• Experimental approaches will use in vitro or in vivo animal paradigms to establish a novel bioactivity of a known drug potentially relevant to a new indication. This can be accomplished through phenotypic in vivo screening, such as Zebra fish or C. elegans. The screening can also be carried out in in vitro target-based biochemical assays. Both in vitro and in vivo screening can lead to the discovery of novel targets, eventual target validation, or perhaps novel pathways related to a particular pathophysiology, with differences in the identification of successful candidates notable between phenotypic and target-based drug discovery efforts.2,3 While the selection of assays and compounds to screen could be done in a completely unbiased manner, a systematic search of biomedical literature and accessible data may be a more efficient way to discover possible targets for evaluation.

• Clinical approaches are based on the analysis of adverse effects or symptom improvements observed either during the clinical trial or during post-marketed use of the drug. These approaches are typically based on serendipitous discoveries of benefits that are tangential to the originally expected benefits.4 In some cases, the repurposing efforts arise in the aftermath of a failed Phase III trial. Sildenafil, for example, was originally intended to treat hypertension and angina pectoris but demonstrated a lack of efficacy in clinical trials. An analysis of its side effects, however, prompted developers to repurpose the drug to address erectile dysfunction.5,6 Similarly, thalidomide, tragically marketed as a sedative in the 1950s, would prove unexpectedly effective at treating erythema nodosum leprosum and multiple myeloma.7,8

• Computational or in silico approaches take advantage of the amount of biomedical data, its accessibility, and high-performance informatics techniques to curate and analyze this data at an unprecedented level. Automated text search methods can digest terabytes of publicly available biomedical literature to identify high-confidence relationships between drugs, targets, biochemical pathways, and human disease. Omics research can plow through genomics, transcriptomics, and proteomics data, including protein structure databases, to look for relationships and similarities between biological targets or pathways. Molecular modeling methods make it possible to go beyond a drug molecule and to explore similar bioactive molecules, targets, and pathways related to them that point to novel indications.

These computational methods enable developers to analyze the largest set of potential targets and pathways, truly enabling “indication hopping,” as it has been described. The main challenge in employing these methods lies in staying focused and properly prioritizing the most promising leads. The proof of a rising interest in in silico drug repurposing is a growing number of start-up tech companies that explicitly focus on developing and fully exploiting in silico approaches to drive the discovery of new indications for known drugs.