1
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Tong H, Phan NVT, Nguyen TT, Nguyen DV, Vo NS, Le L. Review on Databases and Bioinformatic Approaches on Pharmacogenomics of Adverse Drug Reactions. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2021; 14:61-75. [PMID: 33469342 PMCID: PMC7812041 DOI: 10.2147/pgpm.s290781] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 12/26/2020] [Indexed: 12/27/2022]
Abstract
Pharmacogenomics has been used effectively in studying adverse drug reactions by determining the person-specific genetic factors associated with individual response to a drug. Current approaches have revealed the significant importance of sequencing technologies and sequence analysis strategies for interpreting the contribution of genetic variation in developing adverse reactions. Advance in next generation sequencing and platform brings new opportunities in validating the genetic candidates in certain reactions, and could be used to develop the preemptive tests to predict the outcome of the variation in a personal response to a drug. With the highly accumulated available data recently, the in silico approach with data analysis and modeling plays as other important alternatives which significantly support the final decisions in the transformation from research to clinical applications such as diagnosis and treatments for various types of adverse responses.
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Affiliation(s)
- Hang Tong
- School of Biotechnology, International University, Ho Chi Minh City, Vietnam.,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Nga V T Phan
- School of Biotechnology, International University, Ho Chi Minh City, Vietnam.,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Thanh T Nguyen
- Department of Translational Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
| | - Dinh V Nguyen
- Department of Respiratory, Allergy and Clinical Immunology, Vinmec International Hospital, Hanoi, Vietnam.,College of Health Sciences, VinUniversity, Hanoi, Vietnam
| | - Nam S Vo
- Department of Translational Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
| | - Ly Le
- School of Biotechnology, International University, Ho Chi Minh City, Vietnam.,Vietnam National University, Ho Chi Minh City, Vietnam.,Department of Translational Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
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2
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Wang Q, Gan J, Wei K, Berceli SA, Gragnoli C, Wu R. A unified mapping framework of multifaceted pharmacodynamic responses to hypertension interventions. Drug Discov Today 2019; 24:883-889. [PMID: 30690194 PMCID: PMC6492935 DOI: 10.1016/j.drudis.2019.01.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Revised: 01/03/2019] [Accepted: 01/17/2019] [Indexed: 02/04/2023]
Abstract
The personalized therapy for hypertension needs comprehensive knowledge about how blood pressures (BPs; systolic and diastolic) and their pulsatile and steady components are controlled by genetic factors. Here, we propose a unified pharmacodynamic (PD) functional mapping framework for identifying specific quantitative trait loci (QTLs) that mediate multivariate response-dose curves of BP. This framework can characterize how QTLs govern pulsatile and steady components through jointly regulating systolic and diastolic pressures. The model can quantify the genetic effects of individual QTLs on maximal drug effect, the maximal rate of drug response, and the dose window of maximal drug response. This unified mapping framework provides a tool for identifying pharmacological genes potentially useful to design the right medication and right dose for patients.
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Affiliation(s)
- Qian Wang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Jingwen Gan
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Kun Wei
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Scott A Berceli
- Malcom Randall VA Medical Center, Gainesville, FL 32610, USA; Department of Surgery, University of Florida, Box 100128, Gainesville, FL 32610, USA; Department of Biomedical Engineering, University of Florida, Gainesville, FL 32610, USA
| | - Claudia Gragnoli
- Division of Endocrinology, Diabetes, and Metabolic Disease, Translational Medicine, Department of Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA; Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Molecular Biology Laboratory, Bios Biotech Multi Diagnostic Health Center, Rome 00197, Italy
| | - Rongling Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA 17033, USA.
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3
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Keskin O, Farzan N, Birben E, Akel H, Karaaslan C, Maitland-van der Zee AH, Wechsler ME, Vijverberg SJ, Kalayci O. Genetic associations of the response to inhaled corticosteroids in asthma: a systematic review. Clin Transl Allergy 2019; 9:2. [PMID: 30647901 PMCID: PMC6327448 DOI: 10.1186/s13601-018-0239-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 11/16/2018] [Indexed: 02/06/2023] Open
Abstract
There is wide variability in the response to inhaled corticosteroids (ICS) in asthma. While some of this heterogeneity of response is due to adherence and environmental causes, genetic variation also influences response to treatment and genetic markers may help guide treatment. Over the past years, researchers have investigated the relationship between a large number of genetic variations and response to ICS by performing pharmacogenomic studies. In this systematic review we will provide a summary of recent pharmacogenomic studies on ICS and discuss the latest insight into the potential functional role of identified genetic variants. To date, seven genome wide association studies (GWAS) examining ICS response have been published. There is little overlap between identified variants and methodologies vary largely. However, in vitro and/or in silico analyses provide additional evidence that genes discovered in these GWAS (e.g. GLCCI1, FBXL7, T gene, ALLC, CMTR1) might play a direct or indirect role in asthma/treatment response pathways. Furthermore, more than 30 candidate-gene studies have been performed, mainly attempting to replicate variants discovered in GWAS or candidate genes likely involved in the corticosteroid drug pathway. Single nucleotide polymorphisms located in GLCCI1, NR3C1 and the 17q21 locus were positively replicated in independent populations. Although none of the genetic markers has currently reached clinical practise, these studies might provide novel insights in the complex pathways underlying corticosteroids response in asthmatic patients.
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Affiliation(s)
- Ozlem Keskin
- 1Paediatric Allergy and Immunology Department, School of Medicine, Gaziantep University, Gaziantep, Turkey
| | - Niloufar Farzan
- 2Department of Respiratory Medicine, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, Amsterdam, Netherlands
| | - Esra Birben
- 3Pediatric Allergy and Asthma Unit, Hacettepe University School of Medicine, 06100 Ankara, Turkey
| | - Hayriye Akel
- 4Department of Molecular Biology, Faculty of Sciences, Hacettepe University, Ankara, Turkey
| | - Cagatay Karaaslan
- 4Department of Molecular Biology, Faculty of Sciences, Hacettepe University, Ankara, Turkey
| | - Anke H Maitland-van der Zee
- 2Department of Respiratory Medicine, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, Amsterdam, Netherlands.,5Department of Pediatric Respiratory Medicine and Allergy, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, Amsterdam, Netherlands
| | | | - Susanne J Vijverberg
- 2Department of Respiratory Medicine, University of Amsterdam, Amsterdam UMC, Meibergdreef 9, Amsterdam, Netherlands
| | - Omer Kalayci
- 3Pediatric Allergy and Asthma Unit, Hacettepe University School of Medicine, 06100 Ankara, Turkey
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4
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Wei K, Wang Q, Gan J, Zhang S, Ye M, Gragnoli C, Wu R. Mapping genes for drug chronotherapy. Drug Discov Today 2018; 23:1883-1888. [PMID: 29964181 DOI: 10.1016/j.drudis.2018.06.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 05/20/2018] [Accepted: 06/12/2018] [Indexed: 12/29/2022]
Abstract
Genome-wide association studies have been increasingly used to map and characterize genes that contribute to interindividual variation in drug response. Some studies have integrated the pharmacokinetic (PK) and pharmacodynamic (PD) processes of drug reactions into association mapping, gleaning new insight into how genes determine the dynamic relationship of drug effect and drug dose. Here, we present an evolutionary framework by which two distinct concepts, chronopharmacodynamics and heterochrony (describing variation in the timing and rate of developmental events), are married to comprehend the pharmacogenetic architecture of drug response. The resulting new concept, heterochronopharmacodynamics (HCPD), can better interpret how genes influence drug efficacy and drug toxicity according to the circadian rhythm of the body and changes in drug concentration.
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Affiliation(s)
- Kun Wei
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Qian Wang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Jingwen Gan
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Shilong Zhang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Meixia Ye
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Claudia Gragnoli
- Division of Endocrinology, Diabetes, and Metabolic Disease, Translational Medicine, Department of Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA; Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Molecular Biology Laboratory, Bios Biotech Multi Diagnostic Health Center, Rome 00197, Italy
| | - Rongling Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA 17033, USA.
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5
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Zhong WP, Wu H, Chen JY, Li XX, Lin HM, Zhang B, Zhang ZW, Ma DL, Sun S, Li HP, Mai LP, He GD, Wang XP, Lei HP, Zhou HK, Tang L, Liu SW, Zhong SL. Genomewide Association Study Identifies Novel Genetic Loci That Modify Antiplatelet Effects and Pharmacokinetics of Clopidogrel. Clin Pharmacol Ther 2017; 101:791-802. [PMID: 27981573 PMCID: PMC5485718 DOI: 10.1002/cpt.589] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 11/14/2016] [Accepted: 12/03/2016] [Indexed: 12/20/2022]
Abstract
Genetic variants in the pharmacokinetic (PK) mechanism are the main underlying factors affecting the antiplatelet response to clopidogrel. Using a genomewide association study (GWAS) to identify new genetic loci that modify antiplatelet effects in Chinese patients with coronary heart disease, we identified novel variants in two transporter genes (SLC14A2 rs12456693, ATP‐binding cassette [ABC]A1 rs2487032) and in N6AMT1 (rs2254638) associated with P2Y12 reaction unit (PRU) and plasma active metabolite (H4) concentration. These new variants dramatically improved the predictability of PRU variability to 37.7%. The associations between these loci and PK parameters of clopidogrel and H4 were observed in additional patients, and its function on the activation of clopidogrel was validated in liver S9 fractions (P < 0.05). Rs2254638 was further identified to exert a marginal risk effect for major adverse cardiac events in an independent cohort. In conclusion, new genetic variants were systematically identified as risk factors for the reduced efficacy of clopidogrel treatment.
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Affiliation(s)
- W-P Zhong
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangzhou, China.,Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - H Wu
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - J-Y Chen
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangzhou, China.,Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - X-X Li
- Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - H-M Lin
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - B Zhang
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangzhou, China.,Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Z-W Zhang
- Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - D-L Ma
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangzhou, China.,Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - S Sun
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangzhou, China.,Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - H-P Li
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangzhou, China.,Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - L-P Mai
- Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - G-D He
- Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - X-P Wang
- Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - H-P Lei
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangzhou, China.,Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - H-K Zhou
- Guangzhou Seq-Health Medical Technology Co., Ltd, Guangzhou, China.,Guangzhou Genedenovo Biotechnology Co., Ltd, Guangzhou, China
| | - L Tang
- Department of Pharmaceutics, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, China
| | - S-W Liu
- Department of Pharmaceutics, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, China
| | - S-L Zhong
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangzhou, China.,Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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6
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Vizirianakis IS, Mystridis GA, Avgoustakis K, Fatouros DG, Spanakis M. Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review). Oncol Rep 2016; 35:1891-904. [PMID: 26781205 DOI: 10.3892/or.2016.4575] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Accepted: 10/27/2015] [Indexed: 11/05/2022] Open
Abstract
The existing tumor heterogeneity and the complexity of cancer cell biology critically demand powerful translational tools with which to support interdisciplinary efforts aiming to advance personalized cancer medicine decisions in drug development and clinical practice. The development of physiologically based pharmacokinetic (PBPK) models to predict the effects of drugs in the body facilitates the clinical translation of genomic knowledge and the implementation of in vivo pharmacology experience with pharmacogenomics. Such a direction unequivocally empowers our capacity to also make personalized drug dosage scheme decisions for drugs, including molecularly targeted agents and innovative nanoformulations, i.e. in establishing pharmacotyping in prescription. In this way, the applicability of PBPK models to guide individualized cancer therapeutic decisions of broad clinical utility in nanomedicine in real-time and in a cost-affordable manner will be discussed. The latter will be presented by emphasizing the need for combined efforts within the scientific borderlines of genomics with nanotechnology to ensure major benefits and productivity for nanomedicine and personalized medicine interventions.
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Affiliation(s)
- Ioannis S Vizirianakis
- Laboratory of Pharmacology, Department of Pharmaceutical Sciences, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki GR‑54124, Greece
| | - George A Mystridis
- Laboratory of Pharmacology, Department of Pharmaceutical Sciences, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki GR‑54124, Greece
| | - Konstantinos Avgoustakis
- Laboratory of Pharmaceutical Technology, Department of Pharmaceutical Sciences, University of Patras, Patras GR-26504, Greece
| | - Dimitrios G Fatouros
- Laboratory of Pharmaceutical Technology, Department of Pharmaceutical Sciences, Aristotle University of Thessaloniki, Thessaloniki GR-54124, Greece
| | - Marios Spanakis
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion GR-71110, Crete, Greece
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7
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Abstract
There is evidence that genetic factors are implicated in the observed differences in therapeutic responses to the common classes of asthma therapy such as β2-agonists, corticosteroids, and leukotriene modifiers. Pharmacogenomics explores the roles of genetic variation in drug response and continues to be a field of great interest in asthma therapy. Prior studies have focused on candidate genes and recently emphasized genome-wide association analyses. Newer integrative omics and system-level approaches have recently revealed novel understanding of drug response pathways. However, the current known genetic loci only account for a fraction of variability in drug response and ongoing research is needed. While the field of asthma pharmacogenomics is not yet fully translatable to clinical practice, ongoing research should hopefully achieve this goal in the near future buttressed by the recent precision medicine efforts in the USA and worldwide.
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8
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Ocaña J, Sanchez O MP, Carrasco JL. Carryover negligibility and relevance in bioequivalence studies. Pharm Stat 2015; 14:400-8. [PMID: 26175204 DOI: 10.1002/pst.1699] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 03/23/2015] [Accepted: 05/21/2015] [Indexed: 11/11/2022]
Abstract
The carryover effect is a recurring issue in the pharmaceutical field. It may strongly influence the final outcome of an average bioequivalence study. Testing a null hypothesis of zero carryover is useless: not rejecting it does not guarantee the non-existence of carryover, and rejecting it is not informative of the true degree of carryover and its influence on the validity of the final outcome of the bioequivalence study. We propose a more consistent approach: even if some carryover is present, is it enough to seriously distort the study conclusions or is it negligible? This is the central aim of this paper, which focuses on average bioequivalence studies based on 2 × 2 crossover designs and on the main problem associated with carryover: type I error inflation. We propose an equivalence testing approach to these questions and suggest reasonable negligibility or relevance limits for carryover. Finally, we illustrate this approach on some real datasets.
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Affiliation(s)
- Jordi Ocaña
- Department of Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | - Maria P Sanchez O
- Statistical Institute, Faculty of Science, University of Valparaiso, Valparaiso, Chile.,Department of Biology, Faculty of Chemistry and Biology, University of Santiago, Santiago, Chile
| | - Josep L Carrasco
- Department of Public Health, Faculty of Medicine, Universitat de Barcelona, Barcelona, Spain
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9
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Pharmacodynamic genome-wide association study identifies new responsive loci for glucocorticoid intervention in asthma. THE PHARMACOGENOMICS JOURNAL 2015; 15:422-9. [PMID: 25601762 DOI: 10.1038/tpj.2014.83] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Received: 04/23/2014] [Revised: 09/09/2014] [Accepted: 11/07/2014] [Indexed: 12/14/2022]
Abstract
Asthma is a chronic lung disease that has a high prevalence. The therapeutic intervention of this disease can be made more effective if genetic variability in patients' response to medications is implemented. However, a clear picture of the genetic architecture of asthma intervention response remains elusive. We conducted a genome-wide association study (GWAS) to identify drug response-associated genes for asthma, in which 909 622 SNPs were genotyped for 120 randomized participants who inhaled multiple doses of glucocorticoids. By integrating pharmacodynamic properties of drug reactions, we implemented a mechanistic model to analyze the GWAS data, enhancing the scope of inference about the genetic architecture of asthma intervention. Our pharmacodynamic model observed associations of genome-wide significance between dose-dependent response to inhaled glucocorticoids (measured as %FEV1) and five loci (P=5.315 × 10(-7) to 3.924 × 10(-9)), many of which map to metabolic genes related to lung function and asthma risk. All significant SNPs detected indicate a recessive effect, at which the homozygotes for the mutant alleles drive variability in %FEV1. Significant associations were well replicated in three additional independent GWAS studies. Pooled together over these three trials, two SNPs, chr6 rs6924808 and chr11 rs1353649, display an increased significance level (P=6.661 × 10(-16) and 5.670 × 10(-11)). Our study reveals a general picture of pharmacogenomic control for asthma intervention. The results obtained help to tailor an optimal dose for individual patients to treat asthma based on their genetic makeup.
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10
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Vizirianakis IS. Harnessing pharmacological knowledge for personalized medicine and pharmacotyping: Challenges and lessons learned. World J Pharmacol 2014; 3:110-119. [DOI: 10.5497/wjp.v3.i4.110] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Revised: 07/03/2014] [Accepted: 10/29/2014] [Indexed: 02/07/2023] Open
Abstract
The contribution of the genetic make-up to an individual’s capacity has long been recognized in modern pharmacology as a crucial factor leading to therapy inefficiency and toxicity, negatively impacting the economic burden of healthcare and restricting the monitoring of diseases. In practical terms, and in order for drug prescription to be improved toward meeting the personalized medicine concept in drug delivery, the maximum clinical outcome for most, if not all, patients must be achieved, i.e., pharmacotyping. Such a direction although promising and of high expectation from the society, it is however hardly to be afforded for healthcare worldwide. To overcome any existed hurdles, this means that practical clinical utility of personalized medicine decisions have to be documented and validated in the clinical setting. The latter implies for drug delivery the efficient implementation of previously gained in vivo pharmacology experience with pharmacogenomics knowledge. As an approach to work faster and in a more productive way, the elaboration of advanced physiologically based pharmacokinetics models is discussed. And in better clarifying this topic, the example of tamoxifen is thoroughly presented. Overall, pharmacotyping represents a major challenge in modern therapeutics for which pharmacologists need to work in successfully fulfilling this task.
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11
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Wu K, Gamazon ER, Im HK, Geeleher P, White SR, Solway J, Clemmer GL, Weiss ST, Tantisira KG, Cox NJ, Ratain MJ, Huang RS. Genome-wide interrogation of longitudinal FEV1 in children with asthma. Am J Respir Crit Care Med 2014; 190:619-27. [PMID: 25221879 PMCID: PMC4214107 DOI: 10.1164/rccm.201403-0460oc] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Accepted: 08/03/2014] [Indexed: 11/16/2022] Open
Abstract
RATIONALE Most genomic studies of lung function have used phenotypic data derived from a single time-point (e.g., presence/absence of disease) without considering the dynamic progression of a chronic disease. OBJECTIVES To characterize lung function change over time in subjects with asthma and identify genetic contributors to a longitudinal phenotype. METHODS We present a method that models longitudinal FEV1 data, collected from 1,041 children with asthma who participated in the Childhood Asthma Management Program. This longitudinal progression model was built using population-based nonlinear mixed-effects modeling with an exponential structure and the determinants of age and height. MEASUREMENTS AND MAIN RESULTS We found ethnicity was a key covariate for FEV1 level. Budesonide-treated children with asthma had a slight but significant effect on FEV1 when compared with those treated with placebo or nedocromil (P < 0.001). A genome-wide association study identified seven single-nucleotide polymorphisms nominally associated with longitudinal lung function phenotypes in 581 white Childhood Asthma Management Program subjects (P < 10(-4) in the placebo ["discovery"] and P < 0.05 in the nedocromil treatment ["replication"] group). Using ChIP-seq and RNA-seq data, we found that some of the associated variants were in strong enhancer regions in human lung fibroblasts and may affect gene expression in human lung tissue. Genetic mapping restricted to genome-wide enhancer single-nucleotide polymorphisms in lung fibroblasts revealed a highly significant variant (rs6763931; P = 4 × 10(-6); false discovery rate < 0.05). CONCLUSIONS This study offers a strategy to explore the genetic determinants of longitudinal phenotypes, provide a comprehensive picture of disease pathophysiology, and suggest potential treatment targets.
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Affiliation(s)
- Kehua Wu
- Department of Medicine and
- State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing, China; and
| | | | - Hae Kyung Im
- Department of Health Studies, The University of Chicago, Chicago, Illinois
| | | | | | | | - George L. Clemmer
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Kelan G. Tantisira
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
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12
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Crommelin DJA, Sindelar RD, Meibohm B. Genomics, Other “Omic” Technologies, Personalized Medicine, and Additional Biotechnology-Related Techniques. PHARMACEUTICAL BIOTECHNOLOGY 2013. [PMCID: PMC7122419 DOI: 10.1007/978-1-4614-6486-0_8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
The products resulting for biotechnologies continue to grow at an exponential rate, and the expectations are that an even greater percentage of drug development will be in the area of the biologics. In 2011, worldwide there were over 800 new biotech drugs and treatments in development including 23 antisense, 64 cell therapy, 50 gene therapy, 300 monoclonal antibodies, 78 recombinant proteins, and 298 vaccines (PhRMA 2012). Pharmaceutical biotechnology techniques are at the core of most methodologies used today for drug discovery and development of both biologics and small molecules. While recombinant DNA technology and hybridoma techniques were the major methods utilized in pharmaceutical biotechnology through most of its historical timeline, our ever-widening understanding of human cellular function and disease processes and a wealth of additional and innovative biotechnologies have been, and will continue to be, developed in order to harvest the information found in the human genome. These technological advances will provide a better understanding of the relationship between genetics and biological function, unravel the underlying causes of disease, explore the association of genomic variation and drug response, enhance pharmaceutical research, and fuel the discovery and development of new and novel biopharmaceuticals. These revolutionary technologies and additional biotechnology-related techniques are improving the very competitive and costly process of drug development of new medicinal agents, diagnostics, and medical devices. Some of the technologies and techniques described in this chapter are both well established and commonly used applications of biotechnology producing potential therapeutic products now in development including clinical trials. New techniques are emerging at a rapid and unprecedented pace and their full impact on the future of molecular medicine has yet to be imagined.
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Affiliation(s)
- Daan J. A. Crommelin
- Department of Pharmaceutical Sciences, Utrecht University, Utrecht, Utrecht The Netherlands
| | - Robert D. Sindelar
- Department of Pharmaceutical Sciences and Department of Medicine, The University of British Columbia, Vancouver, British Columbia Canada
| | - Bernd Meibohm
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, College of Pharmacy, Memphis, Tennessee USA
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13
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Wang Z, Li H, Wang J, Li J, Wu R. Statistical resolution of missing longitudinal data in clinical pharmacogenomics. Adv Drug Deliv Rev 2013; 65:980-6. [PMID: 23523630 DOI: 10.1016/j.addr.2013.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2013] [Revised: 02/13/2013] [Accepted: 03/13/2013] [Indexed: 11/16/2022]
Abstract
Clinical pharmacogenomics, integrating genomic information with clinical practices to facilitate the prediction of drug response, has recently emerged as a vital area of public health. In clinical trials, phenotypic data on drug response are often longitudinal, with some patients dropping out early due to physiological or other unpredictable reasons. The genetic analysis of such missing longitudinal data presents a significant challenge in clinical pharmacogenomics. We develop a statistical algorithm for detecting haplotypes that control longitudinal responses subject to non-ignorable dropout. The model was derived by incorporating the selection model into a dynamic model - functional mapping, aimed to discover genetic variants that contribute to phenotypic variation in longitudinal traits. The selection models is a statistical approach for analyzing missing longitudinal data by assuming that dropout depends on the outcome of drug response. The model derived can jointly characterize the genetic control of longitudinal responses and dropout events. Simulation studies were performed to investigate the statistical properties of the model and validate its practical usefulness. The model will find its implications for clinical pharmacogenomics toward personalized medicine.
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Affiliation(s)
- Zhong Wang
- Center for Computational Biology, Beijing Forestry University, Beijing, China
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Wang Z, Luo J, Fu G, Wang Z, Wu R. Stochastic modeling of systems mapping in pharmacogenomics. Adv Drug Deliv Rev 2013; 65:912-7. [PMID: 23528445 PMCID: PMC4249941 DOI: 10.1016/j.addr.2013.03.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2013] [Revised: 02/22/2013] [Accepted: 03/13/2013] [Indexed: 12/11/2022]
Abstract
As a basis of personalized medicine, pharmacogenetics and pharmacogenomics that aim to study the genetic architecture of drug response critically rely on dynamic modeling of how a drug is absorbed and transported to target tissues where the drug interacts with body molecules to produce drug effects. Systems mapping provides a general framework for integrating systems pharmacology and pharmacogenomics through robust ordinary differential equations. In this chapter, we extend systems mapping to more complex and more heterogeneous structure of drug response by implementing stochastic differential equations (SDE). We argue that SDE-implemented systems mapping provides a computational tool for pharmacogenetic or pharmacogenomic research towards personalized medicine.
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Affiliation(s)
- Zuoheng Wang
- Department of Biostatistics, Yale University, New Haven, CT 06520, USA
| | - Jiangtao Luo
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Guifang Fu
- Department of Mathematics and Statistics, Utah State University, Logan, UT 84322, USA
| | - Zhong Wang
- Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA
| | - Rongling Wu
- Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA
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Londono D, Chen KM, Musolf A, Wang R, Shen T, Brandon J, Herring JA, Wise CA, Zou H, Jin M, Yu L, Finch SJ, Matise TC, Gordon D. A novel method for analyzing genetic association with longitudinal phenotypes. Stat Appl Genet Mol Biol 2013; 12:241-61. [PMID: 23502345 DOI: 10.1515/sagmb-2012-0070] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Knowledge of genes influencing longitudinal patterns may offer information about predicting disease progression. We developed a systematic procedure for testing association between SNP genotypes and longitudinal phenotypes. We evaluated false positive rates and statistical power to localize genes for disease progression. We used genome-wide SNP data from the Framingham Heart Study. With longitudinal data from two real studies unrelated to Framingham, we estimated three trajectory curves from each study. We performed simulations by randomly selecting 500 individuals. In each simulation replicate, we assigned each individual to one of the three trajectory groups based on the underlying hypothesis (null or alternative), and generated corresponding longitudinal data. Individual Bayesian posterior probabilities (BPPs) for belonging to a specific trajectory curve were estimated. These BPPs were treated as a quantitative trait and tested (using the Wald test) for genome-wide association. Empirical false positive rates and power were calculated. Our method maintained the expected false positive rate for all simulation models. Also, our method achieved high empirical power for most simulations. Our work presents a method for disease progression gene mapping. This method is potentially clinically significant as it may allow doctors to predict disease progression based on genotype and determine treatment accordingly.
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Affiliation(s)
- Douglas Londono
- Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Road, Piscataway, NJ 08854, USA
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16
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Multiple single nucleotide polymorphism analysis using penalized regression in nonlinear mixed-effect pharmacokinetic models. Pharmacogenet Genomics 2013; 23:167-74. [DOI: 10.1097/fpc.0b013e32835dd22c] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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17
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Modeling the Pharmacogenetic Architecture of Drug Response. Pharmacogenomics 2013. [DOI: 10.1016/b978-0-12-391918-2.00017-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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18
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Kim IW, Kim KI, Chang HJ, Yeon B, Bang SJ, Park T, Kwon JS, Kim S, Oh JM. Ethnic variability in the allelic distribution of pharmacogenes between Korean and other populations. Pharmacogenet Genomics 2012; 22:829-36. [DOI: 10.1097/fpc.0b013e328358dd70] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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