{"title":"Relationships between Molecular Properties and Antimycobacterial Activities of Steroids","authors":"J. Rugutt, K. J. Rugutt","doi":"10.1080/10575630290020000","DOIUrl":null,"url":null,"abstract":"Progesterone ( 1 ), cholesterol ( 2 ), stigmasterol ( 3 ), ergosterol endoperoxide ( 4 ), sitosterol ( 5 ), betulin ( 6 ), fusidic acid ( 7 ), chondrilasterol ( 8 ), and ergosterol ( 9 ) have been evaluated against Mycobacterium tuberculosis H37Rv. The maximum antimycobacterial activity seemed to depend on hydrophobicity and the type of substituents on the phytyl moiety on steroidal backbone. The variation in activity was rationalized using quantitative structure-activity relationship (QSAR) models based on several molecular descriptors including van der Waals surface area (VDW A ), van der Waals volume (VDW v ), polarizability, dipole moment, logP, and the differences between the Highest Occupied Molecular Orbital and the Lowest Unoccupied Molecular Orbital (HOMO-LUMO gap). The proposed QSAR models could be developed to predict the antimycobacterial activity of structurally similar steroids and to create a priority list for testing so that time, money, and effort can be focused on the potentially most promising steroids. The implications of QSAR data for the rational design of new antituberculosis agents are discussed. Because mycobacteria degrade steroids to progesterone-type compounds, unambiguous assignments of the high-field proton ( 1 H) and carbon-13 ( 13 C) NMR data of progesterone ( 1 ) were achieved through a combination of modern one- and two-dimensional (2D) NMR techniques.","PeriodicalId":18873,"journal":{"name":"Natural Product Letters","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Natural Product Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/10575630290020000","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
Progesterone ( 1 ), cholesterol ( 2 ), stigmasterol ( 3 ), ergosterol endoperoxide ( 4 ), sitosterol ( 5 ), betulin ( 6 ), fusidic acid ( 7 ), chondrilasterol ( 8 ), and ergosterol ( 9 ) have been evaluated against Mycobacterium tuberculosis H37Rv. The maximum antimycobacterial activity seemed to depend on hydrophobicity and the type of substituents on the phytyl moiety on steroidal backbone. The variation in activity was rationalized using quantitative structure-activity relationship (QSAR) models based on several molecular descriptors including van der Waals surface area (VDW A ), van der Waals volume (VDW v ), polarizability, dipole moment, logP, and the differences between the Highest Occupied Molecular Orbital and the Lowest Unoccupied Molecular Orbital (HOMO-LUMO gap). The proposed QSAR models could be developed to predict the antimycobacterial activity of structurally similar steroids and to create a priority list for testing so that time, money, and effort can be focused on the potentially most promising steroids. The implications of QSAR data for the rational design of new antituberculosis agents are discussed. Because mycobacteria degrade steroids to progesterone-type compounds, unambiguous assignments of the high-field proton ( 1 H) and carbon-13 ( 13 C) NMR data of progesterone ( 1 ) were achieved through a combination of modern one- and two-dimensional (2D) NMR techniques.
黄体酮(1)、胆固醇(2)、豆甾醇(3)、麦角甾醇内过氧化物(4)、谷甾醇(5)、白桦林(6)、福西地酸(7)、软骨醇(8)和麦角甾醇(9)对结核分枝杆菌H37Rv的作用进行了评估。最大抑菌活性似乎取决于疏水性和甾体骨架上植基部分取代基的类型。基于van der Waals表面积(vdwaa)、van der Waals体积(vdwav)、极化率、偶极矩、logP和最高占据轨道与最低未占据轨道之差(HOMO-LUMO gap)等分子描述符,利用定量构效关系(QSAR)模型对活性变化进行了合理化。提出的QSAR模型可以用来预测结构相似的类固醇的抗细菌活性,并创建一个优先测试列表,以便将时间、金钱和精力集中在可能最有前途的类固醇上。讨论了QSAR数据对合理设计新型抗结核药物的意义。由于分枝杆菌将类固醇降解为黄体酮类化合物,因此通过结合现代一维和二维(2D)核磁共振技术,可以确定黄体酮(1)的高场质子(1h)和碳-13 (13c)核磁共振数据。