Rational Design of High-Affinity Peptide Drug Candidates

高亲和力肽候选药物的合理设计

• 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
• 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

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(申请人提供)：新药的研究和开发是一项漫长而昂贵的工作。典型的药物发现过程从疾病靶标的先导识别到先导优化、体外/体内评估、临床前和临床试验，以及FDA的批准。几项研究估计，一种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被遗弃的候选铅，这些候选铅最初是通过筛选针对目标的活性的大量、无重点的化合物库获得的，但由于各种原因在管道中失败。因此，拥有一条库存充足的候选药物管道是确保任何药物发现努力取得成功不可或缺的一部分。最近在解开人类蛋白质组和相互作用组方面取得的巨大进展使得绘制蛋白质-蛋白质相互作用(PPI)的复杂网络成为可能。PPI参与所有的细胞过程，包括生长、维持和死亡，已有文献证明某些PPI的失调是各种疾病的病理基础。识别这些PPI的调节子，进而识别蛋白质的功能，以及将其转化为高含量铅系列的过程，是现代药物发现中的关键活动。我们提出了一种快速的、基于知识的方法来开发几种高亲和力的多肽候选药物，这些候选药物能够调节关键蛋白质的相互作用，并具有作为药物先导的高潜力。多肽是药物发现中的高价值靶点，而来自PPI位点的多肽导向物目前占药物流水线的50%。在第一阶段的研究中，Lynntech和VBI的Garner团队已经明确证明，使用我们独特的方法，将系统生物学和生物信息学工具与先进的高密度多肽微阵列相结合，高通量筛选候选配体，仅从它们的一级序列就可以获得针对目标蛋白质的生物活性多肽配体。通过基于阵列的亲和成熟获得了几个高亲和力的多肽配体(具有NM范围的亲和力)，并且这些配体的子集显示出明显的调节ESRRG相互作用的倾向。我们的第一阶段努力还成功地开发了可通过网络访问的发现引擎和数据库，使该方法所涉及的各个步骤能够在用户协助下实现伪自动化，从而大大加快了这一进程。需要进一步的增强，才能使它成为一个强大的药物发现引擎，用于铅的产生、铅的优化和铅爆炸。第二阶段的提案不仅将开发图形用户界面工具，用于数据分析和流水线中配体的通知向下选择，而且还将阐明选择规则，这些规则将提供从蛋白质序列中获得高价值候选先导药物的最快方法。