PDF(Pubmed)
Abstract：
Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs < 6.3). Our model identifies viscous antibodies with relatively high accuracy not only in our training and test sets, but also for previously reported data. Importantly, we show that the interpretable nature of the model enables the design of mutations that significantly reduce antibody viscosity, which we confirmed experimentally. We expect that this approach can be readily integrated into the drug development process to reduce the need for experimental viscosity screening and improve the identification of antibody candidates with drug-like properties.
摘要：
具有药物样特性的抗体候选物的早期鉴定对于简化安全有效的抗体治疗剂的开发是必要的。对于皮下给药，重要的是要确定具有低自缔合的候选物，以使其配方在高浓度下同时保持低粘度，乳光,和聚合。这里,我们报告了一个可解释的机器学习模型,用于仅使用其可变(Fv)区的序列预测低粘度抗体(IgG1)变体.我们的模型是根据在常见制剂pH（pH5.2）下获得的抗体粘度数据（>100mg/mLmAb浓度）进行训练的。它确定了与粘度相关的抗体的三个关键Fv特征，即它们的等电点，疏水贴片尺寸，以及带负电荷的贴片的数量。在这三个特征中，大多数预测的抗体有高粘度风险，包括我们研究中具有不同抗体种系的抗体（79mAb）以及临床阶段IgG1（94mAb），是具有低Fv等电点(FvpIs<6.3)的那些。我们的模型不仅在我们的训练和测试集中以相对较高的准确性识别粘性抗体，还有以前报道的数据。重要的是,我们表明，该模型的可解释性质使突变的设计，显着降低抗体粘度，我们通过实验证实了这一点。我们期望这种方法可以容易地整合到药物开发过程中，以减少对实验粘度筛选的需要，并改善具有药物样特性的抗体候选物的鉴定。
