Abstract
BackgroundDrug-induced gene expression dataset (for example Connectivity Map, CMap) represent a valuable resource for drug-repurposing, a class of methods for identifying novel indications for approved drugs. Recently, CMap-based methods have successfully applied to identifying drugs for a number of diseases. However, currently few gene expression based methods are available for the repurposing of combined drugs. Increasing evidence has shown that the combination of drugs may valid for novel indications.MethodHere, for this purpose, we presented a simple CMap-based scoring system to predict novel indications for the combination of two drugs. We then confirmed the effectiveness of the predicted drug combination in an animal model of type 2 diabetes.ResultsWe applied the presented scoring system to type 2 diabetes and identified a candidate combination of two drugs, Trolox C and Cytisine. Finally, we confirmed that the predicted combined drugs are effective for the treatment of type 2 diabetes.ConclusionThe presented scoring system represents one novel method for drug repurposing, which would provide helps for greatly extended the space of drugs.
Highlights
Drug repurposing or drug repositioning, which aims to find new therapeutic indications for approved drugs and experimental drugs that fail approval in their initial indication, has offered several advantages over traditional drug development including rescuing stalled pharmaceutical projects, finding therapies for neglected diseases and reducing the time, cost and risk of drug development [1,2]
Here we presented a simple computational scoring system based on Connectivity Map (CMap) and the deregulated gene profile of a given disease
We identified the combination of Trolox C and Cytisine has the potential to treat type 2 diabetes, none of them was reported to have the ability of treating type 2
Summary
Drug repurposing or drug repositioning, which aims to find new therapeutic indications for approved drugs and experimental drugs that fail approval in their initial indication, has offered several advantages over traditional drug development including rescuing stalled pharmaceutical projects, finding therapies for neglected diseases and reducing the time, cost and risk of drug development [1,2]. A number of computational strategies for drug repurposing have been developed [1], including strategies based on the chemical similarity of drugs [3], similarity of drug side effects [4], molecular activity similarity [5], and shared molecular pathology [6] Among these strategies, the method based on similarity of molecular activity generated from global gene expression profiling emerges as a promising approach for drug repurposing [5]. Drug-induced gene expression dataset (for example Connectivity Map, CMap) represent a valuable resource for drug-repurposing, a class of methods for identifying novel indications for approved drugs. Increasing evidence has shown that the combination of drugs may valid for novel indications
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