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Synergistic drug combinations from electronic health records and gene expression.

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.

SUBMITTER: Low YS

PROVIDER: S-EPMC6080645 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Synergistic drug combinations from electronic health records and gene expression.

Low Yen S YS Daugherty Aaron C AC Schroeder Elizabeth A EA Chen William W Seto Tina T Weber Susan S Lim Michael M Hastie Trevor T Mathur Maya M Desai Manisha M Farrington Carl C Radin Andrew A AA Sirota Marina M Kenkare Pragati P Thompson Caroline A CA Yu Peter P PP Gomez Scarlett L SL Sledge George W GW Kurian Allison W AW Shah Nigam H NH

Journal of the American Medical Informatics Association : JAMIA 20170501 3

<h4>Objective</h4>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.<h4>Method</h4>We applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pair ...[more]

PMID: 27940607

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Project description:Background: Drug-induced coagulopathy (DIC) is a severe adverse reaction and has become a significantly increased clinical problem in children. It is crucial to the detection of the DIC safety signal for drug post-marketing scientific supervision purposes. Therefore, this study aimed to detect potential signals for DIC in children using the routine electronic medical record (EMR) data. Methods: This study extracted EMR data from Beijing Children's Hospital between 2009 and 2020. A two-stage modeling method was developed to detect the signal of DIC. We calculated the crude incidence by mining cases of coagulopathy to select the potential suspected drugs; then, propensity score-matched retrospective cohorts of specific screened drugs from the first stage were constructed and estimated the odds ratio (OR) and 95% confidence interval (CI) using conditional logistic regression models. The current literature evidence was used to assess the novelty of the signal. Results:In the study, from a total of 340 drugs, 22 drugs were initially screened as potentially inducing coagulopathy. In total, we identified 19 positive DIC associations. Of these, potential DIC risk of omeprazole (OR: 2.23, 95% CI: 1.88-2.65), chlorpheniramine (OR:3.04, 95% CI:2.56-3.60), and salbutamol sulfate (OR:1.36, 95% CI:1.07-1.73) were three new DIC signals in both children and adults. Twelve associations between coagulopathy and drugs, meropenem (OR: 3.38, 95% CI: 2.72-4.20), cefoperazone sulbactam (OR: 2.80, 95% CI: 2.30-3.41), fluconazole (OR: 2.11, 95% CI: 1.71-2.59), voriconazole (OR: 2.82, 95% CI: 2.20-3.61), ambroxol hydrochloride (OR: 2.12, 95% CI: 1.74-2.58), furosemide (OR: 2.36, 95% CI: 2.08-2.67), iodixanol (OR: 2.21, 95% CI: 1.72-2.85), cefamandole (OR: 1.82, 95% CI: 1.56-2.13), ceftizoxime (OR: 1.95, 95% CI: 1.44-2.63), ceftriaxone (OR: 1.95, 95% CI: 1.44-2.63), latamoxef sodium (OR: 1.76, 95% CI: 1.49-2.07), and sulfamethoxazole (OR: 1.29, 95% CI: 1.01-1.64), were considered as new signals in children. Conclusion: The two-stage algorithm developed in our study to detect safety signals of DIC found nineteen signals of DIC, including twelve new signals in a pediatric population. However, these safety signals of DIC need to be confirmed by further studies based on population study and mechanism research.

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Synergistic drug combinations from electronic health records and gene expression.

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Synergistic drug combinations from electronic health records and gene expression.

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