Affiliation:
1. Thomson Reuters, Corporate Research and Development, London, United Kingdom
Abstract
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 ℓ1-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.
Funder
Thomson Reuters Global Resources
Reference92 articles.
1. Covariance and linear independence;Allen,1997
2. P2RY12 and its role in osteoclast activity and bone remodeling;Authors Listed;Bonekey Rep.,2012
3. Under-reporting of serious adverse drug reactions in Sweden;Backstrom;Pharmacoepidemiology and Drug Safety,2004
4. Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data;Banerjee;The Journal of Machine Learning Research,2008
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