Kinetic Study of anti-HIV drugs by Thermal Decomposition Analysis: A Multilayer Artificial Neural Network Propose

Abstract

Kinetic study by thermal decomposition of antiretroviral drugs, Efavirenz (EFV) and Lamivudine (3TC), usually present in the HIV cocktail, can be done by individual adjustment of the solid decomposition models. However, in some cases unacceptable errors are found using this methodology. To circumvent this problem, here is proposed to use a multilayer perceptron neural network (MLP), with an appropriate algorithm, which constitutes a linearization of the network by setting weights between the input layer and the intermediate one and the use of Kinetic models as activation functions of neurons in the hidden layer. The interconnection weights between that intermediate layer and output layer determines the contribution of each model in the overall fit of the experimental data. Thus, the decomposition is assumed to be a phenomenon that can occur following different kinetic processes. In the investigated data, the kinetic thermal decomposition process was best described by R1 and D4 model for all temperatures to EFV and 3TC, respectively. The residual error adjustment over the network is on average 10$^{3}$ times lower for EFV and 10$^{2}$ times lower for 3TC compared to the best individual kinetic model that describes the process. These improvements in physical adjustment allow detailed study of the process and therefore a more accurate calculation of the kinetic parameters such as the activation energy and frequency factor. It was found Ea=75.230 kJ/mol and ln(A)=3.2190x10$^{-16}$ s$^{-1}$ for EFV and Ea=103.25 kJ/mol and ln(A)= 2.5587X10$^{3}$ s$^{-1}$ for 3TC.