Abstract: In pharmaceutical development, it is very useful to exploit the knowledge of the causal relationship between product quality and critical material attributes (CMA) in developing new formulations and products, and optimizing manufacturing processes. With the big data captured in the pharmaceutical industry, computational intelligence (CI) models could potentially be used to identify critical quality attributes (CQA), CMA and critical process parameters (CPP). The objective of this study was to develop computational intelligence models for pharmaceutical tabletting processes, for which bio-inspired feature selection algorithms were developed and implemented for optimisation while artificial neural network (ANN) was employed to predict the tablet characteristics such as porosity and tensile strength. Various pharmaceutical excipients (MCC PH 101, MCC PH 102, MCC DG, Mannitol Pearlitol 200SD, Lactose, and binary mixtures) were considered. Granules were also produced with dry granulation using roll compaction. The feed powders and granules were then compressed at various compression pressures to produce tablets with different porosities, and the corresponding tensile strengths were measured. For the CI modelling, the efficiency of seven bio-inspired optimization algorithms were explored: grey wolf optimization (GWO), bat optimization (BAT), cuckoo search (CS), flower pollination algorithm (FPA), genetic algorithm (GA), particle swarm optimization (PSO), and social spider optimization (SSO). Two-thirds of the experimental dataset was randomly chosen as the training set, and the remaining was used to validate the model prediction. The model efficiency was evaluated in terms of the average reduction (representing the fraction of selected input variables) and the mean square error (MSE). It was found that the CI models can well predict the tablet characteristics (i.e. porosity and tensile strength). It was also shown that the GWO algorithm was the most accurate in predicting porosity. While the most accurate prediction for the tensile strength was achieved using the SSO algorithm. In terms of the average reduction, the GA algorithm resulted in the highest reduction of inputs (i.e. 60%) for predicting both the porosity and the tensile strength.

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