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Fusaroli M, Raschi E, Poluzzi E, Hauben M. The evolving role of disproportionality analysis in pharmacovigilance. Expert Opin Drug Saf 2024; 23:981-994. [PMID: 38913869 DOI: 10.1080/14740338.2024.2368817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 06/12/2024] [Indexed: 06/26/2024]
Abstract
INTRODUCTION From 2009 to 2015, the IMI PROTECT conducted rigorous studies addressing questions about optimal implementation and significance of disproportionality analyses, leading to the development of Good Signal Detection Practices. The ensuing period witnessed the independent exploration of research paths proposed by IMI PROTECT, accumulating valuable experience and insights that have yet to be seamlessly integrated. AREAS COVERED This state-of-the-art review integrates IMI PROTECT recommendations with recent acquisitions and evolving challenges. It deals with defining the object of study, disproportionality methods, subgrouping, masking, drug-drug interaction, duplication, expectedness, the debated use of disproportionality results as risk measures, integration with other types of data. EXPERT OPINION Despite the ongoing skepticism regarding the usefulness of disproportionality analyses and individual case safety reports, their ability to timely detect safety signals regarding rare and unpredictable adverse reactions remains unparalleled. Moreover, recent exploration into their potential for characterizing safety signals revealed valuable insights concerning potential risk factors and the patient's perspective. To fully realize their potential beyond hypothesis generation and achieve a comprehensive evidence synthesis with other kinds of data and studies, each with their unique limitations and contributions, we need to investigate methods for more transparently communicating disproportionality results and mapping and addressing pharmacovigilance biases.
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Affiliation(s)
- Michele Fusaroli
- Pharmacology Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Emanuel Raschi
- Pharmacology Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Elisabetta Poluzzi
- Pharmacology Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Manfred Hauben
- Department of Family and Community Medicine, New York Medical College, Valhalla, NY, USA
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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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3
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Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug-Drug Interactions. Clin Ther 2023; 45:117-133. [PMID: 36732152 DOI: 10.1016/j.clinthera.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 12/15/2022] [Accepted: 01/09/2023] [Indexed: 02/01/2023]
Abstract
Despite increasing mechanistic understanding, undetected and underrecognized drug-drug interactions (DDIs) persist. This elusiveness relates to an interwoven complexity of increasing polypharmacy, multiplex mechanistic pathways, and human biological individuality. This persistent elusiveness motivates development of artificial intelligence (AI)-based approaches to enhancing DDI detection and prediction capabilities. The literature is vast and roughly divided into "prediction" and "detection." The former relatively emphasizes biological and chemical knowledge bases, drug development, new drugs, and beneficial interactions, whereas the latter utilizes more traditional sources such as spontaneous reports, claims data, and electronic health records to detect novel adverse DDIs with authorized drugs. However, it is not a bright line, either nominally or in practice, and both are in scope for pharmacovigilance supporting signal detection but also signal refinement and evaluation, by providing data-based mechanistic arguments for/against DDI signals. The wide array of intricate and elegant methods has expanded the pharmacovigilance tool kit. How much they add to real prospective pharmacovigilance, reduce the public health impact of DDIs, and at what cost in terms of false alarms amplified by automation bias and its sequelae are open questions. (Clin Ther. 2023;45:XXX-XXX) © 2023 Elsevier HS Journals, Inc.
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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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Dasgupta S, Jayagopal A, Jun Hong AL, Mariappan R, Rajan V. Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation. JMIR Med Inform 2021; 9:e32730. [PMID: 34694230 PMCID: PMC8576589 DOI: 10.2196/32730] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 09/07/2021] [Accepted: 09/18/2021] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Adverse drug events (ADEs) are unintended side effects of drugs that cause substantial clinical and economic burdens globally. Not all ADEs are discovered during clinical trials; therefore, postmarketing surveillance, called pharmacovigilance, is routinely conducted to find unknown ADEs. A wealth of information, which facilitates ADE discovery, lies in the growing body of biomedical literature. Knowledge graphs (KGs) encode information from the literature, where the vertices and the edges represent clinical concepts and their relations, respectively. The scale and unstructured form of the literature necessitates the use of natural language processing (NLP) to automatically create such KGs. Previous studies have demonstrated the utility of such literature-derived KGs in ADE prediction. Through unsupervised learning of the representations (features) of clinical concepts from the KG, which are used in machine learning models, state-of-the-art results for ADE prediction were obtained on benchmark data sets. OBJECTIVE Due to the use of NLP to infer literature-derived KGs, there is noise in the form of false positive (erroneous) and false negative (absent) nodes and edges. Previous representation learning methods do not account for such inaccuracies in the graph. NLP algorithms can quantify the confidence in their inference of extracted concepts and relations from the literature. Our hypothesis, which motivates this work, is that by using such confidence scores during representation learning, the learned embeddings would yield better features for ADE prediction models. METHODS We developed methods to use these confidence scores on two well-known representation learning methods-DeepWalk and Translating Embeddings for Modeling Multi-relational Data (TransE)-to develop their weighted versions: Weighted DeepWalk and Weighted TransE. These methods were used to learn representations from a large literature-derived KG, the Semantic MEDLINE Database, which contains more than 93 million clinical relations. They were compared with Embedding of Semantic Predications, which, to our knowledge, is the best reported representation learning method using the Semantic MEDLINE Database with state-of-the-art results for ADE prediction. Representations learned from different methods were used (separately) as features of drugs and diseases to build classification models for ADE prediction using benchmark data sets. The methods were compared rigorously over multiple cross-validation settings. RESULTS The weighted versions we designed were able to learn representations that yielded more accurate predictive models than the corresponding unweighted versions of both DeepWalk and TransE, as well as Embedding of Semantic Predications, in our experiments. There were performance improvements of up to 5.75% in the F1-score and 8.4% in the area under the receiver operating characteristic curve value, thus advancing the state of the art in ADE prediction from literature-derived KGs. CONCLUSIONS Our classification models can be used to aid pharmacovigilance teams in detecting potentially new ADEs. Our experiments demonstrate the importance of modeling inaccuracies in the inferred KGs for representation learning.
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Affiliation(s)
| | | | - Abel Lim Jun Hong
- School of Computing, National University of Singapore, Singapore, Singapore
| | | | - Vaibhav Rajan
- Department of Information Systems and Analytics, National University of Singapore, Singapore, Singapore
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6
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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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7
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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 2019; 23:2156-2163. [PMID: 30296244 PMCID: PMC6745690 DOI: 10.1109/jbhi.2018.2874533] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [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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Affiliation(s)
- Danai Chasioti
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University, Indianapolis, IN, USA
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Pengyue Zhang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, USA
| | - Samuel Lerner
- Department of Computer Science and Engineering, Ohio State University, Columbus, OH, USA
| | - Sara K. Quinney
- Department of Obstetrics and Gynecology, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Xia Ning
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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8
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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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9
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Pattern Discovery from High-Order Drug-Drug Interaction Relations. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2018; 2:272-304. [DOI: 10.1007/s41666-018-0020-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 03/05/2018] [Accepted: 04/09/2018] [Indexed: 11/26/2022]
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10
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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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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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12
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Zhang P, Du L, Wang L, Liu M, Cheng L, Chiang CW, Wu HY, Quinney SK, Shen L, Li L. A Mixture Dose-Response Model for Identifying High-Dimensional Drug Interaction Effects on Myopathy Using Electronic Medical Record Databases. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015; 4:474-80. [PMID: 26380156 PMCID: PMC4562163 DOI: 10.1002/psp4.53] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Accepted: 05/04/2015] [Indexed: 12/18/2022]
Abstract
Interactions between multiple drugs may yield excessive risk of adverse effects. This increased risk is not uniform for all combinations, although some combinations may have constant adverse effect risks. We developed a statistical model using medical record data to identify drug combinations that induce myopathy risk. Such combinations are revealed using a novel mixture model, comprised of a constant risk model and a dose–response risk model. The dose represents the number of drug combinations. Using an empirical Bayes estimation method, we successfully identified high-dimensional (two to six) drug combinations that are associated with excessive myopathy risk at significantly low local false-discovery rates. From the curve of a dose–response model and high-dimensional drug interaction data, we observed that myopathy risk increases as the drug interaction dimension increases. This is the first time that such a dose–response relationship for high-dimensional drug interactions was observed and extracted from the medical record database.
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Affiliation(s)
- P Zhang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Department of Biostatistics, Indiana University School of Medicine Indianapolis, Indiana, USA
| | - L Du
- Departments of Radiology and Imaging Sciences, Indiana University School of Medicine Indianapolis, Indiana, USA
| | - L Wang
- Biomedical Engineering Institute, College of Automation, Harbin Engineering University Harbin, Heilongjiang, China ; Bioinformatics Research Center, College of Automation, Harbin Engineering University Harbin, Heilongjiang, China
| | - M Liu
- Division Two, Department of Neurology, First Harbin Hospital Harbin, Heilongjiang, China
| | - L Cheng
- Center for Computational Biology and Bioinformatics, 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
| | - H-Y Wu
- Center for Computational Biology and Bioinformatics, 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
| | - 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
| | - L Li
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Department of Biostatistics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Indiana Institute of Personalized Medicine, Indiana University School of Medicine Indianapolis, Indiana, USA
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