Aggregation can be a major challenge in the development of antibody-based pharmaceuticals as it can compromise the quality of the product during bioprocessing, formulation, and drug administration. To avoid aggregation,… Click to show full abstract
Aggregation can be a major challenge in the development of antibody-based pharmaceuticals as it can compromise the quality of the product during bioprocessing, formulation, and drug administration. To avoid aggregation, developability assessment is often run in parallel with functional optimization in the early screening phases to flag and deselect problematic molecules. As developability assessment can be demanding with regard to time and resources, there is a high focus on the development of molecule design strategies for engineering molecules with a high developability potential. Previously, Dudgeon et al. [(2012) Proc. Natl. Acad. Sci. U. S. A. 109, 10879-10884] demonstrated how Asp substitutions at specific positions in human variable domains and single-chain variable fragments could decrease the aggregation propensity. Here, we have investigated whether these Asp substitutions would improve the developability potential of a murine antigen binding fragment (Fab). A full combinatorial library consisting of 393 Fab variants with single, double, and triple Asp substitutions was first screened in silico with Rosetta; thereafter, 26 variants with the highest predicted thermodynamic stability were selected for production. All variants were subjected to a set of developability studies. Interestingly, most variants had thermodynamic stability on par with or improved relative to that of the wild type. Twenty-five of the variants exhibited improved nonspecificity. Half of the variants exhibited improved aggregation resistance. Strikingly, while we observed remarkable improvement in the developability potential, the Asp substitutions had no substantial effect on the antigenic binding affinity. Altogether, by combining the insertion of negative charges and the in silico screen based on computational models, we were able to improve the developability of the Fab rapidly.
               
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