Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge
By Takako Takeda, Ming Hao, Tiejun Cheng, Stephen H. Bryant, Yanli Wang. 2017
120 citations recorded by the source corpus.
Drug–drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our assumption was that a query drug (Dq) and a drug to be examined (De) likely have DDI if the drugs in the interaction network of De are structurally similar to Dq. A network of De describes the associations between the drugs and the proteins relating to PK and PD for De. These include target proteins, proteins interacting with target proteins, enzymes, and transporters for De. We constructed logistic regression models for DDI prediction using only 2D structural similarities between each Dq and the drugs in the network of De. The results indicated that our models could effectively predict DDIs. It was found that integrating structural similarity scores of the drugs relating to both PK and PD of De was crucial for model performance. In particular, the combination of the target- and enzyme-related scores provided the largest increase of the predictive power.Graphical abstract.
Subfields: Computational Theory and Mathematics, Pharmacology, Spectroscopy
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Citations in the corpus
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “Drug-drug interactions are defined through the concepts of synergy and antagonism [283, 284] and are quantified biologically by measuring the dose-effect curves [285, 286] or cell viability [287, 288, 289, 290, 280, 291, 292].”