计算机科学
数量结构-活动关系
计算生物学
生物系统
作者
Pin-Kuang Lai,Amendra Fernando,Theresa K. Cloutier,Yatin R. Gokarn,Jifeng Zhang,Walter Schwenger,Ravi V. J. Chari,Cesar Calero-Rubio,Bernhardt L. Trout
标识
DOI:10.1021/acs.molpharmaceut.0c01073
摘要
Predicting the solution viscosity of monoclonal antibody (mAb) drug products remains as one of the main challenges in antibody drug design, manufacturing, and delivery. In this work, the concentration-dependent solution viscosity of 27 FDA-approved mAbs was measured at pH 6.0 in 10 mM histidine-HCl. Six mAbs exhibited high viscosity (>30 cP) in solutions at 150 mg/mL mAb concentration. Combining molecular modeling and machine learning feature selection, we found that the net charge in the mAbs and the amino acid composition in the Fv region are key features which govern the viscosity behavior. For mAbs whose behavior was not dominated by charge effects, we observed that high viscosity is correlated with more hydrophilic and fewer hydrophobic residues in the Fv region. A predictive model based on the net charges of mAbs and a high viscosity index is presented as a fast screening tool for classifying low- and high-viscosity mAbs.
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