Deng Yuehua, Liu Shiyuan, Jiang Yanbin, Martins Inês C B, Rades Thomas
Guangdong Provincial Key Lab of Green Chemical Product Technology, School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China.
Department of Pharmacy, University of Copenhagen, Universitetsparken 2, 2100 Copenhagen, Denmark.
Pharmaceutics. 2023 Aug 22;15(9):2174. doi: 10.3390/pharmaceutics15092174.
Multicomponent solid forms of low molecular weight drugs, such as co-crystals, salts, and co-amorphous systems, are a result of the combination of an active pharmaceutical ingredient (API) with a pharmaceutically acceptable co-former. These solid forms can enhance the physicochemical and pharmacokinetic properties of APIs, making them increasingly interesting and important in recent decades. Nevertheless, predicting the formation of API multicomponent solid forms in the early stages of formulation development can be challenging, as it often requires significant time and resources. To address this, empirical and computational methods have been developed to help screen for potential co-formers more efficiently and accurately, thus reducing the number of laboratory experiments needed. This review provides a comprehensive overview of current screening and prediction methods for the formation of API multicomponent solid forms, covering both crystalline states (co-crystals and salts) and amorphous forms (co-amorphous). Furthermore, it discusses recent advances and emerging trends in prediction methods, with a particular focus on artificial intelligence.
低分子量药物的多组分固体形式，如共晶体、盐和共无定形体系，是活性药物成分（API）与药学上可接受的共形成物结合的结果。这些固体形式可以增强API的物理化学和药代动力学性质，在近几十年来使其变得越来越有趣且重要。然而，在制剂开发的早期阶段预测API多组分固体形式的形成可能具有挑战性，因为这通常需要大量的时间和资源。为了解决这个问题，已经开发了经验和计算方法来帮助更有效、准确地筛选潜在的共形成物，从而减少所需的实验室实验数量。本综述全面概述了目前用于API多组分固体形式形成的筛选和预测方法，涵盖了晶态（共晶体和盐）和无定形形式（共无定形）。此外，还讨论了预测方法的最新进展和新兴趋势，特别关注人工智能。
