The Future of Artificial Intelligence in Pharmaceutical Product Formulation

Q2 Pharmacology, Toxicology and Pharmaceutics
L. Singh, R. Tiwari, S. Verma, V. Sharma
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引用次数: 1

Abstract

Conventional approach of formulating a new dosage form is a comprehensive task and uses various sources like man, money, time and experimental efforts. The use of AI can help to obtain optimized pharmaceutical formulation with desired (best) attributes. AI minimizes the use of resources and increases the understanding of impact, of independent variable over desired dependent responses/variables. Thus, the aim of present work is to explore the use of Artificial intelligence in designing pharmaceutical products as well as the manufacturing process to get the pharmaceutical product of desired attributes with ease. The review is presenting various aspects of Artificial intelligence like Quality by Design (QbD) & Design of Experiment (DoE) to confirm the quality profile of drug product, reduce interactions among the input variables for the optimization, modelization and various simulation tools used in pharmaceutical manufacturing (scale up and production). Hence, the use of QbD approach in Artificial intelligence is not only useful in understanding the products or process but also helps in building an excellent and economical pharmaceutical product.
人工智能在医药产品配方中的未来
制定新剂型的传统方法是一个综合性的问题,需要各种资源,如人力、财力、时间和实验努力。人工智能的使用可以帮助获得具有所需(最佳)属性的优化药物配方。人工智能最大限度地减少了资源的使用,并增加了对影响的理解,自变量超过了所需的依赖性响应/变量。因此,本工作的目的是探索人工智能在药物产品设计和制造过程中的应用,以轻松获得所需的药物产品。该综述介绍了人工智能的各个方面,如质量设计(QbD)和实验设计(DoE),以确认药品的质量状况,减少优化、建模的输入变量之间的相互作用,以及制药制造(扩大规模和生产)中使用的各种模拟工具。因此,在人工智能中使用QbD方法不仅有助于理解产品或过程,而且有助于构建一种优秀且经济的药品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Drug Delivery Letters
Drug Delivery Letters Pharmacology, Toxicology and Pharmaceutics-Pharmacology, Toxicology and Pharmaceutics (miscellaneous)
CiteScore
1.70
自引率
0.00%
发文量
30
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