Noninvasive prediction of tumor response to gemcitabine using MRI

使用 MRI 无创预测肿瘤对吉西他滨的反应

• 批准号: 9328948
• 负责人: Guanshu Liu
• 金额: $ 37.49万
• 依托单位: HUGO W. MOSER RES INST KENNEDY KRIEGER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-07 至 2021-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9328948
• 关键词: Accelerometer; Anatomy; Animals; Antineoplastic Agents; Biodistribution; Biological Assay; Cells; Chemicals; Clinic; Combined Modality Therapy; Cytidine; Cytosine; Cytosine deaminase; Data Analyses; Deoxycytidine; Deoxycytidine Kinase; Detection; Development; Drug Delivery Systems; Drug effect disorder; Drug resistance; Drug usage; Effectiveness; Encapsulated; Enzymes; Future; Genetic Engineering; Genetically Engineered Mouse; Goals; Histology; Human; Image; Imaging Device; Imaging technology; Immunohistochemistry; Injectable; Label; Liposomes; Magnetic Resonance Imaging; Mainstreaming; Malignant Neoplasms; Malignant neoplasm of pancreas; Measurement; Measures; Medical Imaging; Metabolic; Methods; Modeling; Modification; Monitor; Mus; Names; Pancreatic Ductal Adenocarcinoma; Patients; Pharmaceutical Preparations; Physiological; Play; Protocols documentation; Protons; Public Health; Radioactive; Reverse Transcription; Role; Signal Transduction; Techniques; Technology; Testing; Time; Translating; Translations; United States National Institutes of Health; Unresectable; Water; analog; base; chemotherapy; cytosine analog; design; enzyme activity; gemcitabine; imaging agent; imaging modality; imaging probe; improved; in vivo; killings; metallicity; mouse model; nano; neoplastic cell; non-invasive imaging; pancreatic cancer cells; pancreatic neoplasm; patient stratification; personalized medicine; predicting response; response; therapy outcome; translational study; treatment group; treatment planning; tumor; tumor growth; tumor xenograft; uptake

The prediction of tumor response to chemotherapy can be achieved by elucidating the efficiency of drug delivery to the targeted tumor cells, and the effectiveness of the delivered drug to be activated and act on tumor cells. A non-invasive means that can answer these questions is essential for designing efficient and personalized therapy, and is especially crucial to improve the efficacy of treating pancreatic ductal adenocarcinoma (PDAC), one of the most lethal human malignancies. In the present study, we propose to develop a highly translatable MRI technology to answer the two questions mentioned above in the gemcitabine treatment of PADC, and hence to predict tumor responses. In particular, Our approach is based on a so-called Chemical Exchange Saturation Transfer (CEST) MRI contrast mechanism, by which drugs are imaged directly by their inherently carried exchangeable protons (OH, NH or NH2), at a detectability comparable to that for Gd-based agents. Formulated on the basis of our preliminary results, we hypothesize that agents that contain cytosine and cytidine, for instance gemcitabine, can be detected using CEST MRI, namely cytCEST. We anticipate our approach can be used to predict tumor response to the gemcitabine treatment by assessing the accumulation, biodistribution and retention of the drug in the tumor, without the need for imaging tags or additional agents. To achieve our goal, we will first optimize and validate the cytCEST MRI detection of tumor uptake and biodistribution of gemcitabine. Then we will develop cytCEST MRI as an effective means to detect the activity of deoxycytidine kinase (dCK), one of the most important drug-resistance-related enzymes. Finally the potential of cytCEST MRI to predict the response of pancreatic tumors to therapy will be examined on the treatment in KPC genetically engineered mouse models using three different gemcitabine-based treatments. Successful completion of this project will result in an imaging tool for the prediction of tumor response to gemcitabine using the drug or its analog deoxycytidine directly as the imaging agent, namely label-free because no chemical-modification is needed. It is expected that such a label-free approach can be rapidly translated to the clinic, allowing clinicians to stratify patients prior to (or immediately after) the administration of gemcitabine or other cytosine- or cytidine-based chemotherapeutic drugs and to choose the personalized treatment plan for each group of patients.

肿瘤对化疗的反应可以通过阐明药物的有效性来实现预测 递送到靶向肿瘤细胞，以及递送的药物被激活并作用于肿瘤的有效性 细胞能够回答这些问题的非侵入性手段对于设计高效且 个性化治疗，尤其是提高治疗胰腺导管炎的疗效至关重要 腺癌（PDAC），最致命的人类恶性肿瘤之一。在本研究中，我们建议 开发一种高度可转换的MRI技术，以回答吉西他滨中上述两个问题 治疗PADC，从而预测肿瘤反应。特别是，我们的方法是基于所谓的 化学交换饱和转移（CEST）MRI对比机制，通过该机制直接对药物成像 通过其固有携带的可交换质子（OH、NH或NH2），可检测性与 基于GD的代理商根据我们的初步结果制定，我们假设， 胞嘧啶和胞苷，例如吉西他滨，可以使用CEST MRI，即cytCEST来检测。我们 预期我们的方法可用于通过评估吉西他滨治疗的肿瘤反应， 药物在肿瘤中的蓄积、生物分布和保留，而不需要成像标签， 额外的代理人。为了实现我们的目标，我们将首先优化和验证肿瘤的cytCEST MRI检测 吉西他滨的摄取和生物分布。然后我们将开发cytCEST MRI作为检测的有效手段 脱氧胞苷激酶（dCK）的活性，其是最重要的耐药相关酶之一。最后 将在上检查cytCEST MRI预测胰腺肿瘤对治疗反应的潜力 使用三种不同的基于吉西他滨的治疗在KPC基因工程小鼠模型中进行治疗。 该项目的成功完成将产生一种成像工具，用于预测肿瘤对 吉西他滨直接使用药物或其类似物脱氧胞苷作为显像剂，即无标记，因为 不需要化学改性。预计这种无标签方法可以迅速转化为 诊所，允许临床医生在吉西他滨给药之前（或之后立即）对患者进行分层 或其他基于胞嘧啶或胞嘧啶的化疗药物，并选择个性化的治疗计划， 每一组患者。