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Li Y, Li X, Zhu M, Liu H, Lei Z, Yao X, Liu D. Development of a Physiologically Based Pharmacokinetic Population Model for Diabetic Patients and its Application to Understand Disease-drug-drug Interactions. Clin Pharmacokinet 2024; 63:831-845. [PMID: 38819713 DOI: 10.1007/s40262-024-01383-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/01/2024]
Abstract
INTRODUCTION The activity changes of cytochrome P450 (CYP450) enzymes, along with the complicated medication scenarios in diabetes mellitus (DM) patients, result in the unanticipated pharmacokinetics (PK), pharmacodynamics (PD), and drug-drug interactions (DDIs). Physiologically based pharmacokinetic (PBPK) modeling has been a useful tool for assessing the influence of disease status on CYP enzymes and the resulting DDIs. This work aims to develop a novel diabetic PBPK population model to facilitate the prediction of PK and DDI in DM patients. METHODS First, mathematical functions were constructed to describe the demographic and non-CYP physiological characteristics specific to DM, which were then incorporated into the PBPK model to quantify the net changes in CYP enzyme activities by comparing the PK of CYP probe drugs in DM versus non-DM subjects. RESULTS The results show that the enzyme activity is reduced by 32.3% for CYP3A4/5, 39.1% for CYP2C19, and 27% for CYP2B6, while CYP2C9 activity is enhanced by 38% under DM condition. Finally, the diabetic PBPK model was developed through integrating the DM-specific CYP activities and other parameters and was further used to perform PK simulations under 12 drug combination scenarios, among which 3 combinations were predicted to result in significant PK changes in DM, which may cause DDI risks in DM patients. CONCLUSIONS The PBPK modeling applied herein provides a quantitative tool to assess the impact of disease factors on relevant enzyme pathways and potential disease-drug-drug-interactions (DDDIs), which may be useful for dosing regimen optimization and minimizing the DDI risks associated with the treatment of DM.
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Affiliation(s)
- Yafen Li
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, 100191, China
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
- Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, 100191, China
| | - Xiaonan Li
- Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Miao Zhu
- School of Pharmacy, Fudan University, Shanghai, 200433, China
| | - Huan Liu
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, 100191, China
- Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, 100191, China
| | - Zihan Lei
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, 100191, China
- Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, 100191, China
- Department of Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China
| | - Xueting Yao
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, 100191, China.
- Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, 100191, China.
| | - Dongyang Liu
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, 100191, China.
- Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, 100191, China.
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Dagenais S, Lee C, Cronenberger C, Wang E, Sahasrabudhe V. Proposing a framework to quantify the potential impact of pharmacokinetic drug-drug interactions caused by a new drug candidate by using real world data about the target patient population. Clin Transl Sci 2024; 17:e13741. [PMID: 38445532 PMCID: PMC10915735 DOI: 10.1111/cts.13741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 03/07/2024] Open
Abstract
Drug development teams must evaluate the risk/benefit profile of new drug candidates that perpetrate drug-drug interactions (DDIs). Real-world data (RWD) can inform this decision. The purpose of this study was to develop a predicted impact score for DDIs perpetrated by three hypothetical drug candidates via CYP3A, CYP2D6, or CYP2C9 in type 2 diabetes mellitus (T2DM), obesity, or migraine. Optum Market Clarity was analyzed to estimate use of CYP3A, CYP2D6, or CYP2C9 substrates classified in the University of Washington Drug Interaction Database as moderate sensitive, sensitive, narrow therapeutic index, or QT prolongation. Scoring was based on prevalence of exposure to victim substrates and characteristics (age, polypharmacy, duration of exposure, and number of prescribers) of those exposed. The study population of 14,163,271 adults included 1,579,054 with T2DM, 3,117,753 with obesity, and 410,436 with migraine. For T2DM, 71.3% used CYP3A substrates, 44.3% used CYP2D6 substrates, and 44.3% used CYP2C9 substrates. For obesity, 57.1% used CYP3A substrates, 34.6% used CYP2D6 substrates, and 31.0% used CYP2C9 substrates. For migraine, 64.1% used CYP3A substrates, 44.0% used CYP2D6 substrates, and 28.9% used CYP2C9 substrates. In our analyses, the predicted DDI impact scores were highest for DDIs involving CYP3A, followed by CYP2D6, and CYP2C9 substrates, and highest for T2DM, followed by migraine, and obesity. Insights from RWD can be used to estimate a predicted DDI impact score for pharmacokinetic DDIs perpetrated by new drug candidates currently in development. This score can inform the risk/benefit profile of new drug candidates in a target patient population.
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Affiliation(s)
| | - Christine Lee
- Internal Medicine Research UnitPfizer, Inc.New YorkNYUSA
| | | | - Ellen Wang
- Clinical Pharmacology & BioanalyticsPfizer, Inc.New YorkNYUSA
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Shi Y, Chiang CW, Unroe KT, Oyarzun-Gonzalez X, Sun A, Yang Y, Hunold KM, Caterino J, Li L, Donneyong M, Zhang P. Application of an Innovative Data Mining Approach Towards Safe Polypharmacy Practice in Older Adults. Drug Saf 2024; 47:93-102. [PMID: 37935996 PMCID: PMC11256269 DOI: 10.1007/s40264-023-01370-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/19/2023] [Indexed: 11/09/2023]
Abstract
INTRODUCTION Polypharmacy is common and is associated with higher risk of adverse drug event (ADE) among older adults. Knowledge on the ADE risk level of exposure to different drug combinations is critical for safe polypharmacy practice, while approaches for this type of knowledge discovery are limited. The objective of this study was to apply an innovative data mining approach to discover high-risk and alternative low-risk high-order drug combinations (e.g., three- and four-drug combinations). METHODS A cohort of older adults (≥ 65 years) who visited an emergency department (ED) were identified from Medicare fee-for-service and MarketScan Medicare supplemental data. We used International Classification of Diseases (ICD) codes to identify ADE cases potentially induced by anticoagulants, antidiabetic drugs, and opioids from ED visit records. We assessed drug exposure data during a 30-day window prior to the ED visit dates. We investigated relationships between exposure of drug combinations and ADEs under the case-control setting. We applied the mixture drug-count response model to identify high-order drug combinations associated with an increased risk of ADE. We conducted therapeutic class-based mining to reveal low-risk alternative drug combinations for high-order drug combinations associated with an increased risk of ADE. RESULTS We investigated frequent high-order drug combinations from 8.4 million ED visit records (5.1 million from Medicare data and 3.3 million from MarketScan data). We identified 5213 high-order drug combinations associated with an increased risk of ADE by controlling the false discovery rate at 0.01. We identified 1904 high-order, high-risk drug combinations had potential low-risk alternative drug combinations, where each high-order, high-risk drug combination and its corresponding low-risk alternative drug combination(s) have similar therapeutic classes. CONCLUSIONS We demonstrated the application of a data mining technique to discover high-order drug combinations associated with an increased risk of ADE. We identified high-risk, high-order drug combinations often have low-risk alternative drug combinations in similar therapeutic classes.
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Affiliation(s)
- Yi Shi
- Department of Biostatistics and Health Data Science, Indiana University, 410 West 10th Street, Suite 3000, Indianapolis, IN, USA
| | - Chien-Wei Chiang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Kathleen T Unroe
- School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Aging Research, Regenstrief Institute, Indianapolis, IN, USA
| | | | - Anna Sun
- Department of Biostatistics and Health Data Science, Indiana University, 410 West 10th Street, Suite 3000, Indianapolis, IN, USA
| | - Yuedi Yang
- Department of Biostatistics and Health Data Science, Indiana University, 410 West 10th Street, Suite 3000, Indianapolis, IN, USA
| | - Katherine M Hunold
- Department of Emergency Medicine, The Ohio State University, Columbus, OH, USA
| | - Jeffrey Caterino
- Department of Emergency Medicine, The Ohio State University, Columbus, OH, USA
- Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
| | - Lang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Macarius Donneyong
- College of Pharmacy, The Ohio State University, 500 West 12th Ave., Columbus, OH, USA.
| | - Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University, 410 West 10th Street, Suite 3000, Indianapolis, IN, USA.
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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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Binkheder S, Wu HY, Quinney SK, Zhang S, Zitu MM, Chiang CW, Wang L, Jones J, Li L. PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature. J Biomed Semantics 2022; 13:17. [PMID: 35690873 PMCID: PMC9188713 DOI: 10.1186/s13326-022-00272-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 05/18/2022] [Indexed: 12/28/2022] Open
Abstract
Background Adverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One of the first steps of developing automated text mining tools is building a corpus. Therefore, this study aimed to develop annotation guidelines and a gold standard corpus to facilitate building future automated approaches for mining phenotype definitions contained in the literature. Furthermore, our aim is to improve the understanding of how these published phenotype definitions are presented in the literature and how we annotate them for future text mining tasks. Results Two annotators manually annotated the corpus on a sentence-level for the presence of evidence for phenotype definitions. Three major categories (inclusion, intermediate, and exclusion) with a total of ten dimensions were proposed characterizing major contextual patterns and cues for presenting phenotype definitions in published literature. The developed annotation guidelines were used to annotate the corpus that contained 3971 sentences: 1923 out of 3971 (48.4%) for the inclusion category, 1851 out of 3971 (46.6%) for the intermediate category, and 2273 out of 3971 (57.2%) for exclusion category. The highest number of annotated sentences was 1449 out of 3971 (36.5%) for the “Biomedical & Procedure” dimension. The lowest number of annotated sentences was 49 out of 3971 (1.2%) for “The use of NLP”. The overall percent inter-annotator agreement was 97.8%. Percent and Kappa statistics also showed high inter-annotator agreement across all dimensions. Conclusions The corpus and annotation guidelines can serve as a foundational informatics approach for annotating and mining phenotype definitions in literature, and can be used later for text mining applications. Supplementary Information The online version contains supplementary material available at 10.1186/s13326-022-00272-6.
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Affiliation(s)
- Samar Binkheder
- Department of Biohealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, USA.,Medical Informatics Unit, Department of Medical Education, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Heng-Yi Wu
- Development Science Informatics, Genentech, South San Francisco, CA, USA
| | - Sara K Quinney
- Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shijun Zhang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Md Muntasir Zitu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Chien-Wei Chiang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Lei Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Josette Jones
- Department of Biohealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA. .,, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA.
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Kably B, Launay M, Derobertmasure A, Lefeuvre S, Dannaoui E, Billaud EM. Antifungal Drugs TDM: Trends and Update. Ther Drug Monit 2022; 44:166-197. [PMID: 34923544 DOI: 10.1097/ftd.0000000000000952] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 12/09/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE The increasing burden of invasive fungal infections results in growing challenges to antifungal (AF) therapeutic drug monitoring (TDM). This review aims to provide an overview of recent advances in AF TDM. METHODS We conducted a PubMed search for articles during 2016-2020 using "TDM" or "pharmacokinetics" or "drug-drug-interaction" with "antifungal," consolidated for each AF. Selection was limited to English language articles with human data on drug exposure. RESULTS More than 1000 articles matched the search terms. We selected 566 publications. The latest findings tend to confirm previous observations in real-life clinical settings. The pharmacokinetic variability related to special populations is not specific but must be considered. AF benefit-to-risk ratio, drug-drug interaction (DDI) profiles, and minimal inhibitory concentrations for pathogens must be known to manage at-risk situations and patients. Itraconazole has replaced ketoconazole in healthy volunteers DDI studies. Physiologically based pharmacokinetic modeling is widely used to assess metabolic azole DDI. AF prophylactic use was studied more for Aspergillus spp. and Mucorales in oncohematology and solid organ transplantation than for Candida (already studied). Emergence of central nervous system infection and severe infections in immunocompetent individuals both merit special attention. TDM is more challenging for azoles than amphotericin B and echinocandins. Fewer TDM requirements exist for fluconazole and isavuconazole (ISZ); however, ISZ is frequently used in clinical situations in which TDM is recommended. Voriconazole remains the most challenging of the AF, with toxicity limiting high-dose treatments. Moreover, alternative treatments (posaconazole tablets, ISZ) are now available. CONCLUSIONS TDM seems to be crucial for curative and/or long-term maintenance treatment in highly variable patients. TDM poses fewer cost issues than the drugs themselves or subsequent treatment issues. The integration of clinical pharmacology into multidisciplinary management is now increasingly seen as a part of patient care.
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Affiliation(s)
- Benjamin Kably
- Laboratoire de Pharmacologie-Toxicologie, Hôpital Européen Georges Pompidou, AP-HP Centre
- Faculté de Médecine, Université de Paris, Paris, France
| | - Manon Launay
- Laboratoire de Pharmacologie-Toxicologie-Gaz du sang, Hôpital Nord-CHU Saint Etienne, Saint-Etienne
| | - Audrey Derobertmasure
- Laboratoire de Pharmacologie-Toxicologie, Hôpital Européen Georges Pompidou, AP-HP Centre
| | - Sandrine Lefeuvre
- Laboratoire de Toxicologie et Pharmacocinétique, CHU de Poitiers, Poitiers; and
| | - Eric Dannaoui
- Faculté de Médecine, Université de Paris, Paris, France
- Unité de Parasitologie-Mycologie, Laboratoire de Microbiologie, Hôpital Européen Georges Pompidou, Paris, France
| | - Eliane M Billaud
- Laboratoire de Pharmacologie-Toxicologie, Hôpital Européen Georges Pompidou, AP-HP Centre
- Faculté de Médecine, Université de Paris, Paris, France
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Patrick MT, Bardhi R, Raja K, He K, Tsoi LC. Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources. J Am Med Inform Assoc 2021; 28:1159-1167. [PMID: 33544847 DOI: 10.1093/jamia/ocaa335] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 12/14/2020] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE Drug-drug interactions (DDIs) can result in adverse and potentially life-threatening health consequences; however, it is challenging to predict potential DDIs in advance. We introduce a new computational approach to comprehensively assess the drug pairs which may be involved in specific DDI types by combining information from large-scale gene expression (984 transcriptomic datasets), molecular structure (2159 drugs), and medical claims (150 million patients). MATERIALS AND METHODS Features were integrated using ensemble machine learning techniques, and we evaluated the DDIs predicted with a large hospital-based medical records dataset. Our pipeline integrates information from >30 different resources, including >10 000 drugs and >1.7 million drug-gene pairs. We applied our technique to predict interactions between 37 611 drug pairs used to treat psoriasis and its comorbidities. RESULTS Our approach achieves >0.9 area under the receiver operator curve (AUROC) for differentiating 11 861 known DDIs from 25 750 non-DDI drug pairs. Significantly, we demonstrate that the novel DDIs we predict can be confirmed through independent data sources and supported using clinical medical records. CONCLUSIONS By applying machine learning and taking advantage of molecular, genomic, and health record data, we are able to accurately predict potential new DDIs that can have an impact on public health.
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Affiliation(s)
- Matthew T Patrick
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Redina Bardhi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA.,School of Medicine, Wayne State University, Detroit, Michigan, USA
| | - Kalpana Raja
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA.,Morgridge Institute for Research, Madison, Wisconsin, USA
| | - Kevin He
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Lam C Tsoi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA.,Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
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Wang L, Shendre A, Chiang CW, Cao W, Ning X, Zhang P, Zhang P, Li L. A pharmacovigilance study of pharmacokinetic drug interactions using a translational informatics discovery approach. Br J Clin Pharmacol 2021; 88:1471-1481. [PMID: 33543792 DOI: 10.1111/bcp.14762] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 01/20/2021] [Accepted: 01/25/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND While the pharmacokinetic (PK) mechanisms for many drug interactions (DDIs) have been established, pharmacovigilance studies related to these PK DDIs are limited. Using a large surveillance database, a translational informatics approach can systematically screen adverse drug events (ADEs) for many DDIs with known PK mechanisms. METHODS We collected a set of substrates and inhibitors related to the cytochrome P450 (CYP) isoforms, as recommended by the United States Food and Drug Administration (FDA) and Drug Interactions Flockhart table™. The FDA's Adverse Events Reporting System (FAERS) was used to obtain ADE reports from 2004 to 2018. The substrate and inhibitor information were used to form PK DDI pairs for each of the CYP isoforms and Medical Dictionary for Regulatory Activities (MedDRA) preferred terms used for ADEs in FAERS. A shrinkage observed-to-expected ratio (Ω) analysis was performed to screen for potential PK DDI and ADE associations. RESULTS We identified 149 CYP substrates and 62 CYP inhibitors from the FDA and Flockhart tables. Using FAERS data, only those DDI-ADE associations were considered that met the disproportionality threshold of Ω > 0 for a CYP substrate when paired with at least two inhibitors. In total, 590 ADEs were associated with 2085 PK DDI pairs and 38 individual substrates, with ADEs overlapping across different CYP substrates. More importantly, we were able to find clinical and experimental evidence for the paclitaxel-clopidogrel interaction associated with peripheral neuropathy in our study. CONCLUSION In this study, we utilized a translational informatics approach to discover potentially novel CYP-related substrate-inhibitor and ADE associations using FAERS. Future clinical, population-based and experimental studies are needed to confirm our findings.
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Affiliation(s)
- Lei Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Aditi Shendre
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Chien-Wei Chiang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Weidan Cao
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Xia Ning
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Ping Zhang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Pengyue Zhang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.,Now at Indiana University School of Medicine, Indianapolis, IN, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
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Yao X, Tsang T, Sun Q, Quinney S, Zhang P, Ning X, Li L, Shen L. Mining and visualizing high-order directional drug interaction effects using the FAERS database. BMC Med Inform Decis Mak 2020; 20:50. [PMID: 32183790 PMCID: PMC7079342 DOI: 10.1186/s12911-020-1053-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Background Adverse drug events (ADEs) often occur as a result of drug-drug interactions (DDIs). The use of data mining for detecting effects of drug combinations on ADE has attracted growing attention and interest, however, most studies focused on analyzing pairwise DDIs. Recent efforts have been made to explore the directional relationships among high-dimensional drug combinations and have shown effectiveness on prediction of ADE risk. However, the existing approaches become inefficient from both computational and illustrative perspectives when considering more than three drugs. Methods We proposed an efficient approach to estimate the directional effects of high-order DDIs through frequent itemset mining, and further developed a novel visualization method to organize and present the high-order directional DDI effects involving more than three drugs in an interactive, concise and comprehensive manner. We demonstrated its performance by mining the directional DDIs associated with myopathy using a publicly available FAERS dataset. Results Directional effects of DDIs involving up to seven drugs were reported. Our analysis confirmed previously reported myopathy associated DDIs including interactions between fusidic acid with simvastatin and atorvastatin. Furthermore, we uncovered a number of novel DDIs leading to increased risk for myopathy, such as the co-administration of zoledronate with different types of drugs including antibiotics (ciprofloxacin, levofloxacin) and analgesics (acetaminophen, fentanyl, gabapentin, oxycodone). Finally, we visualized directional DDI findings via the proposed tool, which allows one to interactively select any drug combination as the baseline and zoom in/out to obtain both detailed and overall picture of interested drugs. Conclusions We developed a more efficient data mining strategy to identify high-order directional DDIs, and designed a scalable tool to visualize high-order DDI findings. The proposed method and tool have the potential to contribute to the drug interaction research and ultimately impact patient health care. Availability and implementation http://lishenlab.com/d3i/explorer.html
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Affiliation(s)
- Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Tiffany Tsang
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Qing Sun
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sara Quinney
- Department of Obstetrics and Gynecology, School of Medicine, Indiana University, Indianapolis, IN, 46202, USA
| | - Pengyue Zhang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Xia Ning
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Zhou Y, Hou Y, Shen J, Huang Y, Martin W, Cheng F. Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discov 2020; 6:14. [PMID: 32194980 PMCID: PMC7073332 DOI: 10.1038/s41421-020-0153-3] [Citation(s) in RCA: 983] [Impact Index Per Article: 245.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 03/02/2020] [Indexed: 02/07/2023] Open
Abstract
Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV-host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the "Complementary Exposure" pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Jiayu Shen
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Yin Huang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - William Martin
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195 USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106 USA
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11
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Wang H, Xi J, Wang M, Li A. Dual-Layer Strengthened Collaborative Topic Regression Modeling for Predicting Drug Sensitivity. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:587-598. [PMID: 30106738 DOI: 10.1109/tcbb.2018.2864739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
An effective way to facilitate the development of modern oncology precision medicine is the systematical analysis of the known drug sensitivities that have emerged in recent years. Meanwhile, the screening of drug response in cancer cell lines provides an estimable genomic and pharmacological data towards high accuracy prediction. Existing works primarily utilize genomic or functional genomic features to classify or regress the drug response. Here in this work, by the migration and extension of the conventional merchandise recommendation methods, we introduce an innovation model on accurate drug sensitivity prediction by using dual-layer strengthened collaborative topic regression (DS-CTR), which incorporates not only the graphic model to jointly learn drugs and cell lines feature from pharmacogenomics data but also drug and cell line similarity network model to strengthen the correlation of the prediction results. Using Genomics of Drug Sensitivity in Cancer project (GDSC) as benchmark datasets, the 5-fold cross-validation experiment demonstrates that DS-CTR model significantly improves drug response prediction performance compared with four categories of state-of-the-art algorithms as for both Receiver Operator Curve (ROC) and the Area Under Receiver Operator Curve (AUC). By uncovering the unknown cell-drug associations with advanced literature evidences, our novel model DS-CTR is validated and supported. The model also provides the possibility to make the discovery of new anti-cancer therapeutics in the preclinical trials cheaper and faster.
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12
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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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13
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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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14
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Davazdahemami B, Delen D. A chronological pharmacovigilance network analytics approach for predicting adverse drug events. J Am Med Inform Assoc 2019; 25:1311-1321. [PMID: 30085102 DOI: 10.1093/jamia/ocy097] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 06/29/2018] [Indexed: 12/31/2022] Open
Abstract
Objectives This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs' similarities in terms of the proteins they target in the human body. The focus of this research, though, is particularly centered on predicting the drug-ADE associations for a set of 8 common and high-risk ADEs. Materials and methods large collection of annotated MEDLINE biomedical articles was used to construct a drug-ADE network, and the network was further equipped with information about drugs' target proteins. Several network metrics were extracted and used as predictors in ML algorithms to predict the existence of network edges (ie, associations or relationships). Results Gradient boosted trees (GBTs) as an ensemble ML algorithm outperformed other prediction methods in identifying the drug-ADE associations with an overall accuracy of 92.8% on the validation sample. The prediction model was able to predict drug-ADE associations, on average, 3.84 years earlier than they were actually mentioned in the biomedical literature. Conclusion While network analysis and ML techniques were used in separation in prior ADE studies, our results showed that they, in combination with each other, can boost the power of one another and predict better. Moreover, our results highlight the superior capability of ensemble-type ML methods in capturing drug-ADE patterns compared to the regular (ie, singular), ML algorithms.
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Affiliation(s)
- Behrooz Davazdahemami
- Department of Management Science and Information Systems, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Dursun Delen
- Department of Management Science and Information Systems, Center for Health Systems Innovation, Oklahoma State University, Stillwater, Oklahoma, USA
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15
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Marić I, Winn VD, Borisenko E, Weber KA, Wong RJ, Aziz N, Blumenfeld YJ, El-Sayed YY, Stevenson DK, Shaw GM. Data-driven queries between medications and spontaneous preterm birth among 2.5 million pregnancies. Birth Defects Res 2019; 111:1145-1153. [PMID: 31433567 PMCID: PMC11199711 DOI: 10.1002/bdr2.1580] [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] [Received: 05/14/2019] [Revised: 07/31/2019] [Accepted: 08/05/2019] [Indexed: 12/15/2022]
Abstract
BACKGROUND Our goal was to develop an approach that can systematically identify potential associations between medication prescribed in pregnancy and spontaneous preterm birth (sPTB) by mining large administrative "claims" databases containing hundreds of medications. One such association that we illustrate emerged with antiviral medications used for herpes treatment. METHODS IBM MarketScan® databases (2007-2016) were used. A pregnancy cohort was established using International Classification of Diseases (ICD-9/10) codes. Multiple hypothesis testing and the Benjamini-Hochberg procedure that limited false discovery rate at 5% revealed, among 863 medications, five that showed odds ratios (ORs) <1. The statistically strongest was an association between antivirals and sPTB that we illustrate as a real example of our approach, specifically for treatment of genital herpes (GH). Three groups of women were identified based on diagnosis of GH and treatment during the first 36 weeks of pregnancy: (a) GH without treatment; (b) GH treated with antivirals; (c) no GH or treatment. RESULTS We identified 2,538,255 deliveries. 0.98% women had a diagnosis of GH. Among them, 60.0% received antiviral treatment. Women with treated GH had OR < 1, (OR [95% CI] = 0.91 [0.85, 0.98]). In contrast, women with untreated GH had a small increased risk of sPTB (OR [95% CI] =1.22 [1.14, 1.32]). CONCLUSIONS Data-driven approaches can effectively generate new hypotheses on associations between medications and sPTB. This analysis led us to examine the association with GH treatment. While unknown confounders may impact these findings, our results indicate that women with untreated GH have a modest increased risk of sPTB.
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Affiliation(s)
- Ivana Marić
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Virginia D. Winn
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Kari A. Weber
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ronald J. Wong
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Natali Aziz
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yair J. Blumenfeld
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yasser Y. El-Sayed
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA
| | - David K. Stevenson
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Gary M. Shaw
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
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16
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Bykov K, Schneeweiss S, Glynn RJ, Mittleman MA, Gagne JJ. A Case-Crossover-Based Screening Approach to Identifying Clinically Relevant Drug-Drug Interactions in Electronic Healthcare Data. Clin Pharmacol Ther 2019; 106:238-244. [PMID: 30663781 DOI: 10.1002/cpt.1376] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 12/03/2018] [Indexed: 12/31/2022]
Abstract
We sought to develop a semiautomated screening approach using electronic healthcare data to identify drug-drug interactions (DDIs) that result in clinical outcomes. Using a case-crossover design with 30-day hazard and referent windows, we evaluated codispensed drugs (potential precipitants) in 7,801 patients who experienced rhabdomyolysis while on cytochrome P450 (CYP)3A4-metabolized statins and in 15,147 who experienced bleeding while on dabigatran. Estimates of direct associations between precipitant drugs and outcomes were used to adjust for bias and precipitants' direct effects. The P values were adjusted for multiple testing using the false discovery rate (FDR). From among 460 drugs codispensed with statins, 1 drug (clarithromycin) generated an alert (adjusted odds ratio (OR) 5.83, FDR < 0.05). From among 485 drugs codispensed with dabigatran, 2 drugs (naproxen and enoxaparin, ORs 2.50 and 2.75; FDR < 0.05) generated an alert. All three signals reflected known pharmacologic interactions, confirming the potential of case-crossover-based approaches for DDI screening in electronic healthcare data.
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Affiliation(s)
- Katsiaryna Bykov
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Robert J Glynn
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Murray A Mittleman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Joshua J Gagne
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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17
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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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18
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Li L. Reverse Translational Pharmacology Research Is Driven by Big Data. CPT Pharmacometrics Syst Pharmacol 2018; 7:63-64. [PMID: 29457706 PMCID: PMC5824108 DOI: 10.1002/psp4.12277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Accepted: 01/04/2018] [Indexed: 02/03/2023] Open
Affiliation(s)
- Lang Li
- Department of Biomedical InformaticsCollege of Medicine, The Ohio State UniversityColumbusOhioUSA
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19
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Kasichayanula S, Venkatakrishnan K. Reverse Translation: The Art of Cyclical Learning. Clin Pharmacol Ther 2018; 103:152-159. [DOI: 10.1002/cpt.952] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Accepted: 11/27/2017] [Indexed: 12/18/2022]
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