Background
Monoclonal antibodies (mAbs) and their derivatives are the fastest expanding category of pharmaceuticals. Efficient screening and generation of appropriate therapeutic human antibodies are important and urgent issues in the field of medicine. The successful in vitro biopanning method for antibody screening largely depends on the highly diverse, reliable and humanized CDR library. To rapidly obtain potent human antibodies, we designed and constructed a highly diverse synthetic human single-chain variable fragment (scFv) antibody library greater than a giga in size by phage display. Herein, the novel TIM-3-neutralizing antibodies with immunomodulatory functions derived from this library serve as an example to demonstrate the library's potential for biomedical applications.
Conclusion
The promising results illustrate not only the potential of the DSyn-1 library for biomedical research applications, but also the therapeutic potential of the three novel fully human TIM-3-neutralizing antibodies.
Methods
The library was designed with high stability scaffolds and six complementarity determining regions (CDRs) tailored to mimic human composition. The engineered antibody sequences were optimized for codon usage and subjected to synthesis. The six CDRs with variable length CDR-H3s were individually subjected to β-lactamase selection and then recombined for library construction. Five therapeutic target antigens were used for human antibody generation via phage library biopanning. TIM-3 antibody activity was verified by immunoactivity assays.
Results
We have designed and constructed a highly diverse synthetic human scFv library named DSyn-1 (DCB Synthetic-1) containing 2.5 × 1010 phage clones. Three selected TIM-3-recognizing antibodies DCBT3-4, DCBT3-19, and DCBT3-22 showed significant inhibition activity by TIM-3 reporter assays at nanomolar ranges and binding affinities in sub-nanomolar ranges. Furthermore, clone DCBT3-22 was exceptionally superior with good physicochemical property and a purity of more than 98% without aggregation.