ABSTRACT

Disulfide bonds play a crucial role in folding and structural stabilization of monoclonal antibodies (mAbs). Disulfide bond reduction may happen during the mAb manufacturing process, resulting in low molecular weight species and possible failure to meet product specifications. Although many mitigation strategies have been developed to prevent disulfide reduction, to the best of our knowledge, reforming disulfide bonds from the reduced antibody in manufacturing has not previously been reported. Here, we explored a novel rescue strategy in the downstream process to repair the broken disulfide bonds via in-vitro redox reactions on Protein A resin. Redox conditions including redox pair (cysteine/cystine ratio), pH, temperature, and reaction time were examined to achieve high antibody purity and a high reaction rate. Under the optimal redox condition, >90% reduced antibody could be reoxidized to form an intact antibody on Protein A resin in an hour. In addition, this study showed high flexibility on the range of the intact mAb fraction in the initial reduced mAb sample (the lower limit of intact mAb faction could be 14% based on the data reported in this study). Furthermore, a kinetic model based on elementary oxidative reactions was constructed to help optimize the reoxidation conditions and to predict product purity. Together, the deep understanding of interchain disulfide bond reoxidation, combined with the predictive kinetic model, provided a good foundation to implement a rescue strategy to generate high-purity antibodies with substantial cost savings in manufacturing processes.

摘要

二硫键在单克隆抗体 (mAb) 的折叠和结构稳定方面起着至关重要的作用。在 mAb 制造过程中可能会发生二硫键还原，从而导致低分子量物质和可能不符合产品规格。尽管已经开发了许多缓解策略来防止二硫键还原，但据我们所知，以前没有报道过在制造过程中从还原的抗体中重新形成二硫键。在这里，我们在下游过程中探索了一种新的拯救策略，通过体外修复断裂的二硫键。Protein A 树脂上的氧化还原反应。检查氧化还原条件，包括氧化还原对（半胱氨酸/胱氨酸比）、pH、温度和反应时间，以实现高抗体纯度和高反应速率。在最佳氧化还原条件下，超过 90% 的还原抗体可在 1 小时内在蛋白 A 树脂上重新氧化形成完整抗体。此外，这项研究显示了初始还原 mAb 样品中完整 mAb 部分范围的高度灵活性（根据本研究报告的数据，完整 mAb 部分的下限可能为 14%）。此外，构建了基于基本氧化反应的动力学模型，以帮助优化再氧化条件并预测产品纯度。一起，对链间二硫键再氧化的深刻理解，结合预测动力学模型，