由于传统药物开发成功率低、周期长，临床趋势是应用组学技术来揭示患者的疾病特征和个体化治疗反应。然而，数据的异构形式和靶点分布的不均匀使得药物发现和精准医疗成为一项艰巨的任务。该研究以三阴性乳腺癌（TNBC）的细胞焦亡治疗为范式，利用大型TNBC队列的数据挖掘和药物数据库建立生物因子调节的神经网络，用于快速筛选和优化复合焦亡药物对。随后，使用米托蒽醌和藤黄酸的优选组合制备仿生纳米共晶体，以实现合理的药物递送。在TNBC模型中揭示了所获得的纳米共晶通过核糖体应激调节焦亡基因并触发焦亡级联免疫效应的独特机制。在这项工作中，基于目标组学的智能复合药物发现框架探索了一种创新药物开发范式，该范式重新利用现有药物并实现难治性疾病的精确治疗。© 2024。作者。

Due to low success rates and long cycles of traditional drug development, the clinical tendency is to apply omics techniques to reveal patient-level disease characteristics and individualized responses to treatment. However, the heterogeneous form of data and uneven distribution of targets make drug discovery and precision medicine a non-trivial task. This study takes pyroptosis therapy for triple-negative breast cancer (TNBC) as a paradigm and uses data mining of a large TNBC cohort and drug databases to establish a biofactor-regulated neural network for rapidly screening and optimizing compound pyroptosis drug pairs. Subsequently, biomimetic nanococrystals are prepared using the preferred combination of mitoxantrone and gambogic acid for rational drug delivery. The unique mechanism of obtained nanococrystals regulating pyroptosis genes through ribosomal stress and triggering pyroptosis cascade immune effects are revealed in TNBC models. In this work, a target omics-based intelligent compound drug discovery framework explores an innovative drug development paradigm, which repurposes existing drugs and enables precise treatment of refractory diseases.© 2024. The Author(s).