Wednesday, November 9, 2022

CYP2C18 and knowledge gaps

Pablo Zubiaur & Andrea Gaedigk have an editorial online ahead of print in Pharmacogenomics calling for the inclusion of CYP2C18 in more studies of drug metabolism [PMID: 36331025].

CYP2C18 is in a cluster on chromosome 10 that has CYP2C18, CYP2C19, CYP2C9 and CYP2C8 that spans 500k bases (NCBI gene browser). The authors comment that CYP2C18 is only included in three PharmGKB pathways (there are actually four: clobazam, diclofenac, warfarin and acenocoumarol), while the other genes of the CYP2C locus are in many. CYP2C19 and CYP2C9 have a volume of data annotated in PharmGKB, CYP2C8 is less populated and CYP2C18 has little (see table below). Similarly, CYP2C19, CYP2C9 and CYP2C8 have haplotypes in PharmVar, while CYP2C18 does not. As Zubiaur and Gaedigk discuss, there are comparatively very few PGx peer-reviewed papers about CYP2C18 and PharmGKB depends on these publications for annotations and pathway creation. We strongly agree with the authors that more studies regarding CYP2C18 would be valuable contributions to the field and look forward to curating them into PharmGKB as they are published.

Like many grant-funded projects, PharmGKB is a small team with limited resources and is unable to manually curate all of PubMed, so sometimes papers are published that we have not yet curated. We encourage PharmGKB users to contact us anytime if they identify important papers for us to curate. If users find knowledge gaps or have recommendations for additional pathway candidate genes, please send the relevant references to We also encourage users who are interested in collaborating on a drug pathway to contact us.

Thursday, October 20, 2022

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 

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

Tuesday, October 4, 2022

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?

Monday, September 26, 2022

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

Thursday, September 22, 2022

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 

Prednisone and Prednisolone Pathway, Pharmacokinetics 

Monday, August 29, 2022

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.

Friday, July 29, 2022

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 interpretationSpecific 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