公开(公告)号：US20130184462A1

公开(公告)日：2013-07-18

申请号：US13876658

申请日：2011-09-30

申请人： Santosh Kumar Srivastava ,  Feroz Khan ,  Shikha Gupta ,  Dharmendra K. Yadav ,  Vinay Kumar Khanna

发明人： Santosh Kumar Srivastava ,  Feroz Khan ,  Shikha Gupta ,  Dharmendra K. Yadav ,  Vinay Kumar Khanna

IPC分类号： G06F19/00

CPC分类号： C07D459/00 ,  G16C20/30 ,  G16C20/50

摘要： The present invention relates to the development of a virtual screening model for predicting antipsychotic activity using quantitative structure activity relationship (QSAR), molecular docking, oral bioavailability, ADME and Toxicity studies. The present invention also relates to the development of QSAR model using forward stepwise method of multiple linear regression with leave-one-out validation approach. QSAR model showed activity-descriptors relationship correlating measure (r2) 0.87 (87%) and predictive accuracy of 81% (rCV2=0.81). The present invention specifically showed strong binding affinity of the untested (unknown) novel compounds against anti-psychotic targets viz., Dopamine D2 and Serotonin (5HT2A) receptors through molecular docking approach. Theoretical results were in accord with the in vitro and in vivo experimental data. The present invention further showed compliance of Lipinski's rule of five for oral bioavailability and toxicity risk assessment for all the active Yohimbine derivatives. Therefore, use of developed virtual screening model will definitely facilitate the screening of more effective antipsychotic leads/drugs with improved antipsychotic activity and also reduced the drug discovery cost and duration.

摘要翻译： 本发明涉及使用定量结构活性关系（QSAR），分子对接，口服生物利用度，ADME和毒性研究来预测抗精神病活性的虚拟筛选模型。 本发明还涉及使用多元线性回归的前向逐步方法与离开一个验证方法开发QSAR模型。 QSAR模型显示活动描述符关系相关度（r2）0.87（87％）和预测精度81％（rCV2 = 0.81）。 本发明通过分子对接方法具体显示未测试（未知）新化合物对抗精神病性靶标即多巴胺D2和5-羟色胺（5HT2A）受体的强结合亲和力。 理论结果符合体外和体内实验数据。 本发明进一步显示了所有活跃的育亨宾衍生物的Lipinski的五条规则符合口服生物利用度和毒性风险评估。 因此，使用开发的虚拟筛选模型肯定会有助于筛选更有效的抗精神病药物，抗精神病药物活性更好，降低药物发现成本和持续时间。