Suppr超能文献

一种新型高通量基于 FLIPR Tetra 的方法,用于捕获高融合动力学数据,以指导结构-动力学关系的早期药物发现。

A Novel High-Throughput FLIPR Tetra-Based Method for Capturing Highly Confluent Kinetic Data for Structure-Kinetic Relationship Guided Early Drug Discovery.

机构信息

Mechanistic Biology and Profiling, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.

出版信息

SLAS Discov. 2021 Jun;26(5):684-697. doi: 10.1177/24725552211000676. Epub 2021 Mar 30.

Abstract

Target engagement by small molecules is necessary for producing a physiological outcome. In the past, a lot of emphasis was placed on understanding the thermodynamics of such interactions to guide structure-activity relationships. It is becoming clearer, however, that understanding the kinetics of the interaction between a small-molecule inhibitor and the biological target [structure-kinetic relationship (SKR)] is critical for selection of the optimum candidate drug molecule for clinical trial. However, the acquisition of kinetic data in a high-throughput manner using traditional methods can be labor intensive, limiting the number of molecules that can be tested. As a result, in-depth kinetic studies are often carried out on only a small number of compounds, and usually at a later stage in the drug discovery process. Fundamentally, kinetic data should be used to drive key decisions much earlier in the drug discovery process, but the throughput limitations of traditional methods preclude this. A major limitation that hampers acquisition of high-throughput kinetic data is the technical challenge in collecting substantially confluent data points for accurate parameter estimation from time course analysis. Here, we describe the use of the fluorescent imaging plate reader (FLIPR), a charge-coupled device (CCD) camera technology, as a potential high-throughput tool for generating biochemical kinetic data with smaller time intervals. Subsequent to the design and optimization of the assay, we demonstrate the collection of highly confluent time-course data for various kinase protein targets with reasonable throughput to enable SKR-guided medicinal chemistry. We select kinase target 1 as a special case study with covalent inhibition, and demonstrate methods for rapid and detailed analysis of the resultant kinetic data for parameter estimation. In conclusion, this approach has the potential to enable rapid kinetic studies to be carried out on hundreds of compounds per week and drive project decisions with kinetic data at an early stage in drug discovery.

摘要

小分子与靶标的结合对于产生生理效果是必要的。过去,人们非常重视理解这种相互作用的热力学,以指导结构-活性关系。然而,越来越清楚的是,理解小分子抑制剂与生物靶标之间相互作用的动力学(结构-动力学关系 [SKR])对于选择用于临床试验的最佳候选药物分子至关重要。然而,使用传统方法以高通量方式获取动力学数据可能非常费力,限制了可以测试的分子数量。因此,通常在药物发现过程的后期阶段,才对少数化合物进行深入的动力学研究。从根本上讲,动力学数据应该更早地用于驱动药物发现过程中的关键决策,但传统方法的通量限制排除了这一点。阻碍高通量动力学数据获取的一个主要限制是在时间过程分析中从实质上连续的数据点收集中进行准确参数估计的技术挑战。在这里,我们描述了荧光成像板读数器 (FLIPR) 的使用,这是一种电荷耦合器件 (CCD) 相机技术,作为一种潜在的高通量工具,用于以更小的时间间隔生成生化动力学数据。在对测定法进行设计和优化之后,我们展示了以合理的通量为各种激酶蛋白靶标收集高度连续的时程数据,从而实现 SKR 指导的药物化学。我们选择激酶靶标 1 作为共价抑制的特殊情况研究,并演示了用于对所得动力学数据进行快速和详细分析以进行参数估计的方法。总之,这种方法有可能使每周能够对数百种化合物进行快速动力学研究,并在药物发现的早期阶段使用动力学数据驱动项目决策。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验