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PGxPHGKB

Specific PHGKB|Pharmacogenomics PHGKB|PHGKB

Last Posted: Dec 02, 2021
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Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction between CYP2C19 and CYP2D6 and SSRIs
JMJL Brouwer et al, EJHG, November 16, 2021

Evidence-based dose recommendations were obtained, based on a structured analysis of published literature. In CYP2C19 PM patients, escitalopram dose should not exceed 50% of the normal maximum dose. In CYP2C19 IM patients, this is 75% of the normal maximum dose. Escitalopram should be avoided in UM patients. In CYP2C19 PM patients, citalopram dose should not exceed 50% of the normal maximum dose. In CYP2C19 IM patients, this is 70% (65–75%) of the normal maximum dose. In contrast to escitalopram, no action is needed for CYP2C19 UM patients. In CYP2C19 PM patients, sertraline dose should not exceed 37.5% of the normal maximum dose. No action is needed for CYP2C19 IM and UM patients. In CYP2D6 UM patients, paroxetine should be avoided. No action is needed for CYP2D6 PM and IM patients. In addition, no action is needed for the other gene-drug combinations.

Precision Medicine for Your Practice: Exploring Pharmacogenomic Testing
The Jackson Labs Online Course, 2021

Precision Medicine for Your Practice is a series of short (30 minute), online modules covering specific topics in genomics and precision medicine. In this module, Exploring Pharmacogenomic Testing, you will learn about the benefits and limitations of pharmacogenomic testing, how to determine whether pharmacogenomic testing is appropriate for the patient and the application of test results to patient management.

Underrepresented patient views and perceptions of personalized medication treatment through pharmacogenomics
L Solzbery et al, NPJ Genomic Medicine, November 2021

We compared views and experiences from self-reported White and Black patients, including education level as a covariate across analyses. Black patients were less confident about whether their providers made personalized treatment decisions, and overwhelmingly wanted a greater role for their genetic information in clinical care. Both groups similarly reported that providers asked their opinions regarding medication changes, but White patients were more likely (59% vs. 49%, P?=?0.005) to discuss the impact of personal/genetic makeup on medication response with providers, and Black patients reported initiating such discussions much less frequently (4% vs. 15%, P?=?0.037).

Nine-gene pharmacogenomics profile service: The Mayo Clinic experience
ET Matey et al, The PGX Journal, October 19, 2021

Eighty-two healthy individuals aged 18 and older underwent genotyping of CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, SLCO1B1, HLA-B*58:01, and VKORC1. A PGx pharmacist was involved in ordering, meeting with patients, interpreting, reviewing, and documenting results. Ninety three percent were CYP1A2 rapid metabolizers, 92% CYP3A4 normal metabolizers, and 88% CYP3A5 poor metabolizers; phenotype frequencies for CYP2C19 and CYP2D6 varied. Seventy-three percent had normal functioning SLCO1B1 transporter, 4% carried the HLA-B*58:01 risk variant, and 35% carried VKORC1 and CYP2C9 variants that increased warfarin sensitivity. Pre-emptive PGx testing offered medication improvement opportunity in 56% of participants for commonly used medications.


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Pharmacogenomic analysis of a genetically distinct Indigenous population. External Web Site Icon
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Pharmacogenomic Testing and Patient Perception Inform Pain Pharmacotherapy. External Web Site Icon
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PharmVIP: A Web-Based Tool for Pharmacogenomic Variant Analysis and Interpretation. External Web Site Icon
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The Influence of CYP2D6 and CYP2C19 Genetic Variation on Diabetes Mellitus Risk in People Taking Antidepressants and Antipsychotics. External Web Site Icon
Austin-Zimmerman Isabelle et al. Genes 2021 12(11)
Unmanaged Pharmacogenomic and Drug Interaction Risk Associations with Hospital Length of Stay among Medicare Advantage Members with COVID-19: A Retrospective Cohort Study. External Web Site Icon
Ashcraft Kristine et al. Journal of personalized medicine 2021 11(11)

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Disclaimer: Articles listed in the Public Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.

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