Leveraging genetic interactions for adverse drug-drug interaction prediction
Sheng Qian, Siqi Liang, Haiyuan Yu · PLoS Computational Biology · 2019
In light of increased co-prescription of multiple drugs, the ability to discern and predict drugdrug interactions (DDI) has become crucial to guarantee the safety of patients undergoing treatment with multiple drugs.However, information on DDI profiles is incomplete and the experimental determination of DDIs is labor-intensive and time-consuming.Although previous studies have explored various feature spaces for in silico screening of interacting drug pairs, their use of conventional cross-validation prevents them from achieving generalizable performance on drug pairs where neither drug is seen during training.Here we demonstrate for the first time targets of adversely interacting drug pairs are significantly more likely to have synergistic genetic interactions than non-interacting drug pairs.Leveraging genetic interaction features and a novel training scheme, we construct a gradient boosting-based classifier that achieves robust DDI prediction even for drugs whose interaction profiles are completely unseen during training.We demonstrate that in addition to classification power -including the prediction of 432 novel DDIs-our genetic interaction approach offers interpretability by providing plausible mechanistic insights into the mode of action of DDIs. Author summaryAdverse drug-drug interactions are adverse side effects caused by taking two or more drugs together.As co-prescription of multiple drugs becomes an increasingly prevalent practice, affecting 42.2% of Americans over 65 years old, adverse drug-drug interactions have become a serious safety concern, accounting for over 74,000 emergency room visits and 195,000 hospitalizations each year in the United States alone.Since experimental determination of adverse drug-drug interactions is labor-intensive and time-consuming, various machine learning-based computational approaches have been developed for predicting drug-drug interactions.Considering the fact that drugs effect through binding and modulating the function of their targets, we have explored whether drug-drug interactions can be predicted from the genetic interaction between the gene targets of two drugs, which characterizes the unexpected fitness effect when two genes are simultaneously knocked out.Furthermore, we have built a fast and robust classifier that achieves accurate