Quantitative analysis of pharmacological mechanism by intravital imaging

这项建议来自一个跨学科的团队，该团队在成像、系统生物学和 临床研究(Weissleder、Sorger、Mitchison和Danuser)。它响应RFA-CA-11-005， 并建议在小鼠身上建立和验证定量癌症药理学平台 在单细胞水平上使用实时成像。眼前的目标是确定个人如何 肿瘤中的癌细胞在体内对单剂和双剂治疗的反应是通过测量 药物在肿瘤细胞中的摄取和分布(药代动力学，PK)，以及 下游反应在不同的时间尺度上发挥作用(药物动力学，PD)。我们的 重点将放在Navitoclax的使用上(ABT263；Bcl2和Bclxl的研究拮抗剂) 和吉西他滨(一种成熟的S阶段特异性细胞毒药物)，无论是单独还是在 组合。Aim 1将开发和验证用于测量药物前肿瘤的活细胞生物传感器 亚细胞体内分辨技术在小鼠移植瘤中的状态和药物反应 成像。AIM 2将开发基于新型生物正交体的可注射显像剂 用于荧光标记药物或用18F标记药物的化学物质。然后这些特工将被 使用目标1的生物传感器进行验证。目标3将专注于计算的开发 从活体图像中提取定量数据的方法，以及创建数据的方法- 驱动、多尺度(PK/PD；单细胞动力学和机械/分子)数学模型 这阐明了单一疗法和联合疗法在治疗上的相关差异。 总体而言，我们的目标是了解药物的作用，即：a)定量的(在 处理在时间和空间上可能不同的数据)；b)概率性(考虑单元到单元 和肿瘤对肿瘤的可变性)；c)分子水平的机制；d)后基因组 (分析不同的细胞系和已知遗传差异的患者样本)；e)整合 (假设药物反应的决定因素是多因素的)；f)数学上 复杂(关于质量作用、集中参数和随机建模)；和g) 医学上相关的(通过分析目前正在进行的临床研究和 通过开发可翻译的测量方法)。该项目的成功将导致一个 单细胞癌药理综合平台。这个平台不仅将用于 克服将临床前成果转化为临床环境的主要障碍； 为合理的、预测性的联合治疗提供了知识基础。同样，它 将提高我们开发有效药物的能力，能够抑制新的和现有的靶点， 也可以更广泛地用于其他新兴抗癌药物的开发。

This proposal is from an inter-disciplinary team with expertise in imaging, systems biology and clinical research (Weissleder, Sorger, Mitchison and Danuser). It responds to RFA-CA-11-005, and proposes to create and validate a platform for quantitative cancer pharmacology in mice using live imaging at the single-cell level. The immediate goal is to determine how individual cancer cells in tumors respond to single and dual-agent therapy in vivo by measuring both the uptake and distribution of drugs in tumor cells (pharmaco-kinetics, PK), as well as the multiple downstream responses that play out over different time-scales (pharmaco-dynamics, PD). Our focus will be on the use of navitoclax (ABT263; an investigational antagonist of Bcl-2 and Bcl-Xl) and gemcitabine (a well established, S-phase-specific cytotoxic drug), both individually and in combination. Aim 1 will develop and validate live-cell biosensors for measuring pre-drug tumor states and drug responses in mouse xenograft tumors by sub-cellular resolution intravital imaging. Aim 2 will develop injectable imaging agents based on novel bioorthogonal chemistries that will be used to label drugs fluorescently or with 18F. These agents will then be validated using the biosensors from aim 1. Aim 3 will focus on the development of computational methods for extracting quantitative data from intravital images, and on the creation of data- driven, multi-scale (PK/PD; single cell kinetics and mechanistic/molecular) mathematical models that elucidate therapeutically relevant differences between mono and combination therapy. Overall, our goal is to gain an understanding of drug action that is: a) quantitative (in the treatment of data that may vary in time and space); b) probabilistic (accounting for cell-to-cell and tumor-to-tumor variability); c) mechanistic at the molecular level; d) post-genomic (analyzing diverse cell lines and patient samples with known genetic differences); e) integrative (assuming that determinants of drug response are multi-factorial); f) mathematically sophisticated (with respect to mass-action, lumped parameter and stochastic modeling); and g) medically relevant (by analyzing a combination therapy currently under clinical investigation and by developing translatable measurement methods). Success of the project will result in an integrated platform for single-cell cancer pharmacology. This platform will not only serve to overcome prevailing impediments to the translation of preclinical results into a clinical setting, but will provide the knowledge base for rational and predictive combination therapy. Likewise, it will improve our ability to develop effective drugs capable of inhibiting new and existing targets, and could also be more broadly used in the development of other emerging anti-cancer drugs.