Project 3: Mechanism-Based Design ofCombination Therapies for Pancreatic Cancer

项目3：基于机制的胰腺癌联合疗法设计

• 批准号: 9149696
• 负责人: Tayyaba Hasan
• 金额: $ 22.29万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-09 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9149696
• 关键词: ABCG2 gene; Acute; Biodistribution; Biological; Biological Markers; Cancer Model; Cell Communication; Cell Line; Clinic; Clinical; Clinical Research; Clinical Trials; Collaborations; Colorectal Cancer; Combined Modality Therapy; Data; Development; Disease; Dose; Drug Delivery Systems; Drug Kinetics; Encapsulated; Epidermal Growth Factor Receptor; Erbitux; Erlotinib; Evaluation; FDA approved; Figs - dietary; Funding; Future; Genetic; Genetically Engineered Mouse; Goals; Growth; Heterogeneity; Human; Hypoxia; Image; Immunofluorescence Immunologic; Investigation; KDR gene; Light; Liposomes; Malignant Neoplasms; Malignant neoplasm of pancreas; Modeling; Molecular; Motivation; Neoplasm Metastasis; Outcome; PUVA Photochemotherapy; Pathology; Pathway interactions; Patients; Phase; Pre-Clinical Model; Primary Neoplasm; Publishing; Radio; Regimen; Resistance; Rosa; SN-38; Schedule; Skin Carcinoma; Surface; Survival Rate; Testing; Time; Toxic effect; Translating; Translations; Treatment Efficacy; Treatment outcome; Tumor Burden; Tumor Volume; Tyrosine Kinase Inhibitor; base; bench to bedside; cancer cell; cancer clinical trial; cytotoxic; density; design; dosimetry; image guided; in vivo; inhibitor/antagonist; irinotecan; mouse model; novel; novel therapeutics; outcome forecast; preclinical evaluation; preclinical study; programs; receptor; response; statistics; targeted agent; three dimensional cell culture; three-dimensional modeling; treatment planning; treatment response; tumor

ABSTRACT: Project 3-Mechanism-based Design of Combination Therapies for Pancreatic Cancer Project 3 develops mechanism and imaging-based combinations with photodynamic therapy (PDT) using novel nanoconstructs (NCs) in preclinical models of pancreatic cancer (PanCa), a disease with dismal statistics and tenacious resistance to current therapies. The promise of PDT in clinical and preclinical studies for PanCa in the previous cycle, our own findings in the current cycle, and collaborations within and outside the Program for efficient clinical translation, motivate the current investigations. The underlying hypothesis recognizes that the multiple growth/survival pathways of cancer development and progression demand an approach to combination treatments that exploits interactive mechanisms to achieve meaningful improvements in PanCa management. The strategy is to design combinations in which the first treatment primes/sensitizes the cancer cell for the second and can also be co-delivered, if warranted, for synergistic outcomes. The goals of the Project will be realized in 4 aims, interact heavily with the clinical projects, and build on our published and preliminary data showing superior control of local and metastatic PanCa with certain PDT-based combinations. Aim 1 will establish, in a genetically engineered mouse (GEM) model (Bardeesy Lab), the optimal schedule for a new combination treatment with (benzoporphyrin derivative, BPD)-PDT (FDA approved for AMD, in clinical studies for PanCa) and MM398, (liposomal irinotecan, Merrimack Pharma) a topisomerase I inhibitor that is FDA approved for colorectal cancer and is in phase III PanCa clinical trials. Assisted by a collaboration with Merrimack Pharma, the results of the PDT + MM-398 combination from Aim 1 will be rapidly translated within the funding cycle to clinical studies via Project 2, and could significantly impact the management of PanCa. Recognizing the need for appropriately timed and targeted combinations and based on preliminary data, Aim 2 takes a more forward-looking approach to synthesize EGFR-targeted multi-inhibitor containing liposomal NCs (TLNCs) for co-delivery of BPD and either a chemotherapeutic (SN-38) or a receptor tyrosine kinase inhibitor (XL184, a "dirty" inhibitor of VEGFR2, c-MET and EGFR pathways) to target key PanCa molecular pathways. To further enhance crosstalk within the Program, TLNCs containing Erlotinib will be developed for preclinical evaluation in non-melanoma skin cancers via Project 1 for eventual testing in humans. The optimal surface density of Erbitux will be established in vivo in collaboration with Project 4 and Core C. Aim 3 will test the optimized SN-38/XL184 TLNCs in sophisticated 3D heterocellular models that replicate stromal-cancer cell interactions, and will identify the most cytotoxic and selective constructs for testing in vivo. Aim 4 will evaluate the optimal cell line-specific TLNC from Aim 3 in orthotopic models of PanCa to assess acute tumor burden reduction (local and metastatic) with Core B and survival enhancement following combination treatment (PDT + SN-38/XL184). These novel targeted multi-agent constructs will form the basis of future clinical trials. Cores B and C provide imaging, pathology and statistical support.

摘要：项目3-基于机制的胰腺癌联合治疗设计 项目3开发了使用新的光动力疗法（PDT）的机制和基于成像的组合。 胰腺癌（PanCa）是一种统计数据令人沮丧的疾病， 对现有疗法的顽强抵抗。PDT在PanCa临床和临床前研究中的前景 上一个周期，我们自己在当前周期的发现，以及计划内外的合作， 有效的临床翻译，激励当前的研究。基本假设认为， 癌症发展和进展的多种生长/存活途径需要一种方法， 联合治疗，利用互动机制，实现有意义的改善PanCa 管理该策略是设计组合，其中第一次治疗引发/致敏癌症 细胞的第二个，也可以共同交付，如果有必要，协同成果。的目标 项目将实现4个目标，与临床项目密切互动，并建立在我们已发表的和 初步数据显示某些基于PDT的组合对局部和转移性PanCa的上级控制。 目的1将在基因工程小鼠（GEM）模型（Bardeesy Lab）中建立 用（苯并卟啉衍生物，BPD）-PDT（FDA批准用于AMD，在临床上 PanCa的研究）和MM 398（脂质体伊立替康，梅里马克制药公司），一种拓扑异构酶I抑制剂， FDA批准用于结直肠癌，目前正在进行III期PanCa临床试验。在与以下机构合作的协助下， 梅里马克制药公司，Aim 1的PDT + MM-398组合的结果将在 通过项目2将资金周期转移到临床研究，并可能显著影响PanCa的管理。 认识到有必要在适当的时间和有针对性的组合，并根据初步数据，目标2 采用更具前瞻性的方法来合成EGFR靶向的含有脂质体NCs的多抑制剂 用于共递送BPD和化疗剂（SN-38）或受体酪氨酸激酶抑制剂的TLNC （XL 184，VEGFR 2、c-MET和EGFR通路的“脏”抑制剂）靶向关键PanCa分子通路。 为了进一步增强项目内的串扰，将开发含厄洛替尼的TLNC用于临床前研究。 通过项目1在非黑色素瘤皮肤癌中进行评估，以最终在人类中进行测试。最佳表面 将与项目4和核心C合作在体内确定爱必妥的密度。目标3将测试 在复杂的3D异质细胞模型中优化SN-38/XL 184 TLNC， 相互作用，并将确定用于体内测试的最具细胞毒性和选择性的构建体。目标4将评估 在PanCa原位模型中，来自Aim 3的最佳细胞系特异性TLNC用于评估急性肿瘤负荷 Core B减少（局部和转移性），联合治疗（PDT）后生存率提高 + SN-38/XL184）。这些新的靶向多药结构将成为未来临床试验的基础。核 B和C提供影像学、病理学和统计学支持。