/MNP do not show a high affinity for the adsorption of Pb (II) in aqueous media, polyethyleneimine cross-linked on SD/MNP showed 40 and 66% increases, respectively, in the adsorption of Pb (II) compared to the SD and SD/MNP. It was found that SD/MNP/PEI removes more efficiently lead ions from aqueous solutions than the SD, SD/MNP. Desorption of the lead from the SD/MNP/PEI was conducted. It was proved that SD/MNP/PEI has excellent properties and can be used as a sorbent of multi-use.