Prediction of Antibody Viscosity from Dilute Solution Measurements.
Prediction of Antibody Viscosity from Dilute Solution Measurements.
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DOI：
10.3390/antib12040078
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发表时间：
2023-12-01
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实现治疗效果所需的高抗体剂量通常需要高浓度产品，这可能导致生产和递送中具有挑战性的粘度问题。在早期开发中预测抗体粘度可以在降低后期开发成本方面发挥关键作用。近年来，已经做出了许多努力来通过稀溶液测量来预测抗体粘度。一个关键的发现是，长的柔性复合物的缠结有助于在所需剂量下抗体粘度的急剧上升。这种纠缠模型建立了两体结合亲和力和多体粘度之间的联系。利用这一洞察力，本研究连接稀溶液测量的自缔合高浓度的粘度曲线，以量化这些制度之间的关系。由此产生的模型已表现出成功的预测粘度在高浓度（约150毫克/毫升）的稀溶液测量，只有少数离群值。我们基于物理学的方法提供了对基础物理学的理解，与实验数据的可解释联系，超出训练条件外推的潜力，以及有效解释这些异常值背后的物理力学的能力。进行假设驱动的实验，专门针对异常分子的粘度和弛豫机制，可以让我们解开它们行为的复杂性，从而提高我们模型的性能。
The high antibody doses required to achieve a therapeutic effect often necessitate high-concentration products that can lead to challenging viscosity issues in production and delivery. Predicting antibody viscosity in early development can play a pivotal role in reducing late-stage development costs. In recent years, numerous efforts have been made to predict antibody viscosity through dilute solution measurements. A key finding is that the entanglement of long, flexible complexes contributes to the sharp rise in antibody viscosity at the required dosing. This entanglement model establishes a connection between the two-body binding affinity and the many-body viscosity. Exploiting this insight, this study connects dilute solution measurements of self-association to high-concentration viscosity profiles to quantify the relationship between these regimes. The resulting model has exhibited success in predicting viscosity at high concentrations (around 150 mg/mL) from dilute solution measurements, with only a few outliers remaining. Our physics-based approach provides an understanding of fundamental physics, interpretable connections to experimental data, the potential to extrapolate beyond training conditions, and the capacity to effectively explain the physical mechanics behind these outliers. Conducting hypothesis-driven experiments that specifically target the viscosity and relaxation mechanisms of outlier molecules may allow us to unravel the intricacies of their behavior and, in turn, enhance the performance of our model.