Understanding and predicting aggregation in biopharmaceuticals

目前，开发更便宜的蛋白质疗法的瓶颈之一是下游生物加工和配方步骤的成本。一个关键的问题是在整个生物过程中，治疗活性蛋白的不可逆聚集的损失。在色谱或过滤过程中，当遇到可能导致高粘度甚至沉淀的高浓度蛋白质时，会出现其他问题。这项工作的重点是开发预测方法，以便在生物过程的早期识别有问题的条件。然后，这些可以用于识别蛋白质的变化，以最大限度地减少问题。或者，该方法可用于优化溶剂性质（pH，缓冲液类型和浓度）或寻找其他小分子添加剂，以避免聚集或增加蛋白质的溶解度。我们通过研究抗体和抗体片段来衡量我们的方法，因为它们作为人类治疗方法的重要性日益增加。

Currently, one of the bottle necks to developing cheaper protein therapeutics is the cost of the downstream bioprocessing and formulation steps. A key problem is the loss of active protein therapeutic to irreversible aggregation throughout the bioprocess. Other problems can arise during chromatography or filtration when encountering high protein concentrations which could lead to high viscosites or even precipitation. The focus of this work is to develop predictive methods for identifying problematic conditions early on in the bioprocess. These could then be used for identifying changes to the protein to minimize the problems. Alternatively, the method could be used for optimizing the solvent properties (pH, buffer type and concentration) or finding other small molecule additives to be used in order to avoid aggregation or increase protein solubility. We benchmark our approach by studying antibodies and antibody fragments due to their growing importance as human therapeutics.