Exploiting drug-disease relationships for computational drug repositioning

Abstract

Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.

Figure 1:

Published drug repositioning strategies described in this review are organized according to their primary mode of inference. The dashed arrows connect high-level informational aspects of drugs and diseases with the methods that incorporate these types of information in their approach. Methods are generally categorized as focusing largely on either (i) ‘Direct’ inference, where established or directly measured biomolecular or chemical properties are used to infer therapeutic relationships between drugs and diseases, (ii) ‘Indirect’ inference, where related or higher level data or representations of drugs and diseases is used to infer therapeutic relationships between drugs and diseases or (iii) ‘Simulation’, where therapeutic interactions are inferred through simulation of interactions between drugs and diseases rather than through direct or indirect measurement of their salient properties. We predict that newer methods will move toward integrating multiple forms of therapeutic inference incorporating many forms of both drug- and disease-based data and knowledge to enable the discovery of new uses for drugs—as some of the methods described in this review have taken steps toward.