Application of silver nanoparticles and principal component-artificial neural network models for simultaneous determination of levodopa and benserazide hydrochloride by a kinetic spectrophotometric method

Earth-friendly-assessed silver-nanoparticles spectrophotometric method for rapid and sensitive analysis of Molnupiravir, an FDA-approved candidate for COVID-19: application on pharmaceutical formulation and dissolution test

Molnupiravir is the first oral direct-acting antiviral prodrug recently approved for the COVID-19 pandemic. Here and for the first time, we present a novel, sensitive, robust, and simple silver-nanoparticles spectrophotometric technique for molnupiravir analysis in its capsules and dissolution media. This spectrophotometric technique involved silver-nanoparticles synthesis through a redox reaction between the reducing agent (molnupiravir) and the oxidizing agent (silver nitrate) in presence of polyvinylpyrrolidone as a stabilizing agent. The produced silver-nanoparticles have an intense surface plasmon resonance peak at 416 nm where the measured absorbance values were utilized for the quantitative analysis of molnupiravir. The produced silver-nanoparticles were recognized by using the transmission electron microscope. Under optimal conditions, a good linear rapport was accomplished between molnupiravir concentrations and the corresponding absorbance values in a range of (100-2000) ng/mL with a detection limit of 30 ng/mL. Greenness assessment was implemented using eco-scale scoring and GAPI disclosing the excellent greenness of the suggested technique. The suggested silver-nanoparticles technique was authenticated according to recommendations of the ICH and statistically assessed with the reported liquid chromatographic method without significant differences regarding accuracy or precision. Accordingly, the suggested technique is deemed a green and cheap alternative for assaying molnupiravir due to its reliance primarily on water. Furthermore, the suggested technique's high sensitivity can be employed for investigating molnupiravir bioequivalence in future studies.

Utilization of Localized Surface Plasmon Resonance of Silver Nanoparticles for the Spectrofluorimetric Estimation of Oxymetazoline in Dosage Forms: Application to Aqueous Humor

A simple, novel, cost-effective and highly sensitive spectrofluorimetric method was developed for estimation of the nasal decongestant oxymetazoline (OMZ) whether per se or in its pharmaceutical preparations using colloidal silver nanoparticles (AgNPs). The method is based on the high catalytic potential activity of AgNPs on the fluorescence intensity of OMZ leading to 12-fold increase in its fluorescence intensity. The response was linear over the range of 20.0 to 700.0 ng/mL with lower detection limit of 5.0 ng/mL and limit of quantification of 14.0 ng/mL. The proposed method was applied to the assay of commercial nasal drops, nasal spray and synthetic aqueous humor. Interference likely to be encountered from co-administered drugs was studied. The developed method was optimized and validated as per International Council of Harmonization (ICH). An explanation for the drug-AgNPs interaction was proposed.

Optical sensors based on silver nanoparticles for determination of pharmaceuticals: An overview of advances in the last decade

This review focuses on optical nanosensors based on silver nanoparticles (Ag NPs) and demonstrates their applications in the determination of pharmaceutical compounds in the last decade. Such optical sensors have received high attention in the analytical field owing to their low cost and simplicity since they do not require any complex or expensive instrumentation. This article reviews Ag NP-based optical methods for the determination of pharmaceutical compounds from 2010 to 2020. The reported optical methods are classified into four types: spectrophotometry, spectrofluorimetry, scattering and chemiluminescence. Ag NPs play different roles in the different sensing platforms used by these methods, the details of which are carefully explained in this review. Moreover, the relevant analytical parameters of the developed methods are categorized by role and tabulated. It is hoped that this review will stimulate further research in this field with similar nanostructures.

Using artificial neural network and multivariate calibration methods for simultaneous spectrophotometric analysis of Emtricitabine and Tenofovir alafenamide fumarate in pharmaceutical formulation of HIV drug

Spectrophotometric analysis method based on artificial neural network (ANN), partial least squares regression (PLS) and principal component regression (PCR) models was proposed for the simultaneous determination of Emtricitabine (ETB) and Tenofovir alafenamide fumarate (TAF) in human immunodeficiency virus (HIV) drug. An artificial neural network consisting of two, five, and seven layers with 2,3,5,7, and 9 neurons was trained by applying a feed forward back-propagation learning. In this method, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back propagation (GDX) algorithms were used. Statistical parameters indicated that the ability of LM was better than GDX algorithm. Also, root mean square error (RMSE) and recovery (%) of the PLS and PCR methods showed that PLS has worked better than PCR. The proposed models were compared to the high- performance liquid chromatography (HPLC) as a reference method. Furthermore, the obtained results of the one-way analysis of variance (ANOVA) test at the 95% confidence level represented that there was no significant difference between the proposed and reference methods.

Simultaneous spectrophotometric determination of Cu2+, Hg2+, and Cd2+ ions using 2-(3-hydroxy-1-methylbut-2-enylideneamino)pyridine-3-ol

New complexes of Cu2+, Hg2+, and Cd2+ with a recently synthesized Schiff base derived from 2-(3-hydroxy-1-methylbut-2-enylideneamino)pyridine-3-ol were applied for their simultaneous determination with artificial neural networks. A new analytical method using principal component-feed forward neural networks (PC-FFNNs) and principal component-radial basis function networks (PC-RBFNs) was used. Spectral data was reduced using principal component analysis and subjected to ANNs. The data obtained from synthetic mixtures of metal ions were processed by PC-FFNNs and PC-RBFNs. Performances of the proposed methods were tested with regard to relative standard error of prediction. Limit of detections and limit of quantifications were determined. The results obtained by PC-FFNNs and PC-RBFNs were compared to each other. Under the working conditions, the proposed methods were successfully applied to simultaneous determination of Hg2+, Cu2+, and Cd2+ in different water and soil samples. Concentrations of metal ions in the samples were also determined by flame atomic absorption spectrometry (FAAS) and standard addition method. The amounts of metal ions obtained by the proposed methods were in good agreement with those obtained by FAAS and standard addition method.

Artificial neural network assisted kinetic spectrophotometric technique for simultaneous determination of paracetamol and p-aminophenol in pharmaceutical samples using localized surface plasmon resonance band of silver nanoparticles

Spectrophotometric analysis method based on the combination of the principal component analysis (PCA) with the feed-forward neural network (FFNN) and the radial basis function network (RBFN) was proposed for the simultaneous determination of paracetamol (PAC) and p-aminophenol (PAP). This technique relies on the difference between the kinetic rates of the reactions between analytes and silver nitrate as the oxidizing agent in the presence of polyvinylpyrrolidone (PVP) which is the stabilizer. The reactions are monitored at the analytical wavelength of 420nm of the localized surface plasmon resonance (LSPR) band of the formed silver nanoparticles (Ag-NPs). Under the optimized conditions, the linear calibration graphs were obtained in the concentration range of 0.122-2.425μgmL(-1) for PAC and 0.021-5.245μgmL(-1) for PAP. The limit of detection in terms of standard approach (LODSA) and upper limit approach (LODULA) were calculated to be 0.027 and 0.032μgmL(-1) for PAC and 0.006 and 0.009μgmL(-1) for PAP. The important parameters were optimized for the artificial neural network (ANN) models. Statistical parameters indicated that the ability of the both methods is comparable. The proposed method was successfully applied to the simultaneous determination of PAC and PAP in pharmaceutical preparations.

Determination of Lipoxygenase Activity in Cereals Grains Using a Silver Nanoparticles Assay

A spectrophotometric method based on the use of silver nanoparticles is presented for the determination of lipoxygenase activity in cereal grains. The method involves the synthesis of prism silver nanoparticles with a maximum absorption at 752 nm, followed by the abatement of the signal as a consequence of the oxidation produced by hydroperoxides generated in the enzymatic reaction. The quantification of the hydroperoxides produced by reaction of lipoxygenase with linoleic acid allows the determination of the enzyme activity in cereal grains. Under optimal conditions, the linear range of the calibration curve ranges from 1.0 to 5.0 μM, with a limit of detection of 0.34 μM. The method was validated comparing the results with those obtained by ferrous-xylenol orange method. A relative standard deviation < 5.0% was obtained in all cases and no significant differences were observed (p<0.05).

Formation of plasmonic silver nanoparticles by flavonoid reduction: A comparative study and application for determination of these substances

Formation of plasmonic silver nanoparticles by flavonoid reduction was studied. Effects of the nature and the concentration of a flavonoid and a stabilizer, composition of the solution and the interaction time were revealed. It was found that quercetin, dihydroquercetin, rutin and morin produced an intense surface plasmon resonance band of silver nanoparticles at 415 nm which was linearly related to the concentration of a flavonoid, while chrysin, naringenin and naringin did not produce any remarkable changes. It was used for the spectrophotometric determination of the former four flavonoids with the detection limits of 0.03; 0.06; 0.09 and 0.1 μg mL(-1), respectively. The developed method was applied for the determination of flavonoids in biologically active food additives.

Artificial neural network-genetic algorithm based optimization for the adsorption of methylene blue and brilliant green from aqueous solution by graphite oxide nanoparticle

In this study, graphite oxide (GO) nano according to Hummers method was synthesized and subsequently was used for the removal of methylene blue (MB) and brilliant green (BG). The detail information about the structure and physicochemical properties of GO are investigated by different techniques such as XRD and FTIR analysis. The influence of solution pH, initial dye concentration, contact time and adsorbent dosage was examined in batch mode and optimum conditions was set as pH=7.0, 2 mg of GO and 10 min contact time. Employment of equilibrium isotherm models for description of adsorption capacities of GO explore the good efficiency of Langmuir model for the best presentation of experimental data with maximum adsorption capacity of 476.19 and 416.67 for MB and BG dyes in single solution. The analysis of adsorption rate at various stirring times shows that both dyes adsorption followed a pseudo second-order kinetic model with cooperation with interparticle diffusion model. Subsequently, the adsorption data as new combination of artificial neural network was modeled to evaluate and obtain the real conditions for fast and efficient removal of dyes. A three-layer artificial neural network (ANN) model is applicable for accurate prediction of dyes removal percentage from aqueous solution by GO following conduction of 336 experimental data. The network was trained using the obtained experimental data at optimum pH with different GO amount (0.002-0.008 g) and 5-40 mg/L of both dyes over contact time of 0.5-30 min. The ANN model was able to predict the removal efficiency with Levenberg-Marquardt algorithm (LMA), a linear transfer function (purelin) at output layer and a tangent sigmoid transfer function (tansig) at hidden layer with 10 and 11 neurons for MB and BG dyes, respectively. The minimum mean squared error (MSE) of 0.0012 and coefficient of determination (R(2)) of 0.982 were found for prediction and modeling of MB removal, while the respective value for BG was the MSE and R(2) of 0.001 and 0.981, respectively. The ANN model results show good agreement with experimental data.