Scientific Services Manager, Biologics, Chemical Computing Group ULC, Montreal, QC, Canada.
Computational Biochemistry and Bioinformatics Group, Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT, USA.
Methods Mol Biol. 2023;2552:219-235. doi: 10.1007/978-1-0716-2609-2_11.
A great effort to avoid known developability risks is now more often being made earlier during the lead candidate discovery and optimization phase of biotherapeutic drug development. Predictive computational strategies, used in the early stages of antibody discovery and development, to mitigate the risk of late-stage failure of antibody candidates, are highly valuable. Various structure-based methods exist for accurately predicting properties critical to developability, and, in this chapter, we discuss the history of their development and demonstrate how they can be used to filter large sets of candidates arising from target affinity screening and to optimize lead candidates for developability. Methods for modeling antibody structures from sequence and detecting post-translational modifications and chemical degradation liabilities are also discussed.
目前，人们在生物治疗药物开发的先导候选物发现和优化阶段更加努力地避免已知的可开发性风险。在抗体发现和开发的早期阶段使用的预测计算策略可降低抗体候选物后期失败的风险，具有很高的价值。存在各种基于结构的方法可准确预测对可开发性至关重要的特性，在本章中，我们讨论了它们的发展历史，并展示了如何将它们用于从靶标亲和力筛选中过滤大量候选物，并优化针对可开发性的先导候选物。还讨论了用于从序列建模抗体结构和检测翻译后修饰和化学降解缺陷的方法。
