ClinGen Pharmacogenomics Working Group (PGxWG) Survey To Close Soon

 

The ClinGen Pharmacogenomics Working Group (PGxWG)'s anonymous survey will close soon after this Friday, June 30, 2023. Our goal is to gather opinions and feedback regarding the criteria and terminology that should be used to define clinical validity and actionability for pharmacogenes and variants. If you have not yet had the chance to fill it out or pass it along, please do so soon! 

Pharmacogenomics expertise is not required - we are also looking for responses across the broader global genetics and medical communities as well (clinicians, pharmacists, labs, genetic counselors, etc.) All responses are appreciated, no matter who you are or where you are in the world. If you’ve previously completed the survey, we appreciate your contribution and there's no need to submit a second response. 

The survey can be accessed at: https://stanforduniversity.qualtrics.com/jfe/form/SV_1IdrrPWBXsV2Xt4.  We sincerely appreciate your time and participation, and your willingness to help.

ClinPGx Sessions at PGRN 2023 Conference and ClinGen Summer Workshops

At the upcoming PGRN 2023 annual conference in Memphis, Dr. Teri E. Klein, the principle investigator for PharmGKB, ClinGen, CPIC and PharmCAT,  will hold a town hall discussion on ClinPGx: a single integrated resource for Pharmacogenomics (PGx). Dr. Klein will discuss the challenge of the separation of pharmacogenomic resources from clinical genomic resources, and present a long-term, conceptual framework for broadly integrating the available PGx resources into a single resource, ClinPGx. 

Dr. Klein will also present at the upcoming ClinGen 2023 Summer Workshop Series on June 16 11am PT. Please come join us. We are actively seeking feedback from the PGx and genomics community on the interest and development of ClinPGx to facilitate the incorporation of PGx into standard of care. Zoom link below: 

June 16th, 2023, 11am PT/ 2pm ET 

Join Zoom Meeting : 

https://acmg.zoom.us/j/87027015818?pwd=S2U2OTFhQ1oranFZZjNlTEhHN0FlUT09 

Meeting ID: 870 2701 5818 

Passcode: 83854960 

One tap mobile 

+16699006833,,87027015818# US (San Jose) 

+17193594580,,87027015818# US 

ClinGen Pharmacogenomics Working Group (PGxWG) Survey Open Until June 30, 2023

The ClinGen Pharmacogenomics Working Group (PGxWG)'s anonymous survey is OPEN until June 30, 2023. Our goal is to gather opinions and feedback regarding the criteria and terminology that should be used to define clinical validity and actionability for pharmacogenes and variants. Please help disseminate to ALL (clinicians, pharmacists, labs, genetic counselors, etc.). We are looking for BROAD global participations - please send to your friends and colleagues too!  If you’ve previously completed the survey, we appreciate your contribution and there's no need to submit a second response. 

The survey can be accessed at: https://stanforduniversity.qualtrics.com/jfe/form/SV_1IdrrPWBXsV2Xt4.  We sincerely appreciate your time and participation, and your willingness to help.

Announcement of PharmVar Content Changes

PharmVar continues to evolve and strive to offer high-quality content to our global users. To allow us to bring new clinically relevant content to PharmVar we needed to make some difficult decisions and ‘retire’ several CYP genes. This decision is based on a newly developed points-based rating system (0-100 points) that allows us to prioritize which genes to maintain and which genes to evaluate for future introduction into PharmVar. More detailed information regarding PharmVar gene content and prioritization will be posted under the GENES tab once these changes have taken effect May 12, 2023.

The following genes were not considered pharmacogenes by PharmVar due to their contribution to lipid and steroid metabolism and/or associations with disease and will be retired:  CYP4A11, CYP4A22, CYP4B1, CYP17A1, CYP19A1, CYP21A2, CYP26A1, TBXAS1  and PTGIS (0 points each), though several of these genes have variant and low level clinical annotations on PharmGKB. Other databases such as ClinGen and/or ClinVar may also be consulted for variation annotations. These genes were listed by PharmVar as ‘legacy’ genes. POR (3 points) was also listed as a legacy gene.  The following genes were transitioned into the PharmVar database, but never curated by an expert panel nor any additional data added: CYPs  1A1, 1B1, 2E1, 2F4, 2J2, 2R1, 2S1, 2W1, 3A7 and 3A43. These genes were not deemed to be clinically important pharmacogenes by the PharmVar Steering Committee based on having 0 points in the ranking system and will also be retired. Furthermore, the link to the archived Human Cytochrome P450 (CYP) Allele Nomenclature database record (last version by cypalleles.ki.se in 2017) will be deactivated to discourage use of outdated information (a copy can be requested through support@pharmvar.org).

If new data emerges and rankings change, a gene may be reintroduced to PharmVar. 

 

NAT2 is currently undergoing curation and is anticipated to be transferred from the Databases of Arylamine N-acetyltransferases (NATs) to PharmVar in summer 2023. The introduction of NAT2 into the PharmVar database is timely as CPIC is initiating a guideline for the NAT2/hydralazine gene-drug pair.  Additionally, NAT2 has multiple clinical annotations and mulitple annotated FDA and other regulatory agency labels.

 

As always, PharmVar values your feedback and suggestions support@pharmvar.org.

CYP3A5 genotyping is a more accurate predictor of drug response than race alone

 A new paper in Journal of Clinical Pharmacology from a group at Indiana University [PMID:37042314] implemented genotyping for CYP3A5 in a kidney transplant center.

The team used CPIC guidelines for tacrolimus dosing based on CYP3A5 genotype.

Implementation included provider education and clinical decision support in the electronic medical record.

This study reinforces that CYP3A5 genotype is an important predictor of therapeutic tacrolimus trough concentrations. They demonstrate that CYP3A5 normal and intermediate metabolizers had fewer tacrolimus trough concentrations within the desired range post-transplantation and took longer to achieve therapeutic dose than poor metabolizers. While the authors note they were underpowered to measure outcomes, there was a trend towards transplant rejection or all-cause mortality within the first year of transplant based on CYP3A5 metabolizer phenotype.

The paper highlights how, despite the guidelines from CPIC being published in 2015, the FDA label still currently only has language around race-based dose adjustment rather than giving precise guidance based on genotype:

“The FDA drug label recommends higher starting doses in individuals of African ancestry, but only 70% of African Americans are normal/intermediate metabolizers. CYP3A5 normal/intermediate metabolizers are also found among whites and Asians (East Asian and Central/South Asian) at lower frequencies (14% and 44-55%, respectively).”

“Self-reported African American race is more closely associated with CYP3A5 expresser status than other self-reported race categories, but self-reported race is not an accurate surrogate for genotype.”

The discussion is a reminder that pharmacogenomics can play a key role in reducing bias and fulfilling personalized precision medicine.

“Equality and minimization of bias in healthcare has recently become prioritized by healthcare systems as recognition of racial bias has come to the forefront in many non-healthcare aspects of society”

“One dose standard protocols and using race as a surrogate for genotype can both potentiate racial disparities in tacrolimus dosing. Routine CYP3A5 genotyping is a more accurate predictor of drug response than race alone and deemphasizes race as a biological variable in clinical care”

CPIC Guideline for CYP2D6, CYP2C19, CYP2B6, SLC6A4 and HTR2A Genotypes and Serotonin Reuptake Inhibitor Antidepressants

The CPIC guideline for CYP2D6, CYP2C19, CYP2B6, SLC6A4 and HTR2A genotypes and Serotonin Reuptake Inhibitor Antidepressants has been published in the journal Clinical Pharmacology and Therapeutics. This new guideline updates the CPIC guideline for Selective Serotonin Reuptake Inhibitors and CYP2D6 and CYP2C19, and includes additional Serotonin Reuptake Inhibitor Antidepressants and three additional genes, CYP2B6, HTR2A, and SLC6A4.  

The guideline gives specific prescribing recommendations for:

• paroxetine, fluvoxamine, venlafaxine, and vortioxetine based on CYP2D6 phenotypes
• escitalopram and citalopram based on CYP2C19 phenotypes
• sertraline based on CYP2C19 and CYP2B6 phenotypes

Clinical recommendations are not provided for serotonin reuptake inhibitor antidepressants based on HTR2A and SLC6A4 genotypes because the current evidence is mixed and/or insufficient to support clinical validity and utility.
 
For specific recommendations and further details, please refer to the guideline and supplemental materials on the CPIC website. Annotations of the guideline, including an interactive genotype picker tool for the drugs mentioned above, are available on the PharmGKB website.

It’s national poetry month again!

I am not a mouse.
It’s the reference allele
(hg38).

I am not a mouse.
It's the common variant
In these folks tested.


Saw on minus strand
G is the major allele
In this group tested.


C is the minor
It’s the reference allele
(hg38).


Last year I wrote a haiku to highlight my gripe about authors declaring a patient a *1 but not stating which alleles were tested so not conclusively ruling out the presence of variants. This year I have a series of haiku on another gripe of mine - authors using the term “wild-type” when talking about humans. I’ve been guilty of it in the past but when we know better we do better. The one upside of it is it's a way one can avoid using terms major/minor allele when those might be different in different populations and thus ambiguous, but I think most study participants would not want to be categorized as wild-type, certainly most would not want to be called mutant (unless it comes with some X-men type powers). The terms I would recommend are “reference allele/genotype” (on genome build …) and “comparison allele/genotype”. If using major/minor allele then state explicitly what base that was in the given population, and describe the population, and which strand the allele was measured on (especially for C/G and A/T variants and genes on minus chromosomal strand). We have been seeing problems lately when authors assume the reference allele on the genome build hg38 is the major allele in most populations and that is not always the case. If in doubt give as much information as possible.