Blueprint for antibody biologics developability

蓝图 计算机科学 生化工程 生物信息学 计算生物学 选择(遗传算法) 纳米技术 化学 人工智能 生物 材料科学 工程类 生物化学 机械工程 基因
作者
Carl Mieczkowski,Xuejin Zhang,Dana Lee,Khanh Nguyen,Wei Lv,Yanling Wang,Yue Zhang,Jackie Way,Jean‐Michel Gries
出处
期刊:mAbs [Landes Bioscience]
卷期号:15 (1) 被引量:29
标识
DOI:10.1080/19420862.2023.2185924
摘要

Large-molecule antibody biologics have revolutionized medicine owing to their superior target specificity, pharmacokinetic and pharmacodynamic properties, safety and toxicity profiles, and amenability to versatile engineering. In this review, we focus on preclinical antibody developability, including its definition, scope, and key activities from hit to lead optimization and selection. This includes generation, computational and in silico approaches, molecular engineering, production, analytical and biophysical characterization, stability and forced degradation studies, and process and formulation assessments. More recently, it is apparent these activities not only affect lead selection and manufacturability, but ultimately correlate with clinical progression and success. Emerging developability workflows and strategies are explored as part of a blueprint for developability success that includes an overview of the four major molecular properties that affect all developability outcomes: 1) conformational, 2) chemical, 3) colloidal, and 4) other interactions. We also examine risk assessment and mitigation strategies that increase the likelihood of success for moving the right candidate into the clinic.
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