DMAP: a connectivity map database to enable identification of novel drug repositioning candidates

Abstract

BackgroundDrug repositioning is a cost-efficient and time-saving process to drug development compared to traditional techniques. A systematic method to drug repositioning is to identify candidate drug's gene expression profiles on target disease models and determine how similar these profiles are to approved drugs. Databases such as the CMAP have been developed recently to help with systematic drug repositioning.MethodsTo overcome the limitation of connectivity maps on data coverage, we constructed a comprehensive in silico drug-protein connectivity map called DMAP, which contains directed drug-to-protein effects and effect scores. The drug-to-protein effect scores are compiled from all database entries between the drug and protein have been previously observed and provide a confidence measure on the quality of such drug-to-protein effects.ResultsIn DMAP, we have compiled the direct effects between 24,121 PubChem Compound ID (CID), which were mapped from 289,571 chemical entities recognized from public literature, and 5,196 reviewed Uniprot proteins. DMAP compiles a total of 438,004 chemical-to-protein effect relationships. Compared to CMAP, DMAP shows an increase of 221 folds in the number of chemicals and 1.92 fold in the number of ATC codes. Furthermore, by overlapping DMAP chemicals with the approved drugs with known indications from the TTD database and literature, we obtained 982 drugs and 622 diseases; meanwhile, we only obtained 394 drugs with known indication from CMAP. To validate the feasibility of applying new DMAP for systematic drug repositioning, we compared the performance of DMAP and the well-known CMAP database on two popular computational techniques: drug-drug-similarity-based method with leave-one-out validation and Kolmogorov-Smirnov scoring based method. In drug-drug-similarity-based method, the drug repositioning prediction using DMAP achieved an Area-Under-Curve (AUC) score of 0.82, compared with that using CMAP, AUC = 0.64. For Kolmogorov-Smirnov scoring based method, with DMAP, we were able to retrieve several drug indications which could not be retrieved using CMAP. DMAP data can be queried using the existing C2MAP server or downloaded freely at: http://bio.informatics.iupui.edu/cmapsConclusionsReliable measurements of how drug affect disease-related proteins are critical to ongoing drug development in the genome medicine era. We demonstrated that DMAP can help drug development professionals assess drug-to-protein relationship data and improve chances of success for systematic drug repositioning efforts.

Epigenome-Based Drug Repositioning in Acute Myeloid Leukemia

“…The process of nominating new indications for previously approved drugs, known as drug repositioning, has become increasing attractive to industry and academia due to the substantial decrease in risk of unforeseen adverse events associated with compounds with known safety profiles [1][2][3] . A large number of computational approaches have been developed over the past 10 years that leverage molecular evidence to identify repositioning candidates, such as using differential gene expression [4][5][6][7] . Unfortunately, these methodologies are hindered by their need for specific data types, assay platforms, and data formats, preventing investigators from utilizing newer profiling technologies and expanding beyond gene expression.…”

Leveraging population-based clinical quantitative phenotyping for drug repositioning

“…Identifying new indications for previously approved drugs, known as drug repositioning, is an attractive alternative to the traditional drug discovery paradigm as previously approved drugs have substantially lower risk of unforeseen adverse events 1 . Computational drug repositioning builds on this premise by pre-screening for promising repositioning candidates, with current methods primarily relying on molecular data [2][3][4][5] and/or the biological literature [6][7][8][9][10] . While these methods have been successful in predicting plausible repositioning candidates, a key challenge in computational repositioning is to provide direct evidence of candidate efficacy in humans, rather than relying on surrogate biomarkers or indirect evidence.…”

A physarum-inspired prize-collecting steiner tree approach to identify subnetworks for drug repositioning

“…By repositioning known drugs, drug development time, costs and risks can be reduced significantly [1–3]. There are mainly two challenges to reposition drugs.…”

“…Existing drug repositioning methods can be divided into three categories; data-driven methods [1–3, 6], text-mining methods [7, 8], and network-based methods [3, 9–11]. The data-driven methods reposition drugs by analyzing pharmacological data using statistical and machine learning concepts such as statistical estimations, classification and clustering [1, 6, 10].…”

“…The data-driven methods reposition drugs by analyzing pharmacological data using statistical and machine learning concepts such as statistical estimations, classification and clustering [1, 6, 10]. Because of the overlapping nature of pharmacological data [3, 11], the evaluation process of the data-driven methods is complicated [11].…”