We have launched a user survey to help inform the future direction of PharmGKB. All user responses are greatly appreciated; no matter who you are, where you are in the world or how many times you have used PharmGKB. The survey is split into two parts. The first section takes ~1 minute to complete. If you have time to give us some more information, the second section will take an additional ~5 minutes. Thank you for your contribution.
Thursday, October 21, 2021
Thursday, October 14, 2021
PharmGKB, PharmVar and CPIC have coordinated updates to their SLCO1B1 resources to reflect its release in PharmVar. The PharmVar SLCO1B1 gene page includes new allele functions assigned by CPIC as part of its forthcoming update to the guideline on SLCO1B1 and statins. These new PharmVar allele definitions and CPIC functions have been incorporated into the CPIC database and implementation resources for use with the current simvastatin guideline recommendations. All resources available through PharmGKB have also been updated accordingly. Standardized nomenclature for this drug transporter is an important step forward for clinical implementation of stain pharmacogenetics.
Monday, October 11, 2021
PharmGKB Pediatric (https://pediatric.pharmgkb.org) is a "view" of PharmGKB (https://www.pharmgkb.org) that highlights pharmacogenomic annotations that (1) are based on pediatric studies or (2) may be relevant to pediatrics. All of the annotations found on PharmGKB are also on PharmGKB Pediatric.
A quick way to access pediatric information is to click the blue "Pediatric Pharmacogenomics" button on the website homepage
and follow the link to the "Pediatric Annotations Dashboard".
Also on PharmGKB Pediatric, the menu on the left-hand side of drug and gene page will have a "child" icon if there are pediatric annotations in a particular category (see example below). Additionally, manually-curated summaries of the pediatric annotations and pediatric information from FDA-approved drug labels (see example below)
|Example screenshot of warfarin page with pediatric icons and summary.|
Monday, October 4, 2021
The U.S. National Institutes of Health (NIH) has put out a Request for Information (RFI) on User Experience with Scientific Data Sources and Tools in order to better understand the use of these resources by the scientific community.
We’re asking the pharmacogenomics community to consider responding to the survey and show their support for pharmacogenomics resources like PharmGKB and PharmVar. The RFI closes on October 15.
Monday, September 27, 2021
Version 1.0 of the Pharmacogenomics Clinical Annotation Tool (PharmCAT) has been released today (September 27, 2021). PharmCAT is a software tool that takes genetic data for an individual as VCF file input, interprets the pharmacogene alleles, diplotypes and phenotypes, and generates reports with CPIC's genotype-based drug prescribing recommendations which can be used to inform treatment decisions. This is the first official release of PharmCAT and it contains multiple updates to the previously published beta version.
Version 1.0 extends the coverage of alleles in existing PharmCAT genes and adds more genes, drugs and guidelines from CPIC. Content is sourced from the CPIC database. The PharmCAT site includes a list of genes and drugs (along with prescribing recommendations) included in the report.
New input and output options
v1.0 of PharmCAT enables the input of diplotypes determined by tools or genetic testing reports outside of PharmCAT to be used to predict gene phenotypes and retrieve respective CPIC recommendations. Previously, only “outside” calls for CYP2D6 diplotypes were allowed, but now diplotypes for any gene supported by PharmCAT can be supplied. For example, if you have a CYP2C9 diplotype call in hand, you can input that to PharmCAT so the CYP2C9 phenotype and CPIC recommendations are included in the final report.
The section of PharmCAT that takes diplotype calls and predicts phenotypes (e.g. metabolizer phenotypes) has been encapsulated into its own module. The "phenotyper" module allows for the input of phenotypes determined outside of PharmCAT, such as from genetic testing reports, to be used to retrieve CPIC recommendations. For example, if you have already determined a CYP2C9 phenotype, you can input that to PharmCAT so the CYP2C9 CPIC recommendations for that phenotype will be included in the final report.
The separation of the “phenotyper” code into its own module also means that if you are only interested in predicting pharmacogene diplotypes and/or phenotypes from a VCF file, you can get that information without proceeding to the final human-readable report.
More PharmCAT validation testing
v1.0 has dramatically expanded in silico testing for internal validation of PharmCAT allele matching. We have created a testing system that generates VCF files to match against expected results and report any mismatches. This system successfully tests the NamedAlleleMatcher beyond the published validation in our paper published in January 2020.
We now provide a VCF preprocessor tool to prepare VCF files for the named allele matcher. PharmCAT requires the input VCF to follow the official VCF format specifications (version 4.1 or later) and expects a parsimonious variant representation format to avoid ambiguity. However, user-supplied VCF files may not always follow the official VCF format specifications and can represent genetic variants in different representation formats as a result of varied VCF preparation bioinformatics pipelines. Variant representation formats different from PharmCAT's requirements can cause unexpected technical hurdles and will require additional data preparation. To resolve this issue, PharmCAT provides a VCF preprocessor tool to normalize and prepare VCFs to a format readily digestible by PharmCAT. The VCF preprocessor will automatically download the Human Reference Genome Sequence fasta and index files from the NIH FTP site to normalize genetic variants in VCF. Preprocessed VCF data will include only necessary PGx allele defining positions, which will improve PharmCAT's runtime. Users will also receive a report VCF of missing PGx positions in the original VCF file.
Development of PharmCAT continues! You can expect more releases very soon as we gather feedback from this release and issue more updates to underlying data coming from CPIC and PharmVar.
New features will also include multi-sample VCF support, GVCF support, and FHIR-formatted reports. Keep an eye on the releases page for updates.
Monday, August 16, 2021
The paper describes how to navigate the PharmGKB website to retrieve information on how genes and genetic variations affect drug efficacy and toxicity. It also includes a protocol on how to use PharmGKB to facilitate interpretation of findings for a specific pharmacogenomic variant genotype or metabolizer phenotype.
Friday, July 23, 2021
A previous blog post described our new quantitative system for assigning a level of evidence (LOE) to clinical annotations. We are pleased to announce that our white paper about the system has now been published in Clinical Pharmacology and Therapeutics and is available on the PharmGKB website.
The paper details how each piece of evidence linked to a clinical annotation is scored by an algorithm and how the total score of a clinical annotation is translated into a LOE. Use of this system makes the assignment of LOE to be more consistent across clinical annotations and allows users to better understand why a certain LOE has been assigned to a particular clinical annotation.
Monday, June 28, 2021
PharmGKB has curated new pathways for the pharmacokinetics of the following drugs that are on the CPIC gene-drug pairs list and either have existing guidelines or are slated for future consideration.
A complete list of PharmGKB pathways can be found at https://www.pharmgkb.org/pathways
Monday, June 14, 2021
Aminoglycosides are widely used antibiotics which inhibit protein synthesis in bacteria by binding to the bacterial 16S ribosomal subunit. They are also associated with adverse events, including aminoglycoside-induced hearing loss (AIHL). Patients who carry certain variants in the MT-RNR1 gene, which encodes the 12S ribosomal subunit, are at a greatly increased risk of developing AIHL. These variants cause the 12S subunit to more closely resemble the bacterial 16S subunit, which can lead to aminoglycoside molecules binding to the ribosome and inhibiting protein synthesis. This ultimately results in hearing loss due to cell death in the cochlea.
This is the first CPIC guideline on a mitochondrial gene. As the mitochondrial genome is haploid, rather than diploid, recommendations are given for single variants, rather than for combinations of alleles.
Tuesday, June 8, 2021
PharmGKB and CPIC recently hosted a Twitter chat sharing pharmacogenomics resources as part of NHGRI's Healthcare Providers Genomics Education week (#MedGeneEd21). An archive of the chat is displayed below.#MedGeneEd21 PGx chat - Curated tweets by pharmgkb
Wednesday, May 12, 2021
The Proton Pump Inhibitor Pathway, Pharmacokinetics has been updated and expanded to now highlight four pathways with candidate genes and drug metabolites involved for:
The pathways illustrate the subtle differences between the metabolism of drugs in the class, their different sensitivities to variation in CYP2C19, and aids explanation of the differences in CPIC recommendations for the different drugs (see CPIC Guideline for CYP2C19 and Proton Pump Inhibitor Dosing).
Monday, May 10, 2021
We announced last December that the Clinical Pharmacogenetics Implementation Consortium (CPIC) was launching its Term Standardization Pharmacogenetic Test Results - Part 2 project. The project is currently focused on MT-RNR1, as that guideline wraps up.
The MT-RNR1 Gene/Disease and PGx expert panels have agreed on terminology for MT-RNR1 and aminoglycoside-induced hearing loss for allele clinical function and phenotype. The final terms are posted for review on the CPIC website. Please send any feedback to Dr. Kelly Caudle at firstname.lastname@example.org by May 28th, 2021.
Thursday, May 6, 2021
The allele pull-down menu (also known as the genotype-picker tool) on PharmGKB annotations of CPIC guidelines is an extremely popular feature among users. We are pleased to now expand this functionality to annotations of guidelines from the Royal Dutch Association for the Advancement of Pharmacy - Pharmacogenetics Working Group (DPWG).
The genotype picker tool relies on determining the following information from each guideline:
1. Which alleles are covered by the guideline
2. The assigned functional status of each allele
3. How allele function combinations are mapped to phenotype groups
The DPWG recommendations that are downloadable from the Dutch Pharmacy Organization’s (KNMP) website and annotated on PharmGKB do not typically contain allele functional status or phenotype group mappings. However, this information is often available in KNMP’s ‘gene background’ files which are posted separately. While DPWG generally follows the same allele-to-phenotype mappings as CPIC, there are some gene-specific differences. These differences mean that all DPWG gene mappings need to be curated into PharmGKB separately from CPIC’s mappings. Our mapping process for each gene covered by DPWG guidelines, including curator notes, can be found here. As we now have internally curated mappings of allele function to phenotype for DPWG, more DPWG guidelines can now be used to support our Level 1A clinical annotations.
Genotype pickers are now available for all annotations of DPWG guidelines with recommendations for HLA-B, CYP2D6 and CYP2C19 genotypes. Work is ongoing to curate DPWG-assigned allele functions and phenotype groups for other genes into PharmGKB. As each gene is curated, genotype pickers will be released on relevant guideline annotation pages.
Tuesday, May 4, 2021
PharmVar and PharmGKB are excited to announce that CYP3A4 has been transitioned into the PharmVar database. Check it out here.
Numerous changes and revisions have been made during an extensive curation process including limiting the upstream and downstream regions used for allele definitions and the removal of introns of unknown functional consequence; these revisions caused the retirement of several suballeles or merging of suballeles.
In addition, upgrading to the gene’s current reference sequence (NG_008421.1) caused the c.-392A>G SNP to flip to c.-392G>A; in other words, all alleles that previously had the c.-392A>G SNP now match the RefSeq and are thus no longer showing the variant, while all other alleles gained c.-392G>A. Furthermore, the submission of new data added one novel star allele,CYP3A4*35, several novel suballeles, as well as helped to raise the evidence levels for many alleles from ‘Lim’ to ‘Def’.
Important CYP3A4 information is provided in the ‘Read Me’ document such as reference sequences used and how the PharmVar CAVE tool facilitates comparisons of core allele definitions. All changes and revisions have been summarized in the ‘Change Log’ document. Here we also provide a record of novel haplotypes that have been submitted to PharmVar and have been accepted.
Finally, a big thank you to all CYP3A4 gene experts for volunteering their time and expertise!
Thursday, March 25, 2021
PharmGKB introduces scoring system for variant and clinical annotations and updated Levels of Evidence
As the number of variant and clinical annotations in PharmGKB has increased, it became a challenge to maintain consistency when assessing all the available evidence and assigning a level of evidence to clinical annotations. To address this, PharmGKB began a project at the end of 2019 to improve standardization across clinical annotations by establishing new curator tools and protocols. We are pleased to be able to release the first phase of this project to users today.
Central to this work has been the development of a scoring system which assigns scores to both variant and clinical annotations as a numerical summary of the evidence underlying each annotation. Variant annotations are scored in a five-step process which assesses various attributes found in both the main annotation and in the study parameters. This scoring of variant annotations is not a judgement of study quality. It is a metric used by PharmGKB curators when comparing variant annotations against each other as part of the process of creating and updating clinical annotations.
The scoring process can be described using the following formula, with a list of attributes scored in each step given below. A more detailed description can be found on our Variant Annotation Scoring page.
Step 1 – Phenotype category (toxicity, efficacy, etc.)
Step 2 – Reported p-value
Step 3 – Cohort size
Step 4 – Effect size
Step 5a – Weighting by study type
Step 5b – Weighting by reported association and significance
When a variant annotation is attached to a clinical annotation, the variant annotation’s score contributes to the score for the clinical annotation. Curators can mark any variant annotation which reports an association in the opposing direction to the assertion made in the clinical annotation as a conflicting variant annotation. For example, if a clinical annotation reports that the G allele is associated with decreased response to a drug, a variant annotation which reports that the G allele is associated with increased response would be considered to be conflicting.
Variant annotations which are marked as conflicting are assigned a negative score and receive a tag which can be seen on a clinical annotation’s page. It is important to realize that a variant annotation is only considered to be conflicting within the context of a specific clinical annotation and that the score of the variant annotation only changes in that clinical annotation.
Supporting CPIC and DPWG clinical guidelines or FDA-approved drug labels can also be added to a clinical annotation by curators and can contribute to that annotation’s score.
A clinical annotation’s score is used to assign a suitable level of evidence for the annotation. A table with scoring ranges and detailed descriptions of each level can be found on our level of evidence page. We have introduced a separate scoring range for rare variants to account for the fact that these tend to only be reported in small studies, even though there is an underlying pharmacogenomic association. Information on how PharmGKB defines a rare variant can be found here. A clinical annotation’s score is only used to determine the level of evidence and is not intended to be used to rank or compare clinical annotations within a given level of evidence.
A clinical annotation’s score is now displayed on clinical annotation pages. This includes a score breakdown to indicate how different types of evidence are contributing to the annotation’s score. In rare cases, the team may feel that a clinical annotation’s score is not reflective of the underlying evidence and can, after group discussion and consensus, choose to override the scoring system. When an annotation’s level has been overridden, it is displayed on the annotation page along with a written justification for the override.
This scoring system minimizes subjectivity in the assessment of clinical annotations and makes the assignment of levels of evidence more consistent, reproducible and transparent to users. To complement the scoring system, PharmGKB clinical annotations are now being written to new standards as well as displaying additional information for users.
New Clinical Annotation Features
Clinical annotations will now begin to use a new template and set of standardized sentences to highlight caveats and other considerations to users. They are also now written only on a single drug, drug combination or drug class and a single phenotype category (e.g. dosage, efficacy, etc.). Additionally, we have introduced extra checks to ensure that all level 1 and 2 clinical annotations are supported by two independent pieces of evidence.
PharmGKB now offers two distinct types of clinical annotation: gene-level and variant-level. Variant-level annotations provide genotype-based summaries for a specific rsID (example), while gene-level clinical annotations display summaries for one of more star alleles of a gene (example). These formats have always been part of our clinical annotations, but have now been formalized with templates to standardize annotation writing.
The score and level of evidence assigned to gene-level clinical annotations represents the strength of evidence underlying the association at the level of the gene rather than at the level of the individual variant. A ‘Limited Evidence’ tag is used to highlight alleles which are supported by substantially less evidence than the overall level indicated by the level of evidence. Where possible, we also now display allele function as assigned by CPIC.
These changes mean that clinical annotations are easier to compare against each other and that the level of evidence is more representative of the evidence supporting a phenotypic association for the variant-drug pair. We acknowledge that this has reduced the number of clinical annotations at levels 1B, 2A and 2B however, these annotations are now more consistent and based on quantitative criteria.
This project has entailed detailed review of over 350 clinical annotations by our curation team and, as part of this release, all level 1 and 2 clinical annotations have been rewritten to our new standards and reviewed by at least two curators. Updating clinical annotations at levels 3 and 4 to our new standards will continue as part of our regular curation activities. Users can check the history section of each annotation to see if there has been a recent update.
We are excited to bring the first phase of this project to PharmGKB users and welcome user comments or suggestions sent to email@example.com.
Tuesday, March 23, 2021
Monday, March 1, 2021
A new perspective just out in Genetics in Medicine describes the improvements in the US payer landscape for pharmacogenomics test reimbursement from this past summer and their implications for the field moving forward.
The Medicare Administrative Contractors (MACs) participating in the Molecular Diagnostic Services (MolDx) program released their final local coverage determinations (LCDs) pharmacogenomic testing in July/August.
PharmGKB and CPIC view these as significant advances because of the large number of US Medicare patients impacted. Further, the LCDs state PGx testing as reasonable and necessary when medications have a clinically actionable gene(s)-drug interaction as defined by CPIC guidelines (category A and B) or the FDA (PGx information required for safe drug administration). Coverage for panel testing was also supported if more than one gene on the panel is considered reasonable and necessary for the safe use of a medication or if multiple drugs are being considered that have different relevant gene associations.
The authors’ analysis lists >50 gene/drug pairs that are covered by the LCD and provides a map (below) of MAC regions impacted. They make a strong argument that harmonization of coverage is needed and that standardization, improved clarity in the regulatory landscape, practitioner education, and research to measure downstream clinical outcomes are needed more than ever to fully capture the value of pharmacogenomic testing.
Friday, February 19, 2021
PharmGKB and CPIC partnered with ClinGen last summer to bring curated pharmacogenomics (PGx) to the resource which defines the clinical relevance of genes and variants in the human genome. A new Pharmacogenomics column has been added to ClinGen's curated gene categories. 130 PGx genes curated by PharmGKB and/or CPIC are now listed on the ClinGen website with links back to the PharmGKB and CPIC websites for more detailed information.
ClinGen displays all gene-drug pairs from PharmGKB with Level 1 & 2 Clinical Annotations along with (1) a link to the relevant PharmGKB drug page, (2) the highest annotation level for the gene-drug pair (linked to the explanation of PharmGKB's Levels of Evidence), (3) the date of the last update and (4) a link to view all PharmGKB Clinical Annotations for that gene-drug pair.
ClinGen also displays all CPIC gene-drug pairs with levels A-D. Where applicable, these are grouped by CPIC guideline with (1) a link to the CPIC guideline page and (2) gene-drug pairs list page. Gene-drug pairs with provisional CPIC levels (i.e. those awaiting further evaluation and potentially guideline development) link to the gene-drug pairs list page.
Thursday, February 18, 2021
Clopidogrel is metabolized to its active metabolite by CYP2C19, as shown in the PharmGKB clopidogrel pathway. Patients carrying CYP2C19 no function alleles (e.g. CYP2C19*2) have reduced or no conversion of clopidogrel to the active metabolite, which puts them at an increased risk of cardiovascular events. The CPIC guideline for clopidogrel recommends that CYP2C19 intermediate and poor metabolizers receive alternative antiplatelet therapy.
The companies were found to have violated Hawaii’s consumer protection laws by not disclosing that Plavix would be ineffective for as many as 30% of patients in Hawaii, many of whom are of Asian and Pacific Islander descent. Some CYP2C19 no function variants, such as CYP2C19*2, are found at higher frequencies in Asian and Pacific Islander populations compared to their frequency in European populations (see the CYP2C19 allele frequency table).
Judge Dean Ochiai ruled that Bristol-Myers Squibb Co and Sanofi “knowingly placed Plavix patients at grave risk of serious injury or death in order to substantially increase their profits” over a 12-year period from 1998 to 2010. Information about the effect of CYP2C19 no function alleles on the efficacy of Plavix was added to the drug label in 2009, and a Black Box warning to consider alternate therapy for CYP2C19 poor metabolizers was added in 2010. PharmGKB has annotated the Plavix label and highlights pharmacogenomic information found within the label.
Hawaii Attorney General Clare Connors emphasized the growing impact of pharmacogenomics on the pharmaceutical industry: “The order entered by the court today puts the pharmaceutical industry on notice that it will be held accountable for conduct that deceives the public and places profit above safety”.
In a joint statement, the companies said that “the overwhelming body of scientific evidence demonstrates that Plavix is a safe and effective therapy, including for people of Asian descent.” and that they plan to appeal.
Tuesday, February 16, 2021
Clicking on any SNV on a PharmVar gene page will activate the variation window. The example shown below is for the CYP2C9*2 variant c.430C>T. This view provides SNV positions across all sequences, the link to the NCBI dbSNP identifier (rs number) as well as SNV frequency. There is also a bar providing the option to display all haplotypes with the selected variant.
The middle portion of the variation window displays SNV positions ‘PharmVar-style’ on the gene, transcript and genome (GRCh37 and GRCh38) levels giving positions for both count modes and detailing the reference and variant nucleotides.
It is noted that HGVS and ‘PharmVar-style’ positions may differ for insertion/deletion variants in some instances, which is most likely explained by how sequences are aligned. Also, PharmVar displays single nucleotide insertions as ‘ins’ while HGVS displays them as duplications or ‘dup’. Additional details and examples are can be found in the PharmVar ‘Standards’ document. HGVS annotations are also accessible via API services.
PharmVar welcomes any feedback you may have through firstname.lastname@example.org.
Wednesday, February 10, 2021
In February 2020, we blogged about the FDA's newly released Table of Pharmacogenetic Associations. Since then, there has been much interest in understanding how that table was created, how it compares to the information on the drug labels, and how it compares to the FDA's Table of Pharmacogenomic Biomarkers in Drug Labeling, which has existed for many years and is routinely curated by PharmGKB. With this in mind, PharmGKB has created a section on its FDA-approved drug label annotations for information from the Table of Pharmacogenetic Associations (Figure 1).
Figure 1. Screenshot of part of the FDA-approved drug label annotation for codeine.
We also have a new landing page specifically for FDA-approved drug label annotations that can be sorted and filtered by different criteria in the column headings, including the category of the drug from the Table of Pharmacogenetic Associations ("FDA PGx Association"). The table can be downloaded in TSV format as either the full or filtered version (Figure 2).
Figure 2. Screenshot of the FDA Drug Label Annotations table.
This table can be found on the PharmGKB homepage under the "Annotation" and "Clinical" section (Figure 3) and is in addition to our Drug Label Annotations table that includes labels from multiple regulatory agencies found at the top left corner of the homepage.
Figure 3. PharmGKB homepage.
As a reminder, PharmGKB drug label annotations provide (1) a brief summary of the PGx in the label, (2) an excerpt from the label, including any guidance from the label for patients with a particular genotype/metabolizer phenotype if it exists, (3) specific variants discussed on the label, particularly if there is prescribing guidance for them, and (4) a downloadable highlighted label PDF file. PharmGKB also "tags" labels to indicate certain information, including:
(5) the "PGx Level" tag ("Testing required", "Testing recommended", "Actionable PGx" and "Informative PGx") which is the PharmGKB interpretation of the level of action implied in each label
(6) the "Dosing Info" tag which indicates dosing information based on genotype/metabolizer phenotype exists on the label
(7) the "Alternate Drug" tag which indicates if a drug is either indicated or contraindicated based on genotype/metabolizer status on the label
(8) the "Prescribing Info" tag which indicates if any guidance from the label for patients with a particular genotype/metabolizer phenotype exists on the label
(9) the "Cancer Genome" tag which indicates if the label discusses a gene or variant present in a tumor/cancer cell
(10) the "On FDA Biomarker List" tag if the label is on the FDA's Table of Pharmacogenomic Biomarkers in Drug Labels.
Figure 4. Screenshot of the FDA-approved drug label annotation for irinotecan to illustrate the types of information found in a label annotation.
Wednesday, January 20, 2021
CYP2B6 is the only member of the CYP2B subfamily to encode a functional enzyme. Variation in this gene impacts the metabolism of several clinically important drugs, including efavirenz (see the CPIC guideline and annotation on PharmGKB), methadone and bupropion.
The paper gives an overview of CYP2B6 genetic variation and outlines the gene’s previous nomenclature system prior to being catalogued by PharmVar. Details of CYP2B6 resources on PharmGKB and CPIC as well as reference materials for genetic testing are also provided.
CYP2B6 has now been curated into PharmVar, with some alleles revised to remove SNPs with little or no evidence available to show that they caused a change in CYP2B6 function. Users should also note that the *16 and *18 alleles have been consolidated, with *16 now listed as a suballele of *18. All changes have been recorded and can be found on the PharmVar page for CYP2B6. PharmGKB and CPIC will be updating CYP2B6 information accordingly.
PharmVar would like to thank all members of the CYP2B6 gene expert panel for their efforts in curating this important gene.
Thursday, January 14, 2021
The CPIC guideline for opioid therapy and CYP2D6, OPRM1 and COMT was recently published in Clinical Pharmacology and Therapeutics and has now been annotated on PharmGKB. This guideline is an update to the CPIC guideline for codeine and CYP2D6, but now includes information on two other genes, OPRM1 and COMT, and a number of other opioids.
The guideline gives specific dosing recommendations for CYP2D6 and three drugs; codeine, tramadol and hydrocodone. Codeine and tramadol are not recommended for CYP2D6 ultrarapid metabolizers due to an increased risk of toxicity, nor for CYP2D6 poor metabolizers, where there is a risk of insufficient analgesia. The authors also recommend that CYP2D6 intermediate and poor metabolizers be monitored for analgesic response following the initiation of hydrocodone therapy. If patients with either of these metabolizer phenotypes fail to have an analgesic response to hydrocodone, a non-codeine or non-tramadol opioid can be considered.
Studies looking at the effects of variants in OPRM1 and COMT on opioid response were also assessed as part of this guideline update. OPRM1 encodes the mu opioid receptor, which binds to many opioids, while COMT codes for the enzyme Catechol-O-methyltransferase. COMT methlyates catecholamines and is thought to regulate pain perception. Due to the low quantity and/or quality of available evidence, the authors specifically issued no recommendations for opioid dosing based on OPRM1 or COMT variants. This is an important feature of the guideline, as testing for variants in these two genes in relation to opioid response is included in some pharmacogenomic tests.
In order to fully capture the size and complexity of this guideline, it is covered by multiple guideline annotations on PharmGKB, including extended dosing guidelines with genotype pickers for codeine, tramadol and hydrocodone. All guideline annotations can be accessed via the PharmGKB Clinical Guideline Annotations page. The full guideline manuscript, supplement and relevant implementation resources are also freely available on the CPIC website.
Thursday, January 7, 2021
PharmGKB released a new pharmacokinetics pathway for Cannabidiol, a phytocannabionoid found in the Cannabis sativa plant approved to treat two rare forms of epilepsy.
The pathway was produced by graduate student Kris Oreschak from the University of Colorado Denver Anschutz Medical Campus with guidance from scientific curator Dr Caroline F. Thorn. While there are currently no published studies documenting the pharmacogenomics of cannabidiol, many pharmacogenes are involved in its metabolism presenting future opportunities to look at variable responses.
View all pathways at PharmGKB