Configuring Radioligand Receptor Binding Assays for HTS Using Scintillation Proximity Assay Technology
John W. Carpenter, Carmen Laethem, Frederick R. Hubbard, T. Kris Eckols, Melvyn Baez, Don B. McClure, David L. Nelson, Paul A. Johnston
- 发表年份
- 2003
- 引用次数
- 21
摘要
AbstractRapid progress in the fields of genomics, proteomics, and molecular biology has both increased the numbers of potential drug targets, and facilitated development of assays to screen these targets (1–5). In parallel with these changes, developments in robotics and combinatorial chemical synthesis have driven the production of very large numbers of compounds with potential for pharmacological activity (1–5). The need to screen these large libraries of drug candidates against multiple new targets has stimulated improvements in technology, instrumentation, and automation that have revolutionized the field of drug discovery, and evolved into the field of high throughput screening (HTS) (1,5–9). Radioligand binding assays have historically been the mainstay of drug discovery and drug development (6–8). In the era of HTS, incorporation of scintillation-proximity technology together with improved automation and radiometric-counting instrumentation have served to maintain radioligand receptor-binding as one of the premier tools of drug discovery (1,5,8,9). Radioligand binding assays are extremely versatile, easy to perform, can be automated to provide very high throughput (10–12). The quality of the data allows the determination of drug affinity, allosteric interactions, the existence of receptor subtypes, and estimates of receptor numbers (10–12). This chapter provides an overview of radioligand receptor-binding assays and discusses some of the issues associated with the conversion of traditional filtration assays to a homogeneous scintillating proximity assay (SPA) format that is more compatible with automation and HTS.KeywordsHigh Throughput ScreeningMembrane PreparationRadioligand BindingRadioligand Binding AssayFiltration AssayThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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