It is challenging to design effective drug delivery systems (DDS) that target metastatic breast cancers (MBC) because of lack of competent imaging and image analysis protocols that suitably capture the interactions between DDS and metastatic lesions. Here, we integrate high temporal resolution of in vivo whole-body PET-CT, ex vivo whole-organ optical imaging, high spatial resolution of confocal microscopy, and mathematical modeling, to systematically deconstruct the trafficking of injectable nanoparticle generators encapsulated with polymeric doxorubicin (iNPG-pDox) in pulmonary MBC. iNPG-pDox accumulated substantially in metastatic lungs, compared to healthy lungs. Intratumoral distribution and retention of iNPG-pDox varied with lesion size, possibly induced by locally remodeled microenvironment. We further used multiscale imaging and mathematical simulations to provide improved drug delivery strategies for MBC. Our work presents a multidisciplinary translational toolbox to evaluate transport and interactions of DDS within metastases. This knowledge can be recursively applied to rationally design advanced therapies for metastatic cancers.

由于缺乏适当地捕捉 DDS 和转移性病变之间相互作用的有效成像和图像分析协议，因此设计针对转移性乳腺癌 (MBC) 的有效药物输送系统 (DDS) 具有挑战性。在这里，我们整合了体内全身 PET-CT 的高时间分辨率、离体全器官光学成像、共聚焦显微镜的高空间分辨率和数学建模，系统地解构了用聚合多柔比星封装的可注射纳米粒子发生器的运输。 iNPG-pDox）在肺 MBC 中。与健康肺相比，iNPG-pDox 在转移性肺中大量积累。iNPG-pDox 的瘤内分布和保留随病变大小而变化，可能是由局部重塑的微环境引起的。我们进一步使用多尺度成像和数学模拟来为 MBC 提供改进的药物递送策略。我们的工作提供了一个多学科转化工具箱，用于评估 DDS 在转移灶内的转运和相互作用。这些知识可以递归地应用于合理设计转移性癌症的先进疗法。