Fu-Zheng-Xuan-Fei formula promotes macrophage polarization and Th17/Treg cell homeostasis against the influenza B virus (Victoria strain) infection.

ETHNOPHARMACOLOGICAL RELEVANCE: Fu-Zheng-Xuan-Fei formula (FF) is a prescription that has been clinically used through the basic theory of traditional Chinese medicine (TCM) for treating viral pneumonia. Although FF possesses a prominent clinical therapeutic effect, seldom pharmacological studies have been reported on its anti-influenza B virus (IBV) activity. AIM OF THE STUDY: Influenza is an acute infectious respiratory disease caused by the influenza virus, which has high annual morbidity and mortality worldwide. With a global decline in the COVID-19 control, the infection rate of influenza virus is gradually increasing. Therefore, it is of great importance to develop novel drugs for the effective treatment of influenza virus. Apart from conventional antiviral drugs, TCM has been widely used in the clinical treatment of influenza in China. Therefore, studying the antiviral mechanism of TCM can facilitate the scientific development of TCM. MATERIALS AND METHODS: Madin-Darby canine kidney cells (MDCK) and BALB/c mice were infected with IBV, and FF was added to evaluate the anti-IBV effects of FF both in vitro and in vivo by Western blotting, immunofluorescence, flow cytometry, and pathological assessment. RESULTS: It was found that FF exhibited anti-viral activity against IBV infection both in vivo and in vitro, while inducing macrophage activation and promoting M1 macrophage polarization. In addition, FF effectively regulated the signal transducer and activator of transcription (STAT) signaling pathway-mediated Th17/Treg balance to improve the lung tissue damage caused by IBV infection-induced inflammation. The findings provided the scientific basis for the antiviral mechanism of FF against IBV infection. CONCLUSIONS: This study shows that FF is a potentially effective antiviral drug against IBV infection.

Screening of Inhibitors Against Idiopathic Pulmonary Fibrosis: Few-Shot Machine Learning and Molecule Docking Based Drug Repurposing.

AIM: Idiopathic pulmonary fibrosis is a chronic progressive disorder and is diagnosed as post-COVID fibrosis. Idiopathic pulmonary fibrosis has no effective treatment because of the low therapeutic effects and side effects of currently available drugs. INTRODUCTION: The aim is to screen new inhibitors against idiopathic pulmonary fibrosis from traditional Chinese medicines. METHODS: Few-shot-based machine learning and molecule docking were used to predict the potential activities of candidates and calculate the ligand-receptor interactions. In vitro A549 cell model was taken to verify the effects of the selected leads on idiopathic pulmonary fibrosis. RESULTS AND DISCUSSION: A logistic regression classifier model with an accuracy of 0.82 was built and, combined with molecule docking, used to predict the activities of candidates. 6 leads were finally screened out and 5 of them were in vitro experimentally verified as effective inhibitors against idiopathic pulmonary fibrosis. CONCLUSION: Herbacetin, morusin, swertiamarin, vicenin-2, and vitexin were active inhibitors against idiopathic pulmonary fibrosis. Swertiamarin exhibited the highest anti-idiopathic pulmonary fibrosis effect and should be further in vivo investigated for its activity.

[Virtual screening of active ingredients of traditional Chinese medicine in treating COVID-19 based on molecular docking and molecular dynamic simulation].

We aim to screen out the active components that may have therapeutic effect on coronavirus disease 2019 (COVID-19) from the severe and critical cases' prescriptions in the "Coronavirus Disease 2019 Diagnosis and Treatment Plan (Trial Ninth Edition)" issued by the National Health Commission of the People's Republic of China and explain its mechanism through the interactions with proteins. The ETCM database and SwissADME database were used to screen the active components contained in 25 traditional Chinese medicines in 3 prescriptions, and the PDB database was used to obtain the crystal structures of 4 proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Molecular docking was performed using Autodock Vina and molecular dynamics simulations were performed using GROMACS. Binding energy results showed that 44 active ingredients including xambioona, gancaonin L, cynaroside, and baicalin showed good binding affinity with multiple targets of SARS-CoV-2, while molecular dynamics simulations analysis showed that xambioona bound more tightly to the nucleocapsid protein of SARS-CoV-2 and exerted a potent inhibitory effect. Modern technical methods are used to study the active components of traditional Chinese medicine and show that xambioona is an effective inhibitor of SARS-CoV-2 nucleocapsid protein, which provides a theoretical basis for the development of new anti-SARS-CoV-2 drugs and their treatment methods.