Anti-PD-1 therapies have transformed cancer treatment by restoring antitumor T cell activity. Despite their broad clinical use, variability in treatment response and immune-related adverse events underscore the need for therapeutic optimization. This article provides an integrative overview of the pharmacokinetics (PKs) of anti-PD-1 antibodies—such as nivolumab, pembrolizumab, and cemiplimab—and examines pharmacokinetic–pharmacodynamic (PK-PD) relationships, highlighting the impact of clearance variability on drug exposure, efficacy, and safety. Baseline clearance and its reduction during therapy, together with interindividual variability, emerge as important dynamic biomarkers with potential applicability across different cancer types for guiding individualized dosing strategies. The review also discusses established biomarkers for anti-PD-1 therapies, including tumor PD-L1 expression and immune cell signatures, and their relevance for patient stratification. The evidence supports a shift from traditional weight-based dosing toward adaptive dosing and therapeutic drug monitoring (TDM), especially in long-term responders and cost-containment contexts. Notably, the inclusion of clearance-based biomarkers—such as baseline clearance and its reduction—into therapeutic models represents a key step toward individualized, dynamic immunotherapy. In conclusion, optimizing anti-PD-1 therapy through PK-PD insights and biomarker integration holds promise for improving outcomes and reducing toxicity. Future research should focus on validating PK-based approaches and developing robust algorithms (machine learning models incorporating clearance, tumor burden, and other validated biomarkers) for tailored cancer treatment.
抗PD-1疗法通过恢复抗肿瘤T细胞活性，彻底改变了癌症治疗格局。尽管其临床应用广泛，但治疗反应差异及免疫相关不良事件凸显了治疗优化的必要性。本文系统综述了纳武利尤单抗、帕博利珠单抗及西米普利单抗等抗PD-1抗体的药代动力学特征，深入探讨了药代动力学-药效学关系，重点阐明清除率变异对药物暴露量、疗效和安全性的影响。基线清除率及其治疗期间的动态降低，结合个体间差异，成为具有跨癌种应用潜力的重要动态生物标志物，可用于指导个体化给药策略。本文同时讨论了抗PD-1治疗的成熟生物标志物（包括肿瘤PD-L1表达和免疫细胞特征谱）及其在患者分层中的价值。现有证据支持从传统体重给药模式转向适应性给药和治疗药物监测，特别是在长期应答者及成本控制场景中。值得注意的是，将基于清除率的生物标志物（如基线清除率及其动态变化）纳入治疗模型，标志着个体化动态免疫治疗迈出关键一步。总之，通过药代动力学-药效学机制解析与生物标志物整合来优化抗PD-1治疗，有望提升疗效并降低毒性。未来研究应聚焦于验证基于药代动力学的方法，并开发整合清除率、肿瘤负荷及其他已验证生物标志物的稳健算法（如机器学习模型），以实现精准癌症治疗。
肿瘤(癌症)患者之家