Data mining guided affinity ultrafiltration for rapid screening of SARS-CoV-2 3CL pro inhibitors in medicinal herbs.
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| Názov: | Data mining guided affinity ultrafiltration for rapid screening of SARS-CoV-2 3CL pro inhibitors in medicinal herbs. |
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| Autori: | Wang L; College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, China. Electronic address: 15763681452@163.com., Chen M; Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, China. Electronic address: 15251752501@163.com., Wang H; Department of Drug Dispensing, Zibo Central Hospital, Zibo, Shandong 255020, China. Electronic address: Wanghanxue666@163.com., Sun Q; College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, China. Electronic address: sunqihui1214@163.com., Zheng S; College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, China. Electronic address: 63010073@sdutcm.edu.cn., Liu X; Shandong Province University Traditional Chinese Medicine Antiviral Collaborative Innovation Center, Jinan, Shandong 250355, China. Electronic address: yunliu1123@163.com., Yang Y; Shandong Province University Traditional Chinese Medicine Antiviral Collaborative Innovation Center, Jinan, Shandong 250355, China. Electronic address: yy7204@163.com., Rong R; College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, China. Electronic address: rosierong@163.com. |
| Zdroj: | Journal of pharmaceutical and biomedical analysis [J Pharm Biomed Anal] 2025 Dec 15; Vol. 266, pp. 117122. Date of Electronic Publication: 2025 Aug 16. |
| Spôsob vydávania: | Journal Article |
| Jazyk: | English |
| Informácie o časopise: | Publisher: Elsevier Science Country of Publication: England NLM ID: 8309336 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-264X (Electronic) Linking ISSN: 07317085 NLM ISO Abbreviation: J Pharm Biomed Anal Subsets: MEDLINE |
| Imprint Name(s): | Publication: <2006->: London : Elsevier Science Original Publication: Oxford ; New York : Pergamon Press, c1983- |
| Výrazy zo slovníka MeSH: | Data Mining*/methods , SARS-CoV-2*/drug effects , COVID-19 Drug Treatment* , Antiviral Agents*/pharmacology , Antiviral Agents*/chemistry , Drugs, Chinese Herbal*/pharmacology , Drugs, Chinese Herbal*/chemistry , Coronavirus 3C Proteases*/antagonists & inhibitors , Coronavirus 3C Proteases*/metabolism , Plants, Medicinal*/chemistry, Animals ; Ultrafiltration/methods ; Mice ; Molecular Docking Simulation ; Humans ; COVID-19/virology ; Vero Cells |
| Abstrakt: | Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Clinical studies have demonstrated that many traditional Chinese medicines (TCM) exhibit not only antiviral effects but also efficacy in alleviating clinical symptoms of Coronavirus Disease 2019 (COVID-19). However, the pharmacologically active constituents responsible for their anti-COVID-19 efficacy remain unclear. This study aimed to establish a novel strategy for identifying active ingredients from herbs clinically used for COVID-19. An integrated approach combining data mining with Affinity Ultrafiltration (AUF) was developed. Initially, using a data mining strategy, high-frequency herbs were selected from the herbs clinically used in COVID-19. Furthermore, AUF technology was used to screen for potential bioactive components from the high-frequency herbs. The anti-COVID-19 potential of active compounds was assessed through enzyme activity assays and molecular docking, followed by validation in cellular and animal models. Data mining revealed that Glycyrrhiza uralensis Fisch., Lonicera japonica Thunb. and Forsythia suspensa (Thunb.) Vahl were the high-frequency herbs used in COVID-19. Five potential 3-Chymotrypsin-like protease (3CL pro ) inhibitors were screened from three herbs via AUF and identified using high-resolution mass spectrometry. Further enzymatic and cellular assays demonstrated that Licochalcone C (LCC) and Forsythiaside A (FTA) inhibited Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) replication at micromolar concentrations. Notably, FTA treatment significantly suppressed the elevation of pulmonary parameters and inflammatory mediators induced by SARS-CoV-2 nucleocapsid protein in mice. In summary, this study proposes a novel strategy integrating data mining with AUF to discover active compounds from TCM. Two components, LCC and FTA, were identified as dose-dependent inhibitors of SARS-CoV-2 Omicron strain replication in vitro. (Copyright © 2025 Elsevier B.V. All rights reserved.) |
| Contributed Indexing: | Keywords: 3-Chymotrypsin-like protease; Active ingredients; Affinity ultrafiltration; Pharmacodynamic substances; Severe acute respiratory syndrome coronavirus 2 |
| Substance Nomenclature: | 0 (Antiviral Agents) 0 (Drugs, Chinese Herbal) EC 3.4.22.28 (Coronavirus 3C Proteases) |
| Entry Date(s): | Date Created: 20250820 Date Completed: 20250903 Latest Revision: 20250903 |
| Update Code: | 20250904 |
| DOI: | 10.1016/j.jpba.2025.117122 |
| PMID: | 40834688 |
| Databáza: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />Clinical studies have demonstrated that many traditional Chinese medicines (TCM) exhibit not only antiviral effects but also efficacy in alleviating clinical symptoms of Coronavirus Disease 2019 (COVID-19). However, the pharmacologically active constituents responsible for their anti-COVID-19 efficacy remain unclear. This study aimed to establish a novel strategy for identifying active ingredients from herbs clinically used for COVID-19. An integrated approach combining data mining with Affinity Ultrafiltration (AUF) was developed. Initially, using a data mining strategy, high-frequency herbs were selected from the herbs clinically used in COVID-19. Furthermore, AUF technology was used to screen for potential bioactive components from the high-frequency herbs. The anti-COVID-19 potential of active compounds was assessed through enzyme activity assays and molecular docking, followed by validation in cellular and animal models. Data mining revealed that Glycyrrhiza uralensis Fisch., Lonicera japonica Thunb. and Forsythia suspensa (Thunb.) Vahl were the high-frequency herbs used in COVID-19. Five potential 3-Chymotrypsin-like protease (3CL <sup>pro</sup> ) inhibitors were screened from three herbs via AUF and identified using high-resolution mass spectrometry. Further enzymatic and cellular assays demonstrated that Licochalcone C (LCC) and Forsythiaside A (FTA) inhibited Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) replication at micromolar concentrations. Notably, FTA treatment significantly suppressed the elevation of pulmonary parameters and inflammatory mediators induced by SARS-CoV-2 nucleocapsid protein in mice. In summary, this study proposes a novel strategy integrating data mining with AUF to discover active compounds from TCM. Two components, LCC and FTA, were identified as dose-dependent inhibitors of SARS-CoV-2 Omicron strain replication in vitro.<br /> (Copyright © 2025 Elsevier B.V. All rights reserved.) |
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| ISSN: | 1873-264X |
| DOI: | 10.1016/j.jpba.2025.117122 |