PharmCAT Version 2.0 Released

Version 2.0 of the Pharmacogenomics Clinical Annotation Tool (PharmCAT) has been released October 20, 2022. PharmCAT is a software tool that takes the genetic data (VCF file) of an individual as input, interprets the pharmacogene alleles, diplotypes, and phenotypes, and generates a report with genotype-based drug prescribing recommendations. 

In version 2.0, we have made substantial improvements and changes to the tool based in part on user feedback. Below we outline these features and provide links to read more about each one. We have also slightly reorganized and expanded the documentation found on the PharmCAT website to better support users. 

We hope you will take a look and send any comments, questions or feedback to pharmcat@pharmgkb.org. 

Major PharmCAT v2.0 features:

• PharmCAT now includes available DPWG prescribing guidance (as annotated in the PharmGKB DB) in addition to CPIC recommendations. Read more about the genes and drugs included from DPWG and how they are sourced 
• PharmCAT report has been redesigned for multiple recommendation sources 
• Added functionality for research use only 
• CYP2D6 diplotype calling based on SNPs and INDELs from a VCF file (does not include structural variants/CNVs). Warning: Because structural variation and haplotype can’t be determined from VCF, this functionality shouldn’t be used for clinical purposes. 
• Partial and combination calls for novel combinations of PGx positions included in the PharmCAT allele definitions. This functionality helps users determine if a sample potentially contains a novel allele. 
• Added support for the DPYD genotype based on the lowest activity score as described in CPIC’s fluoropyrimidine guideline (PMID: 29152729) for samples with more than two DPYD variants.
• Extended support of external genotype/phenotype input (see input and output examples and outside call format). This functionality allows users to include genetic test results from other sources in PharmCAT’s report. 
• Reworked PharmCAT command line tool/arguments. These changes are not backwards compatible with previous PharmCAT versions. 
• VCF Preprocessor updates 
• Harmonized the VCF preprocessor command line arguments and flags with PharmCAT
• Unified the output file name patterns of the VCF preprocessor with what PharmCAT uses
• Added a few amenities, e.g., bcftools and bgzip version check, switch to .bgz file suffix for clarity 

 The latest PharmCAT version and extensive documentation is available on PharmCAT.org

There is no ontology term for Phenoconversion in BioPortal

There are two papers ahead of print in Pharmacogenomics both discussing how important phenoconversion is to consider in the implementation of PGx in clinical practice. Phenoconversion in the PGx context is a drug-drug interaction that impacts a drug metabolizing phenotype such that it mimics the effects of a metabolizer genotype. Yet there was no match for phenoconversion in a search of BioPortal (on 10/3/2022) which has over a thousand biomedical ontologies including MeSH, MedDRA, RxNorm and other ones we use for PGx. PharmGKB does collect drug-drug interaction information from drug labels and publications that can potentially be used in the future to help account for phenoconversion. However, while phenoconversion is a well-known phenomenon, the specifics of how phenoconversion affects patient phenotype, especially on top of genotype, has not been quantified (to our knowledge). This makes it difficult to apply drug-drug interaction information to predict how patient genotype-to-phenotype mapping should be altered by information about concomitant drugs the patient takes when using prescribing guidance from CPIC, DPWG or FDA.

Paper 1: Pharmacogenomics in psychiatry - the challenge of cytochrome P450 enzyme phenoconversion and solutions to assist precision dosing. Mostafa S, Polasek TM, Bousman CA, Müeller DJ, Sheffield LJ, Rembach J, Kirkpatrick CM.Pharmacogenomics. 2022 Sep 28:0. doi: 10.2217/pgs-2022-0104. Online ahead of print. [PMID: 36169629]

This review proposes a model for improved clinical decision support that integrates genomics, co-prescribing information, lifestyle and disease factors into precision dosing. Excerpt from the paper: “In psychiatry, the proposed CDSS (Clinical decision support system) powered by MIPD (model-informed precision dosing) would apply precision dosing of psychotropics by accounting for the influence of genetic variations in CYPs; the presence of CYP phenoconversion; and coexisting lifestyle (smoking), pregnancy or disease (cancer) factors…. In this study, clozapine concentrations were better predicted by MIPD accounting for the CYP1A2 inducing effect in smokers homozygous for the CYP1A2*1F allele. This is an example of where environmental (smoking) and PGx (CYP1A2 genotype) factors were used to optimize the MIPD model, resulting in improved predictions of clozapine plasma concentrations. In principle, this approach can be applied across other psychotropics, especially those with a high risk of toxicity in overdose (e.g., tricyclic antidepressants).”

Paper 2: The importance of phenoconversion when using the CYP2D6 genotype in clinical practice. Cicali EJ, Wiisanen K.Pharmacogenomics. 2022 Sep;23(14):749-752. doi: 10.2217/pgs-2022-0087. Epub 2022 Sep 14. [PMID: 36102178]

This is an editorial with a case study describing a patient with chronic pain taking tramadol (among other medications). The patient is then started on an antidepressant and the pain is no longer relieved even at higher doses. Even though the patient tests as a CYP2D6 normal metabolizer the antidepressant fluoxetine has resulted in phenoconversion and clinically the patient now responds as a CYP2D6 poor metabolizer with respect to tramadol. They discuss options to change the antidepressant or the pain therapies. The authors caution that “CYP2D6 genetic test results should be continually evaluated in the light of concomitant medications throughout a patient’s lifetime.”

Searching PubMed to see the impact of phenoconversion is complicated as this word is also used to describe change or evolution of disease phenotypes, but the results by year tracker shows exponentially increased use. A phenoconversion tag specific for drug interaction related phenoconversion, would help people in PGx research identify the relevant papers.

Maybe phenoconversion could be added as a child term to MedDRA under Drug-drug pharmacokinetic interaction?

New tutorial and walkthrough videos now available

We are pleased to announce the release of our PharmGKB walkthroughs and tutorial videos on YouTube. Users can now view detailed video walkthroughs of each of the main annotation types on PharmGKB as well as a longer video combining resources from across the site. We have also produced a series of tutorial videos to help users learn more about key concepts and issues in pharmacogenomics. These videos range from an introduction to the field to an explanation of the star allele nomenclature system for haplotypes. The videos are freely available on the PharmGKB YouTube channel. Links can also be found on our Educational Resources page. 

PharmGKB and Reactome collaborate on two pathways

PharmGKB has collaborated with Reactome for two new pharmacokinetics pathways, Ribavirin, and Prednisone and Prednisolone available via both formats:

Ribavirin Pathway, Pharmacokinetics 

on Reactome 

on PharmGKB

Prednisone and Prednisolone Pathway, Pharmacokinetics 

on Reactome

on PharmGKB

 

Study of vitamin K pathways presents potential new warfarin candidates

Almost two decades after the cloning of VKORC1 and association with warfarin dose, a new pathway of vitamin K cycling has been published. Mishima et al [PMID:35922516] report on the Ferroptosis suppressor protein 1 (FSP1), coded for by the AIFM2 gene, and its mechanism of maintaining vitamin K in the hydroquinone form. While not interacting directly with warfarin, AIFM2 may play a role in vitamin K-related branches in the warfarin PD pathway that warrant investigation for PGx. 

A review of several ferroptosis related proteins (Vabulas, 2021) discusses some variants of AIFM2. The review mentions a functional study of E156A in the FAD cofactor binding domain that found it impaired anti-ferroptotic activity. This variant is not found in dbSNP. A different amino acid change, E156D (rs1272224219C>A), has not been observed in the ALFA populations that dbSNP reports on, while yet another amino acid change, E156V (rs760393626T>A), is extremely rare (found in 1/121216 alleles). The review lists 2 other potential AIFM2 candidates for functional investigation which are more frequently observed: M135T (mapped by PharmGKB to rs10999147A>G) and D288N (mapped to rs2271694C>T).

(Edited 9/20/22) The Warfarin Pathway, Pharmacodynamics has been updated to include the new candidate gene.

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PharmVar GeneFocus paper for SLCO1B1 is published

The PharmVar GeneFocus: SLCO1B1 paper has just been published by Clinical Pharmacology & Therapeutics.

 

This review provides a general overview of SLCO1B1 as well as a deeper dive into its nomenclature. This GeneFocus covers genetic variability, functional impact, clinical relevance, gene nomenclature before and after PharmVar updates, methods for allele characterization and how the new nomenclature impacts pharmacogenetic testing and interpretation. Specific details of changes to allele definitions can be found on the PharmVar SLCO1B1 page and on the Change Log tab of the SLCO1B1 Allele Definition Table available from PharmGKB. This new nomenclature has been used in the recently published CPIC guideline on statin-associated musculoskeletal symptoms.

 

For more details, please see:

PharmVar GeneFocus: SLCO1B1

Clin Pharmacol Ther. 2022 Jul 7. doi: 10.1002/cpt.2705. 

Laura B. Ramsey, Li Gong, Seung-been Lee, Jonathan B. Wagner, Xujia Zhou, Katrin Sangkuhl, Solomon M. Adams, Robert J. Straka, Philip E. Empey, Erin C. Boone, Teri E. Klein, Mikko Niemi, Andrea Gaedigk.

PMID: 35797228

CYP2A6 now released on PharmVar

PharmVar and PharmGKB are excited to share that CYP2A6 has been transitioned into the PharmVar database. CYP2A6 metabolizes several substates including coumarin, nicotine, aflatoxin B1, nitrosamines, and some pharmaceuticals. Owing to its highly polymorphic nature, CYP2A6 activity varies considerably between individuals. Due to the complex nature of the CYP2A gene locus that contains not only CYP2A6, but also the highly similar CYP2A7 and CYP2A13 genes, CYP2A6 genotype analysis and characterization of allelic variants is not trivial. It is therefore of utmost importance to have up-to-date information regarding sequence variation and star allele (haplotype) definitions to facilitate accurate genetic testing, data interpretation and phenotype prediction in the research and clinical settings.  

The PharmVar CYP2A6 gene experts have systematically reviewed and curated all star allele definitions that were previously issued by the CYP450 Nomenclature databases (these were last updated in 2014 and can be accessed through the archive). Some notable changes include:

• Variants and star alleles are now defined using the most current genomic reference sequence
• Several alleles have been merged, revised and/or redesignated (legacy allele designations are cross-referenced)
• Regions used for allele definitions have been updated
• Structural variants including a common conversion at the 3’UTR have been updated to current knowledge and are detailed in the ‘Structural Variation’ document.

Changes made are detailed in the ‘Change Log’ document and other important information about CYP2A6 and the information displayed by PharmVar can be found in the ‘Read Me’ document. All accompanying documents can be accessed at the PharmVar CYP2A6 page at https://www.pharmvar.org/gene/CYP2A6.  

 

Since there are no CPIC guidelines for CYP2A6, the PharmVar CYP2A6 page does not provide information for ‘CPIC clinical function’. The expert panel has, however, compiled a table in the ‘Read Me’ document summarizing function information for selected star alleles.  

 

Lastly, we would like to thank the PharmVar CYP2A6 experts Rachel Tyndale, Alec Langlois, Meghan Chenoweth, Giada Scantamburlo, Charity Nofziger, David Twesigmwe, Rachel Huddart and Andrea Gaedigk for their tireless efforts that made this massive update possible.