Abstract:
To be viable therapeutics, antibodies must be tolerated by the human immune system. Rational approaches to reduce the risk of unwanted immunogenicity involve maximizing the \'humanness\' of the candidate drug. However, despite the emergence of new discovery technologies, many of which start from entirely human gene fragments, most antibody therapeutics continue to be derived from non-human sources with concomitant humanization to increase their human compatibility. Early experimental humanization strategies that focus on CDR loop grafting onto human frameworks have been critical to the dominance of this discovery route but do not consider the context of each antibody sequence, impacting their success rate. Other challenges include the simultaneous optimization of other drug-like properties alongside humanness and the humanization of fundamentally non-human modalities such as nanobodies. Significant efforts have been made to develop in silico methodologies able to address these issues, most recently incorporating machine learning techniques. Here, we outline these recent advancements in antibody and nanobody humanization, focusing on computational strategies that make use of the increasing volume of sequence and structural data available and the validation of these tools. We highlight that structural distinctions between antibodies and nanobodies make the application of antibody-focused in silico tools to nanobody humanization non-trivial. Furthermore, we discuss the effects of humanizing mutations on other essential drug-like properties such as binding affinity and developability, and methods that aim to tackle this multi-parameter optimization problem.
摘要:
为了成为可行的治疗方法,抗体必须被人体免疫系统耐受。减少不必要免疫原性风险的合理方法包括最大化候选药物的“人性”。然而,尽管出现了新的发现技术,其中许多是从完全的人类基因片段开始的,大多数抗体治疗剂继续源自非人来源,伴随人源化以增加它们的人类相容性。早期的实验性人源化策略侧重于CDR环移植到人类框架上,这对这一发现途径的优势至关重要,但不考虑每个抗体序列的背景。影响他们的成功率。其他挑战包括与人类同时优化其他类似药物的特性以及根本上非人类的模态(如纳米抗体)的人源化。已经做出了巨大的努力来开发能够解决这些问题的计算机方法,最近结合了机器学习技术。这里,我们概述了抗体和纳米抗体人源化的最新进展,专注于利用数量不断增加的序列和结构数据的计算策略,以及对这些工具的验证。我们强调,抗体和纳米抗体之间的结构差异使抗体集中的计算机工具在纳米抗体人源化中的应用变得不平凡。此外,我们讨论了人源化突变对其他基本药物样特性的影响,如结合亲和力和可发育性,以及旨在解决这个多参数优化问题的方法。