[摘 要] 小鼠是生物医学研究的基础模型，在人类疾病机制与治疗策略探索中发挥了重要作用，但其固有的遗传与免疫学差异 严重阻碍了基础研究向临床应用的有效转化。因此，开发免疫系统人源化小鼠模型具有重要意义与广阔应用前景。本文系统回顾 人源化小鼠模型的发展历程，从基于细胞或组织移植的第一代模型，跨越到引入人类细胞因子表达及利用CRISPR/Cas9等技术构 建的第二代及更精细的基因工程模型，详细梳理关键技术演进及优化策略。着重探讨新一代模型在改善髓系细胞与NK细胞发育、 精准模拟人类肿瘤免疫微环境等方面的技术突破。此外，文章深入探讨了免疫系统人源化小鼠模型在评估免疫检查点抑制剂、双 特异性抗体、抗体偶联药物、免疫细胞疗法等治疗手段及细胞因子释放综合征等毒性反应预测中的应用现状及面临的挑战。最后， 展望免疫系统人源化及个性化模型在精准医学与临床前评估中的发展趋势，为优化新药研发策略、提高转化成功率提供参考。

[Abstract] While murine models remain the cornerstone of biomedical research and play important roles in the exploration of human disease mechanism and therapeutic strategies, inherent genetic and immunological discrepancies between species constitute a major barrier to the translational success of basic research. Humanized immune system mouse models have therefore emerged with great significance and broad translational potential. This review systematically outlines the evolution of humanized immune system mouse models and delineates the key technical development and optimizing strategies from first-generation models relying on cell or tissue transplantation to second-generation and more refined genetically engineered models, driven by the incorporation of human cytokine expression and CRISPR/Cas9 technology. Special attention is given to technical advancements of the new-generation model in optimizing myeloid and NK cell development and accurately recapitulating the human tumor immune microenvironment. Additionally, this article provides an in-depth analysis of the application and challenges of these humanized immune system mouse models in the evaluation of immune checkpoint inhibitors, bispecific antibodies, antibody-drug conjugates, immune cell therapies and other therapeutic modalities, as well as toxicity prediction such as cytokine release syndrome. Concluding with a perspective on the development of immune system humanized and personalized models in precision medicine and pre-clinical assessment, this review aims to offer insights into optimizing new drug development strategies and promoting translational success rates.

[基金项目] 国家自然科学基金（82350114，82271775）；江苏省基础研究计划（BK20241809）