Biotech Research and Innovation Center, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark.
DTU Compute, Technical University of Denmark, Kgs. Lyngby 2800, Denmark.
Cell Rep. 2017 Sep 19;20(12):2784-2791. doi: 10.1016/j.celrep.2017.08.095.
Signaling networks are nonlinear and complex, involving a large ensemble of dynamic interaction states that fluctuate in space and time. However, therapeutic strategies, such as combination chemotherapy, rarely consider the timing of drug perturbations. If we are to advance drug discovery for complex diseases, it will be essential to develop methods capable of identifying dynamic cellular responses to clinically relevant perturbations. Here, we present a Bayesian dose-response framework and the screening of an oncological drug matrix, comprising 10,000 drug combinations in melanoma and pancreatic cancer cell lines, from which we predict sequentially effective drug combinations. Approximately 23% of the tested combinations showed high-confidence sequential effects (either synergistic or antagonistic), demonstrating that cellular perturbations of many drug combinations have temporal aspects, which are currently both underutilized and poorly understood.
信号网络是非线性和复杂的，涉及到大量动态相互作用状态，这些状态在空间和时间上不断变化。然而，治疗策略，如联合化疗，很少考虑药物干扰的时间。如果我们要推进复杂疾病的药物发现，就必须开发能够识别对临床相关干扰的动态细胞反应的方法。在这里，我们提出了一个贝叶斯剂量反应框架和对肿瘤药物矩阵的筛选，包括黑色素瘤和胰腺癌细胞系中的 10000 种药物组合，从中我们预测了依次有效的药物组合。大约 23%的测试组合显示出高置信度的顺序效应（协同或拮抗），这表明许多药物组合的细胞干扰具有时间方面，这些方面目前既未得到充分利用，也未得到很好的理解。
