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Abstract

-The aim of this work was to develop a predictive model for the relationship of the poorly soluble drug dissolution profile and parameters characterizing 3D printed dosage form. Itraconazole (ITZ) was chosen as an example of a low soluble active pharmaceutical ingredient (API). Different formulations containing ITZ as a model drug substance were designed, printed using fused deposition modeling 3DP technology and tested in a series of dissolution studies. The dissolution data allowed for the construction of eight predictive models using artificial intelligence (AI) tools based on machine learning (ML) with various techniques including regression algorithms, decision trees, artificial neural networks and evolutionary computations with symbolic regression. Thanks to the last method, the mathematical model with the lowest prediction error was obtained. The final optimized equation automatically identified the key variables, based on which, the newly printed formulations with the desired ITZ dissolution pattern confirmed the good predictive ability of the mathematical model