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: 8199938
• 负责人: Sriram Shankar
• 金额: $ 73.48万
• 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 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8199938
• 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. PUBLIC HEALTH RELEVANCE: The high cost of modern drugs in the US is related directly to the various wasted efforts spent chasing compounds selected from random, unfocussed libraries with only a promise of 'drug-like' properties. Our methodology starts from an 'informed' and defined starting point and takes rapid, meaningful strides across the mountainous 'fitness' landscape of peptide ligand space to efficiently reach the summit of ligand fitness. The surfeit of high-value hit-to-lead candidates from our approach will doubtlessly enhance the probability of success for finding new drugs, and will drastically change the approach currently taken for drug discovery.

描述（由申请人提供）：新药的研究和开发是一项长期而昂贵的奋进。典型的药物发现过程从疾病靶点的先导化合物鉴定到先导化合物优化、体外/体内评价、临床前和临床测试以及FDA批准。几项研究估计，开发一种批准的新药的平均成本超过8亿美元。该费用的很大一部分归因于放弃的先导候选物，这些先导候选物最初是从筛选针对靶标的活性的大量未聚焦的化合物文库中获得的，但由于各种原因沿着管道失败。因此，拥有一个储备充足的候选药物管道是保证任何药物发现奋进成功的必要条件。最近在解开人类蛋白质组和相互作用组的巨大进步，允许蛋白质相互作用（PPI）的复杂网络的映射。PPI参与所有细胞过程，包括生长、维持和死亡，并且有文献记载，某些PPI的失调是各种疾病的病理基础。这些PPI的调节剂的鉴定，从而蛋白质功能，以及将这些转化为高含量铅系列的过程是现代药物发现的关键活动。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肽类药物候选物，能够调节关键蛋白质的相互作用，并具有很高的成功潜力作为药物先导。肽是药物发现中的高价值靶标，并且来自PPI位点的基于肽的先导物目前包含>50%的药物管线。在I期研究中，Lynntech和VBI的Garner小组已经明确证明，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肽微阵列相结合，仅从其一级序列中获得靶蛋白的生物活性肽配体是可行的，用于高通量筛选候选配体。从基于阵列的亲和力成熟获得几种高亲和力肽配体（nM范围亲和力），并且这些配体的子集显示出调节ESRRG相互作用的明显倾向。我们的第一阶段工作还成功地开发了一个可通过网络访问的发现引擎和数据库，使用户能够辅助该方法中所涉及的各个步骤的伪自动化，从而大大加快了该过程。需要进一步增强，使其成为潜在客户生成，潜在客户优化和潜在客户爆炸的强大药物发现引擎。第二阶段的提案不仅将开发图形用户界面工具，用于数据分析和管道中配体的知情向下选择，而且还将阐明选择规则，这些规则将为从蛋白质序列中获得高价值先导药物候选物的最快方法提供信息。 公共卫生相关性：在美国，现代药物的高成本与各种浪费的努力直接相关，这些努力是从随机的、未聚焦的库中选择的化合物，这些化合物只具有“药物样”的特性。我们的方法从一个“知情”和定义的起点开始，并采取快速，有意义的跨越山区的肽配体空间的“健身”景观大步有效地达到配体健身的顶峰。从我们的方法中获得的高价值的候选药物无疑会提高发现新药的成功概率，并将彻底改变目前用于药物发现的方法。