Mechanistic Pharmacodynamic Modeling for Drug Combination Responses

药物组合反应的机制药效学建模

• 批准号: 10592423
• 负责人: Marc R. Birtwistle
• 金额: $ 37.23万
• 依托单位: CLEMSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10592423
• 关键词: Address; Affect; Biochemical; Biology; Cancer cell line; Cell Death; Cell Line; Cell Proliferation; Cells; Cessation of life; Data; Data Set; Dose; Drug Combinations; Drug Industry; Encyclopedias; Engineering; FDA approved; Fluorescence; Gene Targeting; Genetic; Industry; Ingestion; Mammalian Cell; Medical; Modeling; Molecular; Multiomic Data; Music; Patients; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacology; Physics; Proliferating; Regulation; Signal Transduction; Structure; Testing; Time; Validation; Variant; Work; design; drug development; drug testing; high dimensionality; improved; innovation; models and simulation; next generation; novel; novel strategies; pharmacodynamic model; precision medicine; predicting response; response; screening; synergism; theories

ABSTRACT – MECHANISTIC PHARMACODYNAMIC MODELING FOR DRUG COMBINATIONS Most industries simulate design options before implementation, but this is rarely possible in the pharmaceutical and medical industries. An important gap is unbiased drug combination response predictions, which is experimentally impractical. A long-term vision of our lab is improving drug development and precision medicine by building “mechanistic pharmacodynamic models” that can simulate drug combination responses. Such models infuse pharmacology concepts with physics and engineering approaches to describe causal, quantitative, and dynamic mechanisms underlying drug response. A foundational premise is that capturing (i) mechanistic, causal network structure, (ii) dose-response, (iii) dynamics, and (iv) cell-cell variability is necessary to improve many combination response predictions. Here, we study how drug combinations affect single-cell proliferation and death fates by merging theoretical and experimental innovation. The first project builds on our recent and one of the most comprehensive mechanistic models for regulation of single-cell proliferation and death dynamics. We will leverage our involvement with a recent LINCS consortium effort that generated a deep molecular characterization of perturbation response dynamics, including dose responses to 8 drugs. We will integrate network biology with mechanistic models using new approaches to obtain candidate models that are consistent with this dataset, and experimentally test drug combination predictions for the 8 drugs. This will for the first time address the prediction of a comprehensive set of drug combination responses across varied mechanisms of action relying on causal biochemical reasoning and also identify novel mechanisms of signaling and drug response through iterative model refinement and experimental validation. The second project builds on our recently developed experimental approach for fluorescence multiplexing called MuSIC. We propose that MuSIC can enable high-dimensional genetic interaction screening in single mammalian cells, which is not yet possible but would be transformative. We will test the approach by evaluating genetic interactions between a recently curated set of 667 gene targets of 1,578 FDA-approved drugs. This work will nominate new network structures not only for use in the first project, but also more generally. The third project also leverages the above mechanistic model but pivots across cell lines with Cancer Cell Line Encyclopedia data for 1,132 cell lines and 24 drugs. An innovative and foundational feature of our model is that it ingests multi-omic data to create a cell line-specific context through “initialization”. We will generate 1,132 model variants with cell line-specific profiles and evaluate predictive capacity for single and prioritized drug combination responses. This project will establish performance of the current models, identify critical modeling gaps for improving predictions, suggest new potentially effective drug combinations, and elucidate mechanisms underlying synergy. Overall, these projects will produce next-generation pharmacodynamic models that move towards filling the drug combination prediction gap that hinders drug development and precision medicine.

药物组合的抽象-机械药效学模型 大多数行业在实施之前都会模拟设计选项，但这在制药行业是不可能的 和医疗行业。一个重要的差距是无偏见的药物联合反应预测，这是 在实验上是不切实际的。我们实验室的长期愿景是改进药物开发和精准医学 通过建立可以模拟药物联合反应的“机制药效学模型”。是这样的 模型向药理学概念注入物理学和工程学方法，以描述因果、定量、 以及药物反应的动态机制。一个基本的前提是捕捉(I)机械性的， 因果网络结构，(Ii)剂量-反应，(Iii)动力学，以及(Iv)细胞-细胞变异性是改善的必要因素 许多组合反应预测。在这里，我们研究药物组合如何影响单细胞 理论创新和实验创新相结合决定了生物的增殖和死亡的命运。第一个项目构建 关于我们最新和最全面的单细胞增殖调控机制模型之一 和死亡动力学。我们将利用我们与Lincs财团最近的一项努力，该财团产生了 扰动反应动力学的深入分子表征，包括对8种药物的剂量反应。我们 将使用新的方法将网络生物学与机械模型相结合，以获得候选模型 与这个数据集一致，并在实验中测试这8种药物的药物组合预测。这将是 首次解决了对各种不同药物组合反应的全面预测 依赖因果生化推理的作用机制，也识别新的信号机制 并通过迭代模型精化和实验验证进行药物响应。第二个项目建立 关于我们最近开发的称为MUSIC的荧光多路复用实验方法。我们建议 音乐可以在单个哺乳动物细胞中实现高维遗传相互作用筛选，这还不是 这是可能的，但将是变革性的。我们将通过评估基因之间的相互作用来测试该方法 最近策划了1,578种FDA批准的药物的667个基因靶点。这项工作将提名新的网络 结构不仅适用于第一个项目，而且适用于更一般的情况。第三个项目还利用 以上机理模型，但通过癌细胞系百科全书数据显示1,132个细胞系 和24种药物。我们模型的一个创新和基本特征是它吸收多组数据来创建一个 通过“初始化”获得特定于单元格的上下文。我们将生成1,132个特定于细胞系的模型变体 介绍和评估单一和优先药物组合反应的预测能力。这个项目将 确定当前模型的性能，确定关键的建模差距以改进预测，建议 新的潜在有效的药物组合，并阐明潜在的协同作用机制。总的来说，这些 项目将生产下一代药效学模型，朝着填充药物组合的方向发展 阻碍药物开发和精准医学的预测差距。