Abstract
Monoclonal antibody (mAb) candidates from high-throughput screening or binding affinity optimization often contain mutations leading to liabilities for further development of the antibody, such as aggregation-prone regions and lack of solubility. In this work, we optimized a candidate integrin α11-binding mAb for developability using molecular modeling, rational design, and hydrophobic interaction chromatography (HIC). A homology model of the parental mAb Fv region was built, and this revealed hydrophobic patches on the surface of the complementarity-determining region loops. A series of 97 variants of the residues primarily responsible for the hydrophobic patches were expressed and their HIC retention times (RT) were measured. As intended, many of the computationally designed variants reduced the HIC RT compared to the parental mAb, and mutating residues that contributed most to hydrophobic patches had the greatest effect on HIC RT. A retrospective analysis was then performed where 3-dimentional protein property descriptors were evaluated for their ability to predict HIC RT using the current series of mAbs. The same descriptors were used to train a simple multi-parameter protein quantitative structure-property relationship model on this data, producing an improved correlation. We also extended this analysis to recently published HIC data for 137 clinical mAb candidates as well as 31 adnectin variants, and found that the surface area of hydrophobic patches averaged over a molecular dynamics sample consistently correlated to the experimental data across a diverse set of biotherapeutics.