Viklund Jenny, Kolmodin Karin, Nordvall Gunnar, Swahn Britt-Marie, Svensson Mats, Gravenfors Ylva, Rahm Fredrik
Department of Medicinal Chemistry AstraZeneca R&D Södertälje , SE-151 85 Södertälje, Sweden.
ACS Med Chem Lett. 2014 Feb 3;5(4):440-5. doi: 10.1021/ml5000433. eCollection 2014 Apr 10.
In order to find optimal core structures as starting points for lead optimization, a multiparameter lead generation workflow was designed with the goal of finding BACE-1 inhibitors as a treatment for Alzheimer's disease. De novo design of core fragments was connected with three predictive in silico models addressing target affinity, permeability, and hERG activity, in order to guide synthesis. Taking advantage of an additive SAR, the prioritized cores were decorated with a few, well-characterized substituents from known BACE-1 inhibitors in order to allow for core-to-core comparisons. Prediction methods and analyses of how physicochemical properties of the core structures correlate to in vitro data are described. The syntheses and in vitro data of the test compounds are reported in a separate paper by Ginman et al. [J. Med. Chem. 2013, 56, 4181-4205]. The affinity predictions are described in detail by Roos et al. [J. Chem. Inf. 2014, DOI: 10.1021/ci400374z].
为了找到最佳核心结构作为先导优化的起点，设计了一种多参数先导生成工作流程，目标是找到作为阿尔茨海默病治疗药物的β-分泌酶1（BACE-1）抑制剂。核心片段的从头设计与三个预测性计算机模拟模型相关联，这些模型涉及靶点亲和力、通透性和人醚-去极化激活的钾离子通道（hERG）活性，以指导合成。利用加和性构效关系（SAR），用已知BACE-1抑制剂中的一些特征明确的取代基修饰优先选择的核心，以便进行核心间比较。描述了预测方法以及核心结构的物理化学性质与体外数据如何相关的分析。测试化合物的合成和体外数据在Ginman等人的另一篇论文中报道[《药物化学杂志》2013年，56卷，4181 - 4205页]。Roos等人[《化学信息杂志》2014年，DOI: 10.1021/ci400374z]详细描述了亲和力预测。
