Optimization of print parameters for batch and continuous manufacturing of three-dimensional (3D) printed dosage forms using artificial intelligence and machine learning.

Three-dimensional printing (3DP) holds significant potential for developing personalized pharmaceutical oral dosage forms (printlets). 3D printing has the advantage of fabricating complex geometric structures for versatile drug release profiles, enhancing patient preference, palatability and swallowability, reducing the pill burden, and increasing dose accuracy. Optimizing printing parameters is crucial in determining the quality of the printlets during dosage development. The integration of machine learning (ML) can reduce production costs and time through parameter optimization based on trained datasets. This research is focused on optimizing parameters for fused deposition modeling (FDM) based batch and continuous printing methods. The algorithm was trained using a three-level full factorial design, which generated data in the form of printlets with different parameters. Both defect and defect-free printlets were analyzed using image segmentation. Machine learning tools including Gaussian Process Regressor (GPR) and Efficient Global Optimization (EGO) were used to predict and select processing parameters for a targeted percentage surface defect. The final trained algorithm predicted new parameter sets for both batch (R