Realizing drug repositioning by adapting a recommendation system to handle the process

Abstract

BackgroundDrug repositioning is the process of identifying new targets for known drugs. It can be used to overcome problems associated with traditional drug discovery by adapting existing drugs to treat new discovered diseases. Thus, it may reduce associated risk, cost and time required to identify and verify new drugs. Nowadays, drug repositioning has received more attention from industry and academia. To tackle this problem, researchers have applied many different computational methods and have used various features of drugs and diseases.ResultsIn this study, we contribute to the ongoing research efforts by combining multiple features, namely chemical structures, protein interactions and side-effects to predict new indications of target drugs. To achieve our target, we realize drug repositioning as a recommendation process and this leads to a new perspective in tackling the problem. The utilized recommendation method is based on Pareto dominance and collaborative filtering. It can also integrate multiple data-sources and multiple features. For the computation part, we applied several settings and we compared their performance. Evaluation results show that the proposed method can achieve more concentrated predictions with high precision, where nearly half of the predictions are true.ConclusionsCompared to other state of the art methods described in the literature, the proposed method is better at making right predictions by having higher precision. The reported results demonstrate the applicability and effectiveness of recommendation methods for drug repositioning.

A Synergistic Anti-Cancer Effect of Troglitazone and Lovastatin in a Human Anaplastic Thyroid Cancer Cell Line and in a Mouse Xenograft Model

“…However, therapies against ATC have not been improved [ 25 ]. One approach to accelerate the discovery is by drug repositioning [ 26 ]. In this study, we explored the therapeutic effects by a combination of troglitazone and lovastatin.…”

A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions

“…The authors used drug chemical structures and protein targets data to build a dual regularized one-class collaborative filtering model that surpassed the previously introduced state-of-the-art models. Ozsoy et al [ 119 ] treated the drug repositioning process as a recommendation process and utilized Pareto dominance and collaborative filtering to identify drug-disease associations. The authors integrated multisource drugs data (protein targets, chemical structures, and side effects) and applied a variety of similarity measures to calculate drug–drug similarities and then used a Pareto dominance model to identify neighbor drugs.…”

“…In order to evaluate the efficiency of potential repositioned drugs, Rakshit et al [ 126 ] introduced a metric called On-Target Ratio (OTR) which is the ratio between the number of drug targets in their proposed disease-specific genes network to the total number of interactions of the same drug in the DrugBank database. Moreover, Ozsoy et al [ 119 ] evaluated their results against ClinicalTrials.gov , which is a collection of publicly and privately funded clinical studies from around the world. The authors also performed a leave-one-out test and benchmarked their model against state-of-the-art models.…”

Correction to: Realizing drug repositioning by adapting a recommendation system to handle the process

“…Following publication of the original article [ 1 ], the authors reported that there was an error in the spelling of the name of one of the authors.…”