Drug repositioning: a machine-learning approach through data integration

Abstract

: Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses.

Figure 1

Flowchart of the analysis. Green boxes indicate data, red boxes indicate processes.

Figure 2

Classification performance and data integration. Receiving Operator Curve for the three separated kernels (GEX: gene expression; CHEM: chemical structure; TAR: molecular targets) and the final joint kernel in respect with ATC classes.

Figure 3

Repositioning overview. Direction of the arrows represent direction of repositioning from one ATC class to another. Thickness and opacity of the edges represent a score-weighted sum of the reclassification events.

Figure 4

Flowchart of the CMap gene expression data analysis. Green boxes indicate data, red boxes indicate processes.

Figure 5

Kernel noise reduction through MDS projections. Values on the x axis correspond to SVM classifiers trained with kernel K (see text) as projected into the subspace spanned by its first x Principal Components. The y axis reports the corresponding 6-fold cross validation error. The green dot indicates minimum error.