Biological activities of drug inactive ingredients. Issue 5 (6th May 2022)

• Record Type: Journal Article
• Authors: Zhang, Chenyang; Mou, Minjie; Zhou, Ying; Zhang, Wei; Lian, Xichen; Shi, Shuiyang; Lu, Mingkun; Sun, Huaicheng; Li, Fengcheng; Wang, Yunxia; Zeng, Zhenyu; Li, Zhaorong; Zhang, Bing; Qiu, Yunqing; Zhu, Feng; Gao, Jianqing
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 5(2022)
• Publication Date: 2022-05-06
• Subjects: drug inactive ingredient -- biological activity -- drug formulation -- database -- artificial intelligence; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac160
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: In a drug formulation (DFM), the major components by mass are not Active Pharmaceutical Ingredient (API) but rather Drug Inactive Ingredients (DIGs). DIGs can reach much higher concentrations than that achieved by API, which raises great concerns about their clinical toxicities. Therefore, the biological activities of DIG on physiologically relevant target are widely demanded by both clinical investigation and pharmaceutical industry. However, such activity data are not available in any existing pharmaceutical knowledge base, and their potentials in predicting the DIG-target interaction have not been evaluated yet. In this study, the comprehensive assessment and analysis on the biological activities of DIGs were therefore conducted. First, the largest number of DIGs and DFMs were systematically curated and confirmed based on all drugs approved by US Food and Drug Administration. Second, comprehensive activities for both DIGs and DFMs were provided for the first time to pharmaceutical community. Third, the biological targets of each DIG and formulation were fully referenced to available databases that described their pharmaceutical/biological characteristics. Finally, a variety of popular artificial intelligence techniques were used to assess the predictive potential of DIGs' activity data, which was the first evaluation on the possibility to predict DIG's activity. As the activities of DIGs are critical for current pharmaceutical studies, this work is expected to have significant implications for the future practice of drug discovery and precision medicine.