Mechanistic Pharmacodynamic Modeling for Drug Combination Responses

药物组合反应的机制药效学建模

• 批准号: 10580895
• 负责人: Marc R. Birtwistle
• 金额: $ 25万
• 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/10580895
• 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; Foundations; Gene Targeting; Genetic; Industry; Ingestion; Mammalian Cell; Medical; Modeling; Molecular; Multiomic Data; Music; Patients; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacology; Physics; Regulation; Signal Transduction; Structure; Testing; Time; Validation; Variant; Vision; 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.

摘要 - 药物组合的机械药效学建模 大多数行业在实施之前模拟了设计选项，但这在药品中很少可能 和医疗行业。一个重要的差距是公正的药物组合反应预测，即 实验上不切实际。我们实验室的长期愿景是改善药物开发和精确医学 通过构建可以模拟药物组合反应的“机械药物大型动力学模型”。这样的 通过物理和工程方法注入药理学概念，以描述因果，定量， 和药物反应的动态机制。基本的前提是捕获（i）机械， 因果网络结构，（ii）剂量反应，（iii）动力学和（iv）细胞细胞变异性是为了改善改善 许多组合响应预测。在这里，我们研究药物组合如何影响单细胞 通过合并理论和实验创新通过合并和死亡命运。第一个项目构建 在我们最近和最全面的单细胞增殖的机械模型中 和死亡动态。我们将利用我们最近的Lincs财团的参与，这产生了 扰动反应动力学的深层分子表征，包括对8种药物的剂量反应。我们 将使用新方法将网络生物学与机械模型整合在一起，以获取候选模型 与该数据集一致，并通过实验测试了8种药物的药物组合预测。这会 首次解决了各种各样的全面药物组合反应的预测 依靠因果生化推理的作用机制，还确定了信号的新机制 和药物反应通过迭代模型的完善和实验验证。第二个项目建立 在我们最近开发的用于荧光多路复用的实验方法中，称为音乐。我们提出了这一点 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查，这还没有 可能，但会变革。我们将通过评估A之间的遗传相互作用来测试该方法 最近策划了1,578种FDA批准药物的667个基因靶标。这项工作将提名新网络 结构不仅用于第一个项目，而且更普遍。第三个项目还利用 上面的机械模型，但跨细胞系与癌细胞系百科全书数据的枢轴有关1,132个细胞系 和24种药物。我们模型的创新性和基础特征是，它摄入多摩变数据以创建一个 通过“初始化”的特定环境。我们将生成具有细胞系特异性的1,132个模型变体 概况并评估单个和优先的药物组合反应的预测能力。这个项目将 建立当前模型的性能，确定关键的建模差距以改善预测，建议 新的潜在有效的药物组合，并阐明了协同作用的基础机制。总体而言，这些 项目将产生下一代药效学模型，以填补药物组合 预测差距阻碍药物开发和精确医学。