Author response: Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Daniel Scott Himmelstein, Antoine Lizée, Christine Hessler, Leo Brueggeman, Sabrina L. Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E. Baranzini · 2017
Of all the data in the world today, 90% was created in the last two years. However, taking advantage of this data in order to advance our knowledge is restricted by how quickly we can access it and analyze it in a proper context. In biomedical research, data is largely fragmented and stored in databases that typically do not “talk” to each other, thus hampering progress. One particular problem in medicine today is that the process of making a new therapeutic drug from scratch is incredibly expensive and inefficient, making it a risky business. Given the low success rate in drug discovery, there is an economic incentive in trying to repurpose an existing drug that has already been shown to be safe and effective towards a new disease or condition. Himmelstein et al. used a computational approach to analyze 50,000 data points – including drugs, diseases, genes and symptoms – from 19 different public databases. This approach made it possible to create more than two million relationships among the data points, which could be used to develop models that predict which drugs currently in use by doctors might be best suited to treat any of 136 common diseases. For example, Himmelstein et al. identified specific drugs currently used to treat depression and alcoholism that could be repurposed to treat smoking addition and epilepsy. These findings provide a new and powerful way to study drug repurposing. While this work was exclusively performed with public data, an expanded and potentially stronger set of predictions could be obtained if data owned by pharmaceutical companies were incorporated. Additional studies will be needed to test the predictions made by the models.