AI Takes The Guesswork Out Of Drug Interactions
AsianScientist (May 3, 2018) – A research team at the Korea Advanced Institute of Technology (KAIST) has developed a computational framework that can accurately predict drug-drug and drug-food interactions. They published their findings in the Proceedings of the National Academy of Sciences.
Drug interactions, including drug-drug interactions (DDIs) and drug-food constituent interactions (DFIs), can trigger unexpected pharmacological effects including adverse drug events (ADEs). The causal mechanisms of ADEs are often unknown. However, current prediction methods do not provide sufficient details beyond the chance of DDI occurrence, and detailed drug information is often unavailable for DDI prediction.
To tackle this problem, Dr. Ryu Jae Yong, Assistant Professor Kim Hyun Uk and Distinguished Professor Lee Sang Yup, all from the Department of Chemical and Biomolecular Engineering at KAIST, developed a computational framework, named DeepDDI, that accurately predicts 86 DDI types for a given drug pair.
DeepDDI takes structural information and names of two drugs in a pair as inputs and predicts relevant DDI types for the input drug pair. It uses a deep neural network to predict 86 DDI types with a mean accuracy of 92.4 percent using the DrugBank gold standard DDI dataset covering 192,284 DDIs contributed by 191,878 drug pairs.
DDI types predicted by DeepDDI are generated in the form of human-readable sentences as outputs, which describe changes in pharmacological effects and the risk of ADEs as a result of the interaction between two drugs in pair.
For example, DeepDDI output sentences describing potential interactions between oxycodone (opioid pain medication) and atazanavir (antiretroviral medication) were generated as follows: “The metabolism of Oxycodone can be decreased when combined with Atazanavir”; and “The risk or severity of adverse effects can be increased when Oxycodone is combined with Atazanavir”. DeepDDI thus provides more specific information on drug interactions beyond the occurrence chance of DDIs or ADEs typically reported to date.
Furthermore, DeepDDI can be used to suggest which drug or food to avoid in order to minimize the chance of adverse drug events or optimize drug efficacy. DeepDDI was applied to 3,288,157 drug-food constituent pairs (2,159 approved drugs and 1,523 well-characterized food constituents) to predict DFIs.
The impact of 256 food constituents on the pharmacological effects of interacting drugs and bioactivities of 149 food constituents was predicted. These prediction results can be useful if an individual is taking medications for a specific chronic disease such as hypertension or diabetes mellitus type 2.
“We have developed a platform technology that will allow precision medicine in the era of the Fourth Industrial Revolution. DeepDDI can serve to provide important information on drug prescriptions and dietary suggestions to maximize health benefits and ultimately help maintain a healthy life in this aging society,” said Lee.
The article can be found at: Ryu et al. (2018) Deep Learning Improves Prediction of Drug–drug and Drug–food Interactions.
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Source: Korea Advanced Institute of Science and Technology; Photo: Pexels.
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