Use of machine learning tool to elucidate and characterize the growth mechanism of an in-situ fluid bed melt granulation

The aim of this work is to follow the agglomeration mechanism of an in situ Fluid Bed Melt Granulation (FBMG) by means of machine learning with Artificial Neural Network (ANN) modelling. Scanning Electron Microscopy (SEM) was used as a complementary tool with particle size analysis to evaluate the effect of the material properties on the quality attributes of the final granules and provide further insight into the growth mechanisms. The experiments were performed using lactose monohydrate as model filler and two grades of polyethylene glycol (PEG 2000 and 6000) as meltable binders in different contents and in different size fractions, respectively. A multilayer perceptron neural network was developed using MATLAB neural network toolbox and the evaluated quality attributes of the final granules were used as a database for the development and selection of the optimal architecture of the ANN model. The Garson equation was used to quantify the relative importance of each independent variable and thus it was established that the particle size had the highest impact on the granule properties. The distribution and immersion granule growth mechanisms were determined to occur for low and high binder particle size, respectively, as confirmed by the SEM pictures, and the response surfaces helped determine the optimal design space of the process and the optimal FBMG conditions