LBPO.CH01 · 化学 · Late-Breaking
AI引导的pH响应性抗体工程改造实现肿瘤选择性靶向并改善治疗指数
AI guided engineering of pH responsive antibodies enables tumor selective targeting and improves the therapeutic index
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
肿瘤组织表现出轻度酸性的细胞外微环境（pH 6.0-6.8），与处于生理pH（约7.4）的正常组织形成对比，利用这一差异有望实现肿瘤选择性抗体靶向并降低全身毒性；然而，将pH依赖性结合合理地工程化引入抗体仍具挑战性。我们开发了一种深度学习模型，可预测抗体互补决定区（CDR）中的突变如何随pH变化调节抗原结合，并将其应用于重新设计一种B7-H3靶向抗体，使其优先在酸性条件下结合。所得抗体表现出强烈的pH依赖性结合，其亲和力比值（pH 6.0/pH 7.4）超过100倍，在代表肿瘤微环境的酸性条件下维持高亲和力，而在生理pH下结合显著降低。当被重新构建为抗体-药物偶联物（ADC）时，这些pH响应性抗体表现出更佳的选择性，并且相较亲本抗体，给药窗口扩展了三倍。这些结果表明，AI驱动的CDR工程改造能够系统性地设计微环境响应性抗体，并提供了一种可推广的策略以提升基于抗体的抗癌疗法的治疗指数。
查看英文原文 English abstract
Tumor tissues exhibit a mildly acidic extracellular microenvironment (pH 6.0-6.8), in contrast to normal tissues at physiological pH (~7.4), and exploiting this difference could enable tumor-selective antibody targeting and reduce systemic toxicity; however, rationally engineering pH-dependent binding into antibodies remains challenging. We developed an deep learning model that predicts how mutations in antibody complementarity-determining regions (CDRs) modulate antigen binding as a function of pH and applied it to redesign a B7-H3-targeting antibody to preferentially bind under acidic conditions. The resulting antibodies showed strong pH-dependent binding, achieving affinity ratios (pH 6.0 / pH 7.4) exceeding 100-fold, with high affinity maintained in acidic conditions representative of the tumor microenvironment and markedly reduced binding at physiological pH. When reformatted as antibody-drug conjugates (ADCs), these pH-responsive antibodies exhibited improved selectivity and a threefold expansion of the drug administration window compared with the parent antibody. These results demonstrate that AI-driven CDR engineering enables systematic design of microenvironment-responsive antibodies and offers a generalizable strategy to enhance the therapeutic index of antibody-based cancer therapeutics.
利益披露 Disclosure
Q. Yu, None..
M. Chen, None..
Y. Lu, None..
Y. Wang, None.