A Computational Approach for Identifying Synergistic Drug Combinations

Gayvert, Kaitlyn; Aly, Omar M.; Platt, James T.; Bosenberg, Marcus W.; Stern, David F.; Elemento, Olivier

doi:10.1371/journal.pcbi.1005308

Cited by 81 publications

(65 citation statements)

References 16 publications

(30 reference statements)

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“…Since CoSynE uses only structural information, once the training dataset has been screened no experimental effort is required to obtain descriptors for the novel compounds that make up future predictions. This offers an advantage over approaches such as that by Kaitlyn et al 19 A disadvantage of CoSynE over prediction methods that use biological information about the organism or test system used in the assay is that the predictions can only be made for the same species that the drug interaction data has been obtained for in the first place.…”

Section: Journal Of Medicinal Chemistrymentioning

confidence: 99%

Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure

et al. 2017

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Combination antibiotic therapies are clinically important in the fight against bacterial infections. However, the search space of drug combinations is large, making the identification of effective combinations a challenging task. Here, we present a computational framework that uses substructure profiles derived from the molecular structures of drugs and predicts antibiotic interactions. Using a previously published data set of 153 drug pairs, we showed that substructure profiles are useful in predicting synergy. We experimentally measured the interaction of 123 new drug pairs, as a prospective validation set for our approach, and identified 37 new synergistic pairs. Of the 12 pairs predicted to be synergistic, 10 were experimentally validated, corresponding to a 2.8-fold enrichment. Having thus validated our methodology, we produced a compendium of interaction predictions for all pairwise combinations among 100 antibiotics. Our methodology can make reliable antibiotic interaction predictions for any antibiotic pair within the applicability domain of the model since it solely requires chemical structures as an input.

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Section: Journal Of Medicinal Chemistrymentioning

confidence: 99%

Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure

et al. 2017

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“…In view of these concerns, in silico methods have been proposed to find candidate drug combinations for further experimental tests (17)(18)(19)(20)(21). Most of the existing methods aim at predicting the synergistic effects of two drugs, as the triple drug combinatorial effect is technically harder to predict and lacks experimental data for model evaluation.…”

Section: Introductionmentioning

confidence: 99%

Network Propagation Predicts Drug Synergy in Cancers

Quang

2018

Cancer Research

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Combination therapies are commonly used to treat patients with complex diseases that respond poorly to single-agent therapies. high-throughput drug screening is a standard method for preclinical prioritization of synergistic drug combinations, but it can be impractical for large drug sets. Computational methods are thus being actively explored; however, most published methods were built on a limited size of cancer cell lines or drugs, and it remains a challenge to predict synergism at a large scale where the diversity within the data escalates the difficulty of prediction. Here, we present a state-of-the-field synergy prediction algorithm, which ranked first in all subchallenges in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. The model was built and evaluated using the largest drug combination screening dataset at the time of the competition, consisting of approximately 11,500 experimentally tested synergy scores of 118 drugs in 85 cancer cell lines. We developed a novel feature extraction strategy by integrating the cross-cell and cross-drug information with a novel network propagation method and then assembled the information in monotherapy and simulated molecular data to predict drug synergy. This represents a significant conceptual advancement of synergy prediction, using extracted features in the form of simulated posttreatment molecular profiles when only the pretreatment molecular profile is available. Our cross-tissue synergism prediction algorithm achieves promising accuracy comparable with the correlation between experimental replicates and can be applied to other cancer cell lines and drugs to guide therapeutic choices. This study presents a novel network propagation-based method that predicts anticancer drug synergy to the accuracy of experimental replicates, which establishes a state-of-the-field method as benchmarked by the pharmacogenomics research community involving models generated by 160 teams. .

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“…Researchers have realized the impossibility of experimentally testing every pairwise drug combinations and therefore many computational models have been created to answer this very issue 10, 11 . However, many current models were created for either specific drug types or cancer types 8 , which inherently limits the applicability of these models. Recently there was a DREAM Challenge, partnered with AstraZeneca, to predict drug synergy based on diverse drugs and cancer types 10, 11 , however this challenge focused on predicting drug synergy, calculated by only one metric.…”

Section: Discussionmentioning

confidence: 99%

“…Quantitative models have been introduced to predict effective drug combinations, however they tend to be limited in scope, either confined to certain drug or cancer types 8 . A recent DREAM Challenge (dreamchallenges.org) 9 teamed up with AstraZeneca and called for models to predict synergy across diverse drugs and cancer types 10, 11 .…”

Section: Introductionmentioning

confidence: 99%

Multi-task learning predicts drug combination synergy in cells and in the clinic

Gilvary

Elemento

2019

Preprint

Self Cite

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18Combination therapies for various cancers have been shown to increase efficacy, lower toxicity, 19 and circumvent resistance. However, despite the promise of combinatorial therapies, the 20 biological mechanisms behind drug synergy have not been fully characterized, and the 21 systematic testing of all possible synergistic therapies is experimentally infeasible due to the 22 47Precision medicine has been instrumental in the creation of novel cancer therapeutics, however 48 many of these promising treatments have been limited by insufficient efficacy or acquired 49 resistance. Cancer cells have exhibited multiple types of resistance mechanisms, such as 50 redundant pathways 1 and pathway reactivation 2 , among others 3-5 . Combination therapies have 51 been proposed as a general strategy to combat therapeutic resistance, as well as increase overall 52 efficacy 6 . Such is the case with the combination of MEK and ERK inhibition to overcome 53 MAPK pathway reactivation 7 . Overall, rational drug combination therapies have the potential to 54 create long-lasting therapeutic strategies against cancer and perhaps many other diseases. 55 56While combination therapies have shown great potential, identifying effective drug pairings 57 presents a difficult challenge. Traditional methods for assessing drug efficacy of single drug 58 agents are not easily applied. With the number of approved or investigational drugs increasing 59 every year, the ability to pairwise test these agents across a wide breadth of disease models 60 becomes virtually impossible. This experimental infeasibility leads to the need for a 61 computational approach to predict drug synergy. 62 63Quantitative models have been introduced to predict effective drug combinations, however they 64 tend to be limited in scope, either confined to certain drug or cancer types 8 . A recent DREAM 65Challenge (dreamchallenges.org) 9 teamed up with AstraZeneca and called for models to predict 66 synergy across diverse drugs and cancer types 10, 11 . Despite >80 distinct computational methods 67 being submitted to harness biological knowledge and classify cells and drugs, with performance 68 matching the accuracy of biological replicates for many cases, there were still numerous drug 69 combinations that consistently performed poorly across all models. 70 71The problem is confounded as there currently is no agreed upon gold standard to measure drug 72 synergy of clinical relevance in vitro, introducing variability across method development and 73 misclassification of valuable drug combinations. There are many different metrics used to 74 measure drug synergy that are all based on models with differing underlying assumptions. 75Fourcquier and Guedi have presented a comprehensive and succinct description of many of these 76 models, how they differ and their practical limitations 12 . The most widely used of these methods 77include Bliss Independence 13, 14 , which assumes no interference/interaction between drugs, and 78Loewe Additivity 14, 15 , which is based on the d...

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A Computational Approach for Identifying Synergistic Drug Combinations

Cited by 81 publications

References 16 publications

Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure

Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure

Network Propagation Predicts Drug Synergy in Cancers

Multi-task learning predicts drug combination synergy in cells and in the clinic

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