Low cost, high temporal resolution optical fiber-based γ-photon sensor for real-time pre-clinical evaluation of cancer-targeting radiopharmaceuticals

光纤 时间分辨率 分辨率(逻辑) 癌症 计算机科学 材料科学 纳米技术 医学 光学 物理 内科学 人工智能 电信
作者
Rahul Lall,Kyoungtae Lee,Shalini Chopra,Averal Kandala,Michael J. Evans,Youngho Seo,Ali M. Niknejad,Mekhail Anwar
出处
期刊:Biosensors and Bioelectronics [Elsevier]
卷期号:247: 115956-115956
标识
DOI:10.1016/j.bios.2023.115956
摘要

Cancer radiopharmaceutical therapies (RPTs) have demonstrated great promise in the treatment of neuroendocrine and prostate cancer, giving hope to late-stage metastatic cancer patients with currently very few treatment options. These therapies have sparked a large amount of interest in pre-clinical research due to their ability to target metastatic disease, with many research efforts focused towards developing and evaluating targeted RPTs for different cancer types in in vivo models. Here we describe a method for monitoring real-time in vivo binding kinetics for the pre-clinical evaluation of cancer RPTs. Recognizing the significant heterogeneity in biodistribution of RPTs among even genetically identical animal models, this approach offers long-term monitoring of the same in vivo organism without euthanasia in contrast to ex vivo tissue dosimetry, while providing high temporal resolution with a low-cost, easily assembled platform, that is not present in small-animal SPECT/CTs. The method utilizes the developed optical fiber-based γ-photon biosensor, characterized to have a wide linear dynamic range with Lutetium-177 (177Lu) activity (0.5–500 μCi/mL), a common radioisotope used in cancer RPT. The probe's ability to track in vivo uptake relative to SPECT/CT and ex vivo dosimetry techniques was verified by administering 177Lu-PSMA-617 to mouse models bearing human prostate cancer tumors (PC3-PIP, PC3-flu). With this method for monitoring RPT uptake, it is possible to evaluate changes in tissue uptake at temporal resolutions <1 min to determine RPT biodistribution in pre-clinical models and better understand dose relationships with tumor ablation, toxicity, and recurrence when attempting to move therapies towards clinical trial validation.
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