Multiscale computational models for developing combination cancer therapy

用于开发癌症联合疗法的多尺度计算模型

基本信息

批准号：
8323312
负责人：
Jessie L.-S. Au
金额：
$ 33.75万
依托单位：
OPTIMUM THERAPEUTICS, LLC
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-01 至 2015-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8323312
关键词：
Accounting Address Animals Antineoplastic Agents Biological Cell Cycle Cell Cycle Progression Cells Combined Modality Therapy Computer Simulation Cultured Cells DNA DNA biosynthesis Development Dose Drug Combinations Drug Exposure Drug Kinetics Drug effect disorder Equation Event Exposure to Frequencies G1 Phase Goals In Vitro Individual Link Location Methods Mitosis Mitotic Modeling Molecular Molecular Target Outcome Paclitaxel Performance Pharmaceutical Preparations Pharmacodynamics Phase Population Positioning Attribute Process Publications Regulation Research Resistance development S Phase Signal Pathway Site Specific qualifier value Statistical Models Suramin Taxane Compound Time Translating Treatment Protocols Treatment outcome Uncertainty Vertebral column base combination cancer therapy cytotoxic cytotoxicity in vivo pharmacodynamic model population based predictive modeling response taxane tumor uptake

项目摘要

DESCRIPTION (provided by applicant): Development of effective combination therapy for cancer is challenging because many cancer drugs act on intersecting signaling pathways that can interfere with each other. For example, it is well established that drug-drug interactivity can change drastically, e.g., from synergy to antagonism, depending on the treatment conditions (drug concentrations, treatment time, and sequencing of the drugs). Further, under in vivo conditions, drug concentrations change with time (i.e., pharmacokinetics or PK) and different drugs have different PK, which make it difficult to translate the findings in cultured cells where drug concentrations are typically kept constant. We propose to develop multiscale, generalizable computational PK and pharmacodynamic (PD) models to address these challenges. First, we will develop predictive in vitro PD models for single agents. These PD models employ a combination of deterministic models (that designate the fate of a single cell based on drug actions and cell cycle location) and probabilistic models (that determine the fate of all cells). These models jointly depict the response of an individual cell and the overall response of whole cell population (as the collective response of individual cells), as mathematical functions of a treatment (drug concentrations, treatment time) and chemosensitivity of a cell. Second, we will develop predictive in vitro PD models for combination therapies. Drugs can interact on two levels, i.e., cell cycle distribution (cell cycle interactivity) and molecular targets (molecular interactivity). We will extend the above approaches for single agents to develop two-drug-combination models for three types of combinations: (a) drugs with only cell cycle interactivity, (b) drugs with molecular interactivity where both drugs have cytotoxic effects, and (c) drugs with molecular interactivity where one drug does not have cytotoxicity on its own but can enhance and reduce the activity of the other drug. Third, we will develop methods to convert in vitro PD to in vivo PD. We will address two issues, i.e., conversion of CxT under in vitro conditions (constant C) to in vivo situations (changing C), and extend the in vitro PD models to include the non-cycling G0 cells present in vivo tumors. Lastly, we will integrate in vitro PD models with in vivo PK models and evaluate the performance of the integrated models. We will develop integrated PK-PD models to describe the in vivo effects of single agents, followed by models for their combinations. Model performance is evaluated in tumor-bearing animals. The proposed models are first-of-its-kind and will enable the computation of outcomes of potential combinations of different drugs and/or different in vivo treatment schedules/sequences. Such predictive models can reduce the uncertainty in outcomes and the amount of experimentation and thereby accelerate the development of effective combination cancer therapies.

描述（由申请人提供）：开发有效的癌症联合疗法具有挑战性，因为许多抗癌药物作用于可能相互干扰的交叉信号传导途径。例如，众所周知，药物-药物相互作用可以根据治疗条件（药物浓度、治疗时间和药物顺序）发生巨大变化，例如从协同作用变为拮抗作用。此外，在体内条件下，药物浓度随时间变化（即药代动力学或 PK），并且不同的药物具有不同的 PK，这使得很难将研究结果转化为药物浓度通常保持恒定的培养细胞中。我们建议开发多尺度、可推广的计算 PK 和药效 (PD) 模型来应对这些挑战。首先，我们将开发单一药物的体外预测帕金森病模型。这些 PD 模型采用确定性模型（根据药物作用和细胞周期位置指定单个细胞的命运）和概率模型（确定所有细胞的命运）的组合。这些模型共同描述了单个细胞的反应和整个细胞群的总体反应（作为单个细胞的集体反应），作为治疗（药物浓度、治疗时间）和细胞化学敏感性的数学函数。其次，我们将开发联合疗法的体外预测帕金森病模型。药物可以在两个层面上相互作用，即细胞周期分布（细胞周期相互作用）和分子靶点（分子相互作用）。我们将扩展上述单一药物的方法，为三种类型的组合开发双药组合模型：（a）仅具有细胞周期相互作用的药物，（b）具有分子相互作用的药物，其中两种药物都具有细胞毒性作用，以及（c））具有分子相互作用的药物，其中一种药物本身不具有细胞毒性，但可以增强和降低另一种药物的活性。第三，我们将开发将体外PD转化为体内PD的方法。我们将解决两个问题，即体外条件（恒定 C）下的 CxT 转换为体内情况（改变 C），并将体外 PD 模型扩展到包括体内肿瘤中存在的非循环 G0 细胞。最后，我们将体外 PD 模型与体内 PK 模型整合并评估整合模型的性能。我们将开发综合 PK-PD 模型来描述单一药物的体内效应，然后开发其组合的模型。在荷瘤动物中评估模型性能。所提出的模型是同类首创，将能够计算不同药物和/或不同体内治疗方案/顺序的潜在组合的结果。这种预测模型可以减少结果的不确定性和实验量，从而加速有效组合癌症疗法的开发。