LAUSR

In the context of increasing application of modelling methods in the field of pharmaceutics, this study aims to reduce the weight of sildenafil orally disintegrating tablets (ODTs) and optimize their formulation through modelling methods. To achieve the goal, the back-propagation neural network (BPNN)–based non-dominated sorting genetic algorithm II (NSGA-II) was introduced to establish the models and to optimize the percentage of magnesium stearate (MgSt), crospovidone (PVPP), and croscarmellose sodium (CCNa) to obtain satisfactory candidate ODTs. Ultimately, the bioequivalence trial was conducted to verify the effectiveness of the formulation. With the support of the neural network, the model showed satisfactory results in the prediction of hardness and disintegration time of ODTs, and the pareto front obtained by the NSGA-II suggested that there was a strong “competition” between disintegration time and hardness. Since disintegration time should be given the priority, the optimal formulation was determined as 1% MgSt, 6% CCNa, and 2.6% PVPP. The bioequivalence trial results indicated a bioequivalence between the test and the reference formulations of sildenafil, and better medication experience for the test formulation. A bioequivalent formulation with better medication experience is successfully prepared using the NSGA-II. It proves that the NSGA-II is applicable to multi-objective optimization of the drug formulation.