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SYNERGx: a computational framework for drug combination synergy prediction

Status: 
Active
Competition: 
2017 Bioinformatics and Computational Biology Competition
Sector: 
Health
Genome Centre(s):
Ontario Genomics
Project Leader(s):
Benjamin Haibe-Kains (Princess Margaret Cancer Centre )
Project Description: 

When just one drug is used to treat cancer, the patient may not respond, or may develop resistance to it. Combination therapy, where two or more drugs are used in treatment, is more likely to be successful. Yet, it is impossible to test all drug combinations in clinical trials due to the high cost of required resources and certain ethical considerations. Computational techniques are therefore required to model the large amount of available data to improve current cancer treatment strategies and propose more efficient combinations of drugs. Dr. Benjamin Haibe-Kains of the Princess Margaret Cancer Centre is developing SYNERGx, a new computational platform that will integrate multiple pharmacogenomic datasets. These datasets will be used to predict possible combinations of known drugs that can act in synergy, meaning that their combined therapeutic efficacy is greater than the sum of their individual effects. The platform will implement analytic tools to improve modeling of synergistic drug effects. Users will have access to highly curated drug-combination pharmacogenetics data and an open-source machine-learning pipeline for drug synergy prediction. SYNERGx will also implement a new way to optimize drug-screening studies to identify novel synergistic combinations that can be further validated in preclinical studies and then in clinical trials. SYNERGx will provide an efficient way to leverage massive investments in pharmacogenomics studies by allowing the integration of otherwise disparate datasets. It represents a major step forward in the design of new therapeutic strategies for cancer.