In Silico Prediction and Bioactivity Evaluation of Chemical Ingredients Against Influenza A Virus From Isatis tinctoria L

Chuipu Cai; Lvjie Xu; Junfeng Fang; Zhao Dai; Qihui Wu; Xiaoyi Liu; Qi Wang; Jiansong Fang; Ai-Lin Liu; Guan-Hua Du

doi:10.3389/fphar.2021.755396

In Silico Prediction and Bioactivity Evaluation of Chemical Ingredients Against Influenza A Virus From Isatis tinctoria L

Front Pharmacol. 2021 Dec 7:12:755396. doi: 10.3389/fphar.2021.755396. eCollection 2021.

Authors

Chuipu Cai^{1

2

3}, Lvjie Xu⁴, Junfeng Fang⁵, Zhao Dai², Qihui Wu², Xiaoyi Liu³, Qi Wang², Jiansong Fang², Ai-Lin Liu⁴, Guan-Hua Du⁴

Affiliations

¹ Division of Data Intelligence, Department of Computer Science, Key Laboratory of Intelligent Manufacturing Technology of Ministry of Education, College of Engineering, Shantou University, Shantou, China.
² Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China.
³ School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China.
⁴ Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
⁵ The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.

Abstract

Influenza A virus (IAV) is one of the major causes of seasonal endemic diseases and unpredictable periodic pandemics. Due to the high mutation rate and drug resistance, it poses a persistent threat and challenge to public health. Isatis tinctoria L. (Banlangen, BLG), a traditional herbal medicine widely used in Asian countries, has been reported to possess strong efficacy on respiratory viruses, including IAV. However, its effective anti-IAV components and the mechanism of actions (MOAs) are not yet fully elucidated. In this study, we first summarized the chemical components and corresponding contents in BLG according to current available chemical analysis literature. We then presented a network-based in silico framework for identifying potential drug candidates against IAV from BLG. A total of 269 components in BLG were initially screened by drug-likeness and ADME (absorption, distribution, metabolism, and excretion) evaluation. Thereafter, network predictive models were built via the integration of compound-target networks and influenza virus-host proteins. We highlighted 23 compounds that possessed high potential as anti-influenza virus agents. Through experimental evaluation, six compounds, namely, eupatorin, dinatin, linarin, tryptanthrin, indirubin, and acacetin, exhibited good inhibitory activity against wild-type H1N1 and H3N2. Particularly, they also exerted significant effects on drug-resistant strains. Finally, we explored the anti-IAV MOAs of BLG and showcased the potential biological pathways by systems pharmacology analysis. In conclusion, this work provides important information on BLG regarding its use in the development of anti-IAV drugs, and the network-based prediction framework proposed here also offers a powerfulful strategy for the in silico identification of novel drug candidates from complex components of herbal medicine.

Keywords: Isatis tinctoria L.; drug discovery; influenza A virus; network-based identification; virtual screening.