用生成模型设计更像人的治疗抗体，降低免疫反应风险。
Generative Humanization for Therapeutic Antibodies
- 将抗体人源化建模为条件生成任务，利用语言模型采样突变序列。
- 生成的抗体既高度人源化，又保持或提升了抗原结合能力。
- 适合抗体药物研发团队在优化流程中快速生成高质量候选分子。
抗体疗法用于治疗当今最棘手的疾病，但药物开发需满足多项标准才能送达患者。人源化是一种序列优化策略，通过使抗体更接近人类序列来降低免疫原性风险——即患者对药物的免疫反应，而目前尚无预测免疫原性的实验室测试方法。然而，现有方法通常只能产生极少的人源化候选物，且可能伴随生物物理性质下降或药效减弱。本文将人源化重新定义为一个条件生成建模任务，从基于人类抗体数据训练的语言模型中采样人源化突变。我们设计了一种采样流程，融合了对抗原结合亲和力等治疗属性的模型，以获得兼具低免疫原性风险与良好治疗特性的候选序列，便于嵌入迭代抗体优化流程。我们通过体外和实验验证表明，在真实治疗项目中，该生成式人源化方法可生成多样化的抗体，既高度人源化，又具备改进的靶抗原结合能力。
原文摘要 · Abstract (English)
Antibody therapies have been employed to address some of today's most challenging diseases, but must meet many criteria during drug development before reaching a patient. Humanization is a sequence optimization strategy that addresses one critical risk called immunogenicity - a patient's immune response to the drug - by making an antibody more "human-like" in the absence of a predictive lab-based test for immunogenicity. However, existing humanization strategies generally yield very few humanized candidates, which may have degraded biophysical properties or decreased drug efficacy. Here, we re-frame humanization as a conditional generative modeling task, where humanizing mutations are sampled from a language model trained on human antibody data. We describe a sampling process that incorporates models of therapeutic attributes, such as antigen binding affinity, to obtain candidate sequences that have both reduced immunogenicity risk and maintained or improved therapeutic properties, allowing this algorithm to be readily embedded into an iterative antibody optimization campaign. We demonstrate in silico and in lab validation that in real therapeutic programs our generative humanization method produces diverse sets of antibodies that are both (1) highly-human and (2) have favorable therapeutic properties, such as improved binding to target antigens.
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