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J Am Med Inform Assoc. 2017 May 1;24(3):565-576. doi: 10.1093/jamia/ocw161.

Synergistic drug combinations from electronic health records and gene expression.

Author information

1
Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
2
twoXAR, Inc., Palo Alto, CA, USA.
3
Clinical Informatics, Stanford University.
4
Department of Statistics, Stanford University.
5
Department of Health Research and Policy, Stanford University.
6
Quantitative Sciences Unit, Stanford University.
7
Palo Alto Medical Foundation Research Institute, Palo Alto, CA, USA.
8
Cancer Prevention Institute of California, Fremont, CA, USA.
9
Division of Oncology, Department of Medicine, Stanford University.

Abstract

Objective:

Using electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.

Method:

We applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis.

Results:

From EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence.

Conclusions:

This is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing.

KEYWORDS:

breast cancer; combination therapies; drug discovery; drug interactions; drug repurposing; electronic health records

PMID:
27940607
PMCID:
PMC6080645
DOI:
10.1093/jamia/ocw161
[Indexed for MEDLINE]
Free PMC Article

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