Predicting the outcome of granulation and tableting processes using different artificial intelligence methods

Abstract

Artificial intelligence methods offer a modern approach in solving different problems in the field of pharmacy, especially in the area of pharmaceutical technology. The main goal of this article is to present artificial intelligence methods that facilitate granulation and tableting processes. Artificial intelligence methods with the major impact in this area include artificial neural networks (ANN), cubist model, random forest method, k-NN and the combination of neuro-fuzzy logic (NFL) and gene expression programming (GEP). Besides giving a brief introduction to the methods listed above, the scientific goal of this paper is to present the on-going use they have in wet granulation process, roll compaction, solving the capping problems of the tablets, as well as helping with the scale-up process and quality improvement of ramipril tablets. In time to come, it is assumed that the diversity and pliability of the artificial intelligence methods can advance in the tablet making process, ensuring the much needed support in data analyzing and solving complex problems of tablet manufacturing that contain various input and output specification. It should always be duly noted, that even though artificial intelligence methods are far superior to the human brain when it comes to problem-solving and multitasking, these methods, and computer programming can never be the head of the operation, they could only mimic that.