Systematic drug repositioning based on clinical side-effects.
Drug repositioning helps fully explore indications for marketed drugs and clinical candidates. Here we show that the clinical side-effects (SEs) provide a human phenotypic profile for the drug, and this profile can suggest additional disease indications. We extracted 3,175 SE-disease relationships by combining the SE-drug relationships from drug labels and the drug-disease relationships from PharmGKB. Many relationships provide explicit repositioning hypotheses, such as drugs causing hypoglycemia are potential candidates for diabetes. We built Naïve Bayes models to predict indications for 145 diseases using the SEs as features. The AUC was above 0.8 in 92% of these models. The method was extended to predict indications for clinical compounds, 36% of the models achieved AUC above 0.7. This suggests that closer attention should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to rationally explore the repositioning potential based on this "clinical phenotypic assay".
Computational Biology, Quantitative Sciences, Medicines Discovery and Development, GlaxoSmithKline, Philadelphia, Pennsylvania, United States of America. Lun.Yang@gmail.com
Lun
L
Yang
Pankaj
P
Agarwal
D019540:Area Under Curve; D002986:Clinical Trials as Topic; D003196:Computer Graphics; D058492:Drug Repositioning / Q000706:statistics & numerical data*; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D021281:Quantitative Structure-Activity Relationship; D012372:ROC Curve