Drug repurposing, identifying novel indications for drugs, bypasses common
drug development pitfalls to ultimately deliver
therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g.
Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a
drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to
drug repurposing, CATNIP, that requires only
biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different
drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between
drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined
drug classes. We found that
adrenergic uptake inhibitors, specifically
amitriptyline and
trimipramine, could be potential
therapies for
Parkinson's disease. Additionally, using CATNIP, we predicted the
kinase inhibitor,
vandetanib, as a possible treatment for
Type 2 Diabetes. Overall, this systematic approach to
drug repurposing lays the groundwork to streamline future
drug development efforts.