Muñiz Castro, B; Elbadawi, M; Ong, JJ; Pollard, T; Song, Z; Gaisford, S; Pérez, G; ... Goyanes, A; + view all Muñiz Castro, B; Elbadawi, M; Ong, JJ; Pollard, T; Song, Z; Gaisford, S; Pérez, G; Basit, AW; Cabalar, P; Goyanes, A; - view fewer (2021) Machine learning predicts 3D printing performance of over 900 drug delivery systems. Journal of Controlled Release , 337 pp. 530-545. 10.1016/j.jconrel.2021.07.046.

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Abstract

Three-dimensional printing (3DP) is a transformative technology that is advancing pharmaceutical research by producing personalized drug products. However, advances made via 3DP have been slow due to the lengthy trial-and-error approach in optimization. Artificial intelligence (AI) is a technology that could revolutionize pharmaceutical 3DP through analyzing large datasets. Herein, literature-mined data for developing AI machine learning (ML) models was used to predict key aspects of the 3DP formulation pipeline and in vitro dissolution properties. A total of 968 formulations were mined and assessed from 114 articles. The ML techniques explored were able to learn and provide accuracies as high as 93% for values in the filament hot melt extrusion process. In addition, ML algorithms were able to use data from the composition of the formulations with additional input features to predict the drug release of 3D printed medicines. The best prediction was obtained by an artificial neural network that was able to predict drug release times of a formulation with a mean error of ±24.29 min. In addition, the most important variables were revealed, which could be leveraged in formulation development. Thus, it was concluded that ML proved to be a suitable approach to modelling the 3D printing workflow.

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