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A retrospective analysis of actionable pharmacogenetic/genomic biomarker language in FDA labels
The primary goal of precision medicine is to maximize the benefit‐risk relationships for individual patients by delivering the right drug to the right patients at the right dose. To achieve this goal, it has become increasingly important to assess gene‐drug interactions (GDIs) in clinical settings....
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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John Wiley and Sons Inc.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8301579/ https://www.ncbi.nlm.nih.gov/pubmed/33742770 http://dx.doi.org/10.1111/cts.13000 |
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author | Yamazaki, Shinji |
author_facet | Yamazaki, Shinji |
author_sort | Yamazaki, Shinji |
collection | PubMed |
description | The primary goal of precision medicine is to maximize the benefit‐risk relationships for individual patients by delivering the right drug to the right patients at the right dose. To achieve this goal, it has become increasingly important to assess gene‐drug interactions (GDIs) in clinical settings. The US Food and Drug Administration (FDA) periodically updates the table of pharmacogenetic/genomic (PGx) biomarkers in drug labeling on their website. As described herein, an effort was made to categorize various PGx biomarkers covered by the FDA‐PGx table into certain groups. There were 2 major groups, oncology molecular targets (OMT) and drug‐metabolizing enzymes and transporters (DMETs), which constitute ~70% of all biomarkers (~33% and ~35%, respectively). These biomarkers were further classified whether their labeling languages could be actionable in clinical practice. For OMT biomarkers, ~70% of biomarkers are considered actionable in clinical practice as they are critical for the selection of appropriate drugs to individual patients. In contrast, ~30% of DMET biomarkers are considered actionable for the dose adjustments or alternative therapies in specific populations, such as CYP2C19 and CYP2D6 poor metabolizers. In addition, the GDI results related to some of the other OMT and DMET biomarkers are considered to provide valuable information to clinicians. However, clinical GDI results on the other DMET biomarkers can possibly be used more effectively for dose recommendation. As the labels of some drugs already recommend the precise doses in specific populations, it will be desirable to have clear language for dose recommendation of other (or new) drugs if appropriate. |
format | Online Article Text |
id | pubmed-8301579 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83015792021-07-27 A retrospective analysis of actionable pharmacogenetic/genomic biomarker language in FDA labels Yamazaki, Shinji Clin Transl Sci Research The primary goal of precision medicine is to maximize the benefit‐risk relationships for individual patients by delivering the right drug to the right patients at the right dose. To achieve this goal, it has become increasingly important to assess gene‐drug interactions (GDIs) in clinical settings. The US Food and Drug Administration (FDA) periodically updates the table of pharmacogenetic/genomic (PGx) biomarkers in drug labeling on their website. As described herein, an effort was made to categorize various PGx biomarkers covered by the FDA‐PGx table into certain groups. There were 2 major groups, oncology molecular targets (OMT) and drug‐metabolizing enzymes and transporters (DMETs), which constitute ~70% of all biomarkers (~33% and ~35%, respectively). These biomarkers were further classified whether their labeling languages could be actionable in clinical practice. For OMT biomarkers, ~70% of biomarkers are considered actionable in clinical practice as they are critical for the selection of appropriate drugs to individual patients. In contrast, ~30% of DMET biomarkers are considered actionable for the dose adjustments or alternative therapies in specific populations, such as CYP2C19 and CYP2D6 poor metabolizers. In addition, the GDI results related to some of the other OMT and DMET biomarkers are considered to provide valuable information to clinicians. However, clinical GDI results on the other DMET biomarkers can possibly be used more effectively for dose recommendation. As the labels of some drugs already recommend the precise doses in specific populations, it will be desirable to have clear language for dose recommendation of other (or new) drugs if appropriate. John Wiley and Sons Inc. 2021-04-09 2021-07 /pmc/articles/PMC8301579/ /pubmed/33742770 http://dx.doi.org/10.1111/cts.13000 Text en © 2021 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Yamazaki, Shinji A retrospective analysis of actionable pharmacogenetic/genomic biomarker language in FDA labels |
title | A retrospective analysis of actionable pharmacogenetic/genomic biomarker language in FDA labels |
title_full | A retrospective analysis of actionable pharmacogenetic/genomic biomarker language in FDA labels |
title_fullStr | A retrospective analysis of actionable pharmacogenetic/genomic biomarker language in FDA labels |
title_full_unstemmed | A retrospective analysis of actionable pharmacogenetic/genomic biomarker language in FDA labels |
title_short | A retrospective analysis of actionable pharmacogenetic/genomic biomarker language in FDA labels |
title_sort | retrospective analysis of actionable pharmacogenetic/genomic biomarker language in fda labels |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8301579/ https://www.ncbi.nlm.nih.gov/pubmed/33742770 http://dx.doi.org/10.1111/cts.13000 |
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