Cargando…

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....

Descripción completa

Detalles Bibliográficos
Autor principal: Yamazaki, Shinji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
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
_version_ 1783726702450442240
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
work_keys_str_mv AT yamazakishinji aretrospectiveanalysisofactionablepharmacogeneticgenomicbiomarkerlanguageinfdalabels
AT yamazakishinji retrospectiveanalysisofactionablepharmacogeneticgenomicbiomarkerlanguageinfdalabels