PharmCAT Version 1.0 Released

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.

Expanded coverage

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.

VCF preprocessor

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.

Future releases

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.

PharmGKB Tutorial Published

We are pleased to announce that an updated PharmGKB tutorial has now been published in Current Protocols.

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. 

Further information about how to use PharmGKB, including a short guided page tour and training exercises, can be found on our website.

White paper on assigning LOE to clinical annotations now published

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.

Further information about scoring of variant annotations and clinical annotations as well as our LOE system can be found on our website.

New PK pathways for drugs on the CPIC gene-drug pairs list

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.  

Antidepressant:

Trimipramine

Inhaled anesthetics:

Desflurane

Enflurane

Halothane

Methoxyflurane

Sevoflurane

NSAIDs:

Flurbiprofen

Lornoxicam

Meloxicam

A complete list of PharmGKB pathways can be found at https://www.pharmgkb.org/pathways

CPIC releases guideline for MT-RNR1 and aminoglycosides

The CPIC guideline for aminoglycoside antibiotics and variation in the mitochondrial gene MT-RNR1 has been published in the journal Clinical Pharmacology and Therapeutics.

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.

 

The guideline and supplemental materials are available on the CPIC website. An annotation of the guideline, including interactive genotype picker tool, is available on the PharmGKB website.

PharmGKB/CPIC Twitter chat for Healthcare Providers Genomics Education week

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

Expanded Pharmacokinetics Pathways for PPIs

The Proton Pump Inhibitor Pathway, Pharmacokinetics has been updated and expanded to now highlight four pathways with candidate genes and drug metabolites involved for:

Omeprazole and esomeprazole, 

Lanzoprazole and dexlansoprazole, 

Pantoprazole and 

Rabeprazole. 

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).