Predicting pharmaceutical inkjet printing outcomes using machine learning.

Inkjet printing has been extensively explored in recent years to produce personalised medicines due to its low cost and versatility. Pharmaceutical applications have ranged from orodispersible films to complex polydrug implants. However, the multi-factorial nature of the inkjet printing process makes formulation (e.

Machine learning predicts 3D printing performance of over 900 drug delivery systems.

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.

M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines.

Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation development through its ability to analyze and continuously monitor large datasets. Fused deposition modeling (FDM) three-dimensional printing (3DP) has made significant advancements in the field of oral drug delivery with personalized drug-loaded formulations being designed, developed and dispensed for the needs of the patient. The FDM 3DP process begins with the production of drug-loaded filaments by hot melt extrusion (HME), followed by the printing of a drug product using a FDM 3D printer.