Exploring the relationship between drug side-effects and therapeutic indications

Abstract

Therapeutic indications and drug side-effects are both measureable human behavioral or physiological changes in response to the treatment. In modern drug development, both inferring potential therapeutic indications and identifying clinically important drug side-effects are challenging tasks. Previous studies have utilized either chemical structures or protein targets to predict indications and side-effects. In this study, we compared indication prediction using side-effect information and side-effect prediction using indication information against models using only chemical structures and protein targets. Experimental results based on 10-fold cross-validation, show that drug side-effects and therapeutic indications are the most predictive features for each other. In addition, we extracted 6,706 statistically highly correlated disease-side-effect pairs from all known drug-disease and drug-side-effect relationships. Many relationship pairs provide explicit repositioning hypotheses (e.g., drugs causing postural hypotension are potential candidates for hypertension) and clear adverse-reaction watch lists (e.g., drugs for heart failure possibly cause impotence). All data sets and highly correlated disease-side-effect relationships are available at http://astro.temple.edu/~tua87106/druganalysis.html.

Figure 1.

Statistics of the side-effect dataset. (a) The number of side-effects per drug. (b) The number of drugs per side-effect.

Figure 2.

Statistics of the therapeutic-indication dataset. (a) The number of therapeutic indications per drug. (b) The number of drugs per therapeutic indication.

Figure 3.

Illustration of the proposed method.

Figure 4.

The averaged ROC comparison of therapeutic indication predictions for various information source combinations using in 10-fold cross validation. Information sources are sorted in legend of the figure according to their AUC score.

Figure 5.

The averaged ROC comparison of drug side-effect predictions for various information source combinations using in 10-fold cross validation. Information sources are sorted in legend of the figure according to their AUC score.

Figure 6.

Statistics of the highly correlated disease-side-effect pairs result (p-value<0.01). (a) The number of side effects per disease (therapeutic indication). (b) The number of diseases (therapeutic indications) per side effect.