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

Abstract

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.

Keywords: Artificial neural network; Emtricitabine; Partial least squares regression; Principal component regression; Spectrophotometry; Tenofovir alafenamide fumarate.