01087nas a2200313 4500008004100000022001400041245014500055210006900200260001500269300000800284490000600292653002800298653001300326653002300339653001100362653002100373653003300394653003100427653001300458653001500471653002400486100002000510700002700530700001600557700002100573700002000594700002400614856013500638 2020 eng d a2059-363500aDrug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection.0 aDrug repurposing for COVID19 using machine learning and mechanis c2020 12 11 a2900 v510aComputational Chemistry10aCOVID-1910adrug repositioning10aHumans10aMachine Learning10aMolecular Docking Simulation10aMolecular Targeted Therapy10aProteins10aSARS-CoV-210aSignal Transduction1 aLoucera, Carlos1 aEsteban-Medina, Marina1 aRian, Kinza1 aFalco, Matias, M1 aDopazo, Joaquin1 aPeƱa-Chilet, Maria uhttps://www.clinbioinfosspa.es/content/drug-repurposing-covid-19-using-machine-learning-and-mechanistic-models-signal-transduction03269nas a2200685 4500008004100000022001400041245011800055210006900173260001500242300000900257490000700266520115400273653001901427653005101446653001701497653002201514653002101536653002601557653002201583653002001605653003001625653001901655653001301674653001101687653003101698653001301729653001401742653002101756653003501777653004101812653002201853100002301875700001701898700001901915700001801934700002301952700001901975700001501994700001702009700001602026700002002042700001602062700002402078700001902102700001802121700002402139700001802163700002502181700001702206700001902223700002302242700002702265700002002292700002502312700002002337700002102357700002602378710005702404856012202461 2019 eng d a2041-172300aCommunity assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.0 aCommunity assessment to advance computational prediction of canc c2019 06 17 a26740 v103 a
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
10aADAM17 Protein10aAntineoplastic Combined Chemotherapy Protocols10aBenchmarking10aBiomarkers, Tumor10aCell Line, Tumor10aComputational Biology10aDatasets as Topic10aDrug Antagonism10aDrug Resistance, Neoplasm10aDrug Synergism10aGenomics10aHumans10aMolecular Targeted Therapy10amutation10aNeoplasms10apharmacogenetics10aPhosphatidylinositol 3-Kinases10aPhosphoinositide-3 Kinase Inhibitors10aTreatment Outcome1 aMenden, Michael, P1 aWang, Dennis1 aMason, Mike, J1 aSzalai, Bence1 aBulusu, Krishna, C1 aGuan, Yuanfang1 aYu, Thomas1 aKang, Jaewoo1 aJeon, Minji1 aWolfinger, Russ1 aNguyen, Tin1 aZaslavskiy, Mikhail1 aJang, In, Sock1 aGhazoui, Zara1 aAhsen, Mehmet, Eren1 aVogel, Robert1 aNeto, Elias, Chaibub1 aNorman, Thea1 aK Y Tang, Eric1 aGarnett, Mathew, J1 aDi Veroli, Giovanni, Y1 aFawell, Stephen1 aStolovitzky, Gustavo1 aGuinney, Justin1 aDry, Jonathan, R1 aSaez-Rodriguez, Julio1 aAstraZeneca-Sanger Drug Combination DREAM Consortium uhttps://www.clinbioinfosspa.es/content/community-assessment-advance-computational-prediction-cancer-drug-combinations