He Liye, Kulesskiy Evgeny, Saarela Jani, Turunen Laura, Wennerberg Krister, Aittokallio Tero, Tang Jing
Institute for Molecular Medicine Finland (FIMM), University of Helsinki, PO Box 33, Helsinki, 00014, Finland.
Department of Mathematics and Statistics, University of Turku, Turku, Finland.
Methods Mol Biol. 2018;1711:351-398. doi: 10.1007/978-1-4939-7493-1_17.
Gene products or pathways that are aberrantly activated in cancer but not in normal tissue hold great promises for being effective and safe anticancer therapeutic targets. Many targeted drugs have entered clinical trials but so far showed limited efficacy mostly due to variability in treatment responses and often rapidly emerging resistance. Toward more effective treatment options, we will need multi-targeted drugs or drug combinations, which selectively inhibit the viability and growth of cancer cells and block distinct escape mechanisms for the cells to become resistant. Functional profiling of drug combinations requires careful experimental design and robust data analysis approaches. At the Institute for Molecular Medicine Finland (FIMM), we have developed an experimental-computational pipeline for high-throughput screening of drug combination effects in cancer cells. The integration of automated screening techniques with advanced synergy scoring tools allows for efficient and reliable detection of synergistic drug interactions within a specific window of concentrations, hence accelerating the identification of potential drug combinations for further confirmatory studies.
在癌症中异常激活但在正常组织中未激活的基因产物或信号通路,极有可能成为有效且安全的抗癌治疗靶点。许多靶向药物已进入临床试验阶段,但迄今为止疗效有限,这主要是由于治疗反应的变异性以及耐药性往往迅速出现。为了获得更有效的治疗方案,我们需要多靶点药物或药物组合,它们能够选择性地抑制癌细胞的活力和生长,并阻断细胞产生耐药性的不同逃逸机制。药物组合的功能分析需要精心的实验设计和强大的数据分析方法。在芬兰分子医学研究所(FIMM),我们开发了一种实验 - 计算流程,用于高通量筛选癌细胞中的药物组合效应。将自动化筛选技术与先进的协同评分工具相结合,能够在特定浓度范围内高效、可靠地检测协同药物相互作用,从而加速潜在药物组合的识别,以便进行进一步的验证研究。