RepCOOL: computational drug repositioning via integrating heterogeneous biological networks
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Journal of Translational Medicine Open Access
RESEARCH
RepCOOL: computational drug repositioning via integrating heterogeneous biological networks Ghazale Fahimian1†, Javad Zahiri1*† , Seyed Shahriar Arab2 and Reza H. Sajedi3
Abstract Background: It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. Methods: In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. Results: The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. Conclusion: Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen. Keywords: Drug repositioning, Drug-diseases interaction, Biological network, Network integration, Machine learning, Breast cancer Background Drug research and development is a complicated, time-consuming, and incredibly expensive process. Previous research reported that it often takes 10–15 years and approximately 1–3 billion dollars to develop a new drug and place it on the market [1–3]. *Correspondence: [email protected] † Ghazale Fahimian and Javad Zahiri contributed equally to this work 1 Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran Full list of author information is available at the end of the article
Although such a huge amount of time and money is expending in this industry, the number of new Food and Drug Administration (FDA)-approved drugs reported annually remains low. So, in consideration of these challenges, discovering a new use for an existing drug, known as drug repositioning or drug repurposing, has been proposed as a solution for such a problem. The goal of drug repositioning is to identify new indications for drugs currently available in the market. Using such approaches can reduce the overall cost of commercialization and also
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