title: 3D QSAR studies, pharmacophore modeling and virtual screening on a series of steroidal aromatase inhibitors.
abstract: Aromatase inhibitors are the most important targets in treatment of estrogen-dependent cancers. In order to search for potent steroidal aromatase inhibitors (SAIs) with lower side effects and overcome cellular resistance, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of SAIs to build 3D QSAR models. The reliable and predictive CoMFA and CoMSIA models were obtained with statistical results (CoMFA: q² = 0.636, r²(ncv) = 0.988, r²(pred) = 0.658; CoMSIA: q² = 0.843, r²(ncv) = 0.989, r²(pred) = 0.601). This 3D QSAR approach provides significant insights that can be used to develop novel and potent SAIs. In addition, Genetic algorithm with linear assignment of hypermolecular alignment of database (GALAHAD) was used to derive 3D pharmacophore models. The selected pharmacophore model contains two acceptor atoms and four hydrophobic centers, which was used as a 3D query for virtual screening against NCI2000 database. Six hit compounds were obtained and their biological activities were further predicted by the CoMFA and CoMSIA models, which are expected to design potent and novel SAIs.
authors.affiliation: Department of Chemistry, Yunnan University, Kunming 650091, Yunnan, China. front701228@gmail.com.
authors.forename: Huiding
authors.initials: H
authors.lastname: Xie
authors.affiliation: Department of Chemistry, School of Pharmaceutical Science & Yunnan Key Laboratory of Pharmacology for Natural Products, Kunming Medical University, Kunming 650500, Yunnan, China. chenneyao16@hotmail.com.
authors.forename: Kaixiong
authors.initials: K
authors.lastname: Qiu
authors.affiliation: Department of Chemistry, Yunnan University, Kunming 650091, Yunnan, China. xgxie@ynu.edu.cn.
authors.forename: Xiaoguang
authors.initials: X
authors.lastname: Xie
mesh_terms: D000465:Algorithms; D001141:Aromatase / Q000378:metabolism; D047072:Aromatase Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D015195:Drug Design; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship
chemical_list: D047072:Aromatase Inhibitors; D001141:Aromatase