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

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

doi:10.3389/fphar.2021.755396

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.

Highlights

Influenza is an acute respiratory viral infection that leads to the continual emergence of seasonal epidemics and occasional global pandemics in humans, causing significant morbidity and mortality worldwide (Krammer et al, 2018)
Drug-likeness is utilized in drug design to describe how “druglike” a particular molecule is with respect to various molecular properties and structural features
plasma protein binding (PPB) ability has significant effects on the pharmacodynamic action of a drug since one of the main mechanisms for drug uptake and distribution is through PPB

Summary

Introduction

Influenza (flu) is an acute respiratory viral infection that leads to the continual emergence of seasonal epidemics and occasional global pandemics in humans, causing significant morbidity and mortality worldwide (Krammer et al, 2018). A recent study has demonstrated that IAV possesses clear auxo-action on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which could boost viral entry into cells and increase the viral load, causing worse lung damage in virus-infected mice. These results emphasize the great importance of influenza prevention, in the context of the ongoing coronavirus disease-2019 (COVID-19) pandemic. Owing to the emergence of drug resistance of conventional anti-influenza drugs, such as inhibitors of neuraminidase (NA), the M2 ion channel, and RNA-dependent RNA polymerase (RdRp), the need for the development of anti-IAV drugs with novel modes of action is highly urgent (Zu et al, 2015; Xu et al, 2020)

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