Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data

Abstract

The application of established drug compounds to novel therapeutic indications, known as drug repositioning, offers several advantages over traditional drug development, including reduced development costs and shorter paths to approval. Recent approaches to drug repositioning employ high-throughput experimental approaches to assess a compound’s potential therapeutic qualities. Here we present a systematic computational approach to predict novel therapeutic indications based on comprehensive testing of molecular signatures in drug-disease pairs. We integrated gene expression measurements from 100 diseases and gene expression measurements on 164 drug compounds yielding predicted therapeutic potentials for these drugs. We demonstrate the ability to recover many known drug and disease relationships using computationally derived therapeutic potentials, and also predict many new indications for these drugs. We experimentally validated a prediction for the anti-ulcer drug cimetidine as a candidate therapeutic in the treatment of lung adenocarcinoma, and demonstrate both in vitro and in vivo using mouse xenograft models. This novel computational method provides a novel and systematic approach to reposition established drugs to treat a wide range of human diseases.

Drug Repositioning Approaches for the Discovery of New Therapeutics for Alzheimer's Disease

“…Sirota et al [66] demonstrated how computational analysis of public gene expression data led to the discovery of new indications for known drugs. The disease-associated gene expression data were first subjected to significance analysis by microarray to establish "disease signatures" for 100 diseases.…”

“…This concept has been validated experimentally. For instance, using this approach, topiramate (an epilepsy drug) was predicted to be a therapeutic for Crohn's disease and ulcerative colitis [66], and, indeed, was found to exert potent efficacy in a rat model of colitis [61].…”

Gene expression signature-based approach identifies a pro-resolving mechanism of action for histone deacetylase inhibitors

“…9 Drug repositioning strategies, which aim to identify if drugs used for one disease could be applied to another, can benefit from this new approach. 11,12,14 The mechanisms of action of many drugs already on the market are not always completely understood. The CMap strategy can be of great help to this purpose, as gene expression signature of a particular drug can be used to query the database and explore connections with existing drugs.…”

“…5,6 Therefore, new concepts regarding the management of inflammation are emerging from these novel approaches: not only is necessary modulation (yet not the abrogation) of pro-inflammatory events, but promotion and enhancement of endogenous pro-resolving pathways, is required to achieve control of the inflammatory response with rapid restoration of tissue homeostasis. 7,8 A novel approach to drug discovery, the Connectivity Map (CMap), proposed by Lamb et al 9,10 and recently validated by Sirota et al, 11 demonstrates how integrating genome-wide gene expression data of drugs and diseases can be used for compound repositioning (i.e., application of known drugs to new indications), or for discovery of new drugs, reducing costs and years required for their development. The CMap is a publicly available collection of genome-wide gene expression signatures of human cells treated with a number of bioactive small molecules, most of which are FDA-approved drugs.…”

Compensating for literature annotation bias when predicting novel drug-disease relationships through Medical Subject Heading Over-representation Profile (MeSHOP) similarity

“…Informatics-based approaches to drug repositioning are exemplified by the identification of known drug targets in genes arising in genome-wide association studies [8], the prediction of structural suitability of a known compound for a new protein target [9,10], systems biology using gene expression patterns [6,11], and the study of side effects [12]. Underlying many of these informatics approaches has been the availability of reference databases containing information about the relationship between genes, drugs and diseases, such as DrugBank [13], Pharmacogenomics Knowledge Base [14,15], and the Comparative Toxicogenomics Database [16].…”