PO.CL05.11 · 临床研究
一种快速患者来源类器官平台指导ERBB靶向双特异性T细胞衔接器的评估
A rapid patient-derived organoids platform guides the evaluation of ERBB-targeting bispecific T-cell engagers
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景:ERBB受体家族(EGFR/HER1、HER2、HER3、HER4)是经充分验证的致癌驱动因素。ERBB靶向双特异性抗体(如EGFRxCD3、HER2xCD3、HER2降解型BsAb)的临床转化受制于缺乏能够快速且同时预测患者特异性疗效和靶向脱靶毒性的模型。优化这些药物包括精细调节亲和力、价态和表位等参数以最大化治疗窗,这需要一个预测性平台,能够在临床相关的时间范围内提供关于抗肿瘤活性和靶向毒性的综合反馈。
方法:我们建立了一个来自乳腺癌、胃癌和非小细胞肺癌的患者来源类器官(PDO)综合生物样本库,以及匹配的正常类器官。我们开发了一种高通量、标准化的与PBMC或T细胞的共培养试验,以评估一组具有不同分子形式的ERBB靶向双特异性抗体。整个工作流程为速度而设计,可在数天内生成关于肿瘤类器官杀伤和正常类器官毒性的平行数据。
结果:我们的平台在3周窗口内为一系列BsAb候选药物提供了关于治疗指数的稳健、定量数据。我们证明,针对肿瘤相关抗原水平的亲和力调节能够优先保留低ERBB表达的正常类器官,同时维持强效的肿瘤杀伤。此外,比较不同的BsAb揭示,某些分子形式或表位选择与细胞因子释放减少和正常类器官毒性减轻相关,而不损害对靶点阳性肿瘤的疗效。这为先导候选药物的选择和优化提供了一种直接、快速的策略。
结论:我们开发了一种快速、可重现的基于PDO的平台,不仅能预测ERBB靶向BsAb的治疗窗,还能为其分子优化提供关键洞见。这种"快速反馈"系统能够在药物开发过程早期基于亲和力、形式和表位选择做出数据驱动的决策,从而显著降低风险并加速BsAb的转化。能够针对人类肿瘤和正常组织背景快速分析多个候选药物,使其成为设计更安全、更有效双特异性抗体的宝贵工具。本研究提出了一种变革性的临床前优化工具。通过在数周内生成预测性的安全性和疗效数据,我们的平台超越了单纯的预测,进入主动指导,赋能下一代BsAb的合理设计,使其在临床试验中具有本质上改善的治疗特征。
查看英文原文 English abstract
Background: The ERBB receptor family (EGFR/HER1, HER2, HER3, HER4) are well-validated oncogenic drivers. The clinical translation of ERBB-targeted bispecific antibodies (e.g., EGFRxCD3, HER2xCD3,HER2 degrading BsAbs) is bottlenecked by the lack of models that can rapidly and concurrently predict patient-specific efficacy and on-target, off-tumor toxicity. Optimizing these drugs including fine-tuning parameters like affinity, valency, and epitope to maximize the therapeutic window, which requires a predictive platform that provides integrated feedback on both anti-tumor activity and on-target toxicity in a clinically relevant timeframe.
Methods: We established a comprehensive biobank of patient-derived organoids (PDOs) from breast, gastric, and non-small cell lung cancers, alongside matched normal organoids. We developed a high-throughput, standardized co-culture assay with PBMCs or T cells to evaluate a panel of ERBB-targeting bispecific antibodies with varying molecular formats. The entire workflow was designed for speed, generating parallel data on tumor organoid killing and normal organoid toxicity within days.
Results: Our platform delivered robust, quantitative data on the therapeutic index for a series of BsAb candidates within a 3-week window. We demonstrated that affinity-tuning towards tumor-associated antigen levels could preferentially spare normal organoids with low ERBB expression while maintaining potent tumor killing. Furthermore, comparing different BsAbs revealed that certain molecular formats or epitope choices were associated with reduced cytokine release and less severe toxicity in normal organoids, without compromising efficacy in target-positive tumors. This provides a direct, rapid strategy for lead candidate selection and optimization.
Conclusion: We have developed a rapid, reproducible PDO-based platform that not only predicts the therapeutic window of ERBB-targeting BsAbs but also provides critical insights for their molecular optimization. This "fast-feedback" system can significantly de-risk and accelerate the translation of BsAbs by enabling data-driven decisions on affinity, format, and epitope selection early in the drug development process. The ability to rapidly profile multiple candidates against a backdrop of human tumor and normal tissues makes this an invaluable tool for designing safer and more effective bispecific antibodies. This study presents a transformative preclinical optimization tool. By generating predictive safety and efficacy data in weeks, our platform moves beyond mere prediction to active guidance, empowering the rational design of next-generation BsAbs with an inherently improved therapeutic profile for clinical trials.
利益披露 Disclosure
Y. Liu, None..
C. Wang, None..
J. Zhao, None..
L. Zeng, None.