Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Biologics Development, Sanofi, Framingham, Massachusetts 01701, United States.
Mol Pharm. 2021 Mar 1;18(3):1167-1175. doi: 10.1021/acs.molpharmaceut.0c01073. Epub 2021 Jan 15.
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.
预测单克隆抗体 (mAb) 药物产品的溶液黏度仍然是抗体药物设计、制造和输送的主要挑战之一。在这项工作中，在 pH 6.0 下，在 10 mM 组氨酸-HCl 中测量了 27 种 FDA 批准的 mAb 的浓度依赖性溶液黏度。在 150 mg/mL mAb 浓度的溶液中，有 6 种 mAb 表现出高黏度 (>30 cP)。通过分子建模和机器学习特征选择，我们发现 mAb 中的净电荷和 Fv 区域中的氨基酸组成是控制黏度行为的关键特征。对于不受电荷效应主导的 mAb，我们观察到高黏度与 Fv 区域中更多的亲水残基和更少的疏水残基相关。提出了一种基于 mAb 的净电荷和高黏度指数的预测模型，作为一种快速筛选工具，用于分类低黏度和高黏度 mAb。
