Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Cambridge, CB2 1EW, UK; Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 10115, Berlin, Germany.
Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Cambridge, CB2 1EW, UK.
Drug Discov Today. 2019 Dec;24(12):2286-2298. doi: 10.1016/j.drudis.2019.09.002. Epub 2019 Sep 10.
Synergistic drug combinations are commonly sought to overcome monotherapy resistance in cancer treatment. To identify such combinations, high-throughput cancer cell line combination screens are performed; and synergy is quantified using competing models based on fundamentally different assumptions. Here, we compare the behaviour of four synergy models, namely Loewe additivity, Bliss independence, highest single agent and zero interaction potency, using the Merck oncology combination screen. We evaluate agreements and disagreements between the models and investigate putative artefacts of each model's assumptions. Despite at least moderate concordance between scores (Pearson's r >0.32, Spearman's ρ>0.34), multiple instances of strong disagreement were observed. Those disagreements are driven by, among others, large differences in tested concentrations, maximum response values and median effective concentrations.
协同药物组合通常被用于克服癌症治疗中的单药耐药性。为了确定这些组合,我们进行了高通量癌细胞系组合筛选,并使用基于根本不同假设的竞争模型来量化协同作用。在这里,我们使用默克肿瘤学组合筛选来比较四种协同作用模型(即 Loewe 加性、Bliss 独立性、最高单药和零相互作用效价)的行为。我们评估了模型之间的一致性和分歧,并研究了每个模型假设的潜在人为因素。尽管评分之间存在至少中等程度的一致性(Pearson r >0.32,Spearman ρ>0.34),但仍观察到多个强烈不一致的实例。这些分歧是由测试浓度、最大响应值和中效浓度的差异等因素引起的。