Optimization of ۳D printing application in pharmaceuticals through artificial intelligence - A review

Background and aims: Ever since the US FDA approved the first ۳D-printed medicine,SPRITAM®, new studies were designed based on ۳D-printed drugs and related drug deliverysystems. The importance of ۳D-printed drugs lies in the fact that the printing process alters someof the properties of the finished product (solubility and absorption for instance). Alongside, artificialintelligence has engaged with medicine more than it ever did in past. Hence, more innovativestudies have been established to help this new way of drug design and formulation through artificialintelligence and machine learning methods. Hopefully, Artificial intelligence may help theprinting process alterations to be more beneficial. The current study scrutinizes previous studies tounderstand how ۳D printing and artificial intelligence interaction can help pharmaceuticals froma more practical point of view rather than theories. Hopefully, this study will help researchersto learn more about proper materials and methods to start using ۳dprinters and AI to design andproduce new drugs. In addition, the fast progress in the use of ۳dprinters and artificial intelligencecalls for more updates for past similar studies.Method: This study is a review of published articles on the Optimization of ۳D printing applicationsin pharmaceuticals through artificial intelligence. In order to find and collect the articles,“artificial intelligence”, “machine learning”, “۳D-print”, “drugs”, and “medicine” were used askeywords and Google Scholar, Science Direct, PubMed, Wiley and etc. were used as databases.Results: The results are categorized into three main subcategories: printability (including thepharmaceutical and related materials that have been used for ۳D printing, their traits, and structure-based printability), application of different methods of AI in ۳d printing optimization, andother interactions between ۳D printing and AI that concerns medicine and drug design. Materialsshould have certain properties to be printable like high heat resistance, impact resistance, chemicalresistance, and rigidity. Another aspect of printability comes from the capability to form andmaintain reproducible ۳D scaffolds that depend on the product. Different machine learning methodswere used to improve the ۳D printed products and each method has its positive and negativecharacteristics. At last, other interactions between ۳D printing and AI were included.Conclusion: When it comes to ۳D printing, Artificial intelligence has the ability to analyze an objectbefore starting the process and it is able to predict the quality of the final product. The use ofmachine learning algorithms also improves the fixation process and reduces manufacturing waste,which is crucial, considering the economical and treatment aspects of medicine and drug design.

کلیدواژه ها:

artificial intelligence ، ۳D print ، medicine ، drug design