RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

Journal of Translational Medicine

Ghazale Fahimian¹  Javad Zahiri¹  Reza H. Sajedi²  Seyed Shahriar Arab³ 

[1] Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran;Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran;Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran;

关键词: Drug repositioning;    Drug-diseases interaction;    Biological network;    Network integration;    Machine learning;    Breast cancer;   

DOI  :  10.1186/s12967-020-02541-3

来源: Springer

【 摘 要 】

BackgroundIt 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.MethodsIn 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.ResultsThe 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.ConclusionResults 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.

【 授权许可】

CC BY