Multiscale Computational Models Guided By Emerging Cellular Dynamics Quantification For Predicting Optimum Immune Checkpoint And Targeted Therapy Schedules

以新兴细胞动力学量化为指导的多尺度计算模型,用于预测最佳免疫检查点和靶向治疗方案

基本信息

  • 批准号:
    9977480
  • 负责人:
  • 金额:
    $ 49.78万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-05-01 至 2025-04-30
  • 项目状态:
    未结题

项目摘要

Project Summary The principal goal of this proposal is to combine multiscale mathematical modeling with novel computational model-driven quantitative experimental platforms to develop a comprehensive and predictive 3D computational framework. Bladder cancer is one of the 10 most common cancers in the United States and in its advanced stages the 5-year survival rates are below 35%. Given the poor outcomes with chemotherapy in advanced cases, immunotherapy has emerged as an exciting domain for exploration. Monoclonal antibodies targeting the PD- 1/PD-L1 “immune checkpoint” pathway have resulted in favorable outcomes in advanced bladder cancer, and 5 drugs targeting this pathway have been approved in the past two years. Unfortunately, the objective response rates of current FDA approved immunotherapy drugs remain less than 25%. An alternative treatment strategy for bladder cancer is small molecule inhibitors (SMIs) of fibroblast growth factor receptor (FGFR3), and early clinical studies using these molecular-targeted agents have shown promise. Recently published data supporting the co-acting combination of potent immune checkpoint inhibitors and specific FGFR3 inhibitors potentially offer an advance in targeted therapeutics for cancer. A powerful and practical way to optimize novel drug combinations for clinical cancer treatment is to use sophisticated, data-driven computational models. Our proposed agent- based model platform will both aid in the characterization of tumor-immune dynamics and also suggest the best strategies for administering therapeutic combinations of immune-checkpoint and receptor kinase inhibitors. The model will be parameterized at the molecular and cellular scales by an innovative high-throughput image quantification pipeline that allows T-cell or cancer cell behaviors and interactions to be observed, tracked, and quantified. Importantly, this model system pipeline can measure the antigen burden on tumor cells and the proportion of the two types of T-cell cytotoxicity (Fas-ligand vs. granule-based). Our experimentally-driven multiscale approach is posed to (1) significantly enhance the current understanding of the impact of differential cell-kill mechanisms on tumor-immune outcomes; (2) optimize the administration of combination therapy and maximize tumor response; and (3) to improve the ability to select the most promising drugs for the clinical trials. While based on tumors of the bladder, the platform that we are developing is easily adaptable for the study of any therapy targeted to immune checkpoint proteins and receptor kinases in any tumor type. The true significance of our work lies in its translational value: our experimental and theoretical studies will be able to test clinically relevant hypotheses regarding the prospect of receptor tyrosine kinase inhibitors and immune checkpoint inhibitors to impact the mechanism of tumor cell kill by immune cells in distinct ways. Cancer is one of the leading causes of death for Americans and at present the overall effectiveness of therapeutic treatments is only approximately 50%. The development treatment optimization tools could have enormous and immediate impact on the lives of millions of people diagnosed with cancer.
项目摘要 该建议的主要目标是将联合收割机多尺度数学建模与新的计算 模型驱动的定量实验平台,以开发一个全面的和预测性的3D计算 框架.膀胱癌是美国10种最常见的癌症之一, 5年生存率低于35%。考虑到晚期病例化疗的效果不佳, 免疫治疗已经成为一个令人兴奋的探索领域。靶向PD的单克隆抗体- 1/PD-L1 "免疫检查点"通路在晚期膀胱癌中产生了有利的结果,5 靶向这一途径的药物在过去两年中已获得批准。不幸的是,客观的反应 目前FDA批准的免疫治疗药物的比率仍然低于25%。替代治疗策略 膀胱癌的治疗是成纤维细胞生长因子受体(FGFR3)的小分子抑制剂(SMI), 使用这些分子靶向剂的临床研究已经显示出希望。最近公布的数据支持 有效的免疫检查点抑制剂和特异性FGFR3抑制剂的共同作用组合可能提供 癌症靶向治疗的进展优化新型药物组合的强大而实用的方法 临床癌症治疗的关键是使用复杂的、数据驱动的计算模型。我们推荐的代理人- 的模型平台将有助于肿瘤免疫动力学的表征,并提出最佳的 用于施用免疫检查点和受体激酶抑制剂的治疗组合的策略。的 该模型将通过创新的高通量图像在分子和细胞尺度上进行参数化 量化管道,允许观察、跟踪和分析T细胞或癌细胞的行为和相互作用, 量化。重要的是,该模型系统管道可以测量肿瘤细胞上的抗原负荷,并且可以测量肿瘤细胞上的抗原负荷。 两种类型的T细胞细胞毒性的比例(Fas-配体与基于颗粒的)。我们的实验驱动 多尺度方法提出(1)显着提高目前的理解差异的影响, 肿瘤免疫结果的细胞杀伤机制;(2)优化联合治疗的施用, 最大化肿瘤反应;和(3)提高为临床试验选择最有希望的药物的能力。 虽然基于膀胱肿瘤,但我们正在开发的平台很容易适用于研究 任何肿瘤类型中靶向免疫检查点蛋白和受体激酶的任何疗法。真正的 我们的工作的意义在于它的转化价值:我们的实验和理论研究将能够测试 关于受体酪氨酸激酶抑制剂和免疫的前景的临床相关假设 检查点抑制剂以不同的方式影响免疫细胞杀死肿瘤细胞的机制。癌是一种 美国人死亡的主要原因以及目前治疗方法的总体有效性 只有大约50%。开发治疗优化工具可以产生巨大而直接的影响 对数百万被诊断患有癌症的人的生活产生影响。

项目成果

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Trachette Jackson其他文献

Trachette Jackson的其他文献

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{{ truncateString('Trachette Jackson', 18)}}的其他基金

Multiscale Computational Models Guided By Emerging Cellular Dynamics Quantification For Predicting Optimum Immune Checkpoint And Targeted Therapy Schedules
以新兴细胞动力学量化为指导的多尺度计算模型,用于预测最佳免疫检查点和靶向治疗方案
  • 批准号:
    10397586
  • 财政年份:
    2020
  • 资助金额:
    $ 49.78万
  • 项目类别:
Multiscale Computational Models Guided By Emerging Cellular Dynamics Quantification For Predicting Optimum Immune Checkpoint And Targeted Therapy Schedules
以新兴细胞动力学量化为指导的多尺度计算模型,用于预测最佳免疫检查点和靶向治疗方案
  • 批准号:
    10624253
  • 财政年份:
    2020
  • 资助金额:
    $ 49.78万
  • 项目类别:

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