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Davis SE, Zabotka L, Desai RJ, Wang SV, Maro JC, Coughlin K, Hernández-Muñoz JJ, Stojanovic D, Shah NH, Smith JC. Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review. Drug Saf 2023; 46:725-742. [PMID: 37340238 DOI: 10.1007/s40264-023-01325-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2023] [Indexed: 06/22/2023]
Abstract
INTRODUCTION Pharmacovigilance programs protect patient health and safety by identifying adverse event signals through postmarketing surveillance of claims data and spontaneous reports. Electronic health records (EHRs) provide new opportunities to address limitations of traditional approaches and promote discovery-oriented pharmacovigilance. METHODS To evaluate the current state of EHR-based medication safety signal identification, we conducted a scoping literature review of studies aimed at identifying safety signals from routinely collected patient-level EHR data. We extracted information on study design, EHR data elements utilized, analytic methods employed, drugs and outcomes evaluated, and key statistical and data analysis choices. RESULTS We identified 81 eligible studies. Disproportionality methods were the predominant analytic approach, followed by data mining and regression. Variability in study design makes direct comparisons difficult. Studies varied widely in terms of data, confounding adjustment, and statistical considerations. CONCLUSION Despite broad interest in utilizing EHRs for safety signal identification, current efforts fail to leverage the full breadth and depth of available data or to rigorously control for confounding. The development of best practices and application of common data models would promote the expansion of EHR-based pharmacovigilance.
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Affiliation(s)
- Sharon E Davis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Ave, Suite 1475, Nashville, TN, 37203, USA
- Vanderbilt University School of Medicine, Nashville, TN, USA
| | | | - Rishi J Desai
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Shirley V Wang
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Judith C Maro
- Harvard Medical School, Boston, MA, USA
- Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | | | | | | | - Nigam H Shah
- School of Medicine, Stanford University, Stanford, CA, USA
- Stanford Health Care, Palo Alto, CA, USA
| | - Joshua C Smith
- Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Ave, Suite 1475, Nashville, TN, 37203, USA.
- Vanderbilt University School of Medicine, Nashville, TN, USA.
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2
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Qiu Y, Zhang Y, Deng Y, Liu S, Zhang W. A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1968-1985. [PMID: 34003753 DOI: 10.1109/tcbb.2021.3081268] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of multiple drugs. Since laboratory researches are often complicated, costly and time-consuming, it's urgent to develop computational approaches to detect drug-drug interactions. In this paper, we conduct a comprehensive review of state-of-the-art computational methods falling into three categories: literature-based extraction methods, machine learning-based prediction methods and pharmacovigilance-based data mining methods. Literature-based extraction methods detect DDIs from published literature using natural language processing techniques; machine learning-based prediction methods build prediction models based on the known DDIs in databases and predict novel ones; pharmacovigilance-based data mining methods usually apply statistical techniques on various electronic data to detect drug-drug interaction signals. We first present the taxonomy of drug-drug interaction detection methods and provide the outlines of three categories of methods. Afterwards, we respectively introduce research backgrounds and data sources of three categories, and illustrate their representative approaches as well as evaluation metrics. Finally, we discuss the current challenges of existing methods and highlight potential opportunities for future directions.
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3
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Hendrickx JO, van Gastel J, Leysen H, Martin B, Maudsley S. High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases. Pharmacol Rev 2020; 72:191-217. [PMID: 31843941 DOI: 10.1124/pr.119.017921] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.
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Affiliation(s)
- Jhana O Hendrickx
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Jaana van Gastel
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Hanne Leysen
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Bronwen Martin
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Stuart Maudsley
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
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4
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Zhu A, Zeng D, Shen L, Ning X, Li L, Zhang P. A super-combo-drug test to detect adverse drug events and drug interactions from electronic health records in the era of polypharmacy. Stat Med 2020; 39:1458-1472. [PMID: 32101641 DOI: 10.1002/sim.8490] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 11/11/2019] [Accepted: 01/14/2020] [Indexed: 11/06/2022]
Abstract
Pharmacoinformatics research has experienced a great deal of successes in detecting drug-induced adverse events (AEs) using large-scale health record databases. In the era of polypharmacy, pharmacoinformatics faces many new challenges, and two significant challenges are to detect high-order drug interactions and to handle strongly correlated drugs. In this article, we propose a super-combo-drug test (SupCD-T) to address the aforementioned two challenges. SupCD-T detects drug interactions by identifying optimal drug combinations with increased AE risks. In addition, SupCD-T increases the statistical powers to detect single-drug effects by combining strongly correlated drugs. Although SupCD-T does not distinguish single-drug effects from their combination effects, it is noticeably more powerful in selecting an individual drug effect in the multiple regression analysis, where confounding justification between two correlated drugs reduces the power in testing the individual drug effects on AEs. Our simulation studies demonstrate that SupCD-T has generally better power comparing with the multiple regression analysis. In addition, SupCD-T is able to select meaningful drug combinations (eg, highly coprescribed drugs). Using electronic health record database, we illustrate the utility of SupCD-T and discover a number of drug combinations that have increased risk in myopathy. Some novel drug combinations have not yet been investigated and reported in the pharmacology research.
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Affiliation(s)
- Anqi Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Xia Ning
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
| | - Pengyue Zhang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
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5
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Wang X, Li L, Wang L, Feng W, Zhang P. Propensity score-adjusted three-component mixture model for drug-drug interaction data mining in FDA Adverse Event Reporting System. Stat Med 2019; 39:996-1010. [PMID: 31880829 PMCID: PMC9292662 DOI: 10.1002/sim.8457] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 10/16/2019] [Accepted: 12/04/2019] [Indexed: 02/04/2023]
Abstract
With increasing trend of polypharmacy, drug‐drug interaction (DDI)‐induced adverse drug events (ADEs) are considered as a major challenge for clinical practice. As premarketing clinical trials usually have stringent inclusion/exclusion criteria, limited comedication data capture and often times small sample size have limited values in study DDIs. On the other hand, ADE reports collected by spontaneous reporting system (SRS) become an important source for DDI studies. There are two major challenges in detecting DDI signals from SRS: confounding bias and false positive rate. In this article, we propose a novel approach, propensity score‐adjusted three‐component mixture model (PS‐3CMM). This model can simultaneously adjust for confounding bias and estimate false discovery rate for all drug‐drug‐ADE combinations in FDA Adverse Event Reporting System (FAERS), which is a preeminent SRS database. In simulation studies, PS‐3CMM performs better in detecting true DDIs comparing to the existing approach. It is more sensitive in selecting the DDI signals that have nonpositive individual drug relative ADE risk (NPIRR). The application of PS‐3CMM is illustrated in analyzing the FAERS database. Compared to the existing approaches, PS‐3CMM prioritizes DDI signals differently. PS‐3CMM gives high priorities to DDI signals that have NPIRR. Both simulation studies and FAERS data analysis conclude that our new PS‐3CMM is a new method that is complement to the existing DDI signal detection methods.
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Affiliation(s)
- Xueying Wang
- Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, Harbin, China
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
| | - Lei Wang
- Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, Harbin, China
| | - Weixing Feng
- Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, Harbin, China
| | - Pengyue Zhang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
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6
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Chasioti D, Yao X, Zhang P, Lerner S, Quinney SK, Ning X, Li L, Shen L. Mining Directional Drug Interaction Effects on Myopathy Using the FAERS Database. IEEE J Biomed Health Inform 2018; 23:2156-2163. [PMID: 30296244 DOI: 10.1109/jbhi.2018.2874533] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Mining high-order drug-drug interaction (DDI) induced adverse drug effects from electronic health record databases is an emerging area, and very few studies have explored the relationships between high-order drug combinations. We investigate a novel pharmacovigilance problem for mining directional DDI effects on myopathy using the FDA Adverse Event Reporting System (FAERS) database. Our paper provides information on the risk of myopathy associated with adding new drugs on the already prescribed medication, and visualizes the identified directional DDI patterns as user-friendly graphical representation. We utilize the Apriori algorithm to extract frequent drug combinations from the FAERS database. We use odds ratio to estimate the risk of myopathy associated with directional DDI. We create a tree-structured graph to visualize the findings for easy interpretation. Our method confirmed myopathy association with previously reported HMG-CoA reductase inhibitors like rosuvastatin, fluvastatin, simvastatin, and atorvastatin. New, previously unidentified but mechanistically plausible associations with myopathy were also observed, such as the DDI between pamidronate and levofloxacin. Additional top findings are gadolinium-based imaging agents, which however are often used in myopathy diagnosis. Other DDIs with no obvious mechanism are also reported, such as that of sulfamethoxazole with trimethoprim and potassium chloride. This study shows the feasibility to estimate high-order directional DDIs in a fast and accurate manner. The results of the analysis could become a useful tool in the specialists' hands through an easy-to-understand graphic visualization.
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7
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Vilar S, Friedman C, Hripcsak G. Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media. Brief Bioinform 2018; 19:863-877. [PMID: 28334070 PMCID: PMC6454455 DOI: 10.1093/bib/bbx010] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 12/28/2016] [Indexed: 11/13/2022] Open
Abstract
Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.
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Affiliation(s)
- Santiago Vilar
- Department of Biomedical Informatics, Columbia University, New York, USA
- Department of Organic Chemistry, University of Santiago de Compostela, Spain
| | - Carol Friedman
- Department of Biomedical Informatics, Columbia University, New York, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, USA
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8
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Zhu A, Zeng D, Zhang P, Li L. Estimating causal log-odds ratio using the case-control sample and its application in the pharmaco-epidemiology study. Stat Methods Med Res 2018; 28:2165-2178. [PMID: 29355073 DOI: 10.1177/0962280217750175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
One important goal in pharmaco-epidemiology studies is to understand the causal relationship between drug exposures and their clinical outcomes, including adverse drug events. In order to achieve this goal, however, we need to resolve several challenges. Most of pharmaco-epidemiology data are observational and confounding is largely present due to many co-medications. The pharmaco-epidemiology study data set is often sampled from large medical record databases using a matched case-control design, and it may not be representative of the original patient population in the medical record databases. Data analysis method needs to handle a large sample size that cannot be handled using existing statistical analysis packages. In this paper, we tackle these challenges both methodologically and computationally. We propose a conditional causal log-odds ratio (OR) definition to characterize causal effects of drug exposures on a binary adverse drug event adjusting for individual level confounders. Using a case-control design, we present a propensity score estimation using only case samples and we provide sufficient conditions for the consistency of the estimation of the causal log-odds ratio using case-based propensity scores. Computationally, we implement a principle component analysis to reduce high-dimensional confounders. Extensive simulation studies are performed to demonstrate superior performance of our method to existing methods. Finally, we apply the proposed method to analyze drug-induced myopathy data sampled from a de-identified subset of medical record database (close to 5 million patient records), The Indiana Network for Patient Care. Our method identified 70 drug-induced myopathy (p < 0.05) out 72 drugs, which have myoathy side effects on their FDA drug labels. These 70 drugs include three statins who are known for their myopathy side effects.
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Affiliation(s)
- Anqi Zhu
- 1 Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Donglin Zeng
- 1 Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Pengyue Zhang
- 2 Center for Computational Biology and Bioinformatics, Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Lang Li
- 2 Center for Computational Biology and Bioinformatics, Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN, USA
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9
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Chiang CW, Zhang P, Wang X, Wang L, Zhang S, Ning X, Shen L, Quinney SK, Li L. Translational High-Dimensional Drug Interaction Discovery and Validation Using Health Record Databases and Pharmacokinetics Models. Clin Pharmacol Ther 2017; 103:287-295. [PMID: 29052226 DOI: 10.1002/cpt.914] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 10/10/2017] [Accepted: 10/10/2017] [Indexed: 11/07/2022]
Abstract
Polypharmacy increases the risk of drug-drug interactions (DDIs). Combining epidemiological studies with pharmacokinetic modeling, we detected and evaluated high-dimensional DDIs among 30 frequent drugs. Multidrug combinations that increased the risk of myopathy were identified in the US Food and Drug Administration Adverse Event Reporting System (FAERS) and electronic medical record (EMR) databases by a mixture drug-count response model. CYP450 inhibition was estimated among the 30 drugs in the presence of 1 to 4 inhibitors using in vitro / in vivo extrapolation. Twenty-eight three-way and 43 four-way DDIs had significant myopathy risk in both databases and predicted increases in the area under the concentration-time curve ratio (AUCR) >2-fold. The high-dimensional DDI of omeprazole, fluconazole, and clonidine was associated with a 6.41-fold (FAERS) and 18.46-fold (EMR) increased risk of myopathy local false discovery rate (<0.005); the AUCR of omeprazole in this combination was 9.35. The combination of health record informatics and pharmacokinetic modeling is a powerful translational approach to detect high-dimensional DDIs.
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Affiliation(s)
- Chien-Wei Chiang
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Pengyue Zhang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, Ohio, USA
| | - Xueying Wang
- Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, Harbin, P.R. China
| | - Lei Wang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, Ohio, USA.,Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, Harbin, P.R. China
| | - Shijun Zhang
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Xia Ning
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Li Shen
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Sara K Quinney
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, Ohio, USA
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10
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Zhang P, Wu H, Chiang C, Wang L, Binkheder S, Wang X, Zeng D, Quinney SK, Li L. Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 7:90-102. [PMID: 29193890 PMCID: PMC5824109 DOI: 10.1002/psp4.12267] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 11/08/2017] [Indexed: 12/18/2022]
Abstract
Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.
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Affiliation(s)
- Pengyue Zhang
- Department of Biomedical InformaticsCollege of Medicine, the Ohio State UniversityColumbusOhioUSA
| | - Heng‐Yi Wu
- Department of Biomedical InformaticsCollege of Medicine, the Ohio State UniversityColumbusOhioUSA
| | - Chien‐Wei Chiang
- Department of Biomedical InformaticsCollege of Medicine, the Ohio State UniversityColumbusOhioUSA
| | - Lei Wang
- Department of Biomedical InformaticsCollege of Medicine, the Ohio State UniversityColumbusOhioUSA
- Intelligent Systems and Bioinformatics Institute, College of Automation, Harbin Engineering UniversityHarbinHeilongjiangChina
| | - Samar Binkheder
- Department of Biohealth InformaticsIndiana University School of Informatics and ComputingIndianapolisIndianaUSA
- Medical Informatics Unit, College of Medicine, King Saud UniversityRiyadhSaudi Arabia
| | - Xueying Wang
- Intelligent Systems and Bioinformatics Institute, College of Automation, Harbin Engineering UniversityHarbinHeilongjiangChina
| | - Donglin Zeng
- Department of BiostatisticsUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Sara K. Quinney
- Department of Obstetrics and GynecologyIndiana UniversityIndianapolisIndianaUSA
| | - Lang Li
- Department of Biomedical InformaticsCollege of Medicine, the Ohio State UniversityColumbusOhioUSA
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11
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Wang X, Zhang P, Chiang CW, Wu H, Shen L, Ning X, Zeng D, Wang L, Quinney SK, Feng W, Li L. Mixture drug-count response model for the high-dimensional drug combinatory effect on myopathy. Stat Med 2017; 37:673-686. [PMID: 29171062 DOI: 10.1002/sim.7545] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 09/21/2017] [Accepted: 10/05/2017] [Indexed: 01/24/2023]
Abstract
Drug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy. The "count" indicates the number of drugs in a combination. One model is called fixed probability mixture drug-count response model with a maximum risk threshold (FMDRM-MRT). The other model is called count-dependent probability mixture drug-count response model with a maximum risk threshold (CMDRM-MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug-count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high-dimensional drug combinatory effects on myopathy. CMDRM-MRT identified and validated (54; 374; 637; 442; 131) 2-way to 6-way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models' parameters and local false discovery rate estimates are evaluated through statistical simulation studies.
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Affiliation(s)
- Xueying Wang
- Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, NO.145-1, Nantong Street, Nangang District, Harbin, 150001, Heilongjiang, China.,Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, 410 W. 10th St., Suite 5000, Indianapolis, IN, 46202, USA
| | - Pengyue Zhang
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, 410 W. 10th St., Suite 5000, Indianapolis, IN, 46202, USA
| | - Chien-Wei Chiang
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, 410 W. 10th St., Suite 5000, Indianapolis, IN, 46202, USA
| | - Hengyi Wu
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, 410 W. 10th St., Suite 5000, Indianapolis, IN, 46202, USA
| | - Li Shen
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, 410 W. 10th St., Suite 5000, Indianapolis, IN, 46202, USA.,Department of Radiology, School of Medicine, Indiana University, 355 W. 16th Street, Suite 4100, Room 4099, Indianapolis, 46202, IN, USA
| | - Xia Ning
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, 410 W. 10th St., Suite 5000, Indianapolis, IN, 46202, USA.,Computer and Information Science, IUPUI, 723 W Michigan St, SL 265, Indianapolis, IN, 46202, USA
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, 3103B McGavran-Greenberg Hall, CB #7420, Chapel Hill, NC, 27599, USA
| | - Lei Wang
- Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, NO.145-1, Nantong Street, Nangang District, Harbin, 150001, Heilongjiang, China.,Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, 410 W. 10th St., Suite 5000, Indianapolis, IN, 46202, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, 975 West Walnut Street, Medical Research and Library Building, IB 130, Indianapolis, 46202, IN, USA
| | - Sara K Quinney
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, 410 W. 10th St., Suite 5000, Indianapolis, IN, 46202, USA.,Department of Obstetrics and Gynecology, School of Medicine, Indiana University, 550 University Blvd, Indianapolis, 46202, IN, USA.,Division of Clinical Pharmacology, School of Medicine, Indiana University, Research Institute (R2), Room 402, 950 West Walnut Street, Indianapolis, 46202, IN, USA
| | - Weixing Feng
- Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, NO.145-1, Nantong Street, Nangang District, Harbin, 150001, Heilongjiang, China
| | - Lang Li
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, 410 W. 10th St., Suite 5000, Indianapolis, IN, 46202, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, 975 West Walnut Street, Medical Research and Library Building, IB 130, Indianapolis, 46202, IN, USA.,Indiana Institute of Personalized Medicine, School of Medicine, Indiana University, Research Institute (R2), Room 402, 950 West Walnut Street, Indianapolis, 46202, IN, USA
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Du L, Chakraborty A, Chiang CW, Cheng L, Quinney SK, Wu H, Zhang P, Li L, Shen L. Graphic Mining of High-Order Drug Interactions and Their Directional Effects on Myopathy Using Electronic Medical Records. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015; 4:481-8. [PMID: 26380157 PMCID: PMC4562164 DOI: 10.1002/psp4.59] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 05/05/2015] [Accepted: 05/13/2015] [Indexed: 11/11/2022]
Abstract
We propose to study a novel pharmacovigilance problem for mining directional effects of high-order drug interactions on an adverse drug event (ADE). Our goal is to estimate each individual risk of adding a new drug to an existing drug combination. In this proof-of-concept study, we analyzed a large electronic medical records database and extracted myopathy-relevant case control drug co-occurrence data. We applied frequent itemset mining to discover frequent drug combinations within the extracted data, evaluated directional drug interactions related to these combinations, and identified directional drug interactions with large effect sizes. Furthermore, we developed a novel visualization method to organize multiple directional drug interaction effects depicted as a tree, to generate an intuitive graphical and visual representation of our data-mining results. This translational bioinformatics approach yields promising results, adds valuable and complementary information to the existing pharmacovigilance literature, and has the potential to impact clinical practice.
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Affiliation(s)
- L Du
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Departments of Radiology and Imaging Sciences, Indiana University School of Medicine Indianapolis, Indiana, USA
| | - A Chakraborty
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Department of Medical and Molecular Genetics, Indiana University School of Medicine Indianapolis, Indiana, USA
| | - C-W Chiang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Department of Medical and Molecular Genetics, Indiana University School of Medicine Indianapolis, Indiana, USA
| | - L Cheng
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Department of Medical and Molecular Genetics, Indiana University School of Medicine Indianapolis, Indiana, USA
| | - S K Quinney
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Department of Obstetrics and Gynecology, Indiana University School of Medicine Indianapolis, Indiana, USA
| | - H Wu
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Department of Medical and Molecular Genetics, Indiana University School of Medicine Indianapolis, Indiana, USA
| | - P Zhang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Department of Medical and Molecular Genetics, Indiana University School of Medicine Indianapolis, Indiana, USA
| | - L Li
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Department of Medical and Molecular Genetics, Indiana University School of Medicine Indianapolis, Indiana, USA
| | - L Shen
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Departments of Radiology and Imaging Sciences, Indiana University School of Medicine Indianapolis, Indiana, USA
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