通过机器学习和高通量实验的闭环集成,设计具有完全杀死选择性的多特异性抗体
Justin Grace1, Pierre-Yves Colin1, Dan Foxler1
1LabGenius Therapeutics, R&D, London, UK.
mAbs
|December 8, 2025
概括
我们开发了EVATM,这是一个设计更好的T细胞参与者 (TCE) 来对抗固体瘤的平台. EVATM 快速优化 TCE 以实现高效率和瘤选择性,克服了关键的安全挑战.
科学领域:
- 免疫学 免疫学 免疫学
- 生物技术是生物技术.
- 计算生物学 计算生物学
背景情况:
- 在瘤外的点毒性限制了T细胞参与剂 (TCE) 在固体瘤中的有效性.
- 开发强效和瘤选择性TCE需要探索广的设计空间.
研究的目的:
- 引入EVATM,一个闭环设计平台,用于优化T细胞参与者.
- 为了证明EVATM在识别强效和瘤选择性TCE候选者的能力.
主要方法:
- EVATM集成了高通量功能测试与多目标贝叶斯优化.
- 代设计-构建-测试-学习循环被用来探索组合的TCE空间.
- 一个HER2×CD3案例研究分析了基于价值,拓,亲和力和间距的44,160个设计.
主要成果:
- 与Sobol基线相比,EVATM实现了强效,瘤选择性候选者的14倍丰富.
- 优化的TCE显示出10 pM以下的强度对HER2高细胞具有近乎完整的疗效.
- 对低HER2模型的选择性超过了10,000倍,与贪门的原则保持一致.
结论:
- EVATM提供了一种高效的,数据驱动的方法,用于快速优化T细胞参与.
- 密度关闭的狂热性成为提高TCE安全性和有效性的关键设计原则.
- 该平台成功地将其推广到第二个目标,展示了其广泛的适用性.
关键词:
人工智能的人工智能贪 贪 是一种贪.在HER2中,HER2是HER2.多特异性抗体 多特异性抗体这是TCEs.这就是VHH VHH.贝叶斯的优化是贝叶斯的优化.机器学习是机器学习.纳米体是一个纳米体.选择性的选择性一个单域抗体的抗体.更多相关视频
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