ABSTRACT The commercial success of bispecific antibodies generally has been hindered by the complexities associated with generating appropriate molecules for both research scale and large scale manufacturing purposes. Bispecific IgG (BsIgG) based on two antibodies that use an identical common light chain can be combined with a minimal set of Fc mutations to drive heavy chain heterodimerization in order to address these challenges. However, the facile generation of common light chain antibodies with properties similar to traditional monoclonal antibodies has not been demonstrated and they have only been used sparingly. Here, we describe the design of a synthetic human antibody library based on common light chains to generate antibodies with biochemical and biophysical properties that are indistinguishable to traditional therapeutic monoclonal antibodies. We used this library to generate diverse panels of well-behaved, high affinity antibodies toward a variety of epitopes across multiple antigens, including mouse 4-1BB, a therapeutically important T cell costimulatory receptor. Over 200 BsIgG toward 4-1BB were generated using an automated purification method we developed that enables milligram-scale production of BsIgG. This approach allowed us to identify antibodies with a wide range of agonistic activity that are being used to further investigate the therapeutic potential of antibodies targeting one or more epitopes of 4-1BB.
Productive common light chain libraries yield diverse panels of high affinity bispecific antibodies
Thomas J. Van Blarcom,Kevin C. Lindquist,Z. Melton,W. Cheung,Christopher R. Wagstrom,D. McDonough,Cendy A. Valle Oseguera,Sheng Ding,Andrea Rossi,S. Potluri,P. Sundar,S. Pitts,M. Sirota,Meritxell Galindo Casas,Yu Yan,Jeffrey J Jones,Z. Roe-Zurz,Surabhi Srivatsa Srinivasan,W. Zhai,J. Pons,A. Rajpal,J. Chaparro-Riggers
Published 2017 in mAbs
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PUBLICATION RECORD
- Publication year
2017
- Venue
mAbs
- Publication date
2017-12-14
- Fields of study
Medicine, Chemistry
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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