单克隆抗体
计算生物学
抗体
计算机科学
免疫学
医学
生物
作者
C. He,Weijin Huang,Xi Wu,Huanzhang Xia
出处
期刊:Biomedicines
[Multidisciplinary Digital Publishing Institute]
日期:2025-08-23
卷期号:13 (9): 2055-2055
标识
DOI:10.3390/biomedicines13092055
摘要
Monoclonal antibodies (mAbs), as potent therapeutic agents, have been widely applied in the treatment of various major diseases, including infectious diseases, autoimmune disorders, cancers, and neurodegenerative diseases. However, early-generation mAbs were limited by high immunogenicity, short half-life, and insufficient affinity, which compromised their therapeutic efficacy. With technological advancements, novel approaches such as high-throughput screening and glycosyl modification have been introduced to improve the performance of mAbs. Furthermore, computer-aided design techniques—including molecular docking, molecular dynamics simulations, and artificial intelligence -based methods—are increasingly being employed to accelerate the optimization process. This review summarizes recent progress in the optimization of therapeutic mAbs, with a focus on technological breakthroughs and applications in affinity enhancement, development of broad-spectrum mAbs, specificity modulation, immunogenicity reduction, and stability improvement. Additionally, it discusses current challenges and future directions in antibody optimization. This review aims to provide insights and references for the development and optimization of next-generation antibody drugs, ultimately promoting the clinical application of safer and more effective mAb-based therapies.
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