溶解度
单克隆抗体
理论(学习稳定性)
抗体
细胞毒性
生化工程
共轭体系
化学
效应器
组合化学
计算生物学
计算机科学
体外
生物
生物化学
聚合物
免疫学
有机化学
工程类
机器学习
作者
Lilia A. Rabia,Alec A. Desai,Harkamal S. Jhajj,Peter M. Tessier
标识
DOI:10.1016/j.bej.2018.06.003
摘要
The widespread use of monoclonal antibodies for therapeutic applications has led to intense interest in optimizing several of their natural properties (affinity, specificity, stability, solubility and effector functions) as well as introducing non-natural activities (bispecificity and cytotoxicity mediated by conjugated drugs). A common challenge during antibody optimization is that improvements in one property (e.g., affinity) can lead to deficits in other properties (e.g., stability). Here we review recent advances in understanding trade-offs between different antibody properties, including affinity, specificity, stability and solubility. We also review new approaches for co-optimizing multiple antibody properties and discuss how these methods can be used to rapidly and systematically generate antibodies for a wide range of applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI