Kaur Kirandeep, Talele Tanaji T
Department of Pharmaceutical Sciences, College of Pharmacy and Allied Health Professions, St. John's University, Jamaica, NY 11439, USA.
J Mol Graph Model. 2008 Nov;27(4):409-20. doi: 10.1016/j.jmgm.2008.07.003. Epub 2008 Jul 30.
A number of CCK(2) antagonists have been reported to play an important role in controlling gastric acid-related conditions, nervous system related disorders and certain types of cancer. To obtain the helpful information for designing potent antagonists with novel structures and to investigate the quantitative structure-activity relationship of a group of 62 different CCK(2) receptor antagonists with varying structures and potencies, CoMFA, CoMSIA, and HQSAR studies were carried out on a series of 1,3,4-benzotriazepine-based CCK(2) receptor antagonists. QSAR models were derived from a training set of 49 compounds. By applying leave-one-out (LOO) cross-validation study, cross-validated (r(cv)(2)) values of 0.673 and 0.608 and non-cross-validated (r(ncv)(2)) values of 0.966 and 0.969 were obtained for the CoMFA and CoMSIA models, respectively. The predictive ability of the CoMFA and CoMSIA models was determined using a test set of 13 compounds, which gave predictive correlation coefficients (r(pred)(2)) of 0.793 and 0.786, respectively. HQSAR was also carried out as a complementary study, and the best HQSAR model was generated using atoms, bonds, hydrogen atoms, and chirality as fragment distinction with fragment size (2-5) and six components showing r(cv)(2) and r(ncv)(2) values of 0.744 and 0.918, respectively. CoMFA steric and electrostatic, CoMSIA hydrophobic and hydrogen bond acceptor fields, and HQSAR atomic contribution maps were used to analyze the structural features of the datasets that govern their antagonistic potency.
据报道,多种CCK(2)拮抗剂在控制胃酸相关病症、神经系统相关疾病以及某些类型的癌症方面发挥着重要作用。为了获取有助于设计具有新颖结构的强效拮抗剂的信息,并研究一组62种结构和效力各异的不同CCK(2)受体拮抗剂的定量构效关系,对一系列基于1,3,4-苯并三氮杂苯的CCK(2)受体拮抗剂进行了比较分子力场分析(CoMFA)、比较分子相似性指数分析(CoMSIA)和全息定量构效关系(HQSAR)研究。定量构效关系(QSAR)模型源自49种化合物的训练集。通过应用留一法(LOO)交叉验证研究,CoMFA模型和CoMSIA模型的交叉验证(r(cv)(2))值分别为0.673和0.608,非交叉验证(r(ncv)(2))值分别为0.966和0.969。使用13种化合物的测试集确定了CoMFA模型和CoMSIA模型的预测能力,其预测相关系数(r(pred)(2))分别为0.793和0.786。还进行了HQSAR作为补充研究,使用原子、键、氢原子和手性作为片段区分,片段大小为(2 - 5),生成了最佳的HQSAR模型,其六个成分的r(cv)(2)和r(ncv)(2)值分别为0.744和0.918。利用CoMFA的空间和静电场、CoMSIA的疏水和氢键受体场以及HQSAR的原子贡献图来分析决定其拮抗效力的数据集的结构特征。