基于药物相似性、分子对接和分子动力学的虚拟筛选策略对一些新型mTOR激酶抑制剂的分子识别以开发抗癌线索

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2023-02-01 DOI:10.1016/j.comtox.2022.100257
Arka Das , Gurubasavaraja Swamy Purawarga Matada , Prasad Sanjay Dhiwar , Nulgumnalli Manjunathaiah Raghavendra , Nahid Abbas , Ekta Singh , Abhishek Ghara , Ganesh Prasad Shenoy
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引用次数: 2

摘要

癌症是全球第二大死因。在众多的抗癌药物靶点中,mTOR是值得关注的。许多第一代mTOR抑制剂已经被批准,针对激酶结构域的第二代mTOR抑制剂很少进入临床试验,但尚未进入市场,并且许多会导致严重的毒性。在这里,我们将重点从锌数据库中发现一些可能有效抑制mTOR激酶的新型激酶抑制剂。为此,采用了基于计算化学和药效团的ZINC数据库检索方法。通过一系列的虚拟筛选分析,发现了5个活跃点。其中,结合能为−8.9 Kcal/mol的化合物4 (ZINC79476038)在结合袋内的相互作用最大。研究证明,这些化合物都有潜在的抑制mTOR激酶的作用,可以成功地开发为抗癌药物。我们进一步证明,这些化合物不仅对肺癌、乳腺癌、结肠癌和其他外周癌症等普通癌症有效,而且对中枢神经系统也同样有效,针对多种脑癌。
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Molecular recognition of some novel mTOR kinase inhibitors to develop anticancer leads by drug-likeness, molecular docking and molecular dynamics based virtual screening strategy

Cancer is the second leading cause of death worldwide. Among various anticancer drug targets, mTOR is noteworthy. Numerous first-generation mTOR inhibitors are already approved and few second-generation mTOR inhibitors targeting the kinase domain are in the clinical trials, but yet to reach the market, and many lead to serious toxicities. Here we are focused to discover some novel kinase inhibitors from the ZINC database which may effectively inhibit mTOR kinase. For this, computational chemistry and pharmacophore-based ZINC database search has been adopted. Series of virtual screening analysis lead to the discovery of 5 active hits. Among these 5, compound 4 (ZINC79476038) having binding energy of −8.9 Kcal/mol shows maximum interactions within the binding pocket. Study proved that all these compounds can potentially inhibit mTOR kinase and can be successfully developed as anticancer agents. We further proved that these compounds are not only active for general cancers like lung, breast, colon, and other peripheral cancers but also equally active in CNS, targeting numerous brain cancers.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
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
期刊最新文献
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