计算机科学
新产品开发
过程分析技术
剂型
过程(计算)
溶解试验
生化工程
人工智能
数据科学
溶解
化学
工程类
在制品
营销
物理化学
生物制药分类系统
业务
操作系统
色谱法
运营管理
作者
Junhuang Jiang,Xiangyu Ma,Defang Ouyang,Robert O. Williams
出处
期刊:Pharmaceutics
[MDPI AG]
日期:2022-10-22
卷期号:14 (11): 2257-2257
被引量:34
标识
DOI:10.3390/pharmaceutics14112257
摘要
Artificial Intelligence (AI)-based formulation development is a promising approach for facilitating the drug product development process. AI is a versatile tool that contains multiple algorithms that can be applied in various circumstances. Solid dosage forms, represented by tablets, capsules, powder, granules, etc., are among the most widely used administration methods. During the product development process, multiple factors including critical material attributes (CMAs) and processing parameters can affect product properties, such as dissolution rates, physical and chemical stabilities, particle size distribution, and the aerosol performance of the dry powder. However, the conventional trial-and-error approach for product development is inefficient, laborious, and time-consuming. AI has been recently recognized as an emerging and cutting-edge tool for pharmaceutical formulation development which has gained much attention. This review provides the following insights: (1) a general introduction of AI in the pharmaceutical sciences and principal guidance from the regulatory agencies, (2) approaches to generating a database for solid dosage formulations, (3) insight on data preparation and processing, (4) a brief introduction to and comparisons of AI algorithms, and (5) information on applications and case studies of AI as applied to solid dosage forms. In addition, the powerful technique known as deep learning-based image analytics will be discussed along with its pharmaceutical applications. By applying emerging AI technology, scientists and researchers can better understand and predict the properties of drug formulations to facilitate more efficient drug product development processes.
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