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治疗性抗体药物研发中,长期存在一个关键难题:若抗体来源于非人生物(如小鼠),进入人体后可能被免疫系统识别为“外来物”,从而引发免疫反应,导致药效下降,甚至出现不良反应。因此,抗体必须经过“人源化”改造,以降低其免疫原性、提升安全性。然而,传统方法往往依赖全局序列比对,难以精准定位导致免疫原性的具体氨基酸位点,改造过程犹如“大海捞针”,耗时耗力。

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    出版时间: 2026-02-13
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    治疗性抗体药物研发中,长期存在一个关键难题:若抗体来源于非人生物(如小鼠),进入人体后可能被免疫系统识别为“外来物”,从而引发免疫反应,导致药效下降,甚至出现不良反应。因此,抗体必须经过“人源化”改造,以降低其免疫原性、提升安全性。然而,传统方法往往依赖全局序列比对,难以精准定位导致免疫原性的具体氨基酸位点,改造过程犹如“大海捞针”,耗时耗力。

    . 抗体人源化迎来“精准导航”. 科技导报, 2026 , 44 (3) : 4 -4 .
    . Science & Technology Review, 2026 , 44 (3) : 4 -4 .
    治疗性抗体药物研发中,长期存在一个关键难题:若抗体来源于非人生物(如小鼠),进入人体后可能被免疫系统识别为“外来物”,从而引发免疫反应,导致药效下降,甚至出现不良反应。因此,抗体必须经过“人源化”改造,以降低其免疫原性、提升安全性。然而,传统方法往往依赖全局序列比对,难以精准定位导致免疫原性的具体氨基酸位点,改造过程犹如“大海捞针”,耗时耗力。
    复旦大学曹志伟和裘天颐团队开发出位点特异性的治疗性抗体免疫原性预测算法——SITA(site-specific immunogenicity for therapeutic antibody),首次实现对抗体表面免疫原性的“位点级”精细预测,为人源化改造提供了一幅清晰的“导航图”。
    SITA的创新之处在于,它采用“球形壳层”与“三角片”2种结构描述模型,能精准提取每个氨基酸周围微环境的物理化学与空间结构特征。同时,团队借助类迁移学习框架,利用大量已知的“抗原-抗体”相互作用数据,增强模型对“抗体-抗体”间免疫原性位点的识别能力,显著提升了预测的可靠性。
    在13个独立测试的抗体-抗体复合物中,SITA在残基水平的预测性能(ROC-AUC)达到0.85,明显优于已有的7种工具。更令人印象深刻的是,在25个已经完成人源化的治疗性抗体案例中,SITA在超过66% 的案例中,仅从其预测的前5个高风险位点里,就成功识别出了实际被科研人员修饰的位点。该算法还能有效区分人源抗体、治疗性抗体与非人源抗体的免疫原性水平,在大规模的实验或者临床前期样本中得到验证。
    未来,研究人员可借助SITA提供的位点免疫原性评分,优先对评分高的关键氨基酸进行精准替换或修饰,从而在显著降低免疫原性的同时,最大程度保留抗体原有的生物活性与疗效。
    (来源:中国科技期刊卓越行动计划入选期刊:《Journal of Pharmaceutical Analysis》,2025,15(6):101316)

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    鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
    小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
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    小菇属 Mycena 11 5.26
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