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Frye MA, Nemeroff CB. Reply to Sun: Dynamic and accessible pharmacogenomic results: a response to pharmacogenomic testing for antidepressant treatment selection. Neuropsychopharmacology 2024; 49:770-771. [PMID: 38361000 PMCID: PMC10948788 DOI: 10.1038/s41386-024-01815-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/17/2024]
Affiliation(s)
- Mark A Frye
- Department of Psychiatry & Psychology, Mayo Clinic, Mayo Clinic Alix School of Medicine, Rochester, MN, USA.
| | - Charles B Nemeroff
- Department of Psychiatry & Behavioral Sciences, University of Texas at Austin Dell Medical School, Austin, TX, USA
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2
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Peruzzi E, Roncato R, De Mattia E, Bignucolo A, Swen JJ, Guchelaar HJ, Toffoli G, Cecchin E. Implementation of pre-emptive testing of a pharmacogenomic panel in clinical practice: Where do we stand? Br J Clin Pharmacol 2023. [PMID: 37926674 DOI: 10.1111/bcp.15956] [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: 09/13/2023] [Revised: 10/19/2023] [Accepted: 10/20/2023] [Indexed: 11/07/2023] Open
Abstract
Adverse drug reactions (ADRs) account for a large proportion of hospitalizations among adults and are more common in multimorbid patients, worsening clinical outcomes and burdening healthcare resources. Over the past decade, pharmacogenomics has been developed as a practical tool for optimizing treatment outcomes by mitigating the risk of ADRs. Some single-gene reactive tests are already used in clinical practice, including the DPYD test for fluoropyrimidines, which demonstrates how integrating pharmacogenomic data into routine care can improve patient safety in a cost-effective manner. The evolution from reactive single-gene testing to comprehensive pre-emptive genotyping panels holds great potential for refining drug prescribing practices. Several implementation projects have been conducted to test the feasibility of applying different genetic panels in clinical practice. Recently, the results of a large prospective randomized trial in Europe (the PREPARE study by Ubiquitous Pharmacogenomics consortium) have provided the first evidence that prospective application of a pre-emptive pharmacogenomic test panel in clinical practice, in seven European healthcare systems, is feasible and yielded a 30% reduction in the risk of developing clinically relevant toxicities. Nevertheless, some important questions remain unanswered and will hopefully be addressed by future dedicated studies. These issues include the cost-effectiveness of applying a pre-emptive genotyping panel, the role of multiple co-medications, the transferability of currently tested pharmacogenetic guidelines among patients of non-European origin and the impact of rare pharmacogenetic variants that are not detected by currently used genotyping approaches.
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Affiliation(s)
- Elena Peruzzi
- Experimental and Clinical Pharmacology, Centro di Riferimento Oncologico di Aviano, Istituti di Ricovero e Cura a Carattere Scientifico, Aviano, Italy
| | - Rossana Roncato
- Experimental and Clinical Pharmacology, Centro di Riferimento Oncologico di Aviano, Istituti di Ricovero e Cura a Carattere Scientifico, Aviano, Italy
- Department of Medicine, University of Udine, Udine, Italy
| | - Elena De Mattia
- Experimental and Clinical Pharmacology, Centro di Riferimento Oncologico di Aviano, Istituti di Ricovero e Cura a Carattere Scientifico, Aviano, Italy
| | - Alessia Bignucolo
- Experimental and Clinical Pharmacology, Centro di Riferimento Oncologico di Aviano, Istituti di Ricovero e Cura a Carattere Scientifico, Aviano, Italy
| | - Jesse J Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands
| | - Giuseppe Toffoli
- Experimental and Clinical Pharmacology, Centro di Riferimento Oncologico di Aviano, Istituti di Ricovero e Cura a Carattere Scientifico, Aviano, Italy
| | - Erika Cecchin
- Experimental and Clinical Pharmacology, Centro di Riferimento Oncologico di Aviano, Istituti di Ricovero e Cura a Carattere Scientifico, Aviano, Italy
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Abstract
Pharmacogenomics is increasingly important to guide objective, safe, and effective individualised prescribing. Personalised prescribing has revolutionised treatments in the past decade, allowing clinicians to maximise drug efficacy and minimise adverse effects based on a person’s genetic profile. Opioids, the gold standard for cancer pain relief, are among the commonest medications prescribed in palliative care practice. This narrative review examines the literature surrounding opioid pharmacogenomics and its applicability to the palliative care cancer population. There is currently limited intersection between the fields of palliative care and pharmacogenomics, but growing evidence presents a need to build linkages between the two disciplines. Pharmacogenomic evidence guiding opioid prescribing is currently available for codeine and tramadol, which relates to CYP2D6 gene variants. However, these medications are prescribed less commonly for pain in palliative care. Research is accelerating with other opioids, where oxycodone (CYP2D6) and methadone (CYP2B6, ABCB1) already have moderate evidence of an association in terms of drug metabolism and downstream analgesic response and side effects. OPRM1 and COMT are receiving increasing attention and have implications for all opioids, with changes in opioid dosage requirements observed but they have not yet been studied widely enough to be considered clinically actionable. Current evidence indicates that incorporation of pharmacogenomic testing into opioid prescribing practice should focus on the CYP2D6 gene and its actionable variants. Although opioid pharmacogenomic tests are not widely used in clinical practice, the progressively reducing costs and rapid turnover means greater accessibility and affordability to patients, and thus, clinicians will be increasingly asked to provide guidance in this area. The upsurge in pharmacogenomic research will likely discover more actionable gene variants to expand international guidelines to impact opioid prescribing. This rapidly expanding area requires consideration and monitoring by clinicians in order for key findings with clinical implications to be accessible, meaningfully interpretable and communicated.
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Effect Modification by Social Determinants of Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions within an Integrated U.S. Healthcare System. J Pers Med 2022; 12:jpm12071145. [PMID: 35887642 PMCID: PMC9319564 DOI: 10.3390/jpm12071145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/09/2022] [Accepted: 07/12/2022] [Indexed: 11/30/2022] Open
Abstract
The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel, we included additional covariates to evaluate aggregate contribution of social determinants and medical comorbidity with the presence of identified gene-x-drug interactions to moderate 90-day hospital readmission (primary outcome). Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45−10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61−4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77−3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88−2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47−3.07) (p < 0.0001), median household income (OR = 1.63, 1.03−2.58) (p = 0.035), male gender (OR = 1.47, 1.21−1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18−1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08−1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk.
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5
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A Review of Prader–Willi Syndrome. ENDOCRINES 2022. [DOI: 10.3390/endocrines3020027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Prader–Willi Syndrome (PWS, OMIM #176270) is a rare complex genetic disorder due to the loss of expression of paternally derived genes in the PWS critical region on chromosome 15q11-q13. It affects multiple neuroendocrine systems and may present failure to thrive in infancy, but then, hyperphagia and morbid obesity starting in early childhood became the hallmark of this condition. Short stature, hypogonadism, sleep abnormalities, intellectual disability, and behavioral disturbances highlight the main features of this syndrome. There have been a significant number of advances in our understanding of the genetic mechanisms underlying the disease, especially discoveries of MAGEL2, NDN, MKRN3, and SNORD116 genes in the pathophysiology of PWS. However, early diagnosis and difficulty in treating some of the disease’s most disabling features remain challenging. As our understanding of PWS continues to grow, so does the availability of new therapies and management strategies available to clinicians and families.
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6
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Crutchley RD, Keuler N. Sub-Analysis of CYP-GUIDES Data: Assessing the Prevalence and Impact of Drug-Gene Interactions in an Ethnically Diverse Cohort of Depressed Individuals. Front Pharmacol 2022; 13:884213. [PMID: 35496293 PMCID: PMC9039251 DOI: 10.3389/fphar.2022.884213] [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: 02/25/2022] [Accepted: 03/23/2022] [Indexed: 12/02/2022] Open
Abstract
Introduction: Minority groups are underrepresented in pharmacogenomics (PGx) research. Recent sub-analysis of CYP-GUIDES showed reduced length of stay (LOS) in depressed patients with CYP2D6 sub-functional status. Our primary objective was to determine whether PGx guided (G) versus standard treatment (S) influenced LOS among different race/ethnic groups. Secondary objectives included prevalence of drug-gene interactions (DGIs) and readmission rates (RAR). Methods: Retrospective sub-analysis of CYP-GUIDES data comprising CYP2D6 phenotypes was reclassified using standardized CYP2D6 genotype to phenotype recommendations from the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Dutch Pharmacogenetics Working Group (DPWG). The Mann-Whitney test was used to determine differences in LOS between groups G and S and Kruskal Wallis test to compare LOS among different race/ethnic groups. Logistic regression was used to determine covariates associated with RAR. Results: This study included 1,459 patients with 67.3% in G group (n = 982). The majority of patients were White (57.5%), followed by Latinos (25.6%) and Blacks (12.3%). Although there were no differences in LOS between G and S groups, Latinos had significant shorter LOS than Whites (p = 0.002). LOS was significantly reduced by 5.6 days in poor metabolizers in group G compared to S (p = 0.002). The proportion of supra functional and ultra-rapid metabolizers (UMs) were 6 and 20.3% using CYP-GUIDES and CPIC/DPWG definitions, respectively. Prevalence of DGIs was 40% with significantly fewer DGIs in Blacks (p < 0.001). Race/ethnicity was significantly associated with RAR (aOR 1.30; p = 0.003). Conclusion: A greater number of patients were classified as CYP2D6 UMs using CPIC/DPWG definitions as compared to CYP-GUIDES definitions. This finding may have clinical implications for using psychotropics metabolized by CYP2D6.
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Affiliation(s)
- Rustin D. Crutchley
- Department of Pharmacotherapy, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Yakima, WA, United States
- *Correspondence: Rustin D. Crutchley,
| | - Nicole Keuler
- School of Pharmacy, University of the Western Cape, Cape Town, South Africa
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7
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David SP, Singh L, Pruitt J, Hensing A, Hulick P, Meltzer DO, O’Donnell PH, Dunnenberger HM. The Contribution of Pharmacogenetic Drug Interactions to 90-Day Hospital Readmissions: Preliminary Results from a Real-World Healthcare System. J Pers Med 2021; 11:jpm11121242. [PMID: 34945714 PMCID: PMC8705172 DOI: 10.3390/jpm11121242] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/21/2021] [Accepted: 11/21/2021] [Indexed: 01/09/2023] Open
Abstract
Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines exist for many medications commonly prescribed prior to hospital discharge, yet there are limited data regarding the contribution of gene-x-drug interactions to hospital readmissions. The present study evaluated the relationship between prescription of CPIC medications prescribed within 30 days of hospital admission and 90-day hospital readmission from 2010 to 2020 in a study population (N = 10,104) who underwent sequencing with a 14-gene pharmacogenetic panel. The presence of at least one pharmacogenetic indicator for a medication prescribed within 30 days of hospital admission was considered a gene-x-drug interaction. Multivariable logistic regression analyzed the association between one or more gene-x-drug interactions with 90-day readmission. There were 2211/2354 (93.9%) admitted patients who were prescribed at least one CPIC medication. Univariate analyses indicated that the presence of at least one identified gene-x-drug interaction increased the risk of 90-day readmission by more than 40% (OR = 1.42, 95% confidence interval (CI) 1.09–1.84) (p = 0.01). A multivariable model adjusting for age, race, sex, employment status, body mass index, and medical conditions slightly attenuated the effect (OR = 1.32, 95% CI 1.02–1.73) (p = 0.04). Our results suggest that the presence of one or more CPIC gene-x-drug interactions increases the risk of 90-day hospital readmission, even after adjustment for demographic and clinical risk factors.
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Affiliation(s)
- Sean P. David
- Department of Family Medicine, NorthShore University Health System, Evanston, IL 60201, USA;
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA; (P.H.); (D.O.M.); (P.H.O.)
- Correspondence:
| | - Lavisha Singh
- Department of Statistics, NorthShore University Health System, Evanston, IL 60201, USA;
| | - Jaclyn Pruitt
- Department of Surgery, NorthShore University Health System, Evanston, IL 60201, USA;
- Outcomes Research Network, NorthShore University Health System, Evanston, IL 60201, USA;
| | - Andrew Hensing
- Outcomes Research Network, NorthShore University Health System, Evanston, IL 60201, USA;
| | - Peter Hulick
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA; (P.H.); (D.O.M.); (P.H.O.)
- Center for Personalized Medicine, NorthShore University Health System, Evanston, IL 60201, USA
| | - David O. Meltzer
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA; (P.H.); (D.O.M.); (P.H.O.)
| | - Peter H. O’Donnell
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA; (P.H.); (D.O.M.); (P.H.O.)
| | - Henry M. Dunnenberger
- Department of Family Medicine, NorthShore University Health System, Evanston, IL 60201, USA;
- Outcomes Research Network, NorthShore University Health System, Evanston, IL 60201, USA;
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8
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Takahashi PY, Ryu E, Cerhan JR, Bielinski SJ, Olson JE. Pathway to Ascertain the Role of Pharmacogenomics in Healthcare Utilization Outcomes [Response to Letter]. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2021; 14:545-546. [PMID: 33986611 PMCID: PMC8111333 DOI: 10.2147/pgpm.s316851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 04/21/2021] [Indexed: 11/23/2022]
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
| | - James R Cerhan
- Division of Epidemiology, 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
| | - Janet E Olson
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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9
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Ruaño G, Tortora J, Robinson S, Baker S, Holford T, Winokur A, Goethe JW. Subanalysis of the CYP-GUIDES Trial: CYP2D6 Functional Stratification and Operational Timeline Selection. Psychiatry Res 2021; 297:113571. [PMID: 33513485 DOI: 10.1016/j.psychres.2020.113571] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 11/10/2020] [Indexed: 02/02/2023]
Abstract
CYP-GUIDES (Cytochrome Psychotropic Genotyping Under Investigation for Decision Support) was a Randomized Controlled Trial comparing 2 outcomes in hospitalized patients with major depressive disorder treated according to the patient's CYP2D6 genotype and functional status versus standard psychotropic therapy. The primary outcome was hospital Length of Stay (LOS) and the secondary was Re-Admission Rate (RAR) 30 days after discharge. Methodology, total results and database of the trial have been published. Here we present a subanalysis that isolated 3 confounders to assess the impact of CYP2D6 therapeutic guidance on LOS: a single Electronic Medical Record, a minimum 3-day LOS, and CYP2D6 functional stratification of patients. CYP2D6 functional stratification enabled subgrouping patients and comparing outcomes according to CYP2D6 functionality within Group G and Group S. Subfunctional patients evidenced a 2-day shorter LOS in Group G compared to Group S. Drug administration for subfunctional patients in Group S evidenced a higher percentage of CYP2D6 substrate psychotropics being prescribed as well as a greater number of prescriptions than in functional patients. We conclude that there was an effect of pharmacogenetic clinical decision support that reduced LOS in patients with CYP2D6 subfunctional status and reduced prescribing of CYP2D6 substrate dependent drugs.
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Affiliation(s)
- Gualberto Ruaño
- Institute of Living at Hartford Hospital, 67 Jefferson Street, Hartford, CT 06106, United States; Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, United States.
| | - Joseph Tortora
- Research Administration, Hartford Hospital, 80 Seymour Street, Hartford, CT 06102, United States.
| | - Saskia Robinson
- Research Administration, Hartford Hospital, 80 Seymour Street, Hartford, CT 06102, United States.
| | - Seth Baker
- Information Technology Services, Hartford Healthcare, 5 Batterson Park Road, Farmington, CT 06032, United States.
| | - Theodore Holford
- Yale School of Public Health, Yale University, 1 Church Street, 6th Floor Suite, New Haven, CT 06510, United States.
| | - Andrew Winokur
- Department of Psychiatry, University of Connecticut School of Medicine, 263 Farmington Avenue, Farmington, CT 06030, United States.
| | - John W Goethe
- Private medical practice, 155 Ayrshire Lane, Avon, CT 06001, United States.
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10
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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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11
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Forster J, Duis J, Butler MG. Pharmacogenetic Testing of Cytochrome P450 Drug Metabolizing Enzymes in a Case Series of Patients with Prader-Willi Syndrome. Genes (Basel) 2021; 12:genes12020152. [PMID: 33498922 PMCID: PMC7912498 DOI: 10.3390/genes12020152] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 01/15/2021] [Accepted: 01/21/2021] [Indexed: 02/08/2023] Open
Abstract
Prader-Willi syndrome (PWS) is associated with co-morbid psychiatric symptoms (disruptive behavior, anxiety, mood disorders, and psychosis) often requiring psychotropic medications. In this clinical case series of 35 patients with PWS, pharmacogenetic testing was obtained to determine allele frequencies predicting variations in activity of cytochrome (CYP) P450 drug metabolizing enzymes 2D6, 2B6, 2C19, 2C9, 3A4, and 1A2. Results were deidentified, collated, and analyzed by PWS genetic subtype: 14 deletion (DEL), 16 maternal uniparental disomy (UPD) and 5 DNA-methylation positive unspecified molecular subtype (PWS Unspec). Literature review informed comparative population frequencies of CYP polymorphisms, phenotypes, and substrate specificity. Among the total PWS cohort, extensive metabolizer (EM) activity prevailed across all cytochromes except CYP1A2, which showed greater ultra-rapid metabolizer (UM) status (p < 0.05), especially among UPD. Among PWS genetic subtypes, there were statistically significant differences in metabolizing status for cytochromes 2D6, 2C19, 2C9, 3A4 and 1A2 acting on substrates such as fluoxetine, risperidone, sertraline, modafinil, aripiprazole, citalopram, and escitalopram. Gonadal steroid therapy may further impact metabolism of 2C19, 2C9, 3A4 and 1A2 substrates. The status of growth hormone treatment may affect CYP3A4 activity with gender specificity. Pharmacogenetic testing together with PWS genetic subtyping may inform psychotropic medication dosing parameters and risk for adverse events.
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Affiliation(s)
- Janice Forster
- Pittsburgh Partnership, PWS, Pittsburgh, PA 15218, USA
- Correspondence:
| | - Jessica Duis
- Section of Genetic and Inherited Metabolic Disease, Department of Pediatrics, Children’s Hospital Colorado, Aurora, CO 80045, USA;
| | - Merlin G. Butler
- Division of Research and Genetics, Departments of Psychiatry & Behavioral Sciences and Pediatrics, University of Kansas Medical Center, Kansas City, KS 66160, USA;
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12
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Boyle J, Stock CJ. CYP2D6 Expression in Veterans Experiencing Opioid Overdose: A Postmortem Review. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2020; 13:289-293. [PMID: 32848444 PMCID: PMC7429207 DOI: 10.2147/pgpm.s261424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 07/20/2020] [Indexed: 12/20/2022]
Abstract
Purpose The purpose is to characterize the CYP2D6 genotype and predict the phenotype of decedents of opioid overdose to determine if the ultrarapid (UM) phenotype is over-represented in opioid overdose deaths. CYP2D6 is the enzyme responsible for metabolism of various opioids implicated in overdose. The UM group may be at greater risk for overdose due to the rapid metabolism of hydrocodone, oxycodone, or tramadol to more active/potent metabolites than their peers with (poor) PM, (intermediate) IM, or (extensive) EM metabolic phenotypes. Patients and Methods Blood samples obtained during autopsy following an opioid overdose from 75 US military veteran decedents prescribed hydrocodone, oxycodone, or tramadol from one VA medical center were analyzed. DNA extraction, CYP2D6 genotyping, and copy number variation (CNV) testing were performed using the iPLEX® genotyping assay and MassARRAY. Phenotype prediction was based on Clinical Pharmacogenetics Implementation Consortium (CPIC) recommendations. Toxicology results were obtained from Medical Examiner reports of the deceased. Prescription medication information was extracted from archived medical records. Results The majority of the sample had a phenotype of EM metabolizer (75%), with 7% of the total sample having a UM metabolizer phenotype. In addition to hydrocodone, oxycodone, and tramadol (found in 41% of opioid positive samples), other opioids found in toxicology tests included diacetylmorphine, fentanyl, buprenorphine, and methadone. Two or more substances, including alcohol, benzodiazepines, and other potentially sedating medications, were found in nearly half of the opioid positive toxicology samples. Conclusion In this study, 7% of veteran decedents of opioid overdose had CYP2D6 UM metabolic phenotype. The small sample size precludes a conclusion that the frequency of UM phenotype is greater than expected in North American Caucasian groups. The findings in this study do not support the hypothesis that the UM phenotype is over-represented in opioid overdose.
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Affiliation(s)
- Julia Boyle
- Department of Pharmacy, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.,Department of Pharmacy Practice, Idaho State University College of Pharmacy, Meridian, ID, USA
| | - Christopher J Stock
- Department of Pharmacy, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
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13
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Rafi I, Crinson I, Dawes M, Rafi D, Pirmohamed M, Walter FM. The implementation of pharmacogenomics into UK general practice: a qualitative study exploring barriers, challenges and opportunities. J Community Genet 2020; 11:269-277. [PMID: 32468238 PMCID: PMC7295877 DOI: 10.1007/s12687-020-00468-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 04/21/2020] [Indexed: 11/04/2022] Open
Abstract
Pharmacogenomics describes interpatient genetic variability in drug responses. Information based on whole genome sequencing will soon open up the field of pharmacogenomics and facilitate the use of genomic information relating to drug metabolism and drug responses. We undertook a qualitative study, aiming to explore the potential barriers, opportunities and challenges facing the implementation of pharmacogenomics into primary care. Semi-structured interviews were undertaken with 18 clinical participants (16 GPs and 2 other clinicians). All interviews were recorded and transcribed verbatim. Using a thematic analysis approach, data items were coded, ordered and themes constructed. Most participants were aged 55–60 years and worked as part-time clinical GPs with other clearly defined roles. The emerging themes covered several areas of concern, including the following: the utility of pharmacogenomics and the value of introducing such testing into primary care; how to educate the primary care workforce and ‘mainstream’ pharmacogenomics; the ethical, legal and social aspects of pharmacogenomics and its impact on patients; and potential impacts on the healthcare system particularly around economics and informatics. Most participants had concerns about pharmacogenomics and felt that there were a number of barriers and challenges to its implementation into routine primary care. Most striking were their concerns around the cost-effectiveness of using pharmacogenomics in primary care. At the same time most recognised the increasing availability of direct-to-consumer testing, and felt that this would drive the need to understand the ethical and social implications of using genomic information in primary care. This study has raised important issues that need to be considered when planning the implementation of pharmacogenomics into clinical practice. Prior to the implementation of genomic testing into day-to-day practice in UK primary care, it is important that considerations around education, cost-effectiveness and informatics are addressed, as well as the impact on patients.
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Affiliation(s)
- I Rafi
- St George's, University of London, London, UK.
| | - I Crinson
- St George's, University of London, London, UK
| | - M Dawes
- Department of Family Practice, University of British Columbia, Vancouver, Canada
| | - D Rafi
- University of Birmingham, Birmingham, UK
| | - M Pirmohamed
- Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK
| | - F M Walter
- The Primary Care Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge, UK
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14
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Bielinski SJ, St Sauver JL, Olson JE, Larson NB, Black JL, Scherer SE, Bernard ME, Boerwinkle E, Borah BJ, Caraballo PJ, Curry TB, Doddapaneni H, Formea CM, Freimuth RR, Gibbs RA, Giri J, Hathcock MA, Hu J, Jacobson DJ, Jones LA, Kalla S, Koep TH, Korchina V, Kovar CL, Lee S, Liu H, Matey ET, McGree ME, McAllister TM, Moyer AM, Muzny DM, Nicholson WT, Oyen LJ, Qin X, Raj R, Roger VL, Rohrer Vitek CR, Ross JL, Sharp RR, Takahashi PY, Venner E, Walker K, Wang L, Wang Q, Wright JA, Wu TJ, Wang L, Weinshilboum RM. Cohort Profile: The Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment Protocol (RIGHT Protocol). Int J Epidemiol 2020; 49:23-24k. [PMID: 31378813 PMCID: PMC7124480 DOI: 10.1093/ije/dyz123] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2019] [Indexed: 12/29/2022] Open
Affiliation(s)
- Suzette J Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Jennifer L St Sauver
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Robert D and Patricia E Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Janet E Olson
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Nicholas B Larson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - John L Black
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Steven E Scherer
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Eric Boerwinkle
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bijan J Borah
- Robert D and Patricia E Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
- Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Pedro J Caraballo
- Division of General Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Timothy B Curry
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Anesthesia and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Robert R Freimuth
- Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Jyothsna Giri
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Matthew A Hathcock
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Jianhong Hu
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Debra J Jacobson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Leila A Jones
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Sara Kalla
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | | | - Viktoriya Korchina
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Christie L Kovar
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Sandra Lee
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Hongfang Liu
- Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Eric T Matey
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Pharmacy, Mayo Clinic, Rochester, MN, USA
| | - Michaela E McGree
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Ann M Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Donna M Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Wayne T Nicholson
- Department of Anesthesia and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA
| | - Lance J Oyen
- Department of Pharmacy, Mayo Clinic, Rochester, MN, USA
| | - Xiang Qin
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Ritika Raj
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Véronique L Roger
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Division of Cardiovascular Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Richard R Sharp
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Eric Venner
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Kimberly Walker
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Liwei Wang
- Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Qiaoyan Wang
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jessica A Wright
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Pharmacy, Mayo Clinic, Rochester, MN, USA
| | - Tsung-Jung Wu
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Liewei Wang
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Richard M Weinshilboum
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
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15
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Ruaño G, Robinson S, Holford T, Mehendru R, Baker S, Tortora J, Goethe JW. Results of the CYP-GUIDES randomized controlled trial: Total cohort and primary endpoints. Contemp Clin Trials 2020; 89:105910. [PMID: 31838256 DOI: 10.1016/j.cct.2019.105910] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 11/19/2019] [Accepted: 12/10/2019] [Indexed: 12/27/2022]
Abstract
CYP-GUIDES (Cytochrome Psychotropic Genotyping Under Investigation for Decision Support) is a randomized controlled trial (RCT) comparing 2 outcomes in hospitalized patients with major depressive disorder (MDD) treated according to the patient's CYP2D6 genotype and functional status versus standard psychotropic therapy. The primary outcome was hospital Length of Stay (LOS) and the secondary outcome was the Re-Admission Rate (RAR) 30 days after discharge. The trial setting was the Institute of Living at Hartford Hospital. CYP2D6 genotyping was implemented to detect common polymorphisms resulting in an enzyme with sub-normal, normal or supra-normal function. The electronic medical record (EMR) was utilized to transmit clinically actionable drug prescribing guidance based on the patient's CYP2D6 genotype to the physician. The RCT recruited 1500 patients, genotyped CYP2D6 in 1459, and randomized 477 to standard therapy (Group S), for whom treatment-as-usual guidance was delivered without consideration of patient CYP2D6 genotype, and 982 to genetically-guided therapy (Group G) where CYP2D6-based treatment recommendations were provided via EMR to physicians. For inpatients in Group G whose CYP2D6 function was sub- or supra-normal, medications primarily metabolized by the CYP2D6 enzyme were proscribed. The RCT developed a database of potential benefit to the field. The genetic, pharmacologic and clinical information is being simultaneously published. Results did not reveal differences in LOS or RAR between Group G and Group S, but confounders may have obscured the effects of pharmacogenetic guidance. We present strategies to assess effects of pharmacogenetic guidance on the most vulnerable patients at either extreme of CYP2D6 function treated with multiple psychotropics.
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Affiliation(s)
- Gualberto Ruaño
- Institute of Living at Hartford Hospital, Genomas Laboratory of Personalized Health, 67 Jefferson Street, Hartford, CT 06106, United States.
| | - Saskia Robinson
- Research Administration, Hartford Hospital, 80 Seymour Street, Hartford, CT 06102, United States.
| | - Theodore Holford
- Yale School of Public Health, Yale University, 1 Church Street, 6th Floor Suite, New Haven, CT 06510, United States.
| | - Raveen Mehendru
- Institute of Living at Hartford Hospital, 200 Retreat Avenue, Hartford, CT 06114, United States.
| | - Seth Baker
- IInformation Technology Services, Hartford Healthcare, 5 Batterson Park Road, Farmington, CT 06032, United States.
| | - Joseph Tortora
- Research Administration, Hartford Hospital, 80 Seymour Street, Hartford, CT 06102, United States.
| | - John W Goethe
- Private Medical Practice, 155 Ayrshire Lane, Avon, CT 06001, United States
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16
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Dunham WC, Weinger MB, Slagle J, Pretorius M, Shah AS, Absi TS, Shotwell MS, Beller M, Thomas E, Vnencak-Jones CL, Freundlich RE, Wanderer JP, Sandberg WS, Kertai MD. CYP2D6 Genotype-guided Metoprolol Therapy in Cardiac Surgery Patients: Rationale and Design of the Pharmacogenetic-guided Metoprolol Management for Postoperative Atrial Fibrillation in Cardiac Surgery (PREEMPTIVE) Pilot Study. J Cardiothorac Vasc Anesth 2019; 34:20-28. [PMID: 31606278 DOI: 10.1053/j.jvca.2019.09.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 08/29/2019] [Accepted: 09/04/2019] [Indexed: 12/25/2022]
Abstract
OBJECTIVES The Preemptive Pharmacogenetic-guided Metoprolol Management for Atrial Fibrillation in Cardiac Surgery (PREEMPTIVE) pilot trial aims to use existing institutional resources to develop a process for integrating CYP2D6 pharmacogenetic test results into the patient electronic health record, to develop an evidence-based clinical decision support tool to facilitate CYP2D6 genotype-guided metoprolol administration in the cardiac surgery setting, and to determine the impact of implementing this CYP2D6 genotype-guided integrated approach on the incidence of postoperative atrial fibrillation (AF), provider, and cost outcomes. DESIGN One-arm Bayesian adaptive design clinical trial. SETTING Single center, university hospital. PARTICIPANTS The authors will screen (including CYP2D6 genotype) up to 600 (264 ± 144 expected under the adaptive design) cardiac surgery patients, and enroll up to 200 (88 ± 48 expected) poor, intermediate, and ultrarapid CYP2D6 metabolizers over a period of 2 years at a tertiary academic center. INTERVENTIONS All consented and enrolled patients will receive the intervention of CYP2D6 genotype-guided metoprolol management based on CYP2D6 phenotype classified as a poor, intermediate, extensive (normal), or ultrarapid metabolizer. MEASUREMENTS AND MAIN RESULTS The primary outcome will be the incidence of postoperative AF. Secondary outcomes relating to rates of CYP2D6 genotype-guided prescription changes, costs, lengths of stay, and implementation metrics also will be investigated. CONCLUSIONS The PREEMPTIVE pilot study is the first perioperative pilot trial to provide essential information for the design of a future, large-scale trial comparing CYP2D6 genotype-guided metoprolol management with a nontailored strategy in terms of managing AF. In addition, secondary outcomes regarding implementation, clinical benefit, safety, and cost-effectiveness in patients undergoing cardiac surgery will be examined.
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Affiliation(s)
- Wills C Dunham
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Matthew B Weinger
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA; Center for Research and Innovation in System Safety, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jason Slagle
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA; Center for Research and Innovation in System Safety, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mias Pretorius
- Department of anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ashish S Shah
- Department of Cardiac Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Tarek S Absi
- Department of Cardiac Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Matthew S Shotwell
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Marc Beller
- Center for Precision Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Erica Thomas
- Department of Pharmaceutical Services, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Cindy L Vnencak-Jones
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Robert E Freundlich
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan P Wanderer
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Warren S Sandberg
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Miklos D Kertai
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA.
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17
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Pharmacogenetic clinical decision support for psychiatric hospitalization: Design of the CYP-GUIDES randomized controlled trial. Contemp Clin Trials 2019; 83:27-36. [DOI: 10.1016/j.cct.2019.06.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 06/04/2019] [Accepted: 06/19/2019] [Indexed: 12/28/2022]
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