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Estévez-Paredes M, Mata-Martín MC, de Andrés F, LLerena A. Pharmacogenomic biomarker information on drug labels of the Spanish Agency of Medicines and Sanitary products: evaluation and comparison with other regulatory agencies. THE PHARMACOGENOMICS JOURNAL 2024; 24:2. [PMID: 38233388 DOI: 10.1038/s41397-023-00321-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 10/07/2023] [Accepted: 12/05/2023] [Indexed: 01/19/2024]
Abstract
This work aimed to analyse the pharmacogenetic information in the Spanish Drug Regulatory Agency (AEMPS) Summary of Products Characteristics (SmPC), evaluating the presence of pharmacogenetic biomarkers, as well as the associated recommendations. A total of 55.4% of the 1891 drug labels reviewed included information on pharmacogenetic biomarker(s). Pharmacogenomic information appears most frequently in the "antineoplastic and immunomodulating agents", "nervous system", and "cardiovascular system" Anatomical Therapeutic Chemical groups. A total of 509 different pharmacogenetic biomarkers were found, of which CYP450 enzymes accounted for almost 34% of the total drug-biomarker associations evaluated. A total of 3679 drug-biomarker pairs were identified, 102 of which were at the 1A level (PharmGKB® classification system), and 33.33% of these drug-pharmacogenetic biomarker pairs were assigned to "actionable PGx", 12.75% to "informative PGx", 4.9% to "testing recommended", and 4.9% to "testing required". The rate of coincidence in the assigned PGx level of recommendation between the AEMPS and regulatory agencies included in the PharmGKB® Drug Label Annotations database (i.e., the FDA, EMA, SWISS Medic, PMDA, and HCSC) ranged from 45% to 65%, being 'actionable level' the most frequent. On the other hand, discrepancies between agencies did not exceed 35%. This study highlights the presence of relevant pharmacogenetic information on Spanish drug labels, which would help avoid interactions, toxicity, or lack of treatment efficacy.
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Affiliation(s)
- María Estévez-Paredes
- INUBE Extremadura Biosanitary Research Institute, Badajoz, Spain
- CICAB Clinical Research Centre, Pharmacogenetics and Personalized Medicine Unit, Badajoz University Hospital, Extremadura Health Service, Badajoz, Spain
| | - M Carmen Mata-Martín
- INUBE Extremadura Biosanitary Research Institute, Badajoz, Spain
- CICAB Clinical Research Centre, Pharmacogenetics and Personalized Medicine Unit, Badajoz University Hospital, Extremadura Health Service, Badajoz, Spain
| | - Fernando de Andrés
- INUBE Extremadura Biosanitary Research Institute, Badajoz, Spain
- CICAB Clinical Research Centre, Pharmacogenetics and Personalized Medicine Unit, Badajoz University Hospital, Extremadura Health Service, Badajoz, Spain
- Department of Analytical Chemistry and Food technology, Faculty of Pharmacy, University of Castilla-La Mancha, Albacete, Spain
| | - Adrián LLerena
- INUBE Extremadura Biosanitary Research Institute, Badajoz, Spain.
- CICAB Clinical Research Centre, Pharmacogenetics and Personalized Medicine Unit, Badajoz University Hospital, Extremadura Health Service, Badajoz, Spain.
- Faculty of Medicine, University of Extremadura, Badajoz, Spain.
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Pharmacogenomic Biomarkers in US FDA-Approved Drug Labels (2000-2020). J Pers Med 2021; 11:jpm11030179. [PMID: 33806453 PMCID: PMC8000585 DOI: 10.3390/jpm11030179] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 02/21/2021] [Accepted: 02/26/2021] [Indexed: 12/21/2022] Open
Abstract
Pharmacogenomics (PGx) is a key subset of precision medicine that relates genomic variation to individual response to pharmacotherapy. We assessed longitudinal trends in US FDA approval of new drugs labeled with PGx information. Drug labels containing PGx information were obtained from Drugs@FDA and guidelines from PharmGKB were used to compare the actionability of PGx information in drug labels across therapeutic areas. The annual proportion of new drug approvals with PGx labeling has increased by nearly threefold from 10.3% (n = 3) in 2000 to 28.2% (n = 11) in 2020. Inclusion of PGx information in drug labels has increased for all clinical areas over the last two decades but most prominently for cancer therapies, which comprise the largest proportion (75.5%) of biomarker–drug pairs for which PGx testing is required. Clinically actionable information was more frequently observed in biomarker–drug pairs associated with cancer drugs compared to those for other therapeutic areas (n = 92 (59.7%) vs. n = 62 (40.3%), p < 0.0051). These results suggest that further evidence is needed to support the clinical adoption of pharmacogenomics in non-cancer therapeutic areas.
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Jeiziner C, Suter K, Wernli U, Barbarino JM, Gong L, Whirl-Carrillo M, Klein TE, Szucs TD, Hersberger KE, Meyer zu Schwabedissen HE. Pharmacogenetic information in Swiss drug labels - a systematic analysis. THE PHARMACOGENOMICS JOURNAL 2021; 21:423-434. [PMID: 33070160 PMCID: PMC8292148 DOI: 10.1038/s41397-020-00195-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 08/18/2020] [Accepted: 10/05/2020] [Indexed: 01/31/2023]
Abstract
Implementation of pharmacogenetics (PGx) and individualization of drug therapy is supposed to obviate adverse drug reactions or therapy failure. Health care professionals (HCPs) use drug labels (DLs) as reliable information about drugs. We analyzed the Swiss DLs to give an overview on the currently available PGx instructions. We screened 4306 DLs applying natural language processing focusing on drug metabolism (pharmacokinetics) and we assigned PGx levels following the classification system of PharmGKB. From 5979 hits, 2564 were classified as PGx-relevant affecting 167 substances. 55% (n = 93) were classified as "actionable PGx". Frequently, PGx information appeared in the pharmacokinetics section and in DLs of the anatomic group "nervous system". Unstandardized wording, appearance of PGx information in different sections and unclear instructions challenge HCPs to identify and interpret PGx information and translate it into practice. HCPs need harmonization and standardization of PGx information in DLs to personalize drug therapies and tailor pharmaceutical care.
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Affiliation(s)
- C. Jeiziner
- grid.6612.30000 0004 1937 0642Pharmaceutical Care Research Group, Department of Pharmaceutical Sciences, University of Basel, Basel, 4001 Switzerland
| | - K. Suter
- grid.6612.30000 0004 1937 0642European Center of Pharmaceutical Medicine, Faculty of Medicine, University of Basel, Basel, 4056 Switzerland
| | - U. Wernli
- grid.6612.30000 0004 1937 0642Pharmaceutical Care Research Group, Department of Pharmaceutical Sciences, University of Basel, Basel, 4001 Switzerland
| | - J. M. Barbarino
- grid.168010.e0000000419368956Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305 USA
| | - L. Gong
- grid.168010.e0000000419368956Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305 USA
| | - M. Whirl-Carrillo
- grid.168010.e0000000419368956Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305 USA
| | - T. E. Klein
- grid.168010.e0000000419368956Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305 USA ,grid.168010.e0000000419368956Department of Medicine, Stanford University, Stanford, CA 94305 USA
| | - T. D. Szucs
- grid.6612.30000 0004 1937 0642European Center of Pharmaceutical Medicine, Faculty of Medicine, University of Basel, Basel, 4056 Switzerland
| | - K. E. Hersberger
- grid.6612.30000 0004 1937 0642Pharmaceutical Care Research Group, Department of Pharmaceutical Sciences, University of Basel, Basel, 4001 Switzerland
| | - H. E. Meyer zu Schwabedissen
- grid.6612.30000 0004 1937 0642Biopharmacy, Department of Pharmaceutical Sciences, University of Basel, Basel, 4056 Switzerland
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Shugg T, Pasternak AL, London B, Luzum JA. Prevalence and types of inconsistencies in clinical pharmacogenetic recommendations among major U.S. sources. NPJ Genom Med 2020; 5:48. [PMID: 33145028 PMCID: PMC7603298 DOI: 10.1038/s41525-020-00156-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 10/05/2020] [Indexed: 12/30/2022] Open
Abstract
Clinical implementation of pharmacogenomics (PGx) is slow. Previous studies have identified some inconsistencies among clinical PGx recommendations, but the prevalence and types of inconsistencies have not been comprehensively analyzed among major PGx guidance sources in the U.S. PGx recommendations from the Clinical Pharmacogenetics Implementation Consortium, U.S. Food and Drug Administration drug labels, and major U.S. professional medical organizations were analyzed through May 24, 2019. Inconsistencies were analyzed within the following elements: recommendation category; whether routine screening was recommended; and the specific biomarkers, variants, and patient groups involved. We identified 606 total clinical PGx recommendations, which contained 267 unique drugs. Composite inconsistencies occurred in 48.1% of clinical PGx recommendations overall, and in 93.3% of recommendations from three sources. Inconsistencies occurred in the recommendation category (29.8%), the patient group (35.4%), and routine screening (15.2%). In conclusion, almost one-half of clinical PGx recommendations from prominent U.S. guidance sources contain inconsistencies, which can potentially slow clinical implementation.
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Affiliation(s)
- Tyler Shugg
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI USA.,Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, IN USA
| | - Amy L Pasternak
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI USA
| | - Bianca London
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI USA.,Senior Health Services at Blue Cross Blue Shield of Michigan Emerging Markets, Southfield, MI USA
| | - Jasmine A Luzum
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI USA
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Varnai R, Szabo I, Tarlos G, Szentpeteri LJ, Sik A, Balogh S, Sipeky C. Pharmacogenomic biomarker information differences between drug labels in the United States and Hungary: implementation from medical practitioner view. THE PHARMACOGENOMICS JOURNAL 2019; 20:380-387. [PMID: 31787752 PMCID: PMC7253355 DOI: 10.1038/s41397-019-0123-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Revised: 11/12/2019] [Accepted: 11/17/2019] [Indexed: 02/07/2023]
Abstract
Pharmacogenomic biomarker availability of Hungarian Summaries of Product Characteristics (SmPC) was assembled and compared with the information in US Food and Drug Administration (FDA) drug labels of the same active substance (July 2019). The level of action of these biomarkers was assessed from The Pharmacogenomics Knowledgebase database. From the identified 264 FDA approved drugs with pharmacogenomic biomarkers in drug label, 195 are available in Hungary. From them, 165 drugs include pharmacogenomic data disposing 222 biomarkers. Most of them are metabolizing enzymes (46%) and pharmacological targets (41%). The most frequent therapeutic area is oncology (37%), followed by infectious diseases (12%) and psychiatry (9%) (p < 0.00001). Most common biomarkers in Hungarian SmPCs are CYP2D6, CYP2C19, estrogen and progesterone hormone receptor (ESR, PGS). Importantly, US labels present more specific pharmacogenomic subheadings, the level of action has a different prominence, and offer more applicable dose modifications than Hungarians (5% vs 3%). However, Hungarian SmPCs are at 9 oncology drugs stricter than FDA, testing is obligatory before treatment. Out of the biomarkers available in US drug labels, 62 are missing completely from Hungarian SmPCs (p < 0.00001). Most of these belong to oncology (42%) and in case of 11% of missing biomarkers testing is required before treatment. In conclusion, more factual, clear, clinically relevant pharmacogenomic information in Hungarian SmPCs would reinforce implementation of pharmacogenetics. Underpinning future perspective is to support regulatory stakeholders to enhance inclusion of pharmacogenomic biomarkers into Hungarian drug labels and consequently enhance personalized medicine in Hungary.
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Affiliation(s)
- Reka Varnai
- Department of Primary Health Care, Medical School, University of Pécs, H-7623, Pécs, Rákóczi u 2, Hungary.,Doctoral School of Health Sciences, Faculty of Health Sciences, University of Pécs, H-7621, Pécs, Vörösmarty u 4, Hungary
| | - Istvan Szabo
- Institute of Sport Sciences and Physical Education, University of Pécs, H-7624, Pécs, Ifjúság útja 6, Hungary.,Faculty of Sciences, Doctoral School of Biology and Sportbiology, University of Pécs, H-7624, Pécs, Ifjúság útja 6, Hungary
| | - Greta Tarlos
- Faculty of Pharmacy, University of Pécs, H-7624, Pécs, Rokus u 2, Hungary
| | - Laszlo Jozsef Szentpeteri
- Institute of Transdisciplinary Discoveries, Medical School, University of Pécs, H-7624, Pécs, Szigeti út 12, Hungary
| | - Attila Sik
- Institute of Transdisciplinary Discoveries, Medical School, University of Pécs, H-7624, Pécs, Szigeti út 12, Hungary
| | - Sandor Balogh
- Department of Primary Health Care, Medical School, University of Pécs, H-7623, Pécs, Rákóczi u 2, Hungary
| | - Csilla Sipeky
- Insitute of Biomedicine, University of Turku, Kiinamyllynkatu 10, FI-20520, Turku, Finland.
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Lauschke VM, Ingelman-Sundberg M. The Importance of Patient-Specific Factors for Hepatic Drug Response and Toxicity. Int J Mol Sci 2016; 17:E1714. [PMID: 27754327 PMCID: PMC5085745 DOI: 10.3390/ijms17101714] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 09/23/2016] [Accepted: 09/27/2016] [Indexed: 02/07/2023] Open
Abstract
Responses to drugs and pharmacological treatments differ considerably between individuals. Importantly, only 50%-75% of patients have been shown to react adequately to pharmacological interventions, whereas the others experience either a lack of efficacy or suffer from adverse events. The liver is of central importance in the metabolism of most drugs. Because of this exposed status, hepatotoxicity is amongst the most common adverse drug reactions and hepatic liabilities are the most prevalent reason for the termination of development programs of novel drug candidates. In recent years, more and more factors were unveiled that shape hepatic drug responses and thus underlie the observed inter-individual variability. In this review, we provide a comprehensive overview of different principle mechanisms of drug hepatotoxicity and illustrate how patient-specific factors, such as genetic, physiological and environmental factors, can shape drug responses. Furthermore, we highlight other parameters, such as concomitantly prescribed medications or liver diseases and how they modulate drug toxicity, pharmacokinetics and dynamics. Finally, we discuss recent progress in the field of in vitro toxicity models and evaluate their utility in reflecting patient-specific factors to study inter-individual differences in drug response and toxicity, as this understanding is necessary to pave the way for a patient-adjusted medicine.
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Affiliation(s)
- Volker M Lauschke
- Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, SE-17177 Stockholm, Sweden.
| | - Magnus Ingelman-Sundberg
- Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, SE-17177 Stockholm, Sweden.
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Vivot A, Boutron I, Ravaud P, Porcher R. Guidance for pharmacogenomic biomarker testing in labels of FDA-approved drugs. Genet Med 2014; 17:733-8. [PMID: 25521333 DOI: 10.1038/gim.2014.181] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2014] [Accepted: 11/11/2014] [Indexed: 12/26/2022] Open
Abstract
PURPOSE The aim of this study was to compare guidance for genetic testing in US Food and Drug Administration (FDA)-approved drug labels in oncology to those of drugs for other therapeutic areas. METHODS We reviewed labels of all the FDA-approved drugs with labels containing pharmacogenomic information. We assessed whether genetic testing was required or recommended before prescription and, if not, the reason for pharmacogenomic labeling. RESULTS We included 140 drugs corresponding to 158 drug-biomarker pairs. Overall, 46 (29%) of 158 pairs stated a requirement or recommendation for genetic biomarker testing in the label. This proportion was higher in oncology than in other areas (62 vs. 12%; P < 0.001). For the 112 drug-biomarker pairs (including 20 in oncology) without recommendation or requirement for genetic testing, the main reasons for pharmacogenomic labeling were change in pharmacologic end points (32%) and higher risk of toxicity (30%). For 11 (10%) pairs (including 1 in oncology), a genetic biomarker was mentioned only to inform that it was not relevant. In oncology, the main reasons for pharmacogenomic labeling were higher risk of toxicity (55%) and definition of the mechanism of action (25%). CONCLUSION Inclusion of biomarkers in drug labels does not always correspond to required or recommended genetic testing, especially outside oncology.Genet Med 17 9, 733-738.
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Affiliation(s)
- Alexandre Vivot
- Centre d'Épidémiologie Clinique, Hôpital Hôtel Dieu, Assistance Publique des Hôpitaux de Paris, Paris, France.,METHODS Team, Unit 1153, INSERM, Paris, France
| | - Isabelle Boutron
- Centre d'Épidémiologie Clinique, Hôpital Hôtel Dieu, Assistance Publique des Hôpitaux de Paris, Paris, France.,METHODS Team, Unit 1153, INSERM, Paris, France.,Faculté de Médecine, University of Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Philippe Ravaud
- Centre d'Épidémiologie Clinique, Hôpital Hôtel Dieu, Assistance Publique des Hôpitaux de Paris, Paris, France.,METHODS Team, Unit 1153, INSERM, Paris, France.,Faculté de Médecine, University of Paris Descartes, Sorbonne Paris Cité, Paris, France.,Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Raphaël Porcher
- Centre d'Épidémiologie Clinique, Hôpital Hôtel Dieu, Assistance Publique des Hôpitaux de Paris, Paris, France.,METHODS Team, Unit 1153, INSERM, Paris, France.,Faculté de Médecine, University of Paris Descartes, Sorbonne Paris Cité, Paris, France
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