Use of advanced analytical tool to predict protein solution properties

抗体疗法在生物制品市场上占有最大份额，通常需要高度浓缩的液体制剂。在许多情况下，在如此高的蛋白质浓度下，溶液相分离或表现出较差的流变性，这限制了它们的可制造性。因此，需要根据对稀蛋白溶液进行测量的模型来预测浓缩液的行为，这对于早期药物开发至关重要，因为没有太多的蛋白质材料可用。在本课程中，我们主要解决浓溶液粘度的预测问题，这仍然是一个悬而未决的问题。这个问题是用生物物理学和软物质物理的实验和计算工具相结合来解决的。学生的主要目标是确定一个称为Huggin系数的稀溶液粘度参数和浓溶液粘度之间的关系，这是由我们的初步研究得出的，表明这些性质之间存在很强的相关性。大多数将高浓度粘度测量与稀溶液性质相关联的方法依赖于蛋白质-蛋白质相互作用的热力学测量。我们的主要前提是，蛋白质-蛋白质相互作用(PPI)的稀溶液测量对蛋白质溶液的流体力学性质不敏感，这是决定粘度的关键因素。我们预计，结合PPI测量和对流体动力相互作用更敏感的哈金系数的值，将改进高浓度粘度的预测因子。该项目分为三个工作包。在第一个工作包(WP1)中，我们将建立哈金系数值和浓溶液粘度测量的实验数据集，并辅之以稀溶液PPI测量，以建立稀溶液和浓溶液参数之间的相关性。该工作包还将包括沉降速度(SV)模式下的分析超速离心法(AUC)，以直接获得稀蛋白质溶液中对蛋白质-蛋白质相互作用的流体力学贡献。完成这些测量后，在第二个工作包(WP2)中，我们将开发根据其他稀溶液性质预测Huggin系数的模型，以阐明流体动力相互作用在低蛋白质浓度下控制粘度的作用。一旦我们了解了流体动力相互作用和稀溶液粘度之间的关系，在第三个工作包(WP3)中，我们将专注于开发预测浓溶液粘度的模型，并建立与稀溶液参数(如k_H)关联的分子基础。我们预计主要成果将是将k_H与浓溶液粘度之间的关联，以及了解其在PPI测量方面的局限性。-根据k_H测量和PPI的其他稀释测量计算浓溶液粘度的预测模型。-对高浓度粘度的原因和减粘辅料的机理有了更好的分子理解，以指导配方的合理设计

Antibody therapeutics, which comprise the largest share of the market in biologics, are often required as highly concentrated liquid formulations. In many cases, at such high protein concentrations, the solutions phase separate or display poor rheological properties, which limits their manufacturability. As such, approaches are needed for predicting the concentrated solution behaviour from models based on measurements made on dilute protein solutions, which is essential for early-stage drug development when not much protein material is available. In this studentship we mainly tackle the problem of predicting the concentrated solution viscosity, which remains an unresolved problem. The problem is solved using using a combination of experimental and computational tools derived from biophysics and soft matter physics.The main goal of the studentship is to determine the relationship between a dilute solution viscosity parameter called the Huggin's coefficient and the concentrated solution viscosity, which is motivated by our preliminary study indicating there exists a strong correlation between these properties. Most approaches for correlating high concentration viscosity measurements with dilute solution properties rely on thermodynamic measurements of protein-protein interactions. Our main premise is that dilute solution measurements of protein-protein interactions (PPIs) are not sensitive to hydrodynamic properties of the protein solutions, which are a key factor in determining the viscosity. We expect combining PPI measurements with values of the Huggin's coefficient, which is much more sensitive to hydrodynamic interactions, will lead to improved predictors of high concentration viscosity. The project is broken up into three workpackages. In the first workpackage (WP1), we will build up an experimental dataset of Huggin's coefficient values and concentrated solution viscosity measurements, complemented with dilute solution PPI measurements to establish correlations between the dilute and concentrated solution parameters. This workpackage will also include analytical ultracentrifugation (AUC) in sedimentation velocity (SV) mode to obtain directly the hydrodynamic contribution to protein-protein interactions in dilute protein solutions. With these measurements complete, in the second workpackage (WP2) we will develop models for predicting the Huggin's coefficient from other dilute solution properties to elucidate the role of hydrodynamic interactions in controlling viscosity at low protein concentrations. Once we understand the relationship between hydrodynamic interactions and dilute solution viscosity, in the third workpackage (WP3) we will focus on developing models for predicting the concentrated solution viscosity and to establish the molecular basis for the correlations with dilute solution parameters such as k_H. We expect the main deliverables will be- A correlation relating k_H to concentrated solution viscosity along with an understanding of its limitations in terms of PPI measurements. - Predictive models for calculating concentrated solution viscosity from measurements of k_H and other dilute measurements of PPIs.- An improved molecular understanding for the causes of high concentration viscosity and the mechanisms of viscosity-reducing excipients to guide rationale design of formulations