Mechanistic Pharmacodynamic Modeling for Drug Combination Responses

摘要-药物组合的机制药效学建模 大多数行业在实施之前模拟设计选项，但这在制药行业中很少可能。 和医疗行业。一个重要的差距是无偏的药物组合反应预测， 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准医疗 通过建立“药效学机制模型”来模拟药物联合反应。等 模型将药理学概念与物理学和工程学方法结合起来，以描述因果关系，定量， 和药物反应的动力学机制。一个基本的前提是，捕获（i）机械， 因果网络结构，（ii）剂量反应，（iii）动力学，（iv）细胞间变异性是必要的，以改善 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 扩散和死亡的命运，融合理论和实验创新。第一个项目建立 关于我们最近和最全面的机制模型之一，调节单细胞增殖， 和死亡动力学我们将利用我们参与最近的LINCS联盟的努力， 扰动响应动力学的深入分子表征，包括对8种药物的剂量响应。我们 将使用新方法将网络生物学与机械模型相结合，以获得候选模型， 与该数据集一致，并通过实验测试8种药物的药物组合预测。这将 第一次解决了对各种药物组合反应的综合预测， 依赖于因果生化推理的作用机制，并确定新的信号传导机制 和药物反应通过迭代模型细化和实验验证。第二个项目建立 我们最近开发的荧光多路复用实验方法称为MuSIC。我们建议 MuSIC可以在单个哺乳动物细胞中进行高维遗传相互作用筛选，这是目前还没有的。 这是可能的，但它将是变革性的。我们将通过评估一个基因组和一个基因组之间的遗传相互作用来测试这种方法。 最近策划了一组1,578种FDA批准药物的667个基因靶点。这项工作将提名新的网络 结构不仅用于第一个项目，而且更普遍。第三个项目还利用了 上述机制模型，但以1，132个细胞系的癌细胞系百科全书数据为中心， 24种药物我们模型的一个创新和基础特征是，它摄取多组学数据来创建一个 细胞系特定的上下文通过“初始化”。我们将生成1，132个具有细胞系特异性 描述并评估单一和优先药物组合反应的预测能力。该项目将 建立当前模型的性能，确定改进预测的关键建模差距，建议 新的潜在有效的药物组合，并阐明协同作用的机制。总的来说，这些 这些项目将产生下一代药效学模型， 预测差距阻碍了药物开发和精准医疗。

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.