Jiang Junhuang, Ma Xiangyu, Ouyang Defang, Williams Robert O
Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78712, USA.
Global Investment Research, Goldman Sachs, New York, NY 10282, USA.
Pharmaceutics. 2022 Oct 22;14(11):2257. doi: 10.3390/pharmaceutics14112257.
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.
基于人工智能（AI）的制剂研发是促进药品研发过程的一种有前景的方法。人工智能是一种通用工具，包含多种可应用于各种情况的算法。以片剂、胶囊、粉末、颗粒等为代表的固体剂型是最广泛使用的给药方式之一。在产品研发过程中，包括关键物料属性（CMA）和加工参数在内的多个因素会影响产品特性，如溶出速率、物理和化学稳定性、粒度分布以及干粉的气溶胶性能。然而，传统的产品研发试错方法效率低下、费力且耗时。人工智能最近已被公认为是一种用于药物制剂研发的新兴前沿工具，并受到了广泛关注。本综述提供了以下见解：（1）人工智能在药学领域的总体介绍以及监管机构的主要指导意见，（2）生成固体剂型数据库的方法，（3）对数据准备和处理的见解，（4）人工智能算法的简要介绍和比较，以及（5）人工智能应用于固体剂型的应用和案例研究信息。此外，还将讨论基于深度学习的图像分析这一强大技术及其药学应用。通过应用新兴的人工智能技术，科学家和研究人员可以更好地理解和预测药物制剂的特性，以促进更高效的药品研发过程。
