Abstract
Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others. Here we present a network inference algorithm to predict uncharacterized drug-drug interactions. Our algorithm takes, as its only input, sets of previously reported interactions, and does not require any pharmacological or biochemical information about the drugs, their targets or their mechanisms of action. Because the models we use are abstract, our approach can deal with adverse interactions, synergistic/antagonistic/suppressing interactions, or any other type of drug interaction. We show that our method is able to accurately predict interactions, both in exhaustive pairwise interaction data between small sets of drugs, and in large-scale databases. We also demonstrate that our algorithm can be used efficiently to discover interactions of new drugs as part of the drug discovery process.
Highlights
Understanding interactions between drugs is becoming increasingly important
We show that our method is able to accurately predict drug interactions, and that it can be used efficiently to discover interactions of new drugs as part of the drug discovery process
We show that our method is able to accurately predict interactions, even in large-scale databases containing thousands of drugs, and that it can be used efficiently to discover interactions of new drugs as part of the drug discovery process
Summary
A recent large-scale study of older adults (ages 57–85) in the U.S found that 29% of them use five or more prescription medications concurrently, and that as many as 4% may be at risk of having a major adverse drug-drug interaction [1]. For this reason, the evaluation of drug interactions is ‘‘an integral part of drug development and regulatory review prior to its market approval’’ [2], and institutions like the FDA put much effort in developing guidelines for in vitro and in vivo studies, as well as for developing in silico models and methods. Antagonistic interactions can be used to study the mechanisms of action of drugs [4], and even suppressing interactions between drugs, in which one drug inhibits the action of the other, have been found to be potentially very relevant in the fight against antibiotic-resistant pathogens [5]
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