Systematic evaluation of drug-disease relationships to identify leads for novel drug uses

Abstract

Drug repositioning refers to the discovery of alternative uses for drugs--uses that are different from that for which the drugs were originally intended. One challenge in this effort lies in choosing the indication for which a drug of interest could be prospectively tested. We systematically evaluated a drug treatment-based view of diseases in order to address this challenge. Suggestions for novel drug uses were generated using a "guilt by association" approach. When compared with a control group of drug uses, the suggested novel drug uses generated by this approach were significantly enriched with respect to previous and ongoing clinical trials.

Figure 1

Pairwise disease correlations under the FDA-approved and practiced views. Each point represents a pairing of two of the 726 diseases in DrDKB that share at least one FDA-approved drug. Each pair of diseases was scored for similarity based on binary correlation calculated using shared FDA approved drug indications (x-axis) and combined FDA- and non-FDA drug uses (y-axis). Zero on either axis indicates no drugs in common between the two diseases, while one indicates an exactly matching set in the pair. The regression line indicates an overall similarity in FDA and combined indication data (r = 0.68, p = 2.2×10⁻¹⁶). However, many pairs of diseases share many FDA approved drugs, and few non-FDA drugs; we exploit these to suggest novel drug uses across the pair of diseases.

Figure 2

Schematic of the guilt-by-association method of suggesting leads for novel drug uses. Given two diseases i, j and their corresponding treatment profiles T_i and T_j, respectively, the suggested novel drug uses for i based on shared treatment profile with j is S_i,j or the difference of the union treatment profile (U_ij) and T_i. Conversely, suggested novel drug uses for j based on i (S_j,i) are obtained from subtracting T_j from U_ij.