Optimizing critical quality attributes of fast disintegrating tablets using artificial neural networks: a scientific benchmark study

The ANN model, integrated with differential evolution, provided a robust tool for optimizing FDT formulations by accurately predicting CQAs and reducing the need for extensive experimental trials. Compared to traditional optimization methods, ANN excels in capturing intricate multi-variable relationships, making it a valuable approach for scaling beyond acetylsalicylic acid to other formulations. This method enhances the consistency and efficiency of tablet formulation, supporting broader pharmaceutical applications.