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Zhang S, Wu H, Wang L, Zhang G, Rocha LM, Shatkay H, Li L. Translational drug-interaction corpus. Database (Oxford) 2022; 2022:baac031. [PMID: 35616099 PMCID: PMC9216474 DOI: 10.1093/database/baac031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/07/2022] [Accepted: 05/06/2022] [Indexed: 12/01/2022]
Abstract
The discovery of drug-drug interactions (DDIs) that have a translational impact among in vitro pharmacokinetics (PK), in vivo PK and clinical outcomes depends largely on the quality of the annotated corpus available for text mining. We have developed a new DDI corpus based on an annotation scheme that builds upon and extends previous ones, where an abstract is fragmented and each fragment is then annotated along eight dimensions, namely, focus, polarity, certainty, evidence, directionality, study type, interaction type and mechanism. The guideline for defining these dimensions has undergone refinement during the annotation process. Our DDI corpus comprises 900 positive DDI abstracts and 750 that are not directly relevant to DDI. The abstracts in corpus are separated into eight categories of DDI or non-DDI evidence: DDI with pharmacokinetic (PK) mechanism, in vivo DDI PK, DDI clinical, drug-nutrition interaction, single drug, not drug related, in vitro pharmacodynamic (PD) and case report. Seven annotators, three annotators with drug-interaction research experience and four annotators with less drug-interaction research experience independently annotated the DDI corpus, where two researchers independently annotated each abstract. After two rounds of annotations with additional training in between, agreement improved from (0.79, 0.96, 0.86, 0.70, 0.91, 0.65, 0.78, 0.90) to (0.93, 0.99, 0.96, 0.94, 0.95, 0.93, 0.96, 0.97) for focus, certainty, evidence, study type, interaction type, mechanisms, polarity and direction, respectively. The novice-level annotators improved from 0.83 to 0.96, while the expert-level annotators stayed in high performance with some improvement, from 0.90 to 0.96. In summary, we achieved 96% agreement among each pair of annotators with regard to the eight dimensions. The annotated corpus is now available to the community for inclusion in their text-mining pipelines. Database URL https://github.com/zha204/DDI-Corpus-Database/tree/master/DDI%20corpus.
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Affiliation(s)
- Shijun Zhang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 1585 Neil Ave, Columbus, OH 43210, USA
| | - Hengyi Wu
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Lei Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 1585 Neil Ave, Columbus, OH 43210, USA
| | - Gongbo Zhang
- Department of Computer and Information Sciences, University of Delaware, 101 Smith Hall, 18 Amstel Ave, Newark, DE 19716, USA
| | - Luis M Rocha
- School of Informatics & Computing, Indiana University, 919 E 10th St, Bloomington, IN 47408, USA
| | - Hagit Shatkay
- Department of Computer and Information Sciences, University of Delaware, 101 Smith Hall, 18 Amstel Ave, Newark, DE 19716, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 1585 Neil Ave, Columbus, OH 43210, USA
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2
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Zhu Y, Chiang C, Wang L, Brock G, Milks MW, Cao W, Zhang P, Zeng D, Donneyong M, Li L. A multistate transition model for statin-induced myopathy and statin discontinuation. CPT Pharmacometrics Syst Pharmacol 2021; 10:1236-1244. [PMID: 34562311 PMCID: PMC8520747 DOI: 10.1002/psp4.12691] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 05/10/2021] [Accepted: 07/07/2021] [Indexed: 12/12/2022] Open
Abstract
The overarching goal of this study was to simultaneously model the dynamic relationships among statin exposure, statin discontinuation, and potentially statin-related myopathic outcomes. We extracted data from the Indiana Network of Patient Care for 134,815 patients who received statin therapy between January 4, 2004, and December 31, 2008. All individuals began statin treatment, some discontinued statin use, and some experienced myopathy and/or rhabdomyolysis while taking the drug or after discontinuation. We developed a militate model to characterize 12 transition probabilities among six different states defined by use or discontinuation of statin and its associated myopathy or rhabdomyolysis. We found that discontinuation of statin therapy was common and frequently early, with 44.4% of patients discontinuing therapy after 1 month, and discontinuation is a strong indicator for statin-induced myopathy (risk ratio, 10.8; p < 0.05). Women more likely than men (p < 0.05) and patients aged 65 years and older had a higher risk than those aged younger than 65 years to discontinue statin use or experience myopathy. In conclusion, we introduce an innovative multistate model that allows clear depiction of the relationship between statin discontinuation and statin-induced myopathy. For the first time, we have successfully demonstrated and quantified the relative risk of myopathy between patients who continued and discontinued statin therapy. Age and sex were two strong risk factors for both statin discontinuation and incident myopathy.
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Affiliation(s)
- Yuxi Zhu
- Division of BiostatisticsCollege of Public HealthThe Ohio State UniversityColumbusOhioUSA
- Department of Biomedical InformaticsCollege of MedicineThe Ohio State UniversityColumbusOhioUSA
| | - Chien‐Wei Chiang
- Department of Biomedical InformaticsCollege of MedicineThe Ohio State UniversityColumbusOhioUSA
| | - Lei Wang
- Department of Biomedical InformaticsCollege of MedicineThe Ohio State UniversityColumbusOhioUSA
| | - Guy Brock
- Department of Biomedical InformaticsCollege of MedicineThe Ohio State UniversityColumbusOhioUSA
| | - M. Wesley Milks
- Department of Internal MedicineCollege of MedicineThe Ohio State UniversityColumbusOhioUSA
| | - Weidan Cao
- Department of Biomedical InformaticsCollege of MedicineThe Ohio State UniversityColumbusOhioUSA
| | - Pengyue Zhang
- BiostatisticsSchool of MedicineIndiana UniversityIndianapolisIndianaUSA
| | - Donglin Zeng
- Department of BiostatisticsUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Macarius Donneyong
- Division of Pharmacy Practice and ScienceCollege of PharmacyThe Ohio State UniversityColumbusOhioUSA
| | - Lang Li
- Department of Biomedical InformaticsCollege of MedicineThe Ohio State UniversityColumbusOhioUSA
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3
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Kichenadasse G, Miners JO, Mangoni AA, Karapetis CS, Hopkins AM, Sorich MJ. Proton Pump Inhibitors and Survival in Patients With Colorectal Cancer Receiving Fluoropyrimidine-Based Chemotherapy. J Natl Compr Canc Netw 2021; 19:1037-1044. [PMID: 33951613 DOI: 10.6004/jnccn.2020.7670] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 10/12/2020] [Indexed: 12/30/2022]
Abstract
BACKGROUND Concomitant use of proton pump inhibitors (PPIs) may negatively affect the efficacy of anticancer drugs such as fluoropyrimidines in patients with colorectal cancer (CRC). The primary objective of this study was to assess whether there is an association between concomitant PPI use and survival outcomes in patients with CRC treated with a fluoropyrimidine-based chemotherapy. PATIENTS AND METHODS A secondary analysis of 6 randomized controlled clinical trials in patients with advanced CRC was conducted using individual patient data through data-sharing platforms. The outcome measures were progression-free survival and overall survival in PPI users and nonusers. Subgroup analysis included the type of chemotherapy, capecitabine versus 5-FU, line of therapy, and addition of a vascular endothelial growth factor receptor inhibitor. Overall pooled hazard ratios (HRs) with 95% confidence intervals were calculated using a random effects model. RESULTS A total of 5,594 patients with advanced CRC across 6 trials and 11 trial arms were included; 902 patients were receiving a PPI at trial entry and initiation of chemotherapy. PPI use was significantly associated with worse overall survival (pooled HR, 1.20; 95% CI, 1.03-1.40; P=.02; I2 for heterogeneity = 69%) and progression-free survival (overall pooled HR, 1.20; 95% CI, 1.05-1.37; P=.009; I2 = 65%) after adjusting for clinical covariates. Furthermore, the association between concomitant PPI use and survival outcomes was similar across most treatment subgroups. CONCLUSIONS We speculate that alterations in the gut microbiome, altered immune milieu within the tumor, and interactions through transporters are potential mechanisms behind this association between PPI use and chemotherapy in patients with CRC, which warrant further study. Concomitant use of PPIs is associated with worse survival outcomes in patients with CRC treated with fluoropyrimidine-based chemotherapy. Clinicians should cautiously consider the concomitant use of PPIs in such patients.
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Affiliation(s)
- Ganessan Kichenadasse
- 1Department of Clinical Pharmacology, College of Medicine and Public Health, and.,2Department of Medical Oncology, Flinders Centre for Innovation in Cancer, Flinders Medical Centre/Flinders University, Bedford Park, South Australia, Australia
| | - John O Miners
- 1Department of Clinical Pharmacology, College of Medicine and Public Health, and
| | - Arduino A Mangoni
- 1Department of Clinical Pharmacology, College of Medicine and Public Health, and
| | - Christos S Karapetis
- 2Department of Medical Oncology, Flinders Centre for Innovation in Cancer, Flinders Medical Centre/Flinders University, Bedford Park, South Australia, Australia
| | - Ashley M Hopkins
- 1Department of Clinical Pharmacology, College of Medicine and Public Health, and
| | - Michael J Sorich
- 1Department of Clinical Pharmacology, College of Medicine and Public Health, and
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Dey S, Zhang P, Ghalwash M, Maduri C, Sow D, Shahn Z. Finding Causal Mechanistic Drug-Drug Interactions from Observational Data. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2021; 2020:363-372. [PMID: 33936409 PMCID: PMC8075465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Many adverse drug reactions (ADRs) are caused by drug-drug interactions (DDIs), meaning they arise from concurrent use of multiple medications. Detecting DDIs using observational data has at least three major challenges: (1) The number of potential DDIs is astronomical; (2) Associations between drugs and ADRs may not be causal due to observed or unobserved confounding; and (3) Frequently co-prescribed drug pairs that each independently cause an ADR do not necessarily causally interact, where causal interaction means that at least some patients would only experience the ADR if they take both drugs. We address (1) through data mining algorithms pre-filtering potential interactions, and (2) and (3) by fitting causal interaction models adjusting for observed confounders and conducting sensitivity analyses for unobserved confounding. We rank candidate DDIs robust to unobserved confounding more likely to be real. Our rigorous approach produces far fewer false positives than past applications that ignored (2) and (3).
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Affiliation(s)
- Sanjoy Dey
- IBM T. J. Watson Research Center, Yorktown Heights, NY, USA
| | - Ping Zhang
- The Ohio State University, Columbus, OH, USA
| | | | | | - Daby Sow
- IBM T. J. Watson Research Center, Yorktown Heights, NY, USA
| | - Zach Shahn
- IBM T. J. Watson Research Center, Yorktown Heights, NY, USA
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5
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Statins, toxicity, and their adverse effects via oxidative imbalance. Toxicology 2021. [DOI: 10.1016/b978-0-12-819092-0.00026-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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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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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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8
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Wang S, Li J, Sun X, Zhang YH, Huang T, Cai Y. Computational Method for Identifying Malonylation Sites by Using Random Forest Algorithm. Comb Chem High Throughput Screen 2018; 23:304-312. [PMID: 30588879 DOI: 10.2174/1386207322666181227144318] [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] [Received: 01/28/2018] [Revised: 09/03/2018] [Accepted: 12/04/2018] [Indexed: 12/12/2022]
Abstract
BACKGROUND As a newly uncovered post-translational modification on the ε-amino group of lysine residue, protein malonylation was found to be involved in metabolic pathways and certain diseases. Apart from experimental approaches, several computational methods based on machine learning algorithms were recently proposed to predict malonylation sites. However, previous methods failed to address imbalanced data sizes between positive and negative samples. OBJECTIVE In this study, we identified the significant features of malonylation sites in a novel computational method which applied machine learning algorithms and balanced data sizes by applying synthetic minority over-sampling technique. METHOD Four types of features, namely, amino acid (AA) composition, position-specific scoring matrix (PSSM), AA factor, and disorder were used to encode residues in protein segments. Then, a two-step feature selection procedure including maximum relevance minimum redundancy and incremental feature selection, together with random forest algorithm, was performed on the constructed hybrid feature vector. RESULTS An optimal classifier was built from the optimal feature subset, which featured an F1-measure of 0.356. Feature analysis was performed on several selected important features. CONCLUSION Results showed that certain types of PSSM and disorder features may be closely associated with malonylation of lysine residues. Our study contributes to the development of computational approaches for predicting malonyllysine and provides insights into molecular mechanism of malonylation.
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Affiliation(s)
- ShaoPeng Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - JiaRui Li
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Xijun Sun
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Yu-Hang Zhang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yudong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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9
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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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10
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Alam K, Crowe A, Wang X, Zhang P, Ding K, Li L, Yue W. Regulation of Organic Anion Transporting Polypeptides (OATP) 1B1- and OATP1B3-Mediated Transport: An Updated Review in the Context of OATP-Mediated Drug-Drug Interactions. Int J Mol Sci 2018. [PMID: 29538325 PMCID: PMC5877716 DOI: 10.3390/ijms19030855] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Organic anion transporting polypeptides (OATP) 1B1 and OATP1B3 are important hepatic transporters that mediate the uptake of many clinically important drugs, including statins from the blood into the liver. Reduced transport function of OATP1B1 and OATP1B3 can lead to clinically relevant drug-drug interactions (DDIs). Considering the importance of OATP1B1 and OATP1B3 in hepatic drug disposition, substantial efforts have been given on evaluating OATP1B1/1B3-mediated DDIs in order to avoid unwanted adverse effects of drugs that are OATP substrates due to their altered pharmacokinetics. Growing evidences suggest that the transport function of OATP1B1 and OATP1B3 can be regulated at various levels such as genetic variation, transcriptional and post-translational regulation. The present review summarizes the up to date information on the regulation of OATP1B1 and OATP1B3 transport function at different levels with a focus on potential impact on OATP-mediated DDIs.
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Affiliation(s)
- Khondoker Alam
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73117, USA.
| | - Alexandra Crowe
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73117, USA.
| | - Xueying Wang
- Center for Computational Biology and Bioinformatics, Indiana Institute of Personalized Medicine, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
| | - Pengyue Zhang
- Center for Computational Biology and Bioinformatics, Indiana Institute of Personalized Medicine, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
| | - Kai Ding
- Department of Biostatistics and Epidemiology, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73126, USA.
| | - Lang Li
- Center for Computational Biology and Bioinformatics, Indiana Institute of Personalized Medicine, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
- Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, USA.
| | - Wei Yue
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73117, USA.
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Biomedical Informatics Approaches to Identifying Drug-Drug Interactions: Application to Insulin Secretagogues. Epidemiology 2018; 28:459-468. [PMID: 28169935 DOI: 10.1097/ede.0000000000000638] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Drug-drug interactions with insulin secretagogues are associated with increased risk of serious hypoglycemia in patients with type 2 diabetes. We aimed to systematically screen for drugs that interact with the five most commonly used secretagogues-glipizide, glyburide, glimepiride, repaglinide, and nateglinide-to cause serious hypoglycemia. METHODS We screened 400 drugs frequently coprescribed with the secretagogues as candidate interacting precipitants. We first predicted the drug-drug interaction potential based on the pharmacokinetics of each secretagogue-precipitant pair. We then performed pharmacoepidemiologic screening for each secretagogue of interest, and for metformin as a negative control, using an administrative claims database and the self-controlled case series design. The overall rate ratios (RRs) and those for four predefined risk periods were estimated using Poisson regression. The RRs were adjusted for multiple estimation using semi-Bayes method, and then adjusted for metformin results to distinguish native effects of the precipitant from a drug-drug interaction. RESULTS We predicted 34 pharmacokinetic drug-drug interactions with the secretagogues, nine moderate and 25 weak. There were 140 and 61 secretagogue-precipitant pairs associated with increased rates of serious hypoglycemia before and after the metformin adjustment, respectively. The results from pharmacokinetic prediction correlated poorly with those from pharmacoepidemiologic screening. CONCLUSIONS The self-controlled case series design has the potential to be widely applicable to screening for drug-drug interactions that lead to adverse outcomes identifiable in healthcare databases. Coupling pharmacokinetic prediction with pharmacoepidemiologic screening did not notably improve the ability to identify drug-drug interactions in this case.
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12
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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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13
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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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14
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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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15
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Leung YH, Turgeon J, Michaud V. Study of Statin- and Loratadine-Induced Muscle Pain Mechanisms Using Human Skeletal Muscle Cells. Pharmaceutics 2017; 9:pharmaceutics9040042. [PMID: 28994701 PMCID: PMC5750648 DOI: 10.3390/pharmaceutics9040042] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 09/30/2017] [Accepted: 10/01/2017] [Indexed: 12/15/2022] Open
Abstract
Many drugs can cause unexpected muscle disorders, often necessitating the cessation of an effective medication. Inhibition of monocarboxylate transporters (MCTs) may potentially lead to perturbation of l-lactic acid homeostasis and muscular toxicity. Previous studies have shown that statins and loratadine have the potential to inhibit l-lactic acid efflux by MCTs (MCT1 and 4). The main objective of this study was to confirm the inhibitory potentials of atorvastatin, simvastatin (acid and lactone forms), rosuvastatin, and loratadine on l-lactic acid transport using primary human skeletal muscle cells (SkMC). Loratadine (IC50 31 and 15 µM) and atorvastatin (IC50 ~130 and 210 µM) demonstrated the greatest potency for inhibition of l-lactic acid efflux at pH 7.0 and 7.4, respectively (~2.5-fold l-lactic acid intracellular accumulation). Simvastatin acid exhibited weak inhibitory potency on l-lactic acid efflux with an intracellular lactic acid increase of 25–35%. No l-lactic acid efflux inhibition was observed for simvastatin lactone or rosuvastatin. Pretreatment studies showed no change in inhibitory potential and did not affect lactic acid transport for all tested drugs. In conclusion, we have demonstrated that loratadine and atorvastatin can inhibit the efflux transport of l-lactic acid in SkMC. Inhibition of l-lactic acid efflux may cause an accumulation of intracellular l-lactic acid leading to the reported drug-induced myotoxicity.
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Affiliation(s)
- Yat Hei Leung
- Faculty of Pharmacy, Université de Montréal, Montreal, QC H2X 0A9, Canada.
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC H2X 0A9, Canada.
| | - Jacques Turgeon
- Faculty of Pharmacy, Université de Montréal, Montreal, QC H2X 0A9, Canada.
| | - Veronique Michaud
- Faculty of Pharmacy, Université de Montréal, Montreal, QC H2X 0A9, Canada.
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC H2X 0A9, Canada.
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16
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Alam K, Pahwa S, Wang X, Zhang P, Ding K, Abuznait AH, Li L, Yue W. Downregulation of Organic Anion Transporting Polypeptide (OATP) 1B1 Transport Function by Lysosomotropic Drug Chloroquine: Implication in OATP-Mediated Drug-Drug Interactions. Mol Pharm 2016; 13:839-51. [PMID: 26750564 PMCID: PMC4970216 DOI: 10.1021/acs.molpharmaceut.5b00763] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Organic anion transporting polypeptide (OATP) 1B1 mediates the hepatic uptake of many drugs including lipid-lowering statins. Decreased OATP1B1 transport activity is often associated with increased systemic exposure of statins and statin-induced myopathy. Antimalarial drug chloroquine (CQ) is also used for long-term treatment of rheumatoid arthritis and systemic lupus erythematosus. CQ is lysosomotropic and inhibits protein degradation in lysosomes. The current studies were designed to determine the effects of CQ on OATP1B1 protein degradation, OATP1B1-mediated transport in OATP1B1-overexpressing cell line, and statin uptake in human sandwich-cultured hepatocytes (SCH). Treatment with lysosome inhibitor CQ increased OATP1B1 total protein levels in HEK293-OATP1B1 cells and in human SCH as determined by OATP1B1 immunoblot. In HEK293-FLAG-tagged OATP1B1 stable cell line, co-immunofluorescence staining indicated that intracellular FLAG-OATP1B1 is colocalized with lysosomal associated membrane glycoprotein (LAMP)-2, a marker protein of late endosome/lysosome. Enlarged LAMP-2-positive vacuoles with FLAG-OATP1B1 protein retained inside were readily detected in CQ-treated cells, consistent with blocking lysosomal degradation of OATP1B1 by CQ. In HEK293-OATP1B1 cells, without pre-incubation, CQ concentrations up to 100 μM did not affect OATP1B1-mediated [(3)H]E217G accumulation. However, pre-incubation with CQ at clinically relevant concentration(s) significantly decreased [(3)H]E217G and [(3)H]pitavastatin accumulation in HEK293-OATP1B1 cells and [(3)H]pitavastatin accumulation in human SCH. CQ pretreatment (25 μM, 2 h) resulted in ∼1.9-fold decrease in Vmax without affecting Km of OATP1B1-mediated [(3)H]E217G transport in HEK293-OATP1B1 cells. Pretreatment with monensin and bafilomycin A1, which also have lysosome inhibition activity, significantly decreased OATP1B1-mediated transport in HEK293-OATP1B1 cells. Pharmacoepidemiologic studies using data from the U.S. Food and Drug Administration Adverse Event Reporting System indicated that CQ plus pitavastatin, rosuvastatin, and pravastatin, which are minimally metabolized by the cytochrome P450 enzymes, led to higher myopathy risk than these statins alone. In summary, the present studies report novel findings that lysosome is involved in degradation of OATP1B1 protein and that pre-incubation with lysosomotropic drug CQ downregulates OATP1B1 transport activity. Our in vitro data in combination with pharmacoepidemiologic studies support that CQ has potential to cause OATP-mediated drug-drug interactions.
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Affiliation(s)
- Khondoker Alam
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma 73117, United States
| | - Sonia Pahwa
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma 73117, United States
| | - Xueying Wang
- Center for Computational Biology and Bioinformatics, Indiana Institute of Personalized Medicine, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Pengyue Zhang
- Center for Computational Biology and Bioinformatics, Indiana Institute of Personalized Medicine, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Kai Ding
- Department of Biostatistics and Epidemiology, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma 73126, United States
| | - Alaa H. Abuznait
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma 73117, United States
| | - Lang Li
- Center for Computational Biology and Bioinformatics, Indiana Institute of Personalized Medicine, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Wei Yue
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma 73117, United States
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17
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Flockhart D, Bies RR, Gastonguay MR, Schwartz SL. Big Data: Challenges and opportunities for clinical pharmacology. Br J Clin Pharmacol 2016; 81:804-6. [PMID: 26845397 DOI: 10.1111/bcp.12896] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Accepted: 01/29/2016] [Indexed: 11/28/2022] Open
Affiliation(s)
- David Flockhart
- Indiana School of Medicine, 1001 West 10th Street, Indianapolis, IN
| | - Robert R Bies
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, Member, Center for Data Enabled Science and Engineering, State University of New York at Buffalo, Buffalo
| | - Marc R Gastonguay
- Metrum Research Group LLC, 2 Tunxis Rd., Ste 112, Tariffville, CT, 06081
| | - Sorell L Schwartz
- Department of Pharmacology & Physiology, Georgetown University Medical Center, 3900 Reservoir Rd, NW, Washington, DC, 20057, USA
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