Prediction of drug combination effects with a minimal set of experiments
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Prediction of drug combination effects with a minimal set of experiments. / Ianevski, Aleksandr; Giri, Anil K.; Gautam, Prson; Kononov, Alexander; Potdar, Swapnil; Saarela, Jani; Wennerberg, Krister; Aittokallio, Tero.
In: Nature Machine Intelligence, Vol. 1, No. 12, 2019, p. 568-577.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Prediction of drug combination effects with a minimal set of experiments
AU - Ianevski, Aleksandr
AU - Giri, Anil K.
AU - Gautam, Prson
AU - Kononov, Alexander
AU - Potdar, Swapnil
AU - Saarela, Jani
AU - Wennerberg, Krister
AU - Aittokallio, Tero
PY - 2019
Y1 - 2019
N2 - High-throughput drug combination screening provides a systematic strategy to discover unexpected combinatorial synergies in pre-clinical cell models. However, phenotypic combinatorial screening with multi-dose matrix assays is experimentally expensive, especially when the aim is to identify selective combination synergies across a large panel of cell lines or patient samples. Here, we implement DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose–response measurements for accurate prediction of drug combination synergy in a given sample. Using a compendium of 23,595 drug combination matrices tested in various cancer cell lines and malaria and Ebola infection models, we demonstrate how cost-effective experimental designs with DECREASE capture almost the same degree of information for synergy and antagonism detection as the fully measured dose–response matrices. Measuring only the matrix diagonal provides an accurate and practical option for combinatorial screening. The minimal-input web implementation enables applications of DECREASE to both pre-clinical and translational studies.
AB - High-throughput drug combination screening provides a systematic strategy to discover unexpected combinatorial synergies in pre-clinical cell models. However, phenotypic combinatorial screening with multi-dose matrix assays is experimentally expensive, especially when the aim is to identify selective combination synergies across a large panel of cell lines or patient samples. Here, we implement DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose–response measurements for accurate prediction of drug combination synergy in a given sample. Using a compendium of 23,595 drug combination matrices tested in various cancer cell lines and malaria and Ebola infection models, we demonstrate how cost-effective experimental designs with DECREASE capture almost the same degree of information for synergy and antagonism detection as the fully measured dose–response matrices. Measuring only the matrix diagonal provides an accurate and practical option for combinatorial screening. The minimal-input web implementation enables applications of DECREASE to both pre-clinical and translational studies.
U2 - 10.1038/s42256-019-0122-4
DO - 10.1038/s42256-019-0122-4
M3 - Journal article
C2 - 32368721
VL - 1
SP - 568
EP - 577
JO - Nature Machine Intelligence
JF - Nature Machine Intelligence
SN - 2522-5839
IS - 12
ER -
ID: 249533230