Platform for Massively Parallel Selection of Aptamer Ligands

描述（由申请人提供）：适配体已经成为目前可用的最有前途的药物先导和诊断配体之一。核酸适体是来源于大型组合文库的核酸配体，通常具有与抗体竞争的亲和力和特异性，但它们在治疗和诊断应用中具有许多显着优势。不幸的是，现有的适体开发过程是低通量和繁琐的，因为DNA或RNA文库仅针对单一目标进行筛选。该项目的重点是开发方法和工具，以允许大型组合筛选同时针对成千上万的蛋白质阵列。这种蛋白质阵列的含量越来越高，具有很高的治疗和诊断价值。实现这一目标的关键是制定必要的步骤来破译哪个适体（一旦被选中）对应于哪个目标。所谓的“下一代”测序将极大地使所提出的过程与本项目中开发的必要的“序列标记”方法相结合。一旦我们的大规模平行适配体选择过程被开发出来，我们将能够在大约一周内创建数千种蛋白质的高亲和力适配体配体。然后，开发的配体可以进一步表征为有希望的候选药物，诊断标签和其他研究应用，最终可能包括个性化医疗。

DESCRIPTION (provided by applicant): Aptamers have emerged as one of the most promising classes of drug leads and diagnostic ligands presently available. Aptamers, nucleic acid ligands derived from large combinatorial libraries, typically have affinities and specificities that rival antibodies, yet they have a number of significant advantages for therapeutic and diagnostic applications. Unfortunately, the existing process for aptamer development is low-throughput and tedious as DNA or RNA libraries are screened against only a single target. This project focuses on developing the methods and tools to allow large combinatorial to be screened against arrays of thousands of proteins simultaneously. Such protein arrays are increasing available with content of high therapeutic and diagnostic value. The key to achieving this is developing the necessary steps to decipher which aptamers (once selected) correspond to which target. So-called "next generation" sequencing will greatly enable the proposed process coupled with the necessary "sequence-tagging" approaches developed in this project. Once our massively parallel aptamer selection process is developed, we will be in a position to create high affinity aptamer ligands to thousands of proteins in roughly 1 week. The developed ligands can then be further characterized as promising drug candidates, diagnostic labels, and other research applications perhaps eventually including personalized medicine. PUBLIC HEALTH RELEVANCE: A recent white paper by the US Federal Drug Administration finds that there exists a critical problem in bringing novel drugs to market, something the FDA describes as the 'pipeline problem'. According to this and other reports drug companies spend an average of $0.8-1.7 billion dollars on the discovery, development and approval of any one individual drug. To make matters worse, the time from the initial testing of a drug candidate and to its eventual marketing can take up to 20 years. Thus, the FDA report strongly urges the incorporation of novel quantitative predictive tools for the assessment of safety and efficacy of new drug leads and diagnostic ligands early in the drug development process. This project provides for the parallel development and evaluation of enormous combinatorial libraries of DNA or RNA 'aptamers' against of thousands of protein targets of potential 'druggable' interest. If successful, the technology could provide for unprecedented throughput of drug leads and diagnostic ligands.