title: Systematic prediction of drug combinations based on clinical side-effects.
abstract: Drug co-prescription (or drug combination) is a therapeutic strategy widely used as it may improve efficacy and reduce side-effect (SE). Since it is impractical to screen all possible drug combinations for every indication, computational methods have been developed to predict new combinations. In this study, we describe a novel approach that utilizes clinical SEs from post-marketing surveillance and the drug label to predict 1,508 novel drug-drug combinations. It outperforms other prediction methods, achieving an AUC of 0.92 compared to an AUC of 0.69 in a previous method, on a much larger drug combination set (245 drug combinations in our dataset compared to 75 in previous work.). We further found from the feature selection that three FDA black-box warned serious SEs, namely pneumonia, haemorrhage rectum, and retinal bleeding, contributed mostly to the predictions and a model only using these three SEs can achieve an average area under curve (AUC) at 0.80 and accuracy at 0.91, potentially with its simplicity being recognized as a practical rule-of-three in drug co-prescription or making fixed-dose drug combination. We also demonstrate this performance is less likely to be influenced by confounding factors such as biased disease indications or chemical structures.
authors.affiliation: 1] Computational Biology, GlaxoSmithKline, Philadelphia, Pennsylvania, United States of America [2] School of Informatics and Computing, Indiana University, Indianapolis, Indiana, United States of America.
authors.forename: Hui
authors.initials: H
authors.lastname: Huang
authors.affiliation: Healthcare Analytics Research, IBM T.J. Watson Research Center, United States of America.
authors.forename: Ping
authors.initials: P
authors.lastname: Zhang
authors.affiliation: Computational Biology, GlaxoSmithKline, Research Triangle Park, North Carolina, United States of America.
authors.forename: Xiaoyan A
authors.initials: XA
authors.lastname: Qu
authors.affiliation: Computational Biology, GlaxoSmithKline, Stevenage, United Kingdom.
authors.forename: Philippe
authors.initials: P
authors.lastname: Sanseau
authors.affiliation: Computational Biology, GlaxoSmithKline, Philadelphia, Pennsylvania, United States of America.
authors.forename: Lun
authors.initials: L
authors.lastname: Yang
mesh_terms: D019540:Area Under Curve; D016208:Databases, Factual; D004338:Drug Combinations; D004347:Drug Interactions; D064420:Drug-Related Side Effects and Adverse Reactions; D006471:Gastrointestinal Hemorrhage / Q000209:etiology; D006801:Humans; D008962:Models, Theoretical; D011014:Pneumonia / Q000209:etiology; D011358:Product Surveillance, Postmarketing; D012372:ROC Curve; D012166:Retinal Hemorrhage / Q000209:etiology
chemical_list: D004338:Drug Combinations