工具箱
药物发现
药品
计算机科学
抗体
药物开发
计算生物学
过程(计算)
生化工程
生物信息学
生物
免疫学
药理学
工程类
程序设计语言
操作系统
作者
Hristo L. Svilenov,Paolo Arosio,Tim Menzen,Peter M. Tessier,Pietro Sormanni
出处
期刊:mAbs
[Informa]
日期:2023-01-11
卷期号:15 (1)
被引量:19
标识
DOI:10.1080/19420862.2022.2164459
摘要
Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.
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