Methods for High-throughput Drug Combination Screening and Synergy Scoring View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-01-18

AUTHORS

Liye He , Evgeny Kulesskiy , Jani Saarela , Laura Turunen , Krister Wennerberg , Tero Aittokallio , Jing Tang

ABSTRACT

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. More... »

PAGES

351-398

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4939-7493-1_17

DOI

http://dx.doi.org/10.1007/978-1-4939-7493-1_17

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100466367

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29344898


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