Rational Design of High-Affinity Peptide Drug Candidates

高亲和力肽候选药物的合理设计

基本信息

批准号：
8320350
负责人：
JOHN E MUELLER
金额：
$ 63.46万
依托单位：
LYNNTECH, INC.
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-01 至 2014-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8320350
关键词：
Accounting Advanced Development Affinity Algorithms Amino Acid Sequence Amino Acids Area Automation Binding Bioinformatics Cell physiology Cessation of life Chemicals Chemistry Cleaved cell Clinical Commit Complex Computational Biology Cyclosporine Data Data Analyses Databases Development Disease Drug Delivery Systems Etiology Evaluation Explosion Future Fuzeon Generations Growth Human In Vitro Knowledge Lead Legal patent Libraries Licensing Ligand Binding Ligands Maintenance Maps Marketing Methodology Methods Online Systems Oxytocin Pathology Peptide Library Peptide Sequence Determination Peptides Phage Display Pharmaceutical Preparations Pharmacologic Substance Phase Preclinical Testing Probability Process Property Protein Binding Proteins Proteome Publications Reagent Sampling Science Screening procedure Signal Transduction Site Somatostatin Structure Systems Biology Techniques Technology Testing Time Validation Variant base cell growth combinatorial commercialization computerized tools cost data mining density design drug candidate drug discovery eptifibatide evaluation/testing experience fitness graphical user interface high throughput screening human disease improved in vivo interest knowledge base lead series payment peptidomimetics phase 1 study phase 2 study pre-clinical protein aminoacid sequence protein function protein phosphatase inhibitor-2 protein protein interaction rapid technique research and development research clinical testing success tool user-friendly wasting web-accessible

项目摘要

DESCRIPTION (provided by applicant): Research and development of new drugs is a protracted and expensive endeavor. The typical drug discovery process evolves from lead identification for a disease target to lead optimization, in vitro/in vivo evaluation, preclinical and clinical testing, and FDA approval. Several studies estimate the average cost of development of a single, approved new drug in excess of $800 million. A large portion of this expense is attributed to abandoned lead candidates that are obtained initially from screening vast, unfocussed libraries of compounds for activity against the target but which fail for various reasons along the pipeline. Thus, having a well stocked pipeline of drug candidates is integral to guaranteeing success in any drug discovery endeavor. Recent vast strides in unraveling the human proteome and interactome have allowed mapping of the complex network of protein-protein interactions (PPIs). PPIs are involved in all cellular processes, including growth, maintenance, and death, and it is documented that the dysregulation of certain PPIs underlies the pathology of various diseases. The identification of modulators of these PPIs, and consequently protein function, and the process of transforming these into high-content lead series are key activities in modern drug discovery. We have proposed a rapid, knowledge-based methodology to develop several high-affinity peptide drug candidates able to modulate key protein interactions, and having high potential for success as drug leads. Peptides are high value targets in drug discovery and peptide-based leads derived from PPI sites currently comprise >50% of pharmaceutical pipelines. In Phase I studies, Lynntech and the Garner group at VBI have demonstrated unequivocally that it is feasible to obtain biologically active peptide ligands to target proteins, from their primary sequence alone, using our unique approach that combines systems biology and bioinformatics tools with an advanced, high-density peptide microarray for high-throughput screening of candidate ligands. Several high- affinity peptide ligands (of nM range affinity) were obtained from array based affinity maturation, and a subset of these ligands displayed a clear proclivity to modulate ESRRG interactions. Our Phase I efforts also have resulted in the successful development of a web-accessible discovery engine and database which enables user-assisted pseudo-automation of the various steps involved in the approach, thereby vastly expediting the process. Further enhancements are required to make this a potent drug discovery engine for lead generation, lead optimization, and lead explosion. The Phase II proposal will not only develop graphic-user interface tools for data analysis and informed down-selection of ligands in a pipeline but also elucidate selection rules that will inform the quickest way to obtain a high value lead drug candidate from protein sequence.

描述（由申请人提供）：新药的研究和开发是一种长期且昂贵的努力。典型的药物发现过程从疾病靶标的铅鉴定中演变为铅优化，体外/体内评估，临床前和临床测试以及FDA批准。几项研究估计，一种已批准的新药的平均开发成本超过8亿美元。这笔费用的很大一部分归因于废弃的主要候选人，这些候选人最初是从筛选庞大的，不合时宜的化合物图书馆来进行的，用于针对目标的活动，但由于管道沿线的各种原因而失败。因此，拥有大量的候选药物管道是确保在任何药物发现努力中取得成功不可或缺的一部分。最近在揭开人蛋白质组和相互作用组的巨大进步允许映射蛋白质 - 蛋白质相互作用（PPI）的复杂网络。 PPI参与了所有细胞过程，包括生长，维持和死亡，并且有证明某些PPI的失调是各种疾病的病理的基础。这些PPI的调节剂及其蛋白质功能的鉴定以及将它们转化为高素质铅系列的过程是现代药物发现中的关键活动。我们提出了一种基于知识的快速方法，以开发几种能够调节关键蛋白质相互作用的高亲和力候选药物，并具有很高的成功潜力作为药物铅。肽是药物发现和基于肽的铅的高价值靶标，这些肽目前来自PPI位点，占药品管道的50％。在第一阶段的研究中，VBI的Lynntech和Garner组无明确地证明，仅利用我们的独特方法将系统生物学和生物信息学工具与高级，高密度的肽型微型微型射击率筛选出高级候选者的独特方法，从其主要的序列中获得生物学活跃的肽配体来靶向蛋白质。从基于阵列的亲和力成熟获得了几种高亲和力肽配体（NM范围亲和力），这些配体的一个子集显示出对调节ESRRG相互作用的明显倾向。我们的第一阶段努力也导致了可访问Web访问的发现引擎和数据库的成功开发，该引擎和数据库能够对该方法中涉及的各个步骤进行用户辅助的伪自动化，从而极大地加快了该过程。需要进一步的增强才能使其成为铅产生，铅优化和铅爆炸的有效药物发现引擎。 II期提案不仅将开发用于数据分析的图形用户界面工具，并在管道中了解配体的下调，还阐明了选择规则，这些规则将为从蛋白质序列中获取高价值铅候选药物的最快方法。