Data-centric feedback loops for next-generation immunotherapy development

Rotem Shalita

doi:10.1038/s41551-026-01785-6

2026-08-28

PRE

Rotem Shalita

Ido Amit *

DOI：10.1038/s41551-026-01785-6

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尽管受基因组学驱动的生物医学数据生成和计算能力呈爆炸式增长，但过去几十年中，进入I期临床试验的候选药物最终获得批准的概率却始终顽固地低。这一悖论表明瓶颈并非在于数据生成，而在于将生物和临床数据转化为指导进展、重新设计或终止的决策。本文认为，药物开发应从线性管道转变为由持续数据反馈驱动的迭代学习系统。我们概述了一个以数据为中心的框架，其中高维、多模态的分子和扰动数据，特别是单细胞和空间读出数据，被用于在发现和临床阶段迭代优化疾病模型、治疗假设、分子设计和患者分层策略。以免疫疗法作为概念验证领域，我们提出来自治疗性扰动的单细胞分子读出既能降低开发风险，又能深化对人类免疫反应的机制理解。最后，我们将此与强化学习进行类比，其中人类的分子和临床数据提供反馈信号，用于更新机制模型并指导后续干预措施的设计。采纳这一范式为更具机制基础、情境感知且转化成功率更高的疗法开辟了道路。本文认为，生物发现和药物开发应通过持续的数据反馈循环联系起来，以迭代优化治疗假设、药物设计和患者分层策略。

期刊

Nature Biomedical Engineering

IF:

26.6

论文数:

1.7K

被引数:

2.0W

机构

Weizmann Institute of Science

学者数:

1.3W

论文数: 1.1W

被引数: 2.3W

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