计算机科学
计算生物学
放射性核素治疗
模块化设计
蓝图
化学
示意图
聚乙二醇化
纳米技术
配体(生物化学)
精密医学
领域(数学)
放射免疫疗法
医学
溶瘤病毒
生化工程
神经科学
个性化医疗
临床试验
转化研究
工程类
癌症治疗
后天抵抗
出处
期刊:Journal of nuclear medicine
[Society of Nuclear Medicine and Molecular Imaging]
日期:2026-08-06
卷期号:: jnumed.125.271853-jnumed.125.271853
标识
DOI:10.2967/jnumed.125.271853
摘要
Radionuclide ligand conjugates (RLCs) have emerged as a powerful therapeutic and theranostic platform in oncology, with clinical validation achieved in prostate-specific membrane antigen- and somatostatin receptor-directed settings. Yet the field now faces a pivotal translational challenge: future expansion will not be determined by radionuclide availability alone, but by whether RLC development can move beyond modular radiochemistry toward an integrated biologic design framework. Current pipelines still tend to optimize targeting ligand, chelator, and radionuclide as separable components, whereas clinical performance is ultimately governed by their interaction with target density, intratumoral distribution, internalization behavior, normal-organ exposure, and adaptive resistance. Here, we assert that the next generation of RLCs should be developed through a translational logic that aligns target biology, ligand pharmacology, isotope physics, and resistance mechanisms from the outset. We first examine why the conventional ligand-chelator-payload model, although foundational, is insufficient to support broad clinical generalization beyond a small number of validated targets. We then highlight 3 underrecognized barriers to expansion: inadequate biologic stratification in target selection, incomplete matching between radionuclide properties and disease architecture, and limited integration of resistance-informed combination strategies. Finally, we propose a practical blueprint for next-generation RLC development centered on biologically prioritized target discovery, disease-contextual isotope selection, and biomarker-guided combination therapy. Reframing RLC development in this way may help move the field from isolated successes to a scalable precision oncology platform.
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