Developability assessment at early-stage discovery to enable development of antibody-derived therapeutics

抗体 计算机科学 药物发现 单克隆抗体 药物开发 药品 医学 生物信息学 药理学 生物 免疫学
作者
Weijie Zhang,Hao Wang,Nan Feng,Yifeng Li,Jijie Gu,Zhuozhi Wang
出处
期刊:Antibody therapeutics [Oxford University Press]
卷期号:6 (1): 13-29 被引量:30
标识
DOI:10.1093/abt/tbac029
摘要

Abstract Developability refers to the likelihood that an antibody candidate will become a manufacturable, safe and efficacious drug. Although the safety and efficacy of a drug candidate will be well considered by sponsors and regulatory agencies, developability in the narrow sense can be defined as the likelihood that an antibody candidate will go smoothly through the chemistry, manufacturing and control (CMC) process at a reasonable cost and within a reasonable timeline. Developability in this sense is the focus of this review. To lower the risk that an antibody candidate with poor developability will move to the CMC stage, the candidate’s developability-related properties should be screened, assessed and optimized as early as possible. Assessment of developability at the early discovery stage should be performed in a rapid and high-throughput manner while consuming small amounts of testing materials. In addition to monoclonal antibodies, bispecific antibodies, multispecific antibodies and antibody-drug conjugates, as the derivatives of monoclonal antibodies, should also be assessed for developability. Moreover, we propose that the criterion of developability is relative: expected clinical indication, and the dosage and administration route of the antibody could affect this criterion. We also recommend a general screening process during the early discovery stage of antibody-derived therapeutics. With the advance of artificial intelligence-aided prediction of protein structures and features, computational tools can be used to predict, screen and optimize the developability of antibody candidates and greatly reduce the risk of moving a suboptimal candidate to the development stage.
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