Modeling of Autocatalytic Hydrolysis of Adefovir Dipivoxil in Solid Formulations

Abstract

The stability and hydrolysis kinetics of a phosphate prodrug, adefovir dipivoxil, in solid formulations were studied. The stability relationship between five solid formulations was explored. An autocatalytic mechanism for hydrolysis could be proposed according to the kinetic behavior which fits the Prout-Tompkins model well. For the classical kinetic models could hardly describe and predict the hydrolysis kinetics of adefovir dipivoxil in solid formulations accurately when the temperature is high, a feedforward multilayer perceptron (MLP) neural network was constructed to model the hydrolysis kinetics. The build-in approaches in Weka, such as lazy classifiers and rule-based learners (IBk, KStar, DecisionTable and M5Rules), were used to verify the performance of MLP. The predictability of the models was evaluated by 10-fold cross-validation and an external test set. It reveals that MLP should be of general applicability proposing an alternative efficient way to model and predict autocatalytic hydrolysis kinetics for phosphate prodrugs.