药品
计算生物学
医学
临床试验
药理学
生物
内科学
作者
Liye He,Evgeny Kulesskiy,Jani Saarela,Laura Turunen,Krister Wennerberg,Tero Aittokallio,Jing Tang
出处
期刊:Methods in molecular biology
日期:2018-01-01
卷期号:: 351-398
被引量:171
标识
DOI:10.1007/978-1-4939-7493-1_17
摘要
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.
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