B Cell Repertoire Molecular Platform for Antibody Drug Discovery

描述（由申请人提供）：本创新分子分析技术（IMAT） SBIR I期拨款提案的具体目的是展示从人类B细胞库中发现用于肿瘤的单克隆抗体（mAb）药物的高通量分子技术的可行性。制药公司花费高达50亿美元生产一种fda批准的单克隆抗体（mAb） （DiMasi & Grabowski, 2007; Nelson et al., 2010）。高单抗开发成本至少部分是由于候选药物的高失败率。在进入临床开发之前，一项将单抗选择提高10%的技术可以为制药行业节省约4亿美元，每个FDA批准的单抗。免疫系统是“试管”，它不断地选择具有强大药物潜力的抗体。“GigaLink”将是一项利用这些抗体选择试管为药物发现项目提供的研究服务。在小鼠系统中，我们的技术最终可以取代B细胞的永生。在人体系统中，我们的技术最终可以取代噬菌体展示。在我们的DNA文库中鉴定出的抗体片段将很容易被修饰成单克隆抗体，因为它们是完全人的，已知在B细胞表面表达，并且已经在体内表现出正选择。在第一阶段，我们将把我们目前的分子和生物信息学方法与哺乳动物表达和亲和力筛选结合起来。该项目将专注于针对表皮生长因子受体（EGFR）的抗体片段，因为许多抗EGFR生物制剂可用于帮助我们测试我们的系统。虽然我们专注于EGFR，但这个项目的主要成果是平台开发——这是国家癌症研究所（NCI） IMAT项目的理想选择。我们将通过执行以下任务来完成特定目标：(i)利用已知的抗egfr抗体片段优化哺乳动物表达和亲和力筛选；实施计算方法，定量地整合基因组和亲缘性数据；（iii）筛选胰腺癌患者B细胞中抗egfr抗体片段。为了获得成功，我们必须达到以下指标：(i)通过哺乳动物表达和亲和力筛选，回收80%的已知抗egfr抗体片段（功率=0.8，¿=0.05）；（ii）利用种系分化率和亲和筛选，发现不少于10个亲和常数(Kaff)<10-7且序列进化率显著（power=0.8，¿=0.05）的抗egfr抗体片段。在II期，我们将专注于从GigaLink抗体片段中获得的全人源单克隆抗体亲和成熟的实验室和计算方法。一旦II期完成，GigaGen将与Morphosys和Adimab等单抗发现服务直接竞争。未来的资助申请将集中于肿瘤靶点，用于发现和开发gigagen专有的胰腺癌靶点药物（Dodson等，2011），但这只有在当前的IMAT项目完成后才有可能。

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.