SAR Analysis/Med Chem (California)

SAR 分析/Med Chem（加利福尼亚州）

• 批准号: 8538722
• 负责人: SHENG DING
• 金额: $ 94.88万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8538722
• 关键词: Binding; Biochemical; Biological Availability; California; Chemicals; Coupled; Data; Descriptor; Development; Docking; Generations; Goals; Homology Modeling; Imagery; Individual; Industry; Lead; Libraries; Modeling; Modification; Pharmaceutical Chemistry; Phase; Production; Property; Publishing; Quantitative Structure-Activity Relationship; Research Personnel; Resource Informatics; Series; Shapes; Solubility; Sphingosine-1-Phosphate Receptor; Structure; Surface; Techniques; Toxic effect; Work; base; computerized tools; design; experience; improved; indexing; model development; next generation; pharmacophore; receptor; tool

The goal of an initial SAR analysis is to understand the influence of structural changes in a way that allows a reasonable prediction of which modification(s) may lead to improved properties. This analysis is therefore tightly coupled with the chemists developing a working hypothesis and strategy for structure optimization. Each class of hits will be profiled based on potency (biochemical/cellular), selectivity, solubility, bioavailability, toxicity, and ease of synthesis. Issues will be identified for each class. Lead compounds are chosen from the hit classes with the most desired profiles and sensible SAR for further improvement. If these experimental data are not available, computational descriptors and models can be used to assess whether one structural class is more amenable to optimization than another. We will also consider binding efficiency index (BEI) and surface binding efficiency index (SEI). [65] For the production phase we plan to implement a number of additional computational tools for such multidimensional SAR and SPR analyses using a variety of filters, models, and visualization techniques to quickly and reproducibly rank chemical series or individual compounds in the context of the probe development project. As the probe optimization proceeds through iterative cycles, analyses performed for S1P receptors incjuded QSAR, pharmacophore model development, and docking studies. Receptor structure-based design will be applied if the target structure is known or a reasonable homology model of the target receptor can be built. Industry standard pharmacophore- and shape-based modeling and visualization tools are available, and are described in the informatics and resources section. In this case, each class of hits will be docked to the receptor structure to find possible mode of action (binding mode). The favored mode of action should be consistent with the initial SAR. The initial SAR, receptor structural information (if available), and experience in medicinal chemistry will be combined in the design of second-generation molecules to overcome at least some of the issues of lead classes. The compounds will be screened and profiled. The data will be utilized to refine the SAR which in turn is applied in the design of next generation libraries until optimized compounds/probes are obtained. The optimized lead should be improved over the initial hit as defined in Table 7. All probes and their associated data will be made available to all researchers in accordance with the published RFA guidance.

初始SAR分析的目的是以一种允许A的方式了解结构变化的影响 合理预测哪些修饰可能导致属性的改善。因此，该分析是 与化学家紧密结合，开发了一个工作的假设和结构优化的策略。每个 命中术将根据效力（生化/蜂窝），选择性，溶解度，生物利用度，介绍一类。 毒性，易于合成。每个班级都会确定问题。铅化合物是从中选择的 以最需要的概况和明智的SAR进行进一步改进的班级。如果这些实验 数据不可用，计算描述符和模型可用于评估一个结构是否 班级比另一个更适合优化。我们还将考虑结合效率指数（BEI）和 表面结合效率指数（SEI）。 [65]在生产阶段，我们计划实施多个 使用多种过滤器的多维SAR和SPR分析的其他计算工具， 模型和可视化技术，以快速和可重复排名化学系列或单个化合物 在调查开发项目的背景下。 随着探针优化通过迭代循环进行，对S1P受体进行的分析进行了分析 QSAR，药效团模型开发和对接研究。基于受体结构的设计将是 如果可以构建目标结构，则可以构建目标受体的合理同源模型。 可以使用行业标准的药效团建模和可视化工具，并且 在信息学和资源部分中进行了描述。在这种情况下，每个命中级都将停靠在 受体结构找到可能的作用模式（结合模式）。偏爱的行动方式应该是 与初始SAR一致。最初的SAR，受体结构信息（如果有）和经验 药物化学将在第二代分子的设计中合并，以克服至少一些 铅班的问题。这些化合物将进行筛选和介绍。数据将用于完善 依次将SAR应用于下一代库的设计，直到优化化合物/探针 获得。应在表7中定义的初始点击中改进优化的引线。所有探针和 他们的相关数据将根据已发表的RFA指南提供给所有研究人员。