  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.
