| Chapter title |
Methods for High-throughput Drug Combination Screening and Synergy Scoring
|
|---|---|
| Chapter number | 17 |
| Book title |
Cancer Systems Biology
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7493-1_17 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7492-4, 978-1-4939-7493-1
|
| Authors |
Liye He, Evgeny Kulesskiy, Jani Saarela, Laura Turunen, Krister Wennerberg, Tero Aittokallio, Jing Tang, He, Liye, Kulesskiy, Evgeny, Saarela, Jani, Turunen, Laura, Wennerberg, Krister, Aittokallio, Tero, Tang, Jing |
| 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. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Finland | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 33% |
| Members of the public | 1 | 33% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 1 | <1% |
| Unknown | 171 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 37 | 22% |
| Researcher | 30 | 17% |
| Student > Master | 17 | 10% |
| Student > Bachelor | 15 | 9% |
| Student > Postgraduate | 7 | 4% |
| Other | 22 | 13% |
| Unknown | 44 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 46 | 27% |
| Agricultural and Biological Sciences | 22 | 13% |
| Medicine and Dentistry | 14 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 8 | 5% |
| Immunology and Microbiology | 5 | 3% |
| Other | 17 | 10% |
| Unknown | 60 | 35% |