3D-QSAR-Assisted Drug Design: Identification of a Potent Quinazoline-Based Aurora Kinase Inhibitor

Yi Yu Ke, Hui Yi Shiao, Yung Chang Hsu, Chang Ying Chu, Wen Chieh Wang, Yen Chun Lee, Wen Hsing Lin, Chun Hwa Chen, John T.A. Hsu, Chun Wei Chang, Cheng Wei Lin, Teng Kuang Yeh, Yu Sheng Chao, Mohane Selvaraj Coumar, Hsing Pang Hsieh

研究成果: Article同行評審

27 引文 斯高帕斯(Scopus)


We describe the 3D-QSAR-assisted design of an Aurora kinaseA inhibitor with improved physicochemical properties, invitro activity, and invivo pharmacokinetic profiles over those of the initial lead. Three different 3D-QSAR models were built and validated by using a set of 66 pyrazole (ModelI) and furanopyrimidine (ModelII) compounds with IC50 values toward Aurora kinaseA ranging from 33nM to 10.5μM. The best 3D-QSAR model, ModelIII, constructed with 24 training set compounds from both series, showed robustness (r2CV=0.54 and 0.52 for CoMFA and CoMSIA, respectively) and superior predictive capacity for 42 test set compounds (R2pred=0.52 and 0.67, CoMFA and CoMSIA). Superimposition of CoMFA and CoMSIA ModelIII over the crystal structure of Aurora kinaseA suggests the potential to improve the activity of the ligands by decreasing the steric clash with Val147 and Leu139 and by increasing hydrophobic contact with Leu139 and Gly216 residues in the solvent-exposed region of the enzyme. Based on these suggestions, the rational redesign of furanopyrimidine 24 (clogP=7.41; AuroraA IC50=43nM; HCT-116 IC50=400nM) led to the identification of quinazoline 67 (clogP=5.28; AuroraA IC50=25nM; HCT-116 IC50=23nM). Rat invivo pharmacokinetic studies showed that 67 has better systemic exposure after i.v. administration than 24, and holds potential for further development.

頁(從 - 到)136-148
出版狀態Published - 2013 1月

All Science Journal Classification (ASJC) codes

  • 生物化學
  • 分子醫學
  • 藥理
  • 藥物發現
  • 藥理學、毒理學和藥劑學 (全部)
  • 有機化學


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