{"title":"从fda批准的药物中开发尼帕病毒药物:综合计算方法","authors":"Panneerselvam Theivendren , Selvaraj Kunjiappan , Parasuraman Pavadai , Natarajan Kiruthiga , Anusuya Murugavel , Avinash Dayalan","doi":"10.1016/j.comtox.2025.100354","DOIUrl":null,"url":null,"abstract":"<div><div>The Nipah virus (NiV) is a highly virulent zoonotic pathogen that presents a substantial risk to public health, as limited therapeutic interventions are available. The present study utilizes a computational methodology to discover pharmaceutical substances that have been received from the database consisting of 4344 U.S. Food and Drug Administration (FDA)-approved drugs and have the potential to be repurposed to treat NiV infection. We have used molecular docking and dynamics simulation to evaluate the binding affinity and stability of the drugs against the key viral target, Ephrin-B2. The findings of our study demonstrate the presence of numerous FDA-approved drugs that display favourable binding interactions with the target of Ephrin-B2. Within this FDA-approved data set of drugs, we have identified certain FDA-approved drugs, such as Guamecycline, Ergotamine, Sancycline, Entrectinib, and Atogepant, which showed considerably better binding scores. The dynamic behaviour of ligand–protein interaction was evaluated using molecular dynamics simulation, which offered valuable insights into drug-target complexes’ temporal stability and conformational alterations. The results of docking studies indicate to active ingredients Guamecycline, Ergotamine, Sancycline, Entrectinib and Atogepant having notable inhibition of the Ephrin-B2 protein. According to the findings from the MD simulation, it was noted that Guamecycline displayed significant interaction with the Ephrin-B2 protein. Therefore, Guamecycline shows potential as a suitable primary chemical for treating NiV. Further, the sub-structures of Guamecycline were used to optimize and substantiate the stability of Guamecycline; in this relation sub, structure <strong>ZINC000169368545</strong> was correlated with Guamecycline, and the observed result showed that the Guamecycline was better lead moiety to inhibit the target Ephrin-B2.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100354"},"PeriodicalIF":3.1000,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of Nipah virus drugs from FDA-approved drugs: An integrated computational approach\",\"authors\":\"Panneerselvam Theivendren , Selvaraj Kunjiappan , Parasuraman Pavadai , Natarajan Kiruthiga , Anusuya Murugavel , Avinash Dayalan\",\"doi\":\"10.1016/j.comtox.2025.100354\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The Nipah virus (NiV) is a highly virulent zoonotic pathogen that presents a substantial risk to public health, as limited therapeutic interventions are available. The present study utilizes a computational methodology to discover pharmaceutical substances that have been received from the database consisting of 4344 U.S. Food and Drug Administration (FDA)-approved drugs and have the potential to be repurposed to treat NiV infection. We have used molecular docking and dynamics simulation to evaluate the binding affinity and stability of the drugs against the key viral target, Ephrin-B2. The findings of our study demonstrate the presence of numerous FDA-approved drugs that display favourable binding interactions with the target of Ephrin-B2. Within this FDA-approved data set of drugs, we have identified certain FDA-approved drugs, such as Guamecycline, Ergotamine, Sancycline, Entrectinib, and Atogepant, which showed considerably better binding scores. The dynamic behaviour of ligand–protein interaction was evaluated using molecular dynamics simulation, which offered valuable insights into drug-target complexes’ temporal stability and conformational alterations. The results of docking studies indicate to active ingredients Guamecycline, Ergotamine, Sancycline, Entrectinib and Atogepant having notable inhibition of the Ephrin-B2 protein. According to the findings from the MD simulation, it was noted that Guamecycline displayed significant interaction with the Ephrin-B2 protein. Therefore, Guamecycline shows potential as a suitable primary chemical for treating NiV. Further, the sub-structures of Guamecycline were used to optimize and substantiate the stability of Guamecycline; in this relation sub, structure <strong>ZINC000169368545</strong> was correlated with Guamecycline, and the observed result showed that the Guamecycline was better lead moiety to inhibit the target Ephrin-B2.</div></div>\",\"PeriodicalId\":37651,\"journal\":{\"name\":\"Computational Toxicology\",\"volume\":\"34 \",\"pages\":\"Article 100354\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Toxicology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468111325000143\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TOXICOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111325000143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
Development of Nipah virus drugs from FDA-approved drugs: An integrated computational approach
The Nipah virus (NiV) is a highly virulent zoonotic pathogen that presents a substantial risk to public health, as limited therapeutic interventions are available. The present study utilizes a computational methodology to discover pharmaceutical substances that have been received from the database consisting of 4344 U.S. Food and Drug Administration (FDA)-approved drugs and have the potential to be repurposed to treat NiV infection. We have used molecular docking and dynamics simulation to evaluate the binding affinity and stability of the drugs against the key viral target, Ephrin-B2. The findings of our study demonstrate the presence of numerous FDA-approved drugs that display favourable binding interactions with the target of Ephrin-B2. Within this FDA-approved data set of drugs, we have identified certain FDA-approved drugs, such as Guamecycline, Ergotamine, Sancycline, Entrectinib, and Atogepant, which showed considerably better binding scores. The dynamic behaviour of ligand–protein interaction was evaluated using molecular dynamics simulation, which offered valuable insights into drug-target complexes’ temporal stability and conformational alterations. The results of docking studies indicate to active ingredients Guamecycline, Ergotamine, Sancycline, Entrectinib and Atogepant having notable inhibition of the Ephrin-B2 protein. According to the findings from the MD simulation, it was noted that Guamecycline displayed significant interaction with the Ephrin-B2 protein. Therefore, Guamecycline shows potential as a suitable primary chemical for treating NiV. Further, the sub-structures of Guamecycline were used to optimize and substantiate the stability of Guamecycline; in this relation sub, structure ZINC000169368545 was correlated with Guamecycline, and the observed result showed that the Guamecycline was better lead moiety to inhibit the target Ephrin-B2.
期刊介绍:
Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs