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J Am Med Inform Assoc. 2014 Oct;21(e2):e278-86. doi: 10.1136/amiajnl-2013-002512. Epub 2014 Mar 18.

Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties.

Author information

1
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
2
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, Tennessee, USA Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Abstract

OBJECTIVE:

Drug-drug interactions (DDIs) are an important consideration in both drug development and clinical application, especially for co-administered medications. While it is necessary to identify all possible DDIs during clinical trials, DDIs are frequently reported after the drugs are approved for clinical use, and they are a common cause of adverse drug reactions (ADR) and increasing healthcare costs. Computational prediction may assist in identifying potential DDIs during clinical trials.

METHODS:

Here we propose a heterogeneous network-assisted inference (HNAI) framework to assist with the prediction of DDIs. First, we constructed a comprehensive DDI network that contained 6946 unique DDI pairs connecting 721 approved drugs based on DrugBank data. Next, we calculated drug-drug pair similarities using four features: phenotypic similarity based on a comprehensive drug-ADR network, therapeutic similarity based on the drug Anatomical Therapeutic Chemical classification system, chemical structural similarity from SMILES data, and genomic similarity based on a large drug-target interaction network built using the DrugBank and Therapeutic Target Database. Finally, we applied five predictive models in the HNAI framework: naive Bayes, decision tree, k-nearest neighbor, logistic regression, and support vector machine, respectively.

RESULTS:

The area under the receiver operating characteristic curve of the HNAI models is 0.67 as evaluated using fivefold cross-validation. Using antipsychotic drugs as an example, several HNAI-predicted DDIs that involve weight gain and cytochrome P450 inhibition were supported by literature resources.

CONCLUSIONS:

Through machine learning-based integration of drug phenotypic, therapeutic, structural, and genomic similarities, we demonstrated that HNAI is promising for uncovering DDIs in drug development and postmarketing surveillance.

PMID:
24644270
PMCID:
PMC4173180
DOI:
10.1136/amiajnl-2013-002512
[Indexed for MEDLINE]
Free PMC Article

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