Discovery of Selective LRRK2 Inhibitors Guided by Computational Analysis and Molecular Modeling

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• 作者：Huifen Chen ; Bryan K. Chan ; Jason Drummond ; Anthony A. Estrada ; Janet Gunzner-Toste ; Xingrong Liu ; Yichin Liu ; John Moffat ; Daniel Shore ; Zachary K. Sweeney ; Thuy Tran ; Shumei Wang ; Guiling Zhao ; Haitao Zhu ; Daniel J. Burdick
• 刊名：Journal of Medicinal Chemistry
• 出版年：2012
• 出版时间：June 14, 2012
• 年：2012
• 卷：55
• 期：11
• 页码：5536-5545
• 全文大小：496K
• 年卷期：v.55,no.11(June 14, 2012)
• ISSN：1520-4804

文摘

Mutations in the genetic sequence of leucine-rich repeat kinase 2 (LRRK2) have been linked to increased LRRK2 activity and risk for the development of Parkinson鈥檚 disease (PD). Potent and selective small molecules capable of inhibiting the kinase activity of LRRK2 will be important tools for establishing a link between the kinase activity of LRRK2 and PD. In the absence of LRRK2 kinase domain crystal structures, a LRRK2 homology model was developed that provided robust guidance in the hit-to-lead optimization of small molecule LRRK2 inhibitors. Through a combination of molecular modeling, sequence analysis, and matched molecular pair (MMP) activity cliff analysis, a potent and selective lead inhibitor was discovered. The selectivity of this compound could be understood using the LRRK2 homology model, and application of this learning to a series of 2,4-diaminopyrimidine inhibitors in a scaffold hopping exercise led to the identification of highly potent and selective LRRK2 inhibitors that were also brain penetrable.