B Cell Repertoire Molecular Platform for Antibody Drug Discovery

用于抗体药物发现的 B 细胞库分子平台

基本信息

批准号：
8756836
负责人：
David Scott Johnson
金额：
$ 24.91万
依托单位：
GIGAGEN, INC.
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-01 至 2015-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8756836
关键词：
Affinity Antibodies Applications Grants B cell repertoire B lymphocyte immortalization B-Lymphocytes Bioinformatics Biological Products Blood Cancer Patient Capital Cells Clinical Computing Methodologies Contracts Cost Savings DNA Library DNA Sequence Data Detection Development Diagnostic Disease Drug Industry Elderly Epidermal Growth Factor Receptor Evolution Failure Fertilization in Vitro Future Generations Genomics Human Immune Immune system Immunoglobulin Fragments Institutes Laboratories Lead Licensing Life Light Malignant Neoplasms Malignant neoplasm of pancreas Mammalian Cell Marketing Methods Metric Microfluidics Molecular Molecular Analysis Monoclonal Antibodies Mus National Cancer Institute Phage Display Pharmaceutical Preparations Pharmacologic Substance Phase Phase I Clinical Trials Productivity Research Services Small Business Innovation Research Grant Staging Surface Surveys System Technology Testing Therapeutic Therapeutic Agents Therapeutic antibodies Tube United States Food and Drug Administration United States National Institutes of Health Work biological research cell immortalization clinical efficacy cost cytokine drug discovery human monoclonal antibodies immunogenicity improved in vivo innovation medical specialties new technology novel therapeutics oncology oncology service primary outcome programs public health relevance research and development screening

项目摘要

DESCRIPTION (provided by applicant): Specific Aim of this Innovative Molecular Analysis Technologies (IMAT) SBIR Phase I grant proposal is to show the feasibility of a high-throughput molecular technology for discovery of monoclonal antibody (mAb) drugs for oncology from human B cell repertoires. Drug companies spend up to $5 billion to produce a single FDA-approved monoclonal antibody, or mAb (DiMasi & Grabowski, 2007; Nelson et al., 2010). High mAb development cost is at least partly due to the high failure rate of candidates. A technology that improves mAb selection by just 10% before entering clinical development could save the drug industry ~$400 million per FDA- approved mAb. Immune systems are "test tubes" that constantly select for antibodies with strong potential as drugs. GigaLink" will be a research service for drug discovery programs that leverages these antibody selection test tubes. In mouse systems, our technology could eventually replace B cell immortalization. In human systems, our technology could eventually replace phage display. Antibody fragments identified in our DNA libraries will be easily modified into mAbs, because they are fully human, are already known to express on the surface of B cells, and have already shown positive selection in vivo. In Phase I we will integrate our current molecular and bioinformatic methods with mammalian expression and affinity screening. The project will focus on antibody fragments against epidermal growth factor receptor (EGFR), because many anti-EGFR biologics are available to help us test our system. Though we focus on EGFR, the primary outcome of this project is platform development - ideal for the IMAT program at the National Cancer Institute (NCI). We will accomplish the Specific Aim by performing the following tasks: (i) Optimize mammalian expression and affinity screening using known anti-EGFR antibody fragments; (ii) Implement computational methods for quantitatively integrating genomic and affinity data; and (iii) Screen pancreatic cancer patient B cells for anti-EGFR antibody fragments. To be successful, we must achieve the following metrics: (i) Recover >80% of known anti-EGFR antibody fragments by mammalian expression and affinity screening (power=0.8, ¿=0.05); and (ii) Use germline divergence rates and affinity screening to discover no less than ten anti-EGFR antibody fragments with affinity constant (Kaff)<10-7 and significant sequence evolution rates (power=0.8,¿=0.05). In Phase II, we will focus on laboratory and computational methods for affinity maturation of fully human monoclonal antibodies from GigaLink" antibody fragments. Once Phase II is complete, GigaGen will compete directly with mAb discovery services such as Morphosys and Adimab. Future grant applications will focus on oncology targets for discovery and development of GigaGen-proprietary drugs for pancreatic cancer targets (Dodson et al., 2011), but this will only be possible once the current IMAT project has been completed.

描述（由适用提供）：这种创新的分子分析技术（IMAT）SBIR I期授予建议的具体目的是显示高通量分子技术用于发现单克隆抗体（MAB）药物用于人类B细胞谱的肿瘤学的可行性。制药公司花费高达50亿美元，生产单一的FDA批准的单克隆抗体或MAB（Dimasi＆Grabowski，2007; Nelson et al。，2010）。高MAB开发成本至少部分是由于候选人的高失败率。在进入临床开发之前，仅将mAB选择提高10％的技术可以节省每1亿美元的FDA-批准的MAB约4亿美元。免疫系统是“试管”，它不断选择具有强大潜力作为药物的抗体。 GigaLink" will be a research service for drug discovery programs that leverages these antibody selection test tubes. In mouse systems, our technology could eventually replace B cell immunotialization. In human systems, our technology could eventually replace phage display. Antibody fragments identified in our DNA libraries will be easily modified into mAbs, because they are fully human, are already known to express on the surface of B cells, and have already shown positive selection in vivo. In Phase I we will integrate我们目前具有哺乳动物表达的分子和生物信息，该项目将集中于针对表皮生长因子受体（EGFR）的抗体碎片，因为许多抗EGFR生物学可以帮助我们测试我们的系统，尽管我们的egfr是EGFR。任务：（i）使用已知的抗EGFR抗体片段优化哺乳动物的表达和亲和力筛选；（ii）实施定量整合基因组和亲和力数据的计算方法；（iii）筛查胰腺癌患者B细胞用于抗EGFR抗体片段。要成功，我们必须实现以下指标：（i）通过哺乳动物表达和亲和力筛选恢复已知的抗EGFR抗体片段的80％（功率= 0.8，»= 0.05）；（ii）使用生殖线差异率和亲和力筛选发现具有亲和力常数（KAFF）<10-7和显着序列演化速率（功率= 0.8，€= 0.05）的不少于十个抗EGFR抗体片段。 In Phase II, we will focus on laboratory and computational methods for affinity maturation of fully human monoclonal antibodies from GigaLink" antibody fragments. Once Phase II is complete, GigaGen will compete directly with mAb discovery services such as Morphosys and Adimab. Future grant applications will focus on oncology targets for discovery and development of GigaGen-proprietary drugs for pancreatic cancer targets (Dodson et al., 2011年），但是只有在当前的IMAT项目完成后才有可能。