Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties

Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.

Automated summary

Antibody drugs need to have high-binding affinity for target antigens and favorable drug-like properties. Traditional approaches in antibody drug development require extensive experimentation to produce and optimize candidates. Integrating new methods can optimize the process of selecting antibodies with desired properties. This article summarizes techniques that can complement conventional tools, reduce physicochemical liabilities in initial discovery, and optimize antibody features during early-stage engineering. Biophysical and computational approaches are also highlighted to predict degradation pathways and reduce extensive experimentation.