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Camilleri M, Jencks K. Pharmacogenetics in IBS: update and impact of GWAS studies in drug targets and metabolism. Expert Opin Drug Metab Toxicol 2024; 20:319-332. [PMID: 38785066 PMCID: PMC11139426 DOI: 10.1080/17425255.2024.2349716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 04/26/2024] [Indexed: 05/25/2024]
Abstract
INTRODUCTION Medications are frequently prescribed for patients with irritable bowel syndrome (IBS) or disorders of gut brain interaction. The level of drug metabolism and modifications in drug targets determine medication efficacy to modify motor or sensory function as well as patient response outcomes. AREAS COVERED The literature search included PubMed searches with the terms: pharmacokinetics, pharmacogenomics, epigenetics, clinical trials, irritable bowel syndrome, disorders of gut brain interaction, and genome-wide association studies. The main topics covered in relation to irritable bowel syndrome were precision medicine, pharmacogenomics related to drug metabolism, pharmacogenomics related to mechanistic targets, and epigenetics. EXPERT OPINION Pharmacogenomics impacting drug metabolism [CYP 2D6 (cytochrome P450 2D6) or 2C19 (cytochrome P450 2C19)] is the most practical approach to precision medicine in the treatment of IBS. Although there are proof of concept studies that have documented the importance of genetic modification of transmitters or receptors in altering responses to medications in IBS, these principles have rarely been applied in patient response outcomes. Genome-wide association (GWAS) studies have now documented the association of symptoms with genetic variation but not the evaluation of treatment responses. Considerably more research, particularly focused on patient response outcomes and epigenetics, is essential to impact this field in clinical medicine.
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Affiliation(s)
- Michael Camilleri
- Clinical Enteric Neuroscience Translational and Epidemiological Research (CENTER), Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| | - Kara Jencks
- Clinical Enteric Neuroscience Translational and Epidemiological Research (CENTER), Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
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Johnson D, Del Fiol G, Kawamoto K, Romagnoli KM, Sanders N, Isaacson G, Jenkins E, Williams MS. Genetically guided precision medicine clinical decision support tools: a systematic review. J Am Med Inform Assoc 2024; 31:1183-1194. [PMID: 38558013 PMCID: PMC11031215 DOI: 10.1093/jamia/ocae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 02/06/2024] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
OBJECTIVES Patient care using genetics presents complex challenges. Clinical decision support (CDS) tools are a potential solution because they provide patient-specific risk assessments and/or recommendations at the point of care. This systematic review evaluated the literature on CDS systems which have been implemented to support genetically guided precision medicine (GPM). MATERIALS AND METHODS A comprehensive search was conducted in MEDLINE and Embase, encompassing January 1, 2011-March 14, 2023. The review included primary English peer-reviewed research articles studying humans, focused on the use of computers to guide clinical decision-making and delivering genetically guided, patient-specific assessments, and/or recommendations to healthcare providers and/or patients. RESULTS The search yielded 3832 unique articles. After screening, 41 articles were identified that met the inclusion criteria. Alerts and reminders were the most common form of CDS used. About 27 systems were integrated with the electronic health record; 2 of those used standards-based approaches for genomic data transfer. Three studies used a framework to analyze the implementation strategy. DISCUSSION Findings include limited use of standards-based approaches for genomic data transfer, system evaluations that do not employ formal frameworks, and inconsistencies in the methodologies used to assess genetic CDS systems and their impact on patient outcomes. CONCLUSION We recommend that future research on CDS system implementation for genetically GPM should focus on implementing more CDS systems, utilization of standards-based approaches, user-centered design, exploration of alternative forms of CDS interventions, and use of formal frameworks to systematically evaluate genetic CDS systems and their effects on patient care.
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Affiliation(s)
- Darren Johnson
- Department of Genomic Health, Geisinger Health Systems, Danville, PA 17822, United States
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States
| | - Katrina M Romagnoli
- Department of Genomic Health, Geisinger Health Systems, Danville, PA 17822, United States
| | - Nathan Sanders
- School of Medicine, Geisinger Health Systems, Danville, PA 17822, United States
| | - Grace Isaacson
- Family Medicine, Penn Highlands Healthcare, DuBois, PA 16830, United States
| | - Elden Jenkins
- School of Medicine, Noorda College of Osteopathic Medicine, Provo, UT 84606, United States
| | - Marc S Williams
- Department of Genomic Health, Geisinger Health Systems, Danville, PA 17822, United States
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Biswas M, Hossain MS, Ahmed Rupok T, Hossain MS, Sukasem C. The association of CYP2C19 LoF alleles with adverse clinical outcomes in stroke patients taking clopidogrel: An updated meta-analysis. Clin Transl Sci 2024; 17:e13792. [PMID: 38581109 PMCID: PMC10997845 DOI: 10.1111/cts.13792] [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: 12/07/2023] [Revised: 03/11/2024] [Accepted: 03/15/2024] [Indexed: 04/08/2024] Open
Abstract
The aggregated risk of recurrent stroke in stroke/transient ischemic attack (TIA) patients carrying CYP2C19 LoF alleles who take clopidogrel has not been investigated recently, and the available research is limited. This study aimed to perform an updated meta-analysis to assess the association between CYP2C19 LoF alleles and the risk of recurrent stroke in patients taking clopidogrel. Databases were searched for the literature on eligible studies. The end points were recurrent stroke, composite vascular events, and bleeding events. Odds ratios (ORs) were calculated using RevMan software, where p < 0.05 was considered statistically significant. Patients carrying CYP2C19 LoF alleles who were treated with clopidogrel had a significantly increased risk of recurrent ischemic stroke compared with non-carriers (OR 2.18, 96% CI 1.80-2.63; p < 0.00001). The risk of recurrent stroke was only significantly different in Asian patients (OR 2.29, 96% CI 1.88-2.80; p < 0.00001) but not in patients of other ethnicities; however, there were a limited number of studies in other ethnic groups. Both observational studies (OR 2.83, 96% CI 2.20-3.65; p < 0.00001) and RCTs (OR 1.48, 96% CI 1.10-1.98; p = 0.009) found associations with a significantly increased risk of recurrent ischemic stroke. Asian stroke patients or TIA patients carrying CYP2C19 LoF alleles and taking clopidogrel were at a significantly higher risk of recurrent ischemic stroke than non-carriers. Significantly increased risk of recurrent ischemic stroke was found in both observational studies and RCTs.
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Affiliation(s)
- Mohitosh Biswas
- Department of PharmacyUniversity of RajshahiRajshahiBangladesh
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
- Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC)Ramathibodi HospitalBangkokThailand
| | | | | | | | - Chonlaphat Sukasem
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
- Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC)Ramathibodi HospitalBangkokThailand
- Pharmacogenomics and Precision Medicine, The Preventive Genomics & Family Check‐up Services Center, Bumrungrad International HospitalBangkokThailand
- Faculty of Pharmaceutical SciencesBurapha UniversitySaensuk, MueangChonburiThailand
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Bastaki K, Velayutham D, Irfan A, Adnan M, Mohammed S, Mbarek H, Qoronfleh MW, Jithesh PV. Forging the path to precision medicine in Qatar: a public health perspective on pharmacogenomics initiatives. Front Public Health 2024; 12:1364221. [PMID: 38550311 PMCID: PMC10977610 DOI: 10.3389/fpubh.2024.1364221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 02/20/2024] [Indexed: 04/02/2024] Open
Abstract
Pharmacogenomics (PGx) is an important component of precision medicine that promises tailored treatment approaches based on an individual's genetic information. Exploring the initiatives in research that help to integrate PGx test into clinical setting, identifying the potential barriers and challenges as well as planning the future directions, are all important for fruitful PGx implementation in any population. Qatar serves as an exemplar case study for the Middle East, having a small native population compared to a diverse immigrant population, advanced healthcare system, national genome program, and several educational initiatives on PGx and precision medicine. This paper attempts to outline the current state of PGx research and implementation in Qatar within the global context, emphasizing ongoing initiatives and educational efforts. The inclusion of PGx in university curricula and healthcare provider training, alongside precision medicine conferences, showcase Qatar's commitment to advancing this field. However, challenges persist, including the requirement for population specific implementation strategies, complex genetic data interpretation, lack of standardization, and limited awareness. The review suggests policy development for future directions in continued research investment, conducting clinical trials for the feasibility of PGx implementation, ethical considerations, technological advancements, and global collaborations to overcome these barriers.
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Affiliation(s)
- Kholoud Bastaki
- Clinical and Pharmacy Practice Department, College of Pharmacy, QU Health, Qatar University, Doha, Qatar
| | - Dinesh Velayutham
- College of Health & Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar
| | - Areeba Irfan
- College of Health & Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar
| | - Mohd Adnan
- College of Health & Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar
| | - Sawsan Mohammed
- College of Medicine, Pre-Clinical Education Department, QU Health, Qatar University, Doha, Qatar
| | | | - M. Waild Qoronfleh
- Q3 Research Institute (QRI), Research & Policy Division, Ann Arbor, MI, United States
| | - Puthen Veettil Jithesh
- College of Health & Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar
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Kanegusuku ALG, Chan CW, O'Donnell PH, Yeo KTJ. Implementation of pharmacogenomics testing for precision medicine. Crit Rev Clin Lab Sci 2024; 61:89-106. [PMID: 37776898 DOI: 10.1080/10408363.2023.2255279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 08/31/2023] [Indexed: 10/02/2023]
Abstract
Great strides have been made in the past decade to lower barriers to clinical pharmacogenomics implementation. Nevertheless, PGx consultation prior to prescribing therapeutics is not yet mainstream. This review addresses the current climate surrounding PGx implementation, focusing primarily on strategies for implementation at academic institutions, particularly at The University of Chicago, and provides an up-to-date guide of resources supporting the development of PGx programs. Remaining challenges and recent strategies for overcoming these challenges to implementation are discussed.
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Affiliation(s)
| | - Clarence W Chan
- Departments of Pathology, The University of Chicago, Chicago, IL, USA
| | - Peter H O'Donnell
- Department of Medicine, The University of Chicago, Chicago, IL, USA
- Center for Personalized Therapeutics, The University of Chicago, Chicago, IL, USA
| | - Kiang-Teck J Yeo
- Departments of Pathology, The University of Chicago, Chicago, IL, USA
- Center for Personalized Therapeutics, The University of Chicago, Chicago, IL, USA
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Li LJ, Legeay S, Gagnon AL, Frigon MP, Tessier L, Tremblay K. Moving towards the implementation of pharmacogenetic testing in Quebec. Front Genet 2024; 14:1295963. [PMID: 38234998 PMCID: PMC10791884 DOI: 10.3389/fgene.2023.1295963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024] Open
Abstract
Clinical implementation of pharmacogenetics (PGx) into routine care will elevate the current paradigm of treatment decisions. However, while PGx tests are increasingly becoming reliable and affordable, several barriers have limited their widespread usage in Canada. Globally, over ninety successful PGx implementors can serve as models. The purpose of this paper is to outline the PGx implementation barriers documented in Quebec (Canada) to suggest efficient solutions based on existing PGx clinics and propose an adapted clinical implementation model. We conclude that the province of Quebec is ready to implement PGx.
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Affiliation(s)
- Ling Jing Li
- Centre Intégré Universitaire de Santé et de Services Sociaux Du Saguenay-Lac-Saint-Jean (Chicoutimi University Hospital), Research Center, Saguenay, QC, Canada
- Medicine Department, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Saguenay, QC, Canada
| | - Samuel Legeay
- Centre Intégré Universitaire de Santé et de Services Sociaux Du Saguenay-Lac-Saint-Jean (Chicoutimi University Hospital), Research Center, Saguenay, QC, Canada
- Medicine Department, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Saguenay, QC, Canada
- University Angers, [CHU Angers], Inserm, CNRS, MINT, Angers, France
| | - Ann-Lorie Gagnon
- Centre Intégré Universitaire de Santé et de Services Sociaux Du Saguenay-Lac-Saint-Jean (Chicoutimi University Hospital), Research Center, Saguenay, QC, Canada
| | - Marie-Pier Frigon
- Centre Intégré Universitaire de Santé et de Services Sociaux Du Saguenay-Lac-Saint-Jean (Chicoutimi University Hospital), Research Center, Saguenay, QC, Canada
- Pediatrics Department, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Laurence Tessier
- Centre Intégré Universitaire de Santé et de Services Sociaux Du Saguenay-Lac-Saint-Jean (Chicoutimi University Hospital), Research Center, Saguenay, QC, Canada
- Pharmacology-Physiology Department, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Saguenay, QC, Canada
| | - Karine Tremblay
- Centre Intégré Universitaire de Santé et de Services Sociaux Du Saguenay-Lac-Saint-Jean (Chicoutimi University Hospital), Research Center, Saguenay, QC, Canada
- Pharmacology-Physiology Department, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Saguenay, QC, Canada
- Centre de Recherche Du Centre Hospitalier Universitaire de Sherbrooke (CR-CHUS), Sherbrooke, QC, Canada
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Bousman CA, Maruf AA, Marques DF, Brown LC, Müller DJ. The emergence, implementation, and future growth of pharmacogenomics in psychiatry: a narrative review. Psychol Med 2023; 53:7983-7993. [PMID: 37772416 PMCID: PMC10755240 DOI: 10.1017/s0033291723002817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 08/24/2023] [Accepted: 08/30/2023] [Indexed: 09/30/2023]
Abstract
Psychotropic medication efficacy and tolerability are critical treatment issues faced by individuals with psychiatric disorders and their healthcare providers. For some people, it can take months to years of a trial-and-error process to identify a medication with the ideal efficacy and tolerability profile. Current strategies (e.g. clinical practice guidelines, treatment algorithms) for addressing this issue can be useful at the population level, but often fall short at the individual level. This is, in part, attributed to interindividual variation in genes that are involved in pharmacokinetic (i.e. absorption, distribution, metabolism, elimination) and pharmacodynamic (e.g. receptors, signaling pathways) processes that in large part, determine whether a medication will be efficacious or tolerable. A precision prescribing strategy know as pharmacogenomics (PGx) assesses these genomic variations, and uses it to inform selection and dosing of certain psychotropic medications. In this review, we describe the path that led to the emergence of PGx in psychiatry, the current evidence base and implementation status of PGx in the psychiatric clinic, and finally, the future growth potential of precision psychiatry via the convergence of the PGx-guided strategy with emerging technologies and approaches (i.e. pharmacoepigenomics, pharmacomicrobiomics, pharmacotranscriptomics, pharmacoproteomics, pharmacometabolomics) to personalize treatment of psychiatric disorders.
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Affiliation(s)
- Chad A. Bousman
- The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Psychiatry, University of Calgary, AB, Canada
- Department of Medical Genetics, University of Calgary, Calgary, AB, Canada
- Departments of Physiology and Pharmacology, and Community Health Sciences, University of Calgary, Calgary, AB, Canada
- AB Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- Department of Psychiatry, University of Melbourne, Melbourne, VIC, Australia
| | - Abdullah Al Maruf
- The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Psychiatry, University of Calgary, AB, Canada
- College of Pharmacy, Rady Faculty of Health Sciences, Winnipeg, MB, Canada
| | | | | | - Daniel J. Müller
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Wurzburg, Wurzburg, Germany
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Mitaly S, Wright J, El Melik R, Matey E. Pharmacists' role in supporting the return of over 10,000 preemptive pharmacogenomics results: The Mayo Clinic experience. Am J Health Syst Pharm 2023; 80:1733-1742. [PMID: 37478473 DOI: 10.1093/ajhp/zxad159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Indexed: 07/23/2023] Open
Abstract
PURPOSE To develop a pharmacist-driven, exploratory pharmacogenomics implementation model with the goal of creating a process for pharmacists to interpret pharmacogenomics results from RIGHT 10K Study samples and provide electronic consults to providers. SUMMARY A train-the-trainer model program was initiated whereby pharmacogenomics pharmacists developed a documentation template and a quick reference guide as a standard guide to train other pharmacists. Pharmacists completed electronic consults (e-consults) reviewing pharmacogenomics results, with reference to drug-gene interactions, for patients with "semi-urgent" and "clinically actionable" results, defined as those indicating a potential for gene-drug interactions to cause major harm and those indicating a potential for an adverse drug reaction or reduced efficacy, respectively. Outcomes measured included the number of consults over time, number and role of pharmacists involved, average time to complete e-consults over time, and gene-drug pairs for semi-urgent consults per month. A total of 395 pharmacists were trained. The total number of e-consults completed was 2,843: 61 semi-urgent and 2,782 clinically actionable consults. The average time spent per consult was 24 minutes, and the average number of e-consults per pharmacist was 7. CYP2C19-clopidogrel was the most common gene-drug pair targeted in semi-urgent consults. CONCLUSION Pharmacy leaders planning to implement similar pharmacogenomics programs can utilize this data to estimate hiring needs for future pharmacogenomics implementation, while also considering the potential additional cost of developing resources.
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Affiliation(s)
- Serena Mitaly
- Department of Pharmacy, Mayo Clinic, Rochester, MN, USA
| | | | | | - Eric Matey
- Department of Pharmacy, Mayo Clinic, Rochester, MN, USA
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Sanchez-Ruiz JA, Leibman NI, Larson NB, Jenkins GD, Ahmed AT, Nunez NA, Biernacka JM, Winham SJ, Weinshilboum RM, Wang L, Frye MA, Ozerdem A. Age-Dependent Sex Differences in the Prevalence of Selective Serotonin Reuptake Inhibitor Treatment: A Retrospective Cohort Analysis. J Womens Health (Larchmt) 2023; 32:1229-1240. [PMID: 37856151 PMCID: PMC10621660 DOI: 10.1089/jwh.2022.0484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023] Open
Abstract
Background: Antidepressants are among the most prescribed medications in the United States. The aim of this study was to explore the prevalence of antidepressant prescriptions and investigate sex differences and age-sex interactions in adults enrolled in the Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT) study. Materials and Methods: We conducted a retrospective analysis of the RIGHT study. Using electronic prescriptions, we assessed 12-month prevalence of antidepressant treatment. Sex differences and age-sex interactions were evaluated using multivariable logistic regression and flexible recursive smoothing splines. Results: The sample consisted of 11,087 participants (60% women). Antidepressant prescription prevalence was 22.24% (27.96% women, 13.58% men). After adjusting for age and enrollment year, women had significantly greater odds of antidepressant prescription (odds ratio = 2.29; 95% confidence interval = 2.07, 2.54). Furthermore, selective serotonin reuptake inhibitors (SSRIs) had a significant age-sex interaction. While SSRI prescriptions in men showed a sustained decrease with age, there was no such decline for women until after reaching ∼50 years of age. There are important limitations to consider in this study. Electronic prescription data were cross-sectional; information on treatment duration or adherence was not collected; this cohort is not nationally representative; and enrollment occurred over a broad period, introducing confounding by changes in temporal prescribing practices. Conclusions: Underscored by the significant interaction between age and sex on odds of SSRI prescription, our results warrant age to be incorporated as a mediator when investigating sex differences in mental illness, especially mood disorders and their treatment.
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Affiliation(s)
| | - Nicole I. Leibman
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Nicholas B. Larson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Gregory D. Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Ahmed T. Ahmed
- The Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA
| | - Nicolas A. Nunez
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joanna M. Biernacka
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Stacey J. Winham
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Richard M. Weinshilboum
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Liewei Wang
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark A. Frye
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Aysegul Ozerdem
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, Minnesota, USA
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McDermott JH, Newman W. Introduction to pharmacogenetics. Drug Ther Bull 2023; 61:168-172. [PMID: 37788890 DOI: 10.1136/dtb.2023.000009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
There is considerable interindividual variability in the effectiveness and safety of medicines. Although the reasons for this are multifactorial, it is well recognised that genetic changes impacting the absorption or metabolism of these drugs play a significant contributory role. Understanding how these pharmacogenetic variants impact response to medicines, and leveraging this knowledge to guide prescribing, could have significant benefits for patients and health services. This article provides an introduction to the field of pharmacogenetics, including its nomenclature, the existing evidence base and the current state of implementation globally. We discuss the challenges in translating pharmacogenetic research into clinical practice and highlight the considerable benefits which can emerge in those health services where implementation is successful.
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Affiliation(s)
- John Henry McDermott
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, UK
- Division of Evolution, Infection and Genomics, School of Biological Sciences, The University of Manchester, Manchester, UK
| | - William Newman
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, UK
- Division of Evolution, Infection and Genomics, School of Biological Sciences, The University of Manchester, Manchester, UK
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Stewart S, Dodero-Anillo JM, Guijarro-Eguinoa J, Arias P, Gómez López De Las Huertas A, Seco-Meseguer E, García-García I, Ramírez García E, Rodríguez-Antolín C, Carcas AJ, Rodriguez-Novoa S, Rosas-Alonso R, Borobia AM. Advancing pharmacogenetic testing in a tertiary hospital: a retrospective analysis after 10 years of activity. Front Pharmacol 2023; 14:1292416. [PMID: 37927587 PMCID: PMC10622662 DOI: 10.3389/fphar.2023.1292416] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 10/02/2023] [Indexed: 11/07/2023] Open
Abstract
The field of pharmacogenetics (PGx) holds great promise in advancing personalized medicine by adapting treatments based on individual genetic profiles. Despite its benefits, there are still economic, ethical and institutional barriers that hinder its implementation in our healthcare environment. A retrospective analysis approach of anonymized data sourced from electronic health records was performed, encompassing a diverse patient population and evaluating key parameters such as prescribing patterns and test results, to assess the impact of pharmacogenetic testing. A head-to-head comparison with previously published activity results within the same pharmacogenetic laboratory was also conducted to contrast the progress made after 10 years. The analysis revealed significant utilization of pharmacogenetic testing in daily clinical practice, with 1,145 pharmacogenetic tests performed over a 1-year period and showing a 35% growth rate increase over time. Of the 17 different medical departments that sought PGx tests, the Oncology department accounted for the highest number, representing 58.47% of all genotyped patients. A total of 1,000 PGx tests were requested for individuals susceptible to receive a dose modification based on genotype, and 76 individuals received a genotype-guided dose adjustment. This study presents a comprehensive descriptive analysis of real-world data obtained from a public tertiary hospital laboratory specialized in pharmacogenetic testing, and presents data that strongly endorse the integration of pharmacogenetic testing into everyday clinical practice.
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Affiliation(s)
- Stefan Stewart
- Clinical Pharmacology Department, IdiPAZ, La Paz University Hospital, Madrid, Spain
| | | | | | - Pedro Arias
- Pharmacogenetics Laboratory, Genetics Department, La Paz University Hospital, Madrid, Spain
| | | | | | - Irene García-García
- Clinical Pharmacology Department, IdiPAZ, La Paz University Hospital, Madrid, Spain
| | - Elena Ramírez García
- Clinical Pharmacology Department, IdiPAZ, La Paz University Hospital, Madrid, Spain
- Pharmacology Department, School of Medicine, Universidad Autónoma de Madrid, Madrid, Spain
| | - Carlos Rodríguez-Antolín
- Experimental Therapies and Novel Biomarkers in Cancer, Hospital La Paz Institute for Health Research—IdiPAZ, Madrid, Spain
| | - Antonio J. Carcas
- Clinical Pharmacology Department, IdiPAZ, La Paz University Hospital, Madrid, Spain
- Pharmacology Department, School of Medicine, Universidad Autónoma de Madrid, Madrid, Spain
| | - Sonia Rodriguez-Novoa
- Genetics of Metabolic Diseases Laboratory, Genetics Department, La Paz University Hospital, Madrid, Spain
| | - Rocio Rosas-Alonso
- Pharmacogenetics Laboratory, Genetics Department, La Paz University Hospital, Madrid, Spain
- Experimental Therapies and Novel Biomarkers in Cancer, Hospital La Paz Institute for Health Research—IdiPAZ, Madrid, Spain
| | - Alberto M. Borobia
- Clinical Pharmacology Department, IdiPAZ, La Paz University Hospital, Madrid, Spain
- Pharmacology Department, School of Medicine, Universidad Autónoma de Madrid, Madrid, Spain
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12
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Kapoor E, Faubion SS, Kuhle CL, Kling JM, Miller VM, Fokken S, Mara KC, Moyer AM. The effect of genetic variation in estrogen transportation and metabolism on the severity of menopause symptoms: A study from the RIGHT 10K cohort. Maturitas 2023; 176:107797. [PMID: 37595497 PMCID: PMC10478674 DOI: 10.1016/j.maturitas.2023.107797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 07/08/2023] [Accepted: 07/11/2023] [Indexed: 08/20/2023]
Abstract
OBJECTIVE The severity of menopause-related symptoms varies considerably among women. The determinants of this variation are incompletely understood. The aim of this study was to assess the association between genetic variation in estrogen metabolism and transport pathways and the severity of menopause symptoms. METHODS This was a cross-sectional study of 60 peri- and postmenopausal women in the Mayo Clinic RIGHT study (which involved sequencing of genes involved in drug metabolism and transport), who had also been evaluated in the Women's Health Clinic at Mayo Clinic in Rochester, MN. All participants completed the Menopause Rating Scale (MRS) for assessment of menopause symptoms, including hot flashes. The association between severity of menopause symptoms and the variation in genes encoding 8 enzymes and transporters involved in estrogen metabolism was evaluated. RESULTS Lower CYP3A4 activity and higher COMT activity were associated with lower severity of somatic menopause symptoms (p = 0.04 and 0.06, respectively). These associations did not persist after adjustment for hormone therapy use. No differences in MRS scores or hot flash severity were noted among other genetic variant groups. Age at natural menopause was not affected by variations in the genes studied. CONCLUSION The current study did not show an association between genetic variation in estrogen metabolism and transport pathways and the severity of menopause symptoms. Further studies with larger sample sizes may be required to understand this potentially complex association.
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Affiliation(s)
- Ekta Kapoor
- Center for Women's Health, Mayo Clinic, Rochester, MN, USA; Menopause and Women's Sexual Health Clinic, Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA; Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Mayo Clinic, Rochester, MN, USA; Women's Health Research Center, Mayo Clinic, Rochester, MN, USA.
| | - Stephanie S Faubion
- Center for Women's Health, Mayo Clinic, Rochester, MN, USA; Division of General Internal Medicine, Mayo Clinic, Jacksonville, FL, USA
| | - Carol L Kuhle
- Center for Women's Health, Mayo Clinic, Rochester, MN, USA; Menopause and Women's Sexual Health Clinic, Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Juliana M Kling
- Center for Women's Health, Mayo Clinic, Rochester, MN, USA; Division of Women's Health Internal Medicine, Mayo Clinic, Scottsdale, AZ, USA
| | - Virginia M Miller
- Emerita Staff, Departments of Surgery and Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Shawn Fokken
- Menopause and Women's Sexual Health Clinic, Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Kristin C Mara
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Ann M Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
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13
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Cataldi M, Celentano C, Bencivenga L, Arcopinto M, Resnati C, Manes A, Dodani L, Comnes L, Vander Stichele R, Kalra D, Rengo G, Giallauria F, Trama U, Ferrara N, Cittadini A, Taglialatela M. Identification of Drugs Acting as Perpetrators in Common Drug Interactions in a Cohort of Geriatric Patients from Southern Italy and Analysis of the Gene Polymorphisms That Affect Their Interacting Potential. Geriatrics (Basel) 2023; 8:84. [PMID: 37736884 PMCID: PMC10514861 DOI: 10.3390/geriatrics8050084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/19/2023] [Accepted: 08/22/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Pharmacogenomic factors affect the susceptibility to drug-drug interactions (DDI). We identified drug interaction perpetrators among the drugs prescribed to a cohort of 290 older adults and analysed the prevalence of gene polymorphisms that can increase their interacting potential. We also pinpointed clinical decision support systems (CDSSs) that incorporate pharmacogenomic factors in DDI risk evaluation. METHODS Perpetrator drugs were identified using the Drug Interactions Flockhart Table, the DRUGBANK website, and the Mayo Clinic Pharmacogenomics Association Table. Allelic variants affecting their activity were identified with the PharmVar, PharmGKB, dbSNP, ensembl and 1000 genome databases. RESULTS Amiodarone, amlodipine, atorvastatin, digoxin, esomperazole, omeprazole, pantoprazole, simvastatin and rosuvastatin were perpetrator drugs prescribed to >5% of our patients. Few allelic variants affecting their perpetrator activity showed a prevalence >2% in the European population: CYP3A4/5*22, *1G, *3, CYP2C9*2 and *3, CYP2C19*17 and *2, CYP2D6*4, *41, *5, *10 and *9 and SLC1B1*15 and *5. Few commercial CDSS include pharmacogenomic factors in DDI-risk evaluation and none of them was designed for use in older adults. CONCLUSIONS We provided a list of the allelic variants influencing the activity of drug perpetrators in older adults which should be included in pharmacogenomics-oriented CDSSs to be used in geriatric medicine.
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Affiliation(s)
- Mauro Cataldi
- Department of Neuroscience, Reproductive Sciences and Dentistry, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (C.C.); (C.R.); (A.M.); (L.D.); (M.T.)
| | - Camilla Celentano
- Department of Neuroscience, Reproductive Sciences and Dentistry, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (C.C.); (C.R.); (A.M.); (L.D.); (M.T.)
| | - Leonardo Bencivenga
- Department of Translational Medical Sciences, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (L.B.); (M.A.); (G.R.); (F.G.); (N.F.); (A.C.)
- Gérontopôle de Toulouse, Institut du Vieillissement, CHU de Toulouse, Cité de la Santé, Place Lange, 31300 Toulouse, France
| | - Michele Arcopinto
- Department of Translational Medical Sciences, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (L.B.); (M.A.); (G.R.); (F.G.); (N.F.); (A.C.)
| | - Chiara Resnati
- Department of Neuroscience, Reproductive Sciences and Dentistry, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (C.C.); (C.R.); (A.M.); (L.D.); (M.T.)
| | - Annalaura Manes
- Department of Neuroscience, Reproductive Sciences and Dentistry, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (C.C.); (C.R.); (A.M.); (L.D.); (M.T.)
| | - Loreta Dodani
- Department of Neuroscience, Reproductive Sciences and Dentistry, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (C.C.); (C.R.); (A.M.); (L.D.); (M.T.)
| | - Lucia Comnes
- Datawizard, Via Salaria 719a, 00138 Rome, Italy;
| | - Robert Vander Stichele
- Heymans Institute of Pharmacology, Ghent University, C. Heymanslaan 10, 9000 Ghent, Belgium; (R.V.S.); (D.K.)
- European Institute for Innovation through Health Data, c/o Department Medical Informatics and Statistics, Ghent University Hospital, C. Heymanslaan 10, 9000 Ghent, Belgium
| | - Dipak Kalra
- Heymans Institute of Pharmacology, Ghent University, C. Heymanslaan 10, 9000 Ghent, Belgium; (R.V.S.); (D.K.)
- European Institute for Innovation through Health Data, c/o Department Medical Informatics and Statistics, Ghent University Hospital, C. Heymanslaan 10, 9000 Ghent, Belgium
| | - Giuseppe Rengo
- Department of Translational Medical Sciences, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (L.B.); (M.A.); (G.R.); (F.G.); (N.F.); (A.C.)
- Istituti Clinici Scientifici—ICS Maugeri S.p.A., Via Bagni Vecchi 1, 82037 Telese, Italy
| | - Francesco Giallauria
- Department of Translational Medical Sciences, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (L.B.); (M.A.); (G.R.); (F.G.); (N.F.); (A.C.)
| | - Ugo Trama
- General Directorate for Health Protection and Coordination of the Regional Health System, Regione Campania, Centro Direzionale Is. C3, 80132 Naples, Italy;
| | - Nicola Ferrara
- Department of Translational Medical Sciences, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (L.B.); (M.A.); (G.R.); (F.G.); (N.F.); (A.C.)
- Istituti Clinici Scientifici—ICS Maugeri S.p.A., Via Bagni Vecchi 1, 82037 Telese, Italy
| | - Antonio Cittadini
- Department of Translational Medical Sciences, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (L.B.); (M.A.); (G.R.); (F.G.); (N.F.); (A.C.)
| | - Maurizio Taglialatela
- Department of Neuroscience, Reproductive Sciences and Dentistry, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (C.C.); (C.R.); (A.M.); (L.D.); (M.T.)
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Zaver HB, Ghoz H, Malviya B, Bali A, Antwi S, Moyer AM, Bi Y. Pilot Study: Personalized Medicine in Endoscopy, Can Pharmacogenomics Predict Response to Conscious Sedation? J Pers Med 2023; 13:1107. [PMID: 37511720 PMCID: PMC10381361 DOI: 10.3390/jpm13071107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 06/26/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Adequate response to moderate (conscious) sedation varies significantly between individuals. Polymorphisms in genes encoding drug metabolizing enzymes can lead to inter-individual variability in drug efficacy, potentially influencing sedation requirements during endoscopic procedures. OBJECTIVES The aim of this study was to assess the potential role of inter-individual variation in inherited polymorphisms of drug-metabolizing enzymes, cytochrome P450 (CYP450), specifically CYP3A4 and CYP3A5, in sedation requirements for outpatient endoscopic procedures. METHODS A retrospective analysis of sedation requirements and pharmacogenomics data in 106 unique patients who received outpatient esophagogastroduodenoscopy (EGD), colonoscopy, or both between December 2011 and February 2019 was conducted. Patients were divided into two groups based on their sedation requirements during endoscopy (high vs. normal sedation). RESULTS Patients with reduced a CYP2C19 metabolism (poor + intermediate metabolizers) (odds ratio [OR] = 0.38, 95% confidence interval [CI]: 0.16-0.91, p = 0.03), poor CYP3A5 metabolism (OR = 0.25, 95% CI: 0.095-0.65, p = 0.0046), and poor UGT1A1 (OR = 2.76, 95% CI: 1.07-7.13, p = 0.08) had higher odds of requiring normal sedation compared to those with CYP2C19 increased metabolism, CYP3A5 intermediate metabolism, and UGT1A1 intermediate metabolism. CONCLUSION Information about inter-individual variation in (CYP450) genes may be useful for determining the sedation requirements for outpatient endoscopic procedures. We found that patients with reduced CYP2C19 metabolism, poor CYP3A5 metabolism, and poor UGT1A1 metabolism were more likely to require normal sedation requirements during outpatient endoscopic procedures.
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Affiliation(s)
- Himesh B. Zaver
- Department of Internal Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Hassan Ghoz
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, USA (Y.B.)
| | - Balkishan Malviya
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, USA (Y.B.)
| | - Aman Bali
- Department of Internal Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Samuel Antwi
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Ann M. Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA;
| | - Yan Bi
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, USA (Y.B.)
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15
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Fahim SM, Alexander CSW, Qian J, Ngorsuraches S, Hohmann NS, Lloyd KB, Reagan A, Hart L, McCormick N, Westrick SC. Current published evidence on barriers and proposed strategies for genetic testing implementation in health care settings: A scoping review. J Am Pharm Assoc (2003) 2023; 63:998-1016. [PMID: 37119989 DOI: 10.1016/j.japh.2023.04.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 04/20/2023] [Accepted: 04/22/2023] [Indexed: 05/01/2023]
Abstract
BACKGROUND The slow uptake of genetic testing in routine clinical practice warrants the attention of researchers and practitioners to find effective strategies to facilitate implementation. OBJECTIVES This study aimed to identify the barriers to and strategies for pharmacogenetic testing implementation in a health care setting from published literature. METHODS A scoping review was conducted in August 2021 with an expanded literature search using Ovid MEDLINE, Web of Science, International Pharmaceutical Abstract, and Google Scholar to identify studies reporting implementation of pharmacogenetic testing in a health care setting, from a health care system's perspective. Articles were screened using DistillerSR and findings were organized using the 5 major domains of Consolidated Framework for Implementation Research (CFIR). RESULTS A total of 3536 unique articles were retrieved from the above sources, with only 253 articles retained after title and abstract screening. Upon screening the full texts, 57 articles (representing 46 unique practice sites) were found matching the inclusion criteria. We found that most reported barriers and their associated strategies to the implementation of pharmacogenetic testing surrounded 2 CFIR domains: intervention characteristics and inner settings. Factors relating to cost and reimbursement were described as major barriers in the intervention characteristics. In the same domain, another major barrier was the lack of utility studies to provide evidence for genetic testing uptake. Technical hurdles, such as integrating genetic information to medical records, were identified as an inner settings barrier. Collaborations and lessons from early implementers could be useful strategies to overcome majority of the barriers across different health care settings. Strategies proposed by the included implementation studies to overcome these barriers are summarized and can be used as guidance in future. CONCLUSION Barriers and strategies identified in this scoping review can provide implementation guidance for practice sites that are interested in implementing genetic testing.
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16
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Smith DM, Wake DT, Dunnenberger HM. Pharmacogenomic Clinical Decision Support: A Scoping Review. Clin Pharmacol Ther 2023; 113:803-815. [PMID: 35838358 DOI: 10.1002/cpt.2711] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 07/10/2022] [Indexed: 11/06/2022]
Abstract
Clinical decision support (CDS) is often cited as an essential part of pharmacogenomics (PGx) implementations. A multitude of strategies are available; however, it is unclear which strategies are effective and which metrics are used to quantify clinical utility. The objective of this scoping review was to aggregate previous studies into a cohesive depiction of the current state of PGx CDS implementations and identify areas for future research on PGx CDS. Articles were included if they (i) described electronic CDS tools for PGx and (ii) reported metrics related to PGx CDS. Twenty of 3,449 articles were included and provided data on PGx CDS metrics from 15 institutions, with 93% of programs located at academic medical centers. The most common tools in CDS implementations were interruptive post-test alerts. Metrics for clinical response and alert response ranged from 12-73% and 21-98%, respectively. Few data were found on changes in metrics over time and measures that drove the evolution of CDS systems. Relatively few data were available regarding support of optimal approaches for PGx CDS. Post-test alerts were the most widely studied approach, and their effectiveness varied greatly. Further research on the usability, effectiveness, and optimization of CDS tools is needed.
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Affiliation(s)
- D Max Smith
- MedStar Health, Columbia, Maryland, USA.,Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Dyson T Wake
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Henry M Dunnenberger
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
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17
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McDermott JH, Sharma V, Keen J, Newman WG, Pirmohamed M. The Implementation of Pharmacogenetics in the United Kingdom. Handb Exp Pharmacol 2023; 280:3-32. [PMID: 37306816 DOI: 10.1007/164_2023_658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
There is considerable inter-individual variability in the effectiveness and safety of pharmaceutical interventions. This phenomenon can be attributed to a multitude of factors; however, it is widely acknowledged that common genetic variation affecting drug absorption or metabolism play a substantial contributory role. This is a concept known as pharmacogenetics. Understanding how common genetic variants influence responses to medications, and using this knowledge to inform prescribing practice, could yield significant advantages for both patients and healthcare systems. Some health services around the world have introduced pharmacogenetics into routine practice, whereas others are less advanced along the implementation pathway. This chapter introduces the field of pharmacogenetics, the existing body of evidence, and discusses barriers to implementation. The chapter will specifically focus on efforts to introduce pharmacogenetics in the NHS, highlighting key challenges related to scale, informatics, and education.
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Affiliation(s)
- John H McDermott
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Videha Sharma
- Division of Informatics, Imaging and Data Science, Centre for Health Informatics, The University of Manchester, Manchester, UK
| | - Jessica Keen
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - William G Newman
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Munir Pirmohamed
- Department of Pharmacology and Therapeutics, Wolfson Centre for Personalised Medicine, University of Liverpool, Liverpool, UK.
- Liverpool University Hospital Foundation NHS Trust, Liverpool, UK.
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18
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Zhao FL, Zhang Q, Wang SH, Hong Y, Zhou S, Zhou Q, Geng PW, Luo QF, Yang JF, Chen H, Cai JP, Dai DP. Identification and drug metabolic characterization of four new CYP2C9 variants CYP2C9*72- *75 in the Chinese Han population. Front Pharmacol 2022; 13:1007268. [PMID: 36582532 PMCID: PMC9792615 DOI: 10.3389/fphar.2022.1007268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 12/01/2022] [Indexed: 12/15/2022] Open
Abstract
Cytochrome 2C9 (CYP2C9), one of the most important drug metabolic enzymes in the human hepatic P450 superfamily, is required for the metabolism of 15% of clinical drugs. Similar to other CYP2C family members, CYP2C9 gene has a high genetic polymorphism which can cause significant racial and inter-individual differences in drug metabolic activity. To better understand the genetic distribution pattern of CYP2C9 in the Chinese Han population, 931 individuals were recruited and used for the genotyping in this study. As a result, seven synonymous and 14 non-synonymous variations were identified, of which 4 missense variants were designated as new alleles CYP2C9*72, *73, *74 and *75, resulting in the amino acid substitutions of A149V, R150C, Q214H and N418T, respectively. When expressed in insect cell microsomes, all four variants exhibited comparable protein expression levels to that of the wild-type CYP2C9 enzyme. However, drug metabolic activity analysis revealed that these variants exhibited significantly decreased catalytic activities toward three CYP2C9 specific probe drugs, as compared with that of the wild-type enzyme. These data indicate that the amino acid substitution in newly designated variants can cause reduced function of the enzyme and its clinical significance still needs further investigation in the future.
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Affiliation(s)
- Fang-Ling Zhao
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, China,Peking University Fifth School of Clinical Medicine, Beijing, China
| | - Qing Zhang
- Department of Cardiovascular, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Shuang-Hu Wang
- Laboratory of Clinical Pharmacy, The Sixth Affiliated Hospital of Wenzhou Medical University, The People’s Hospital of Lishui, Lishui, China
| | - Yun Hong
- Department of Gastroenterology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Shan Zhou
- Peking University Fifth School of Clinical Medicine, Beijing, China
| | - Quan Zhou
- Laboratory of Clinical Pharmacy, The Sixth Affiliated Hospital of Wenzhou Medical University, The People’s Hospital of Lishui, Lishui, China
| | - Pei-Wu Geng
- Laboratory of Clinical Pharmacy, The Sixth Affiliated Hospital of Wenzhou Medical University, The People’s Hospital of Lishui, Lishui, China
| | - Qing-Feng Luo
- Department of Gastroenterology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Jie-Fu Yang
- Department of Cardiovascular, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Hao Chen
- Department of Cardiovascular, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China,*Correspondence: Da-Peng Dai, ; Jian-Ping Cai, ; Hao Chen,
| | - Jian-Ping Cai
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, China,*Correspondence: Da-Peng Dai, ; Jian-Ping Cai, ; Hao Chen,
| | - Da-Peng Dai
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, China,Peking University Fifth School of Clinical Medicine, Beijing, China,*Correspondence: Da-Peng Dai, ; Jian-Ping Cai, ; Hao Chen,
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19
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Pasternak AL, Ward K, Irwin M, Okerberg C, Hayes D, Fritsche L, Zoellner S, Virzi J, Choe HM, Ellingrod V. Identifying the prevalence of clinically actionable drug-gene interactions in a health system biorepository to guide pharmacogenetics implementation services. Clin Transl Sci 2022; 16:292-304. [PMID: 36510710 PMCID: PMC9926071 DOI: 10.1111/cts.13449] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 10/14/2022] [Accepted: 10/24/2022] [Indexed: 12/15/2022] Open
Abstract
Understanding patterns of drug-gene interactions (DGIs) is important for advancing the clinical implementation of pharmacogenetics (PGx) into routine practice. Prior studies have estimated the prevalence of DGIs, but few have confirmed DGIs in patients with known genotypes and prescriptions, nor have they evaluated clinician characteristics associated with DGI-prescribing. This retrospective chart review assessed prevalence of DGI, defined as a medication prescription in a patient with a PGx phenotype that has a clinical practice guideline recommendation to adjust therapy or monitor drug response, for patients enrolled in a research genetic biorepository linked to electronic health records (EHRs). The prevalence of prescriptions for medications with pharmacogenetic (PGx) guidelines, proportion of prescriptions with DGI, location of DGI prescription, and clinical service of the prescriber were evaluated descriptively. Seventy-five percent (57,058/75,337) of patients had a prescription for a medication with a PGx guideline. Up to 60% (n = 26,067/43,647) of patients had at least one DGI when considering recommendations to adjust or monitor therapy based on genotype. The majority (61%) of DGIs occurred in outpatient prescriptions. Proton pump inhibitors were the most common DGI medication for 11 of 12 clinical services. Almost 25% of patients (n = 10,706/43,647) had more than one unique DGI, and, among this group of patients, 61% had a DGI with more than one gene. These findings can inform future clinical implementation by identifying key stakeholders for initial DGI prescriptions, helping to inform workflows. The high prevalence of multigene interactions identified also support the use of panel PGx testing as an implementation strategy.
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Affiliation(s)
- Amy L. Pasternak
- Department of Clinical PharmacyUniversity of Michigan College of PharmacyAnn ArborMichiganUSA,Michigan MedicineUniversity of Michigan HealthAnn ArborMichiganUSA
| | - Kristen Ward
- Department of Clinical PharmacyUniversity of Michigan College of PharmacyAnn ArborMichiganUSA,Michigan MedicineUniversity of Michigan HealthAnn ArborMichiganUSA
| | - Madison Irwin
- Department of Clinical PharmacyUniversity of Michigan College of PharmacyAnn ArborMichiganUSA,Michigan MedicineUniversity of Michigan HealthAnn ArborMichiganUSA
| | - Carl Okerberg
- Michigan MedicineUniversity of Michigan HealthAnn ArborMichiganUSA
| | - David Hayes
- Department of Clinical PharmacyUniversity of Michigan College of PharmacyAnn ArborMichiganUSA
| | - Lars Fritsche
- Department of BiostatisticsUniversity of Michigan School of Public HealthAnn ArborMichiganUSA
| | - Sebastian Zoellner
- Department of BiostatisticsUniversity of Michigan School of Public HealthAnn ArborMichiganUSA
| | - Jessica Virzi
- Michigan MedicineUniversity of Michigan HealthAnn ArborMichiganUSA
| | - Hae Mi Choe
- Department of Clinical PharmacyUniversity of Michigan College of PharmacyAnn ArborMichiganUSA,Michigan MedicineUniversity of Michigan HealthAnn ArborMichiganUSA
| | - Vicki Ellingrod
- Department of Clinical PharmacyUniversity of Michigan College of PharmacyAnn ArborMichiganUSA
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20
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A Theory-Informed Systematic Review of Barriers and Enablers to Implementing Multi-Drug Pharmacogenomic Testing. J Pers Med 2022; 12:jpm12111821. [PMID: 36579514 PMCID: PMC9696651 DOI: 10.3390/jpm12111821] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/20/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022] Open
Abstract
PGx testing requires a complex set of activities undertaken by practitioners and patients, resulting in varying implementation success. This systematic review aimed (PROSPERO: CRD42019150940) to identify barriers and enablers to practitioners and patients implementing pharmacogenomic testing. We followed PRISMA guidelines to conduct and report this review. Medline, EMBASE, CINAHL, PsycINFO, and PubMed Central were systematically searched from inception to June 2022. The theoretical domain framework (TDF) guided the organisation and reporting of barriers or enablers relating to pharmacogenomic testing activities. From the twenty-five eligible reports, eleven activities were described relating to four implementation stages: ordering, facilitating, interpreting, and applying pharmacogenomic testing. Four themes were identified across the implementation stages: IT infrastructure, effort, rewards, and unknown territory. Barriers were most consistently mapped to TDF domains: memory, attention and decision-making processes, environmental context and resources, and belief about consequences.
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21
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Public Attitudes toward Pharmacogenomic Testing and Establishing a Statewide Pharmacogenomics Database in the State of Minnesota. J Pers Med 2022; 12:jpm12101615. [PMID: 36294754 PMCID: PMC9604616 DOI: 10.3390/jpm12101615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 11/07/2022] Open
Abstract
The clinical adoption and implementation of pharmacogenomics (PGx) beyond academic medical centers remains slow, restricting the general population from benefitting from this important component of personalized medicine. As an initial step in the statewide initiative of PGx implementation in Minnesota, we engaged community members and assessed attitudes towards PGx testing and acceptability of establishing a secure statewide PGx database for clinical and research use among Minnesota residents. Data was collected from 808 adult attendees at the 2021 Minnesota State Fair through an electronic survey. Eighty-four percent of respondents felt comfortable getting a PGx test for clinical care. Most respondents trusted health professionals (78.2%) and researchers (73.0%) to keep their PGx data private. The majority expressed their support and interest in participating in a statewide PGx database for clinical and research use (64–72%). Higher acceptability of the statewide PGx database was associated with younger age, higher education, higher health literacy, having health insurance, and prior genetic testing. The study sample representing Minnesota residents expressed high acceptability of receiving PGx testing and willingness to participate in PGx data sharing for clinical and research use. Community support and engagement are needed to advance PGx implementation and research on the state scale.
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22
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Goh LL, Lim CW, Leong KP, Ong KH. TPMT and NUDT15 testing for thiopurine therapy: A major tertiary hospital experience and lessons learned. Front Pharmacol 2022; 13:837164. [PMID: 36210828 PMCID: PMC9537458 DOI: 10.3389/fphar.2022.837164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Variants in thiopurine methyltransferase (TPMT) and nudix hydrolase 15 (NUDT15) are associated with an accumulation of cytotoxic metabolites leading to increased risk of drug-related toxicity with standard doses of thiopurine drugs. We established TPMT and NUDT15 genetic testing for clinical use and evaluated the utilization, service outcomes and potential value of multi-gene PGx testing for 210 patients that underwent pharmacogenetics (PGx) testing for thiopurine therapy with the aim to optimize service delivery for future prescribing. The test was most commonly ordered for Gastroenterology (40.0%) and Neurology (31.4%), with an average turnaround time of 2 days. Following testing, 24.3% patients were identified as intermediate or poor metabolizers, resulting in 51 recommendations for a drug or dose change in thiopurine therapy, which were implemented in 28 (54.9%) patients. In the remaining patients, 14 were not adjusted and 9 had no data available. Focusing on drug gene interactions available for testing in our laboratory, multi-gene PGx results would present opportunities for treatment optimization for at least 33.8% of these patients who were on 2 or more concurrent medications with actionable PGx guidance. However, the use of PGx panel testing in clinical practice will require the development of guidelines and education as revealed by a survey with the test providers. The evaluation demonstrated successful implementation of single gene PGx testing and this experience guides the transition to a pre-emptive multi-gene testing approach that provides the opportunity to improve clinical care.
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Affiliation(s)
- Liuh Ling Goh
- Molecular Diagnostic Laboratory, Personalized Medicine Service, Tan Tock Seng Hospital, Singapore, Singapore
- *Correspondence: Liuh Ling Goh,
| | - Chia Wei Lim
- Molecular Diagnostic Laboratory, Personalized Medicine Service, Tan Tock Seng Hospital, Singapore, Singapore
| | - Khai Pang Leong
- Molecular Diagnostic Laboratory, Personalized Medicine Service, Tan Tock Seng Hospital, Singapore, Singapore
- Department of Rheumatology, Allergy & Immunology, Tan Tock Seng Hospital, Singapore, Singapore
| | - Kiat Hoe Ong
- Molecular Diagnostic Laboratory, Personalized Medicine Service, Tan Tock Seng Hospital, Singapore, Singapore
- Department of Haematology, Tan Tock Seng Hospital, Singapore, Singapore
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23
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McDermott JH, Leach M, Sen D, Smith CJ, Newman WG, Bath PM. The role of CYP2C19 genotyping to guide antiplatelet therapy following ischemic stroke or transient ischemic attack. Expert Rev Clin Pharmacol 2022; 15:811-825. [PMID: 35912831 PMCID: PMC9612933 DOI: 10.1080/17512433.2022.2108401] [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] [Indexed: 11/04/2022]
Abstract
Introduction Clopidogrel is an antiplatelet agent recommended for secondary prevention of ischemic stroke (IS) and transient ischemic attack (TIA). Conversion of clopidogrel to its active metabolite by hepatic cytochrome P450-2C19 (CYP2C19) is essential for the inhibition of the P2Y12 receptor and subsequent platelet aggregation to prevent thrombotic events. CYP2C19 is highly polymorphic, with over 30 loss of function (LoF) alleles. This review considers whether there is sufficient data to support genotype guided antiplatelet therapy after stroke. Areas covered A systematic literature review retrieved articles, which describe the interaction between CYP2C19 genotype and clinical outcomes following IS or TIA when treated with clopidogrel. The review documents efforts to identify optimal antiplatelet regimens and explores the value genotype guided antiplatelet therapy. The work outlines the contemporary understanding of clopidogrel metabolism and appraises evidence linking CYP2C19 LoF variants with attenuated platelet inhibition and poorer outcomes. Expert opinion There is good evidence that CYP2C19 LoF allele carriers of Han-Chinese ancestry have increased risk for further vascular events following TIA or IS when treated with clopidogrel. The evidence base is less certain in other populations. The expansion of pharmacogenetics into routine clinical practice will facilitate further research and help tailor other aspects of secondary prevention.
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Affiliation(s)
- John H McDermott
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, M13 9WL, UK.,The Division of Evolution, Infection and Genomics, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Marc Leach
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, M13 9WL, UK
| | - Dwaipayan Sen
- Greater Manchester Comprehensive Stroke Centre, Geoffrey Jefferson Brain Research Centre, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Salford, UK
| | - Craig J Smith
- Greater Manchester Comprehensive Stroke Centre, Geoffrey Jefferson Brain Research Centre, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Salford, UK.,Division of Cardiovascular Sciences, Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - William G Newman
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, M13 9WL, UK.,The Division of Evolution, Infection and Genomics, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Philip M Bath
- Stroke Trials Unit, Mental Health & Clinical Neuroscience, University of Nottingham, Nottingham, UK.,Stroke, Nottingham University Hospitals NHS Trust, Queen's Medical Centre, Nottingham, UK
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24
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Blagec K, Swen JJ, Koopmann R, Cheung KC, Crommentuijn-van Rhenen M, Holsappel I, Konta L, Ott S, Steinberger D, Xu H, Cecchin E, Dolžan V, Dávila-Fajardo CL, Patrinos GP, Sunder-Plassmann G, Turner RM, Pirmohamed M, Guchelaar HJ, Samwald M. Pharmacogenomics decision support in the U-PGx project: Results and advice from clinical implementation across seven European countries. PLoS One 2022; 17:e0268534. [PMID: 35675343 PMCID: PMC9176797 DOI: 10.1371/journal.pone.0268534] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 04/26/2022] [Indexed: 12/18/2022] Open
Abstract
Background The clinical implementation of pharmacogenomics (PGx) could be one of the first milestones towards realizing personalized medicine in routine care. However, its widespread adoption requires the availability of suitable clinical decision support (CDS) systems, which is often impeded by the fragmentation or absence of adequate health IT infrastructures. We report results of CDS implementation in the large-scale European research project Ubiquitous Pharmacogenomics (U-PGx), in which PGx CDS was rolled out and evaluated across more than 15 clinical sites in the Netherlands, Spain, Slovenia, Italy, Greece, United Kingdom and Austria, covering a wide variety of healthcare settings. Methods We evaluated the CDS implementation process through qualitative and quantitative process indicators. Quantitative indicators included statistics on generated PGx reports, median time from sampled upload until report delivery and statistics on report retrievals via the mobile-based CDS tool. Adoption of different CDS tools, uptake and usability were further investigated through a user survey among healthcare providers. Results of a risk assessment conducted prior to the implementation process were retrospectively analyzed and compared to actual encountered difficulties and their impact. Results As of March 2021, personalized PGx reports were produced from 6884 genotyped samples with a median delivery time of twenty minutes. Out of 131 invited healthcare providers, 65 completed the questionnaire (response rate: 49.6%). Overall satisfaction rates with the different CDS tools varied between 63.6% and 85.2% per tool. Delays in implementation were caused by challenges including institutional factors and complexities in the development of required tools and reference data resources, such as genotype-phenotype mappings. Conclusions We demonstrated the feasibility of implementing a standardized PGx decision support solution in a multinational, multi-language and multi-center setting. Remaining challenges for future wide-scale roll-out include the harmonization of existing PGx information in guidelines and drug labels, the need for strategies to lower the barrier of PGx CDS adoption for healthcare institutions and providers, and easier compliance with regulatory and legal frameworks.
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Affiliation(s)
- Kathrin Blagec
- Institute of Artificial Intelligence, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Jesse J Swen
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, Leiden, The Netherlands
| | - Rudolf Koopmann
- Diagnosticum Center for Human Genetics, Frankfurt am Main, Germany.,Institute for Human Genetics, Justus Liebig University, Giessen, Germany
| | - Ka-Chun Cheung
- Medicines Information Centre, Royal Dutch Pharmacists Association (KNMP), The Hague, The Netherlands
| | | | - Inge Holsappel
- Medicines Information Centre, Royal Dutch Pharmacists Association (KNMP), The Hague, The Netherlands
| | - Lidija Konta
- Diagnosticum Center for Human Genetics, Frankfurt am Main, Germany
| | - Simon Ott
- Institute of Artificial Intelligence, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Daniela Steinberger
- Diagnosticum Center for Human Genetics, Frankfurt am Main, Germany.,Institute for Human Genetics, Justus Liebig University, Giessen, Germany
| | - Hong Xu
- Institute of Artificial Intelligence, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Erika Cecchin
- Experimental and Clinical Pharmacology Unit, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, Italy
| | - Vita Dolžan
- Faculty of Medicine, Institute of Biochemistry and Molecular Genetics, Pharmacogenetics Laboratory, University of Ljubljana, Ljubljana, Slovenia
| | - Cristina Lucía Dávila-Fajardo
- Clinical Pharmacy Department, Hospital Universitario Virgen de las Nieves, Instituto de Investigación Biosanitaria Granada (Ibs.Granada), Granada, Spain
| | - George P Patrinos
- Department of Pharmacy, Laboratory of Pharmacogenomics and Individualized Therapy, University of Patras School of Health Sciences, Patras, Greece
| | - Gere Sunder-Plassmann
- Division of Nephrology and Dialysis, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
| | - Richard M Turner
- The Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- Department of Molecular and Clinical Pharmacology, Royal Liverpool University Hospital and University of Liverpool, Liverpool, United Kingdom
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, Leiden, The Netherlands
| | - Matthias Samwald
- Institute of Artificial Intelligence, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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25
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Wang L, Scherer SE, Bielinski SJ, Muzny DM, Jones LA, Black JL, Moyer AM, Giri J, Sharp RR, Matey ET, Wright JA, Oyen LJ, Nicholson WT, Wiepert M, Sullard T, Curry TB, Vitek CRR, McAllister TM, Sauver JL, Caraballo PJ, Lazaridis KN, Venner E, Qin X, Hu J, Kovar CL, Korchina V, Walker K, Doddapaneni H, Wu TJ, Raj R, Denson S, Liu W, Chandanavelli G, Zhang L, Wang Q, Kalra D, Karow MB, Harris KJ, Sicotte H, Peterson SE, Barthel AE, Moore BE, Skierka JM, Kluge ML, Kotzer KE, Kloke K, Vander Pol JM, Marker H, Sutton JA, Kekic A, Ebenhoh A, Bierle DM, Schuh MJ, Grilli C, Erickson S, Umbreit A, Ward L, Crosby S, Nelson EA, Levey S, Elliott M, Peters SG, Pereira N, Frye M, Shamoun F, Goetz MP, Kullo IJ, Wermers R, Anderson JA, Formea CM, El Melik RM, Zeuli JD, Herges JR, Krieger CA, Hoel RW, Taraba JL, Thomas SR, Absah I, Bernard ME, Fink SR, Gossard A, Grubbs PL, Jacobson TM, Takahashi P, Zehe SC, Buckles S, Bumgardner M, Gallagher C, Fee-Schroeder K, Nicholas NR, Powers ML, Ragab AK, Richardson DM, Stai A, Wilson J, Pacyna JE, Olson JE, Sutton EJ, Beck AT, Horrow C, Kalari KR, Larson NB, Liu H, Wang L, Lopes GS, Borah BJ, Freimuth RR, Zhu Y, Jacobson DJ, Hathcock MA, Armasu SM, McGree ME, Jiang R, Koep TH, Ross JL, Hilden M, Bosse K, Ramey B, Searcy I, Boerwinkle E, Gibbs RA, Weinshilboum RM. Implementation of preemptive DNA sequence-based pharmacogenomics testing across a large academic medical center: The Mayo-Baylor RIGHT 10K Study. Genet Med 2022; 24:1062-1072. [PMID: 35331649 PMCID: PMC9272414 DOI: 10.1016/j.gim.2022.01.022] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/25/2022] [Accepted: 01/26/2022] [Indexed: 12/12/2022] Open
Abstract
PURPOSE The Mayo-Baylor RIGHT 10K Study enabled preemptive, sequence-based pharmacogenomics (PGx)-driven drug prescribing practices in routine clinical care within a large cohort. We also generated the tools and resources necessary for clinical PGx implementation and identified challenges that need to be overcome. Furthermore, we measured the frequency of both common genetic variation for which clinical guidelines already exist and rare variation that could be detected by DNA sequencing, rather than genotyping. METHODS Targeted oligonucleotide-capture sequencing of 77 pharmacogenes was performed using DNA from 10,077 consented Mayo Clinic Biobank volunteers. The resulting predicted drug response-related phenotypes for 13 genes, including CYP2D6 and HLA, affecting 21 drug-gene pairs, were deposited preemptively in the Mayo electronic health record. RESULTS For the 13 pharmacogenes of interest, the genomes of 79% of participants carried clinically actionable variants in 3 or more genes, and DNA sequencing identified an average of 3.3 additional conservatively predicted deleterious variants that would not have been evident using genotyping. CONCLUSION Implementation of preemptive rather than reactive and sequence-based rather than genotype-based PGx prescribing revealed nearly universal patient applicability and required integrated institution-wide resources to fully realize individualized drug therapy and to show more efficient use of health care resources.
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Affiliation(s)
- Liewei Wang
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN,Division of Clinical Pharmacology, Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN
| | - Steven E. Scherer
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | - Suzette J. Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Donna M. Muzny
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Leila A. Jones
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | - John Logan Black
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Ann M. Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Jyothsna Giri
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | | | | | | | | | - Wayne T. Nicholson
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Mathieu Wiepert
- Department of Information Technology, Mayo Clinic, Rochester, MN
| | - Terri Sullard
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | - Timothy B. Curry
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN,Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | | | | | - Jennifer L. Sauver
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN,Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | - Pedro J. Caraballo
- Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Konstantinos N. Lazaridis
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN,Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Eric Venner
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Xiang Qin
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Jianhong Hu
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Christie L. Kovar
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Viktoriya Korchina
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Kimberly Walker
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | | | - Tsung-Jung Wu
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Ritika Raj
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Shawn Denson
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Wen Liu
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Gauthami Chandanavelli
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Lan Zhang
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Qiaoyan Wang
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Divya Kalra
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Mary Beth Karow
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | | | - Hugues Sicotte
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Sandra E. Peterson
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Amy E. Barthel
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Brenda E. Moore
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | | | - Michelle L. Kluge
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Katrina E. Kotzer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Karen Kloke
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | | | - Heather Marker
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | - Joseph A. Sutton
- Department of Information Technology, Mayo Clinic, Rochester, MN
| | | | | | - Dennis M. Bierle
- Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | | | | | | | - Audrey Umbreit
- Department of Pharmacy, Mayo Clinic Health System, Mankato, MN
| | - Leah Ward
- Department of Pharmacy, Mayo Clinic, Jacksonville, FL
| | - Sheena Crosby
- Department of Pharmacy, Mayo Clinic, Jacksonville, FL
| | | | - Sharon Levey
- Department of Clinical Genomics, Mayo Clinic, Scottsdale, AZ
| | - Michelle Elliott
- Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Steve G. Peters
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Naveen Pereira
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Mark Frye
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN
| | - Fadi Shamoun
- Department of Cardiovascular Medicine Mayo Clinic, Phoenix, AZ
| | - Matthew P. Goetz
- Division of Medical Oncology, Department of Oncology, Mayo Clinic, Rochester, MN
| | | | - Robert Wermers
- Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | | | | | | | | | | | | | | | | | - Scott R. Thomas
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | - Imad Absah
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | | | - Stephanie R. Fink
- Division of Community Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Andrea Gossard
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | | | | | - Paul Takahashi
- Division of Community Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | | | - Susan Buckles
- Department of Public Affairs, Mayo Clinic, Rochester, MN
| | | | | | | | | | - Melody L. Powers
- Biospecimens Accessioning and Processing Laboratory, Mayo Clinic, Rochester, MN
| | - Ahmed K. Ragab
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | | | - Anthony Stai
- Department of Information Technology, Mayo Clinic, Rochester, MN
| | - Jaymi Wilson
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | - Joel E. Pacyna
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN
| | - Janet E. Olson
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN,Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN
| | - Erica J. Sutton
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN
| | - Annika T. Beck
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN
| | - Caroline Horrow
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN
| | - Krishna R. Kalari
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Nicholas B. Larson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Hongfang Liu
- Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Liwei Wang
- Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Guilherme S. Lopes
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN,Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Bijan J. Borah
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN,Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Robert R. Freimuth
- Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Ye Zhu
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | - Debra J. Jacobson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Matthew A. Hathcock
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Sebastian M. Armasu
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Michaela E. McGree
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Ruoxiang Jiang
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | | | | | | | | | | | | | - Eric Boerwinkle
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX,Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX,School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
| | - Richard A. Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX,Corresponding Authors (), ()
| | - Richard M. Weinshilboum
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN,Division of Clinical Pharmacology, Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN,Corresponding Authors (), ()
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26
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Lopes GS, Lopes JL, Bielinski SJ, Armasu SM, Zhu Y, Cavanaugh DC, Moyer AM, Jacobson DJ, Wang L, Jiang R, St. Sauver JL, Larson NB. Identification of sex-specific genetic associations in response to opioid analgesics in a White, non-Hispanic cohort from Southeast Minnesota. THE PHARMACOGENOMICS JOURNAL 2022; 22:117-123. [PMID: 35102242 PMCID: PMC8975736 DOI: 10.1038/s41397-022-00265-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 01/10/2022] [Indexed: 11/27/2022]
Abstract
The study of sex-specific genetic associations with opioid response may improve the understanding of inter-individual variability in pain treatments. We investigated sex-specific associations between genetic variation and opioid response. We identified participants in the RIGHT Study prescribed codeine, tramadol, hydrocodone, and oxycodone between 01/01/2005 and 12/31/2017. Prescriptions were collapsed into codeine/tramadol and hydrocodone/oxycodone. Outcomes included poor pain control and adverse reactions within six weeks after prescription date. We performed gene-level and single-variant association analyses stratified by sex. We included 7169 non-Hispanic white participants and a total of 1940 common and low-frequency variants (MAF > 0.01). Common variants in MACROD2 (rs76026520), CYP1B1 (rs1056837, rs1056836), and CYP2D6 (rs35742686) were associated with outcomes. At the gene level, FAAH, SCN1A, and TYMS had associations for men and women, and NAT2, CYP3A4, CYP1A2, and SLC22A2 had associations for men only. Our findings highlight the importance of considering sex in association studies on opioid response.
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27
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Lopes JL, Harris K, Karow MB, Peterson SE, Kluge ML, Kotzer KE, Lopes GS, Larson NB, Bielinski SJ, Scherer SE, Wang L, Weinshilboum RM, Black JL, Moyer AM. Targeted Genotyping in Clinical Pharmacogenomics: What Is Missing? J Mol Diagn 2022; 24:253-261. [PMID: 35041929 PMCID: PMC8961466 DOI: 10.1016/j.jmoldx.2021.11.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/09/2021] [Accepted: 11/29/2021] [Indexed: 01/01/2023] Open
Abstract
Clinical pharmacogenomic testing typically uses targeted genotyping, which only detects variants included in the test design and may vary among laboratories. To evaluate the potential patient impact of genotyping compared with sequencing, which can detect common and rare variants, an in silico targeted genotyping panel was developed based on the variants most commonly included in clinical tests and applied to a cohort of 10,030 participants who underwent sequencing for CYP1A2, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, DPYD, SLCO1B1, TPMT, UGT1A1, and VKORC1. The results of in silico targeted genotyping were compared with the clinically reported sequencing results. Of the 10,030 participants, 2780 (28%) had at least one potentially clinically relevant variant/allele identified by sequencing that would not have been detected in a standard targeted genotyping panel. The genes with the largest number of participants with variants only detected by sequencing were SLCO1B1, DPYD, and CYP2D6, which affected 13%, 6.3%, and 3.5% of participants, respectively. DPYD (112 variants) and CYP2D6 (103 variants) had the largest number of unique variants detected only by sequencing. Although targeted genotyping detects most clinically significant pharmacogenomic variants, sequencing-based approaches are necessary to detect rare variants that collectively affect many patients. However, efforts to establish pharmacogenomic variant classification systems and nomenclature to accommodate rare variants will be required to adopt sequencing-based pharmacogenomics.
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Affiliation(s)
- Jaime L. Lopes
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Kimberley Harris
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Mary Beth Karow
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Sandra E. Peterson
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Michelle L. Kluge
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Katrina E. Kotzer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Guilherme S. Lopes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Nicholas B. Larson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | | | - Steven E. Scherer
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
| | - Richard M. Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
| | - John L. Black
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Ann M. Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota,Address correspondence to Ann M. Moyer, M.D., Ph.D., Mayo Clinic, 200 First St SW, Rochester, MN 55905.
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The Value of Pharmacogenetics to Reduce Drug-Related Toxicity in Cancer Patients. Mol Diagn Ther 2022; 26:137-151. [PMID: 35113367 PMCID: PMC8975257 DOI: 10.1007/s40291-021-00575-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/19/2021] [Indexed: 10/19/2022]
Abstract
Many anticancer drugs cause adverse drug reactions (ADRs) that negatively impact safety and reduce quality of life. The typical narrow therapeutic range and exposure-response relationships described for anticancer drugs make precision dosing critical to ensure safe and effective drug exposure. Germline mutations in pharmacogenes contribute to inter-patient variability in pharmacokinetics and pharmacodynamics of anticancer drugs. Patients carrying reduced-activity or loss-of-function alleles are at increased risk for ADRs. Pretreatment genotyping offers a proactive approach to identify these high-risk patients, administer an individualized dose, and minimize the risk of ADRs. In the field of oncology, the most well-studied gene-drug pairs for which pharmacogenetic dosing recommendations have been published to improve safety are DPYD-fluoropyrimidines, TPMT/NUDT15-thiopurines, and UGT1A1-irinotecan. Despite the presence of these guidelines, the scientific evidence showing the benefits of pharmacogenetic testing (e.g., improved safety and cost-effectiveness) and the development of efficient multi-gene genotyping panels, routine pretreatment testing for these gene-drug pairs has not been implemented widely in the clinic. Important considerations required for widespread clinical implementation include pharmacogenetic education of physicians, availability or allocation of institutional resources to build an efficient clinical infrastructure, international standardization of guidelines, uniform adoption of guidelines by regulatory agencies leading to genotyping requirements in drug labels, and development of cohesive reimbursement policies for pretreatment genotyping. Without clinical implementation, the potential of pharmacogenetics to improve patient safety remains unfulfilled.
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29
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Identification of pharmacogenetic variants from large scale next generation sequencing data in the Saudi population. PLoS One 2022; 17:e0263137. [PMID: 35089958 PMCID: PMC8797234 DOI: 10.1371/journal.pone.0263137] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 01/12/2022] [Indexed: 11/19/2022] Open
Abstract
It is well documented that drug responses are related to Absorption, Distribution, Metabolism, and Excretion (ADME) characteristics of individual patients. Several studies have identified genetic variability in pharmacogenes, that are either directly responsible for or are associated with ADME, giving rise to individualized treatments. Our objective was to provide a comprehensive overview of pharmacogenetic variation in the Saudi population. We mined next generation sequencing (NGS) data from 11,889 unrelated Saudi nationals, to determine the presence and frequencies of known functional SNP variants in 8 clinically relevant pharmacogenes (CYP2C9, CYP2C19, CYP3A5, CYP4F2, VKORC1, DPYD, TPMT and NUDT15), recommended by the Clinical Pharmacogenetics Implementation Consortium (CPIC), and collectively identified 82 such star alleles. Functionally significant pharmacogenetic variants were prevalent especially in CYP genes (excluding CYP3A5), with 10-44.4% of variants predicted to be inactive or to have decreased activity. In CYP3A5, inactive alleles (87.5%) were the most common. Only 1.8%, 0.7% and 0.7% of NUDT15, TPMT and DPYD variants respectively, were predicted to affect gene activity. In contrast, VKORC1 was found functionally, to be highly polymorphic with 53.7% of Saudi individuals harboring variants predicted to result in decreased activity and 31.3% having variants leading to increased metabolic activity. Furthermore, among the 8 pharmacogenes studied, we detected six rare variants with an aggregated frequency of 1.1%, that among several other ethnicities, were uniquely found in Saudi population. Similarly, within our cohort, the 8 pharmacogenes yielded forty-six novel variants predicted to be deleterious. Based upon our findings, 99.2% of individuals from the Saudi population carry at least one actionable pharmacogenetic variant.
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30
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Lee KH, Kang DY, Kim HH, Kim YJ, Kim HJ, Kim JH, Song EY, Yun J, Kang H. Reducing severe cutaneous adverse and type B adverse drug reactions using pre-stored human leukocyte antigen genotypes. Clin Transl Allergy 2022; 12:e12098. [PMID: 35070271 PMCID: PMC8760506 DOI: 10.1002/clt2.12098] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 11/26/2021] [Accepted: 12/21/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Several type B adverse drug reactions (ADRs), especially severe cutaneous adverse reactions (SCARs), are associated with particular human leukocyte antigen (HLA) genotypes. However, pre-stored HLA information obtained from other clinical workups has not been used to prevent ADRs. We aimed to simulate the preemptive use of pre-stored HLA information in electronic medical records to evaluate whether this information can prevent ADRs. METHODS We analyzed the incidence and the risk of ADRs for selected HLA alleles (HLA-B*57:01, HLA-B*58:01, HLA-A*31:01, HLA-B*15:02, HLA-B*15:11, HLA-B*13:01, HLA-B*59:01, and HLA-A*32:01) and seven drugs (abacavir, allopurinol, carbamazepine, oxcarbazepine, dapsone, methazolamide, and vancomycin) using pre-stored HLA information of transplant patients based on the Pharmacogenomics Knowledge Base guidelines and experts' consensus. RESULTS Among 11,988 HLA-tested transplant patients, 4092 (34.1%) had high-risk HLA alleles, 4583 (38.2%) were prescribed risk drugs, and 580 (4.8%) experienced type B ADRs. Patients with HLA-B*58:01 had a significantly higher incidence of type B ADR and SCARs associated with allopurinol use than that of patients without HLA-B*58:01 (17.2% vs. 11.9%, odds ratio [OR] 1.53 [95% confidence interval {CI} 1.09-2.13], p = 0.001, 2.3% versus 0.3%, OR 7.13 [95% CI 2.19-22.69], p < 0.001). Higher risks of type B ADR and SCARs were observed in patients taking carbamazepine or oxcarbazepine if they had one of HLA-A*31:01, HLA-B*15:02, or HLA-B*15:11 alleles. Vancomycin and dapsone use in HLA-A*32:01 and HLA-B*13:01 carriers, respectively, showed trends toward increased risk of type B ADRs. CONCLUSION Utilization of pre-stored HLA data can prevent type B ADRs including SCARs by screening high-risk patients.
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Affiliation(s)
- Kye Hwa Lee
- Department of Information MedicineAsan Medical CenterSeoulSouth Korea
| | - Dong Yoon Kang
- Drug Safety CenterSeoul National University HospitalSeoulSouth Korea
| | - Hyun Hwa Kim
- Drug Safety CenterSeoul National University HospitalSeoulSouth Korea
| | - Yi Jun Kim
- Institute of Convergence MedicineEwha Womans University Mokdong HospitalSeoulSouth Korea
| | - Hyo Jung Kim
- Department of Digital HealthSamsung Advanced Institute for Health Science and TechnologySungkyunkwan UniversitySeoulSouth Korea
| | - Ju Han Kim
- Seoul National University Biomedical Informatics and Systems Biomedical Informatics Research CenterDivision of Biomedical InformaticsSeoul National University College of MedicineSeoulSouth Korea
| | - Eun Young Song
- Department of Molecular Medicine and Biopharmaceutical SciencesGraduate School of Convergence Science and Technology and College of MedicineMedical Research CenterSeoul National UniversitySeoulSouth Korea
| | - James Yun
- Department of Immunology and RheumatologyNepean HospitalSydneyNew South WalesAustralia
- Faculty of Medicine and HealthThe University of SydneySydneyNew South WalesAustralia
| | - Hye‐Ryun Kang
- Drug Safety CenterSeoul National University HospitalSeoulSouth Korea
- Institute of Allergy and Clinical ImmunologySeoul National University Medical Research CenterSeoul National University College of MedicineSeoulSouth Korea
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31
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Por ED, Selig DJ, Chin GC, DeLuca JP, Oliver TG, Livezey JR. Evaluation of Pharmacogenomics Testing of Cytochrome P450 Enzymes in the Military Health System From 2015 to 2020. Mil Med 2021; 187:1-8. [PMID: 34967404 DOI: 10.1093/milmed/usab098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/01/2021] [Accepted: 03/02/2021] [Indexed: 11/14/2022] Open
Abstract
Pharmacogenomics (PGx) plays a fundamental role in personalized medicine, providing an evidence-based treatment approach centered on the relationship between genomic variations and their effect on drug metabolism. Cytochrome P450 (CYP450) enzymes are responsible for the metabolism of most clinically prescribed drugs and a major source of variability in drug pharmacokinetics and pharmacodynamics. To assess the prevalence of PGx testing within the Military Health System (MHS), testing of specific CYP450 enzymes was evaluated. Data were retrospectively obtained from the Military Health System Management Analysis and Reporting Tool (M2) database. Patient demographics were identified for each test, along with TRICARE status, military treatment facility, clinic, and National Provider Identifier. A total of 929 patients received 1,833 PGx tests, predominantly composed of active duty/guard service members (N = 460; 49.5%), with highest testing rates in the army (51.5%). An even distribution in testing was observed among gender, with the highest rates in Caucasians (41.7%). Of the CYP enzymes assessed, CYP2C19 and CYP2D6 accounted for 87.8% of all PGx CYP testing. The majority of patients were tested in psychiatry clinics (N = 496; 53.4%) and primary care clinics (N = 233; 25.1%), accounting for 56.4% and 24.8% of all tests, respectively. Testing was found to be provider driven, suggesting a lack of a standardized approach to PGx and its application in patient care within the MHS. We initially recommend targeted education and revising testing labels to be more uniform and informative. Long-term recommendations include establishing pharmacy-driven protocols and point-of-care PGx testing to optimize patient outcomes.
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Affiliation(s)
- Elaine D Por
- Experimental Therapeutics, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | - Daniel J Selig
- Experimental Therapeutics, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | - Geoffrey C Chin
- Experimental Therapeutics, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | - Jesse P DeLuca
- Experimental Therapeutics, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | - Thomas G Oliver
- Division of Clinical Pharmacology and Medical Toxicology, Uniformed Services University, Bethesda, MD 20814, USA
| | - Jeffrey R Livezey
- Division of Clinical Pharmacology and Medical Toxicology, Uniformed Services University, Bethesda, MD 20814, USA
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32
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Evaluating the feasibility of performing pharmacogenetic
guided‐medication
therapy management in a retirement community: A prospective, single arm study. JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY 2021. [DOI: 10.1002/jac5.1570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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33
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Wang Y, Xiao F, Chen Y, Xiao LD, Wang LY, Zhan Y, Xiong XL, Zhou G, Liu R, Ouyang DS, Li Z, McLeod HL, Zhang W, Li Q, Liu ZQ, Zhou HH, Yin JY. Analytics of the clinical implementation of pharmacogenomics testing in 12 758 individuals. Clin Transl Med 2021; 11:e586. [PMID: 34841710 PMCID: PMC8571952 DOI: 10.1002/ctm2.586] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/20/2021] [Accepted: 09/14/2021] [Indexed: 11/17/2022] Open
Affiliation(s)
- Yang Wang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Fan Xiao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Yan Chen
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Le-Dong Xiao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Lei-Yun Wang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Yan Zhan
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Xing-Liang Xiong
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Gang Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Rong Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Dong-Sheng Ouyang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Zhi Li
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Howard L McLeod
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Geriatric Oncology Consortium, Tampa, Florida, USA.,USF Taneja College of Pharmacy, Tampa, Florida, USA
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Qing Li
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Zhao-Qian Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Hong-Hao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China
| | - Ji-Ye Yin
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China.,Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, P. R. China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, P. R. China.,National Clinical Research Center for Geriatric Disorders, Changsha, P. R. China.,Hunan Key Laboratory of Precise Diagnosis and Treatment of Gastrointestinal Tumor, Changsha, P. R. China
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34
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Mroz P, Michel S, Allen JD, Meyer T, McGonagle EJ, Carpentier R, Vecchia A, Schlichte A, Bishop JR, Dunnenberger HM, Yohe S, Thyagarajan B, Jacobson PA, Johnson SG. Development and Implementation of In-House Pharmacogenomic Testing Program at a Major Academic Health System. Front Genet 2021; 12:712602. [PMID: 34745204 PMCID: PMC8564018 DOI: 10.3389/fgene.2021.712602] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 09/16/2021] [Indexed: 12/26/2022] Open
Abstract
Pharmacogenomics (PGx) studies how a person's genes affect the response to medications and is quickly becoming a significant part of precision medicine. The clinical application of PGx principles has consistently been cited as a major opportunity for improving therapeutic outcomes. Several recent studies have demonstrated that most individuals (> 90%) harbor PGx variants that would be clinically actionable if prescribed a medication relevant to that gene. In multiple well-conducted studies, the results of PGx testing have been shown to guide therapy choice and dosing modifications which improve treatment efficacy and reduce the incidence of adverse drug reactions (ADRs). Although the value of PGx testing is evident, its successful implementation in a clinical setting presents a number of challenges to molecular diagnostic laboratories, healthcare systems, providers and patients. Different molecular methods can be applied to identify PGx variants and the design of the assay is therefore extremely important. Once the genotyping results are available the biggest technical challenge lies in turning this complex genetic information into phenotypes and actionable recommendations that a busy clinician can effectively utilize to provide better medical care, in a cost-effective, efficient and reliable manner. In this paper we describe a successful and highly collaborative implementation of the PGx testing program at the University of Minnesota and MHealth Fairview Molecular Diagnostic Laboratory and selected Pharmacies and Clinics. We offer detailed descriptions of the necessary components of the pharmacogenomic testing implementation, the development and technical validation of the in-house SNP based multiplex PCR based assay targeting 20 genes and 48 SNPs as well as a separate CYP2D6 copy number assay along with the process of PGx report design, results of the provider and pharmacists usability studies, and the development of the software tool for genotype-phenotype translation and gene-phenotype-drug CPIC-based recommendations. Finally, we outline the process of developing the clinical workflow that connects the providers with the PGx experts within the Molecular Diagnostic Laboratory and the Pharmacy.
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Affiliation(s)
- Pawel Mroz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Stephen Michel
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Josiah D Allen
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, MN, United States
| | - Tim Meyer
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States
| | - Erin J McGonagle
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, MN, United States
| | | | | | | | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, MN, United States.,Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Henry M Dunnenberger
- Mark R Neaman Center for Personalized Medicine Center, NorthShore University HealthSystem, Evanston, IL, United States
| | - Sophia Yohe
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Pamala A Jacobson
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, MN, United States
| | - Steven G Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States
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35
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Clinical implementation of drug metabolizing gene-based therapeutic interventions worldwide. Hum Genet 2021; 141:1137-1157. [PMID: 34599365 DOI: 10.1007/s00439-021-02369-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 09/09/2021] [Indexed: 02/05/2023]
Abstract
Over the last few years, the field of pharmacogenomics has gained considerable momentum. The advances of new genomics and bioinformatics technologies propelled pharmacogenomics towards its implementation in the clinical setting. Since 2007, and especially the last-5 years, many studies have focused on the clinical implementation of pharmacogenomics while identifying obstacles and proposed strategies and approaches for overcoming them in the real world of primary care as well as outpatients and inpatients clinics. Here, we outline the recent pharmacogenomics clinical implementation projects and provide details of the study designs, including the most predominant and innovative, as well as clinical studies worldwide that focus on outpatients and inpatient clinics, and primary care. According to these studies, pharmacogenomics holds promise for improving patients' health in terms of efficacy and toxicity, as well as in their overall quality of life, while simultaneously can contribute to the minimization of healthcare expenditure.
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36
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Tafazoli A, Guchelaar HJ, Miltyk W, Kretowski AJ, Swen JJ. Applying Next-Generation Sequencing Platforms for Pharmacogenomic Testing in Clinical Practice. Front Pharmacol 2021; 12:693453. [PMID: 34512329 PMCID: PMC8424415 DOI: 10.3389/fphar.2021.693453] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
Pharmacogenomics (PGx) studies the use of genetic data to optimize drug therapy. Numerous clinical centers have commenced implementing pharmacogenetic tests in clinical routines. Next-generation sequencing (NGS) technologies are emerging as a more comprehensive and time- and cost-effective approach in PGx. This review presents the main considerations for applying NGS in guiding drug treatment in clinical practice. It discusses both the advantages and the challenges of implementing NGS-based tests in PGx. Moreover, the limitations of each NGS platform are revealed, and the solutions for setting up and management of these technologies in clinical practice are addressed.
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Affiliation(s)
- Alireza Tafazoli
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, Bialystok, Poland
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
- Leiden Network of Personalized Therapeutics, Leiden, Netherlands
| | - Wojciech Miltyk
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Adam J. Kretowski
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Jesse J. Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
- Leiden Network of Personalized Therapeutics, Leiden, Netherlands
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37
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Wake DT, Smith DM, Kazi S, Dunnenberger HM. Pharmacogenomic Clinical Decision Support: A Review, How-to Guide, and Future Vision. Clin Pharmacol Ther 2021; 112:44-57. [PMID: 34365648 PMCID: PMC9291515 DOI: 10.1002/cpt.2387] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 07/28/2021] [Indexed: 02/06/2023]
Abstract
Clinical decision support (CDS) is an essential part of any pharmacogenomics (PGx) implementation. Increasingly, institutions have implemented CDS tools in the clinical setting to bring PGx data into patient care, and several have published their experiences with these implementations. However, barriers remain that limit the ability of some programs to create CDS tools to fit their PGx needs. Therefore, the purpose of this review is to summarize the types, functions, and limitations of PGx CDS currently in practice. Then, we provide an approachable step‐by‐step how‐to guide with a case example to help implementers bring PGx to the front lines of care regardless of their setting. Particular focus is paid to the five “rights” of CDS as a core around designing PGx CDS tools. Finally, we conclude with a discussion of opportunities and areas of growth for PGx CDS.
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Affiliation(s)
- Dyson T Wake
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - D Max Smith
- MedStar Health, Columbia, Maryland, USA.,Georgetown University Medical Center, Washington, DC, USA
| | - Sadaf Kazi
- Georgetown University Medical Center, Washington, DC, USA.,National Center for Human Factors in Healthcare, MedStar Health Research Institute Washington, Washington, DC, USA
| | - Henry M Dunnenberger
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
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38
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Multisite investigation of strategies for the clinical implementation of pre-emptive pharmacogenetic testing. Genet Med 2021; 23:2335-2341. [PMID: 34282303 PMCID: PMC8633054 DOI: 10.1038/s41436-021-01269-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 06/28/2021] [Accepted: 06/29/2021] [Indexed: 11/08/2022] Open
Abstract
PURPOSE The increased availability of clinical pharmacogenetic (PGx) guidelines and decreasing costs for genetic testing have slowly led to increased utilization of PGx testing in clinical practice. Pre-emptive PGx testing, where testing is performed in advance of drug prescribing, is one means to ensure results are available at the time of prescribing decisions. However, the most efficient and effective methods to clinically implement this strategy remain unclear. METHODS In this report, we compare and contrast implementation strategies for pre-emptive PGx testing by 15 early-adopter institutions. We surveyed these groups, collecting data on testing approaches, team composition, and workflow dynamics, in addition to estimated third-party reimbursement rates. RESULTS We found that while pre-emptive PGx testing models varied across sites, institutions shared several commonalities, including methods to identify patients eligible for testing, involvement of a precision medicine clinical team in program leadership, and the implementation of pharmacogenes with Clinical Pharmacogenetics Implementation Consortium guidelines available. Finally, while reimbursement rate data were difficult to obtain, the data available suggested that reimbursement rates for pre-emptive PGx testing remain low. CONCLUSION These findings should inform the establishment of future implementation efforts at institutions considering a pre-emptive PGx testing program.
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Tang NY, Pei X, George D, House L, Danahey K, Lipschultz E, Ratain MJ, O'Donnell PH, Yeo KTJ, van Wijk XMR. Validation of a Large Custom-Designed Pharmacogenomics Panel on an Array Genotyping Platform. J Appl Lab Med 2021; 6:1505-1516. [PMID: 34263311 DOI: 10.1093/jalm/jfab056] [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: 01/05/2021] [Accepted: 05/07/2021] [Indexed: 11/14/2022]
Abstract
BACKGROUND Pharmacogenomics has the potential to improve patient outcomes through predicting drug response. We designed and evaluated the analytical performance of a custom OpenArray® pharmacogenomics panel targeting 478 single-nucleotide variants (SNVs). METHODS Forty Coriell Institute cell line (CCL) DNA samples and DNA isolated from 28 whole-blood samples were used for accuracy evaluation. Genotyping calls were compared to at least 1 reference method: next-generation sequencing, Sequenom MassARRAY®, or Sanger sequencing. For precision evaluation, 23 CCL samples were analyzed 3 times and reproducibility of the assays was assessed. For sensitivity evaluation, 6 CCL samples and 5 whole-blood DNA samples were analyzed at DNA concentrations of 10 ng/µL and 50 ng/µL, and their reproducibility and genotyping call rates were compared. RESULTS For 443 variants, all samples assayed had concordant calls with at least 1 reference genotype and also demonstrated reproducibility. However, 6 of these 443 variants showed an unsatisfactory performance, such as low PCR amplification or insufficient separation of genotypes in scatter plots. Call rates were comparable between 50 ng/µL DNA (99.6%) and 10 ng/µL (99.2%). Use of 10 ng/µL DNA resulted in an incorrect call for a single sample for a single variant. Thus, as recommended by the manufacturer, 50 ng/µL is the preferred concentration for patient genotyping. CONCLUSIONS We evaluated a custom-designed pharmacogenomics panel and found that it reliably interrogated 437 variants. Clinically actionable results from selected variants on this panel are currently used in clinical studies employing pharmacogenomics for clinical decision-making.
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Affiliation(s)
- Nga Yeung Tang
- Department of Pathology, Advanced Technology Clinical Laboratory, The University of Chicago, Chicago, IL
| | - Xun Pei
- Department of Pathology, Advanced Technology Clinical Laboratory, The University of Chicago, Chicago, IL
| | - David George
- Department of Pathology, Advanced Technology Clinical Laboratory, The University of Chicago, Chicago, IL
| | - Larry House
- Department of Pathology, Advanced Technology Clinical Laboratory, The University of Chicago, Chicago, IL
| | - Keith Danahey
- Center for Personalized Therapeutics, The University of Chicago, Chicago, IL.,Center for Research Informatics, The University of Chicago, Chicago, IL
| | - Elizabeth Lipschultz
- Center for Personalized Therapeutics, The University of Chicago, Chicago, IL.,Center for Research Informatics, The University of Chicago, Chicago, IL
| | - Mark J Ratain
- Center for Personalized Therapeutics, The University of Chicago, Chicago, IL.,Department of Medicine, The University of Chicago, Chicago, IL
| | - Peter H O'Donnell
- Center for Personalized Therapeutics, The University of Chicago, Chicago, IL.,Department of Medicine, The University of Chicago, Chicago, IL
| | - Kiang-Teck J Yeo
- Department of Pathology, Advanced Technology Clinical Laboratory, The University of Chicago, Chicago, IL.,Center for Personalized Therapeutics, The University of Chicago, Chicago, IL
| | - Xander M R van Wijk
- Department of Pathology, Advanced Technology Clinical Laboratory, The University of Chicago, Chicago, IL.,Center for Personalized Therapeutics, The University of Chicago, Chicago, IL
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Langmia IM, Just KS, Yamoune S, Brockmöller J, Masimirembwa C, Stingl JC. CYP2B6 Functional Variability in Drug Metabolism and Exposure Across Populations-Implication for Drug Safety, Dosing, and Individualized Therapy. Front Genet 2021; 12:692234. [PMID: 34322158 PMCID: PMC8313315 DOI: 10.3389/fgene.2021.692234] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/14/2021] [Indexed: 12/11/2022] Open
Abstract
Adverse drug reactions (ADRs) are one of the major causes of morbidity and mortality worldwide. It is well-known that individual genetic make-up is one of the causative factors of ADRs. Approximately 14 million single nucleotide polymorphisms (SNPs) are distributed throughout the entire human genome and every patient has a distinct genetic make-up which influences their response to drug therapy. Cytochrome P450 2B6 (CYP2B6) is involved in the metabolism of antiretroviral, antimalarial, anticancer, and antidepressant drugs. These drug classes are commonly in use worldwide and face specific population variability in side effects and dosing. Parts of this variability may be caused by single nucleotide polymorphisms (SNPs) in the CYP2B6 gene that are associated with altered protein expression and catalytic function. Population variability in the CYP2B6 gene leads to changes in drug metabolism which may result in adverse drug reactions or therapeutic failure. So far more than 30 non-synonymous variants in CYP2B6 gene have been reported. The occurrence of these variants show intra and interpopulation variability, thus affecting drug efficacy at individual and population level. Differences in disease conditions and affordability of drug therapy further explain why some individuals or populations are more exposed to CYP2B6 pharmacogenomics associated ADRs than others. Variabilities in drug efficacy associated with the pharmacogenomics of CYP2B6 have been reported in various populations. The aim of this review is to highlight reports from various ethnicities that emphasize on the relationship between CYP2B6 pharmacogenomics variability and the occurrence of adverse drug reactions. In vitro and in vivo studies evaluating the catalytic activity of CYP2B6 variants using various substrates will also be discussed. While implementation of pharmacogenomic testing for personalized drug therapy has made big progress, less data on pharmacogenetics of drug safety has been gained in terms of CYP2B6 substrates. Therefore, reviewing the existing evidence on population variability in CYP2B6 and ADR risk profiles suggests that, in addition to other factors, the knowledge on pharmacogenomics of CYP2B6 in patient treatment may be useful for the development of personalized medicine with regards to genotype-based prescription.
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Affiliation(s)
- Immaculate M. Langmia
- Institute of Clinical Pharmacology, University Hospital of Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Katja S. Just
- Institute of Clinical Pharmacology, University Hospital of Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Sabrina Yamoune
- Institute of Clinical Pharmacology, University Hospital of Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Jürgen Brockmöller
- Department of Clinical Pharmacology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany
| | - Collen Masimirembwa
- African Institute of Biomedical Science and Technology (AiBST), Harare, Zimbabwe
| | - Julia C. Stingl
- Institute of Clinical Pharmacology, University Hospital of Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
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Schilff M, Sargsyan Y, Hofhuis J, Thoms S. Stop Codon Context-Specific Induction of Translational Readthrough. Biomolecules 2021; 11:biom11071006. [PMID: 34356630 PMCID: PMC8301745 DOI: 10.3390/biom11071006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/28/2021] [Accepted: 07/01/2021] [Indexed: 12/11/2022] Open
Abstract
Premature termination codon (PTC) mutations account for approximately 10% of pathogenic variants in monogenic diseases. Stimulation of translational readthrough, also known as stop codon suppression, using translational readthrough-inducing drugs (TRIDs) may serve as a possible therapeutic strategy for the treatment of genetic PTC diseases. One important parameter governing readthrough is the stop codon context (SCC)-the stop codon itself and the nucleotides in the vicinity of the stop codon on the mRNA. However, the quantitative influence of the SCC on treatment outcome and on appropriate drug concentrations are largely unknown. Here, we analyze the readthrough-stimulatory effect of various readthrough-inducing drugs on the SCCs of five common premature termination codon mutations of PEX5 in a sensitive dual reporter system. Mutations in PEX5, encoding the peroxisomal targeting signal 1 receptor, can cause peroxisomal biogenesis disorders of the Zellweger spectrum. We show that the stop context has a strong influence on the levels of readthrough stimulation and impacts the choice of the most effective drug and its concentration. These results highlight potential advantages and the personalized medicine nature of an SCC-based strategy in the therapy of rare diseases.
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Affiliation(s)
- Mirco Schilff
- Department of Child and Adolescent Health, University Medical Center, 37075 Göttingen, Germany; (M.S.); (Y.S.); (J.H.)
| | - Yelena Sargsyan
- Department of Child and Adolescent Health, University Medical Center, 37075 Göttingen, Germany; (M.S.); (Y.S.); (J.H.)
| | - Julia Hofhuis
- Department of Child and Adolescent Health, University Medical Center, 37075 Göttingen, Germany; (M.S.); (Y.S.); (J.H.)
- Department of Biochemistry and Molecular Medicine, Medical School, Bielefeld University, 33615 Bielefeld, Germany
| | - Sven Thoms
- Department of Child and Adolescent Health, University Medical Center, 37075 Göttingen, Germany; (M.S.); (Y.S.); (J.H.)
- Department of Biochemistry and Molecular Medicine, Medical School, Bielefeld University, 33615 Bielefeld, Germany
- Correspondence: ; Tel.: +49-521-106-86502
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Lopes GS, Bielinski S, Moyer AM, Jacobson DJ, Wang L, Jiang R, Larson NB, Miller VM, Zhu Y, Cavanaugh DC, St Sauver J. Sex differences in type and occurrence of adverse reactions to opioid analgesics: a retrospective cohort study. BMJ Open 2021; 11:e044157. [PMID: 34193479 PMCID: PMC8246359 DOI: 10.1136/bmjopen-2020-044157] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVES Sex as a biological variable affects response to opioids. However, few reports describe the prevalence of specific adverse reactions to commonly prescribed opioids in men and women separately. A large cohort was used to investigate sex differences in type and occurrence of adverse reactions associated with use of codeine, tramadol, oxycodone and hydrocodone. DESIGN Retrospective cohort study. SETTING Participants in the Right Drug, Right Dose, Right Time (RIGHT) Study. PARTICIPANTS The medical records of 8457 participants in the RIGHT Study who received an opioid prescription between 1 January 2004 and 31 December 2017 were reviewed 61% women, 94% white, median age (Q1-Q3)=58 (47-66). PRIMARY AND SECONDARY OUTCOME MEASURES Adverse reactions including gastrointestinal, skin, psychiatric and nervous system issues were collected from the allergy section of each patient's medical record. Sex differences in the risk of adverse reactions due to prescribed opioids were modelled using logistic regression adjusted for age, body mass index, race and ethnicity. RESULTS From 8457 participants (of which 449 (5.3%) reported adverse reactions), more women (6.5%) than men (3.4%) reported adverse reactions to at least one opioid (OR (95% CI)=2.3 (1.8 to 2.8), p<0.001). Women were more likely to report adverse reactions to tramadol (OR (95% CI)=2.8 (1.8 to 4.4), p<0.001) and oxycodone (OR (95% CI)=2.2 (1.7 to 2.9), p<0.001). Women were more likely to report gastrointestinal (OR (95% CI)=3.1 (2.3 to 4.3), p<0.001), skin (OR (95% CI)=2.1 (1.4 to 3.3), p=0.001) and nervous system issues (OR (95% CI)=2.3 (1.3 to 4.2), p=0.004). CONCLUSIONS These findings support the importance of sex as a biological variable to be factored into pain management studies.
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Affiliation(s)
- Guilherme S Lopes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Suzette Bielinski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Ann M Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Debra J Jacobson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Liwei Wang
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Ruoxiang Jiang
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Nicholas B Larson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Virginia M Miller
- Department of Surgery and Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Ye Zhu
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Dana C Cavanaugh
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Jennifer St Sauver
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
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Implementing Pharmacogenomics Testing: Single Center Experience at Arkansas Children's Hospital. J Pers Med 2021; 11:jpm11050394. [PMID: 34064668 PMCID: PMC8150685 DOI: 10.3390/jpm11050394] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 02/07/2023] Open
Abstract
Pharmacogenomics (PGx) is a growing field within precision medicine. Testing can help predict adverse events and sub-therapeutic response risks of certain medications. To date, the US FDA lists over 280 drugs which provide biomarker-based dosing guidance for adults and children. At Arkansas Children’s Hospital (ACH), a clinical PGx laboratory-based test was developed and implemented to provide guidance on 66 pediatric medications for genotype-guided dosing. This PGx test consists of 174 single nucleotide polymorphisms (SNPs) targeting 23 clinically actionable PGx genes or gene variants. Individual genotypes are processed to provide per-gene discrete results in star-allele and phenotype format. These results are then integrated into EPIC- EHR. Genomic indicators built into EPIC-EHR provide the source for clinical decision support (CDS) for clinicians, providing genotype-guided dosing.
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Eghbali N, Alhanai T, Ghassemi MM. Patient-Specific Sedation Management via Deep Reinforcement Learning. Front Digit Health 2021; 3:608893. [PMID: 34713090 PMCID: PMC8521809 DOI: 10.3389/fdgth.2021.608893] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 02/04/2021] [Indexed: 02/04/2023] Open
Abstract
Introduction: Developing reliable medication dosing guidelines is challenging because individual dose-response relationships are mitigated by both static (e. g., demographic) and dynamic factors (e.g., kidney function). In recent years, several data-driven medication dosing models have been proposed for sedatives, but these approaches have been limited in their ability to assess interindividual differences and compute individualized doses. Objective: The primary objective of this study is to develop an individualized framework for sedative-hypnotics dosing. Method: Using publicly available data (1,757 patients) from the MIMIC IV intensive care unit database, we developed a sedation management agent using deep reinforcement learning. More specifically, we modeled the sedative dosing problem as a Markov Decision Process and developed an RL agent based on a deep deterministic policy gradient approach with a prioritized experience replay buffer to find the optimal policy. We assessed our method's ability to jointly learn an optimal personalized policy for propofol and fentanyl, which are among commonly prescribed sedative-hypnotics for intensive care unit sedation. We compared our model's medication performance against the recorded behavior of clinicians on unseen data. Results: Experimental results demonstrate that our proposed model would assist clinicians in making the right decision based on patients' evolving clinical phenotype. The RL agent was 8% better at managing sedation and 26% better at managing mean arterial compared to the clinicians' policy; a two-sample t-test validated that these performance improvements were statistically significant (p < 0.05). Conclusion: The results validate that our model had better performance in maintaining control variables within their target range, thereby jointly maintaining patients' health conditions and managing their sedation.
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Affiliation(s)
- Niloufar Eghbali
- Human Augmentation and Artificial Intelligence Laboratory, Department of Computer Science, Michigan State University, East Lansing, MI, United States
| | - Tuka Alhanai
- Laboratory for Computer-Human Intelligence, Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Mohammad M. Ghassemi
- Human Augmentation and Artificial Intelligence Laboratory, Department of Computer Science, Michigan State University, East Lansing, MI, United States
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Liko I, Corbin L, Tobin E, Aquilante CL, Lee YM. Implementation of a pharmacist-provided pharmacogenomics service in an executive health program. Am J Health Syst Pharm 2021; 78:1094-1103. [PMID: 33772264 DOI: 10.1093/ajhp/zxab137] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
PURPOSE We describe the implementation of a pharmacist-provided pharmacogenomics (PGx) service in an executive health program (EHP) at an academic medical center. SUMMARY As interest in genomic testing grows, pharmacists have the opportunity to advance the use of PGx in EHPs, in collaboration with other healthcare professionals. In November 2018, a pharmacist-provided PGx service was established in the EHP at the University of Colorado Hospital. The team members included 3 physicians, a pharmacist trained in PGx, a registered dietitian/exercise physiologist, a nurse, and 2 medical assistants. We conducted 4 preimplementation steps: (1) assessment of the patient population, (2) selection of a PGx test, (3) establishment of a visit structure, and (4) selection of a billing model. The PGx consultations involved two 1-hour visits. The first visit encompassed pretest PGx education, review of the patient's current medications and previous medication intolerances, and DNA sample collection for genotyping. After this visit, the pharmacist developed a therapeutic plan based on the PGx test results, discussed the results and plan with the physician, and created a personalized PGx report. At the second visit, the pharmacist reviewed the PGx test results, personalized the PGx report, and discussed the PGx-guided therapeutic plan with the patient. Overall, the strategy worked well; minor challenges included evaluation of gene-drug pairs with limited PGx evidence, communication of information to non-EHP providers, scheduling issues, and reimbursement. CONCLUSION The addition of a PGx service within an EHP was feasible and provided pharmacists the opportunity to lead PGx efforts and collaborate with physicians to expand the precision medicine footprint at an academic medical center.
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Affiliation(s)
- Ina Liko
- Department of Pharmaceutical Sciences, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO,USA
| | - Lisa Corbin
- Division of General Internal Medicine, University of Colorado School of Medicine, Aurora, CO,USA
| | - Eric Tobin
- Department of Pharmaceutical Sciences, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO,USA
| | - Christina L Aquilante
- Department of Pharmaceutical Sciences, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO,USA
| | - Yee Ming Lee
- Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO,USA
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Patel JN, Boselli D, Jandrisevits EJ, Hamadeh IS, Salem A, Meadors P, Walsh D. Potentially actionable pharmacogenetic variants and symptom control medications in oncology. Support Care Cancer 2021; 29:5927-5934. [PMID: 33758969 DOI: 10.1007/s00520-021-06170-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 03/18/2021] [Indexed: 01/26/2023]
Abstract
PURPOSE We estimated the prevalence of potentially actionable pharmacogenetic (PGx) variants related to symptom control medications (SCMs) based on institutional prescribing patterns and correlated presenting symptoms with SCM prescribing. METHODS This was a retrospective study of adult ambulatory cancer patients undergoing electronic distress screening (EDS) within 90 days of intake to the cancer hospital. We estimated the proportion prescribed SCM(s) with PGx evidence within 90 days of intake. Those with potentially actionable variants were estimated using population frequency data from 1000 genomes. The expected number at risk of altered drug response was estimated. The associations between symptom scores and SCM(s) were estimated with logistic regression and threshold analyses performed with receiver operating characteristic (ROC) curves. RESULTS Of 6985 patients, 3222 (46%) received ≥ one SCM. Of these, 2760 (86%) received SCM(s) with PGx evidence for CYP2B6, CYP2C19, CYP2D6, or SLC6A4; 2719 (84%) received a drug metabolized by CYP2D6, most commonly hydrocodone (40.4%), ondansetron (35.6%), oxycodone (24.2%), and/or tramadol (7.1%). Based on this, about one quarter were expected to have altered metabolism and/or drug response. One third were prescribed two or more SCMs with PGx evidence. About half reported at least one severe symptom, which significantly correlated with SCM prescribing (p < 0.001). Threshold scores were identified that highly correlated with SCM prescribing for anxiety, depression, nausea, neuropathy, pain, and sleep. CONCLUSION About half presented with significant symptom burden, which highly correlated with SCM prescribing. Most received SCMs with PGx evidence. Preemptive PGx testing for these variants should be evaluated in prospective trials to evaluate the impact on symptom control.
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Affiliation(s)
- Jai N Patel
- Department of Cancer Pharmacology & Pharmacogenomics, Atrium Health Levine Cancer Institute, 1021 Morehead Medical Drive, Charlotte, NC, 28204, USA.
| | - Danielle Boselli
- Department of Biostatistics, Atrium Health Levine Cancer Institute, Charlotte, NC, USA
| | - Elizabeth J Jandrisevits
- Department of Cancer Pharmacology & Pharmacogenomics, Atrium Health Levine Cancer Institute, 1021 Morehead Medical Drive, Charlotte, NC, 28204, USA
| | - Issam S Hamadeh
- Department of Cancer Pharmacology & Pharmacogenomics, Atrium Health Levine Cancer Institute, 1021 Morehead Medical Drive, Charlotte, NC, 28204, USA
| | - Ahmed Salem
- Department of Cancer Pharmacology & Pharmacogenomics, Atrium Health Levine Cancer Institute, 1021 Morehead Medical Drive, Charlotte, NC, 28204, USA
| | - Patrick Meadors
- Section of Psycho-oncology, Center for Supportive Care and Survivorship, Atrium Health Levine Cancer Institute, Charlotte, NC, USA
| | - Declan Walsh
- Department of Supportive Oncology, Atrium Health Levine Cancer Institute, Charlotte, NC, USA
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Zhang L, Sarangi V, Ho MF, Moon I, Kalari KR, Wang L, Weinshilboum RM. SLCO1B1: Application and Limitations of Deep Mutational Scanning for Genomic Missense Variant Function. Drug Metab Dispos 2021; 49:395-404. [PMID: 33658230 PMCID: PMC8042483 DOI: 10.1124/dmd.120.000264] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 02/17/2021] [Indexed: 01/07/2023] Open
Abstract
SLCO1B1 (solute carrier organic anion transporter family member 1B1) is an important transmembrane hepatic uptake transporter. Genetic variants in the SLCO1B1 gene have been associated with altered protein folding, resulting in protein degradation and decreased transporter activity. Next-generation sequencing (NGS) of pharmacogenes is being applied increasingly to associate variation in drug response with genetic sequence variants. However, it is difficult to link variants of unknown significance with functional phenotypes using "one-at-a-time" functional systems. Deep mutational scanning (DMS) using a "landing pad cell-based system" is a high-throughput technique designed to analyze hundreds of gene open reading frame (ORF) missense variants in a parallel and scalable fashion. We have applied DMS to analyze 137 missense variants in the SLCO1B1 ORF obtained from the Exome Aggregation Consortium project. ORFs containing these variants were fused to green fluorescent protein and were integrated into "landing pad" cells. Florescence-activated cell sorting was performed to separate the cells into four groups based on fluorescence readout indicating protein expression at the single cell level. NGS was then performed and SLCO1B1 variant frequencies were used to determine protein abundance. We found that six variants not previously characterized functionally displayed less than 25% and another 12 displayed approximately 50% of wild-type protein expression. These results were then functionally validated by transporter studies. Severely damaging variants identified by DMS may have clinical relevance for SLCO1B1-dependent drug transport, but we need to exercise caution since the relatively small number of severely damaging variants identified raise questions with regard to the application of DMS to intrinsic membrane proteins such as organic anion transporter protein 1B1. SIGNIFICANCE STATEMENT: The functional implications of a large numbers of open reading frame (ORF) "variants of unknown significance" (VUS) in transporter genes have not been characterized. This study applied deep mutational scanning to determine the functional effects of VUS that have been observed in the ORF of SLCO1B1(s olute carrier organic anion transporter family member 1B1). Several severely damaging variants were identified, studied, and validated. These observations have implications for both the application of deep mutational scanning to intrinsic membrane proteins and for the clinical effect of drugs and endogenous compounds transported by SLCO1B1.
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Affiliation(s)
- Lingxin Zhang
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics (L.Z., M.-F.H., I.M., L.W., R.M.W.), Division of Biomedical Statistics and Informatics, Department of Health Sciences Research (V.S., K.R.K.), and Mayo Clinic Center for Individualized Medicine (L.W., R.M.W.), Mayo Clinic, Rochester, Minnesota
| | - Vivekananda Sarangi
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics (L.Z., M.-F.H., I.M., L.W., R.M.W.), Division of Biomedical Statistics and Informatics, Department of Health Sciences Research (V.S., K.R.K.), and Mayo Clinic Center for Individualized Medicine (L.W., R.M.W.), Mayo Clinic, Rochester, Minnesota
| | - Ming-Fen Ho
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics (L.Z., M.-F.H., I.M., L.W., R.M.W.), Division of Biomedical Statistics and Informatics, Department of Health Sciences Research (V.S., K.R.K.), and Mayo Clinic Center for Individualized Medicine (L.W., R.M.W.), Mayo Clinic, Rochester, Minnesota
| | - Irene Moon
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics (L.Z., M.-F.H., I.M., L.W., R.M.W.), Division of Biomedical Statistics and Informatics, Department of Health Sciences Research (V.S., K.R.K.), and Mayo Clinic Center for Individualized Medicine (L.W., R.M.W.), Mayo Clinic, Rochester, Minnesota
| | - Krishna R Kalari
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics (L.Z., M.-F.H., I.M., L.W., R.M.W.), Division of Biomedical Statistics and Informatics, Department of Health Sciences Research (V.S., K.R.K.), and Mayo Clinic Center for Individualized Medicine (L.W., R.M.W.), Mayo Clinic, Rochester, Minnesota
| | - Liewei Wang
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics (L.Z., M.-F.H., I.M., L.W., R.M.W.), Division of Biomedical Statistics and Informatics, Department of Health Sciences Research (V.S., K.R.K.), and Mayo Clinic Center for Individualized Medicine (L.W., R.M.W.), Mayo Clinic, Rochester, Minnesota
| | - Richard M Weinshilboum
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics (L.Z., M.-F.H., I.M., L.W., R.M.W.), Division of Biomedical Statistics and Informatics, Department of Health Sciences Research (V.S., K.R.K.), and Mayo Clinic Center for Individualized Medicine (L.W., R.M.W.), Mayo Clinic, Rochester, Minnesota
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Caraballo PJ, Sutton JA, Giri J, Wright JA, Nicholson WT, Kullo IJ, Parkulo MA, Bielinski SJ, Moyer AM. Integrating pharmacogenomics into the electronic health record by implementing genomic indicators. J Am Med Inform Assoc 2021; 27:154-158. [PMID: 31591640 DOI: 10.1093/jamia/ocz177] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 08/19/2019] [Accepted: 09/11/2019] [Indexed: 12/27/2022] Open
Abstract
Pharmacogenomics (PGx) clinical decision support integrated into the electronic health record (EHR) has the potential to provide relevant knowledge to clinicians to enable individualized care. However, past experience implementing PGx clinical decision support into multiple EHR platforms has identified important clinical, procedural, and technical challenges. Commercial EHRs have been widely criticized for the lack of readiness to implement precision medicine. Herein, we share our experiences and lessons learned implementing new EHR functionality charting PGx phenotypes in a unique repository, genomic indicators, instead of using the problem or allergy list. The Gen-Ind has additional features including a brief description of the clinical impact, a hyperlink to the original laboratory report, and links to additional educational resources. The automatic generation of genomic indicators from interfaced PGx test results facilitates implementation and long-term maintenance of PGx data in the EHR and can be used as criteria for synchronous and asynchronous CDS.
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Affiliation(s)
- Pedro J Caraballo
- Division of General Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph A Sutton
- Department of Information Technology, Mayo Clinic, Rochester, Minnesota
| | - Jyothsna Giri
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Jessica A Wright
- Department of Pharmacy Services, Mayo Clinic, Rochester, Minnesota, USA
| | - Wayne T Nicholson
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark A Parkulo
- Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, USA
- Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Suzette J Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Ann M Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
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Takahashi PY, Ryu E, Bielinski SJ, Hathcock M, Jenkins GD, Cerhan JR, Olson JE. No Association Between Pharmacogenomics Variants and Hospital and Emergency Department Utilization: A Mayo Clinic Biobank Retrospective Study. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2021; 14:229-237. [PMID: 33603442 PMCID: PMC7886254 DOI: 10.2147/pgpm.s281645] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 12/29/2020] [Indexed: 02/06/2023]
Abstract
Background The use of pharmacogenomics data is increasing in clinical practice. However, it is unknown if pharmacogenomics data can be used more broadly to predict outcomes like hospitalization or emergency department (ED) visit. We aim to determine the association between selected pharmacogenomics phenotypes and hospital utilization outcomes (hospitalization and ED visits). Methods This cohort study utilized 10,078 patients from the Mayo Clinic Biobank in the RIGHT protocol with sequence and interpreted phenotypes for 10 selected pharmacogenes including CYP2D6, CYP2C9, CYP2C19, CYP3A5, HLA B 5701, HLA B 5702, HLA B 5801, TPMT, SLCO1B1, and DPYD. The primary outcome was hospitalization with ED visits as a secondary outcome. We used Cox proportional hazards model to test the association between each pharmacogenomics phenotype and the risk of the outcomes. Results During the follow-up period (median [in years] = 7.3), 13% (n=1354) and 8% (n=813) of the subjects experienced hospitalization and ED visits, respectively. Compared to subjects who did not experience hospitalization, hospitalized patients were older (median age [in years]: 67 vs 65), poorer self-rated health (15% vs 4.7% for fair/poor), and higher disease burden (median number of chronic conditions: 7 vs 4) at baseline. There was no association of hospitalization with any of the pharmacogenomics phenotypes. The pharmacogenomics phenotypes were not associated with disease burden, a well-established risk factor for hospital utilization outcomes. Similar findings were observed for patients with ED visits during the follow-up period. Conclusion We found no association of 10 well-established pharmacogenomics phenotypes with either hospitalization or ED visits in this relatively large biobank population and outside the context of specific drug use related to these genes. Traditional risk factors for hospitalization like age and self-rated health were much more likely to predict hospitalization and/or ED visits than this pharmacogenomics information.
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Affiliation(s)
- Paul Y Takahashi
- Division of Community Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Euijung Ryu
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Suzette J Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Matthew Hathcock
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Gregory D Jenkins
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - James R Cerhan
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Janet E Olson
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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Wake DT, Bell GC, Gregornik DB, Ho TT, Dunnenberger HM. Synthesis of major pharmacogenomics pretest counseling themes: a multisite comparison. Pharmacogenomics 2021; 22:165-176. [PMID: 33461326 DOI: 10.2217/pgs-2020-0168] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The accessibility of pharmacogenomic (PGx) testing has grown substantially over the last decade and with it has arisen a demand for patients to be counseled on the use of these tests. While guidelines exist for the use of PGx results; objective determinants for who should receive PGx testing remain incomplete. PGx clinical services have been created to meet these screening and education needs and significant variability exists between these programs. This article describes the practices of four PGx clinics during pretest counseling sessions. A description of the major tenets of the benefits, limitations and risks of testing are compiled. Additional tools are provided to serve as a foundation for those wishing to begin or expand their own counseling service.
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Affiliation(s)
- Dyson T Wake
- Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Gillian C Bell
- Genetics & Personalized Medicine Department, Mission Health, Asheville, NC 28803, USA
| | - David B Gregornik
- Pharmacogenomics Program, Children's Minnesota, Minneapolis, MN 55404, USA
| | - Teresa T Ho
- Department of Pharmacotherapeutics & Clinical Research, University of South Florida Taneja College of Pharmacy, Tampa, FL 33612, USA
| | - Henry M Dunnenberger
- Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
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