RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

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

Fig. 1

Schematic flowchart of the proposed drug repositioning method

Fig. 2

Schematic representation of reconstructing nine new drug-disease networks

Fig. 3

Performance of different classifiers in a tenfold cross validation procedure in PRIDICT dataset. Classifiers include support vector machine (SVM), decision tree (DT), linear regression (LR), naïve Bayes (NB) and random forest (RF)

Fig. 4

Performance comparison of RepCOOL with other methods in terms of AUC based on the obtained results in PREDICT dataset

Fig. 5

Performance of different classifiers in a tenfold cross-validation procedure in repODB dataset. Classifiers include support vector machine (SVM), decision tree (DT), linear regression (LR), naïve Bayes (NB) and random forest (RF)

Fig. 6

Structural relationship between the repurposed (highlighted by rectangles) and FDA-approved drugs for the treatment of breast cancer. a Heat map of the merged repurposed and FDA-approved drugs based on the distance matrix. b Heat map of repurposed and FDA-approved drugs based on the correlation matrix. c Cluster dendrogram of repurposed and FDA-approved drugs based on the distance matrix. The highest and the lowest structural correlation are indicated in blue and red, respectively

Fig. 7

The inhibitory effect of different concentrations of Tamoxifen on the growth of BT474 cells. The results were presented as a percentage relative to the control and graph was plotted using GraphPad Prism 6.01 software