Peer Review #2 of "Computational drug repositioning based on side-effects mined from social media (v0.1)"
2016
Drug repositioning methods attempt to identify novel therapeutic indications for marketed drugs.Strategies include the use of side-effects to assign new disease indications, based on the premise that both therapeutic effects and side-effects are measurable physiological changes resulting from drug intervention.Drugs with similar side-effects might share a common mechanism of action linking side-effects with disease treatment, or may serve as a treatment by "rescuing" a disease phenotype on the basis of their side-effects; therefore it may be possible to infer new indications based on the similarity of side-effect profiles.While existing methods leverage side-effect data from clinical studies and drug labels, evidence suggests this information is often incomplete due to under-reporting.Here, we describe a novel computational method that uses side-effect data mined from social media to generate a sparse undirected graphical model using inverse covariance estimation with l 1 -norm regularization.Results show that known indications are well recovered while current trial indications can also be identified, suggesting that sparse graphical models generated using side-effect data mined from social media may be useful for computational drug repositioning.