B CELL REPERTOIRE MOLECULAR PLATFORM FOR ANTIBODY DRUG DISCOVERY


Year of Award:
2014
Award Type:
R43
Project Number:
CA187852
RFA Number:
PAR-13-327
Technology Track:
Molecular & Cellular Analysis Technologies
PI/Project Leader:
JOHNSON, DAVID SCOTT
Other PI or Project Leader:
N/A
Institution:
GIGAGEN, INC.
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)