期刊
JOURNAL OF CONTROLLED RELEASE
卷 352, 期 -, 页码 833-839出版社
ELSEVIER
DOI: 10.1016/j.jconrel.2022.10.040
关键词
Chemistry classification; Artificial intelligence; Machine-learning formulation
This perspective article highlights the Chemistry Classification System (CCS) as a flexible and innovative approach to drug classification, emphasizing the use of machine-learning models and AI to guide formulation development based on unique physicochemical properties.
This perspective article draws a distinction between some of the well-known drug classification systems and a Chemistry Classification System (CCS). Rather than have drug classification based on some simple properties like solubility and permeability or route of systemic elimination, a CCS results in more than four or five classes and each class has distinct properties that impact formulation development. This perspective provides and outline of 13 classes, but a CCS is a flexible system that introduces a thought process for classification. The number of classes is not rigid, and chemists are encouraged to adapt these methods to their own situations. A CCS utilizes machine-learning models and artificial intelligence (AI) to estimate physicochemical properties that result in unique, frequently observed dissolution, Absorption, Distribution, Metabolism, and Excretion (ADME) properties to guide formulation development.
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