选择(遗传算法)
计算机科学
候选药物
药物开发
计算生物学
抗体
过程(计算)
药物发现
药品
药理学
生化工程
机器学习
生物信息学
生物
免疫学
工程类
操作系统
作者
Alexander Jarasch,Hans Koll,Jörg T. Regula,Martin Bader,Apollon Papadimitriou,Hubert Kettenberger
摘要
Therapeutic antibodies and antibody derivatives comprise the majority of today's biotherapeutics. Routine methods to generate novel antibodies, such as immunization and phage-display, often give rise to several candidates with desired functional properties. On the contrary, resource-intense steps such as the development of a cell line, a manufacturing process, or a formulation, are typically carried out for only one candidate. Therefore, "developability," that is, the likelihood for the successful development of a lead candidate into a stable, manufacturable, safe, and efficacious drug, may be used as an additional selection criterion. Employing a set of small-scale, fast, and predictive tests addressing biochemical and biophysical features, as well as in vivo fate can help to identify a clinical candidate molecule with promising properties at an early stage of drug development. This article gives an overview of existing methods for developability testing and shows how these assays can be interlaced in the lead selection process.
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