Biological activities of drug inactive ingredients

Author:

Zhang Chenyang1,Mou Minjie1,Zhou Ying12,Zhang Wei1,Lian Xichen1,Shi Shuiyang1,Lu Mingkun1,Sun Huaicheng1,Li Fengcheng1,Wang Yunxia1,Zeng Zhenyu3,Li Zhaorong3,Zhang Bing3,Qiu Yunqing2,Zhu Feng13ORCID,Gao Jianqing14ORCID

Affiliation:

1. College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University , Hangzhou 310058, China

2. State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University , 79 QingChun Road, Hangzhou, Zhejiang 310000, China

3. Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare , Hangzhou 330110, China

4. Westlake Laboratory of Life Sciences and Biomedicine , Hangzhou, Zhejiang, China

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.

Funder

Natural Science Foundation of Zhejiang Province

National Natural Science Foundation of China

National High-Level Talents Special Support Plan of China

University Project

Key Research and Development Program of Zhejiang Province

Fundamental Research Fund for Central Universities

Westlake Laboratory of Life Sciences and Biomedicine

Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare

Alibaba Cloud

Information Technology Center of Zhejiang University

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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