Harnessing artificial intelligence for the next generation of 3D printed medicines

Abstract

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State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

“…Additionally, 3D printing, an automated and digitalized technology, has exceptional advantages in many fields and is also used in the pharmaceutics industry to drive personalized medicine [162]. With 3D printing technology, preparations can be produced directly from a computer-aided design model using layer-by-layer manners, such as fused deposition modeling, direct powder extrusion and powder binding.…”

“…With 3D printing technology, preparations can be produced directly from a computer-aided design model using layer-by-layer manners, such as fused deposition modeling, direct powder extrusion and powder binding. An ANN can also be applied to optimize the process parameters, inspect quality and inspire innovative dosage-form design in 3D-printed drugs [30,111,162,163].…”

Direct Powder Extrusion 3D Printing of Praziquantel to Overcome Neglected Disease Formulation Challenges in Paediatric Populations

“…HME has been recently used to obtain filaments for the preparation of personalised medicines using threedimensional (3D) printing [25]. Three-dimensional printing (3DP) is an innovative additive manufacturing technique, capable of converting 3D computer models into real objects by the sequential deposition of material in a layer by layer manner [26][27][28][29][30][31][32][33][34][35][36]. Currently, the most evaluated 3DP technique in the pharmaceutical area is fused deposition modelling (FDM), as a result of the low printer cost, the good quality of the final product, and the direct use of filaments obtained by HME [37].…”

Machine Learning Uncovers Adverse Drug Effects on Intestinal Bacteria

“…The pharmaceutical industry currently screens potential drugs for a range of other toxic effects, to increase the chance of these being identified at an early, preclinical, stage [ 40 ]. Increasingly, industry professionals are looking to advanced predictive techniques, such as machine learning (ML), to streamline toxicology testing and increase the chance of early identification [ 41 , 42 , 43 ]. ML has been successfully used to predict cardiovascular toxicity and drug–drug interactions and is predicted to play a significant role in pharma’s transition to Industry 4.0 [ 44 , 45 , 46 , 47 , 48 ].…”