Folkesson Evelina, Sakshaug B Cristoffer, Hoel Andrea D, Klinkenberg Geir, Flobak Åsmund
Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
Department of Biotechnology, SINTEF Materials and Chemistry, Trondheim, Norway.
Front Syst Biol. 2023 Feb 27;3:1112831. doi: 10.3389/fsysb.2023.1112831. eCollection 2023.
Drug combinations have been proposed to combat drug resistance in cancer, but due to the large number of possible drug targets, testing of all possible combinations of drugs is challenging. Computational models of a disease hold great promise as tools for prediction of response to treatment, and here we constructed a logical model integrating signaling pathways frequently dysregulated in cancer, as well as pathways activated upon DNA damage, to study the effect of clinically relevant drug combinations. By fitting the model to a dataset of pairwise combinations of drugs targeting MEK, PI3K, and TAK1, as well as several clinically approved agents (palbociclib, olaparib, oxaliplatin, and 5FU), we were able to perform model simulations that allowed us to predict more complex drug combinations, encompassing sets of three and four drugs, with potentially stronger effects compared to pairwise drug combinations. All predicted third-order synergies, as well as a subset of non-synergies, were successfully confirmed by experiments in the colorectal cancer cell line HCT-116, highlighting the strength of using computational strategies to rationalize drug testing.
已经有人提出使用联合药物来对抗癌症中的耐药性，但由于可能的药物靶点数量众多，对所有可能的药物组合进行测试具有挑战性。疾病的计算模型作为预测治疗反应的工具具有很大的前景，在此我们构建了一个逻辑模型，整合了癌症中经常失调的信号通路以及DNA损伤时激活的通路，以研究临床相关联合药物的效果。通过将模型拟合到靶向MEK、PI3K和TAK1的药物以及几种临床批准药物（帕博西尼、奥拉帕利、奥沙利铂和5-氟尿嘧啶）的成对组合数据集，我们能够进行模型模拟，从而预测更复杂的联合药物，包括三种和四种药物的组合，与成对药物组合相比可能具有更强的效果。所有预测的三阶协同作用以及一部分非协同作用，都在结肠癌细胞系HCT-116的实验中得到了成功证实，突出了使用计算策略使药物测试合理化的优势。
