A phenome-guided drug repositioning through a latent variable model

Abstract

BackgroundThe phenome represents a distinct set of information in the human population. It has been explored particularly in its relationship with the genome to identify correlations for diseases. The phenome has been also explored for drug repositioning with efforts focusing on the search space for the most similar candidate drugs. For a comprehensive analysis of the phenome, we assumed that all phenotypes (indications and side effects) were inter-connected with a probabilistic distribution and this characteristic may offer an opportunity to identify new therapeutic indications for a given drug. Correspondingly, we employed Latent Dirichlet Allocation (LDA), which introduces latent variables (topics) to govern the phenome distribution.ResultsWe developed our model on the phenome information in Side Effect Resource (SIDER). We first developed a LDA model optimized based on its recovery potential through perturbing the drug-phenotype matrix for each of the drug-indication pairs where each drug-indication relationship was switched to “unknown” one at the time and then recovered based on the remaining drug-phenotype pairs. Of the probabilistically significant pairs, 70% was successfully recovered. Next, we applied the model on the whole phenome to narrow down repositioning candidates and suggest alternative indications. We were able to retrieve approved indications of 6 drugs whose indications were not listed in SIDER. For 908 drugs that were present with their indication information, our model suggested alternative treatment options for further investigations. Several of the suggested new uses can be supported with information from the scientific literature.ConclusionsThe results demonstrated that the phenome can be further analyzed by a generative model, which can discover probabilistic associations between drugs and therapeutic uses. In this regard, LDA serves as an enrichment tool to explore new uses of existing drugs by narrowing down the search space.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-267) contains supplementary material, which is available to authorized users.

Integrating Clinical Phenotype and Gene Expression Data to Prioritize Novel Drug Uses

“…Many studies have analyzed either molecular-level states induced by disease or drugs, or phenotypic profiling from human individuals with the goal of drug repositioning. [9][10][11] For example, the Connectivity Map elucidates relationships between small molecule drugs and diseases. 12 In previous works, we proposed a method based on a guilt-byassociation approach to predict new ones 13 and integrated clinical phenotypes from electronic medical records.…”

“…Many studies have analyzed either molecular‐level states induced by disease or drugs, or phenotypic profiling from human individuals with the goal of drug repositioning . For example, the Connectivity Map elucidates relationships between small molecule drugs and diseases .…”

RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks

“…In recent years, different approaches were exploited for repurposing drugs, including network, text mining, machine learning and semantic inference based approaches. Recently, networkbased approach attracted more attention and was widely used in computational drug repositioning due to the capability of using ever-increasing large scale biological datasets such as genetic, pharmacogenomics, clinical and chemical data [2,5,[7][8][9][10][11][12][13][14].…”

Evaluating disease similarity using latent Dirichlet allocation

“…We use Latent Dirichlet Allocation (LDA) [15], an unsupervised probabilistic method of learning topics associated with text. In the biomedical space, LDA has been used to analyze electronic health records [16,17], drug labels [1], and adverse event data [18].…”