Modulation of glycosylation in therapeutic proteins is a critical aspect to their development and production. The levels of various glycan moieties greatly impact the therapeutic protein’s overall efficacy and safety. As such, controlling the glycan levels and understanding potential levers that impact them is highly desirable. Various computational tools exist to understand these levers and quantify their impact on this critical quality attribute (CQA). Here we present a review on recent advances of these computational tools, how these advances further our understanding of the glycosylation pathway, and their potential applications. We focus on both mechanistic models for N-linked glycosylation, including the vesicular and maturation model, for predicting glycosylation profiles and providing insights into the glycosylation pathway itself. We also discuss data-driven models for predicting glycosylation profiles and identifying process levers for glycosylation.

治疗性蛋白质中糖基化的调节是其开发和生产的关键方面。各种聚糖部分的水平极大地影响了治疗性蛋白质的整体功效和安全性。因此，非常需要控制聚糖水平并了解影响它们的潜在杠杆。存在各种计算工具来了解这些杠杆并量化它们对这一关键质量属性 (CQA) 的影响。在这里，我们回顾了这些计算工具的最新进展，这些进展如何进一步加深我们对糖基化途径的理解，以及它们的潜在应用。我们专注于 N-连接糖基化的两种机制模型，包括囊泡和成熟模型，用于预测糖基化谱并提供对糖基化途径本身的见解。