SAR Analysis/Med Chem (Florida)

SAR 分析/Med Chem（佛罗里达）

• 批准号: 8120939
• 负责人: WILLIAM R ROUSH
• 金额: $ 269.44万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8120939
• 关键词: Binding; Biochemical; Biological Availability; Chemicals; Coupled; Data; Descriptor; Development; Docking; Florida; 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分析的目标是了解结构变化的影响， 合理预测哪种改性可导致改进的性能。因此，该分析 与化学家发展一个工作假设和结构优化策略紧密结合。每个 命中类别将基于效力（生物化学/细胞）、选择性、溶解度、生物利用度 毒性和易于合成。将为每个类确定问题。铅化合物选自 点击类与最理想的配置文件和合理的SAR进一步改善。如果这些实验 数据不可用，计算描述符和模型可以用来评估一个结构是否 类比另一个类更适合优化。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，我们计划实施一些 使用各种滤波器进行这种多维SAR和SPR分析的附加计算工具， 模型和可视化技术，以快速和可重复地对化学系列或单个化合物进行排名 在探测器开发项目的背景下。 随着探针优化通过迭代循环进行，对S1 P受体进行的分析包括 QSAR、药效团模型开发和对接研究。基于受体结构的设计将是 如果靶结构是已知的或者可以建立靶受体的合理同源性模型，则应用该方法。 可以使用行业标准的基于药效团和形状的建模和可视化工具，并且 在信息学和资源部分描述。在这种情况下，每个命中类别都将停靠到 受体结构，以找到可能的作用模式（结合模式）。受欢迎的行动方式应该是 与初始SAR一致。初始SAR、受体结构信息（如可用）和 药物化学将结合在第二代分子的设计，以克服至少一些 of the issues问题of lead铅classes类.将对化合物进行筛选和分析。这些数据将被用于改进 SAR进而应用于下一代文库的设计，直到优化化合物/探针 获得了优化的潜在客户应在表7中定义的初始命中的基础上进行改进。所有探头和 他们的相关数据将根据已发布的RFA指南提供给所有研究人员。