ClinGen Pharmacogenomics Working Group (PGxWG) Survey

The ClinGen Pharmacogenomics Working Group (PGxWG) has launched a survey to solicit feedback about the criteria and terminology that should be used to define clinical validity and actionability for pharmacogenes and variants. 

The ClinGen PGxWG is a multi-disciplinary team of researchers and professionals with expertise in pharmacogenomics (PGx), clinical pharmacology, medical genetics, regulatory affairs, and molecular diagnostics. It was launched in February 2022 with the goal of developing a framework of tiered standard terminology and definitions that reflect clinical significance for genes and genomic variants implicated in drug response, in order to facilitate the incorporation of PGx knowledge into ClinGen and more consistent interpretation of PGx variants identified by panel testing and/or sequencing.

The survey is open now and can be accessed at: https://stanforduniversity.qualtrics.com/jfe/form/SV_bKqeKf2YmCVS1LM.  All responses are greatly appreciated, no matter who you are or where you are in the world. This survey does assume some PGx familiarity, though we plan to launch another survey targeting those with less PGx experience in the near future. The survey takes approximately 10 minutes to complete. We sincerely appreciate your time and attention, and your willingness to help.

PharmVar updates for CYP3A4 star allele definitions

 PharmVar announces several updates for CYP3A4 star allele definitions.

Retirement of the CYP3A4*1G allele: this allele was defined by a common variant in intron 10 (c.1026+12G>A) which was also found on many other haplotypes (or star alleles). PharmVar transiently designated the CYP3A4*1G allele as *36 due to a possible role of c.1025+12G>A being involved in the regulation of CYP3A4 expression. However, owing to the growing body of inconsistent findings regarding associations of c.1026+12G>A and higher or lower expression levels and/or CYP3A4 activity, PharmVar withdrew this redesignation in January 2023 (v5.2.17) which led to the retirement of the CYP3A4*36 (former *1G) allele. Per PharmVar rules, intronic variants are only utilized for star allele definitions if there is convincing evidence that the variant impacts protein function. Therefore, c.1026+12G>A was also removed from all other star allele definitions.

CYP3A4 gene regulation is complex and appears to be governed by a layer of processes, among them long noncoding RNAs, microRNAs and transcription factors which may also influence CYP3A5 activity. Furthermore, there is substrate overlap between CYP3A4 and CYP3A5 and thus, variation in the CYP3A5 gene, further complicates the characterization of CYP3A4 allele function. Investigators are encouraged to include c.1026+12G>A in their carefully designed investigations to produce conclusive evidence regarding the functional impact of c.1026+12G>A.      

We would also like to highlight the addition of a novel star allele, CYP3A4*38 which is characterized by two variants which on their own define CYP3A4*3 and *11. Noteworthy, the CYP3A4*3-defining variant c.1334T>C (p.M445T) has also been found together with the intronic SNP defining CYP3A4*22; this allele was designated CYP3A4*37. Consequently, samples heterozygous for these SNPs could have CYP3A4*1/*37 or *3/*22 or *1/*38 or *3/*11 genotypes, respectively. Since the functional impact of c.1334T>C (p.M445T) remains elusive it is unknown whether alternate genotypes differ in function.

Lastly, the evidence level of several alleles has been updated from ‘Limited’ or ‘Moderate’ to ‘Definitive’ indicating that these alleles are now fully characterized. 

These efforts were only possible by the dedicated work of the PharmVar Team and the CYP3A4 gene experts for volunteering their time and expertise.

CYP4F2 is now fully curated by PharmVar

CYP4F2 contributes to the synthesis of cholesterol, steroids and other lipids. It has been shown to regulate the bioavailability of vitamin E and vitamin K, a co-factor that is critical to blood clotting. Variations in this important pharmacogene can affect vitamin K levels and thus, the dosing of vitamin K antagonists such as the widely used anticoagulant drug warfarin (CPIC level A and PharmGKB 1A evidence level) among others.

We are excited to announce that CYP4F2 is now fully curated by PharmVar and its gene page content reviewed by an international expert panel. Furthermore, the PharmVar Team has generated new data to provide a more comprehensive catalog of genetic variation of this gene. Not only have the two previously defined CYP4F2*2 and *3 now been fully characterized, several other novel haplotypes (or star alleles) have been identified and designated by PharmVar. Notably, the new and relatively commonly observed CYP4F2*4 allele has both sequence variants that otherwise define *2 (c.34T>G, W12G) and *3(c.1297G>A, V433M), respectively while the other three novel star alleles (CYP4F2*5, *6 and *7) are each characterized by a single amino acid change. Interestingly, CYP4F2*5 and *6 appear to be absent or rare in Asian populations; in contrast, *7 seems to be mostly present in African populations and their descendants. These new star alleles may contribute to unexplained variability in daily warfarin dosage requirements in non-White populations. We encourage the research and clinical communities to include this new knowledge in their investigations.

PharmGKB selected in the first list of Global Core Biodata Resources

We are pleased to announce that PharmGKB is included in the first list of Global Core Biodata Resources (GCBRs), a collection of resources whose long term funding and sustainability is critical to life science and biomedical research worldwide.

The Global Biodata Coalition (GBC) brings together major public and charitable funders, with the aim to “stabilize sustainable financial support for the global biodata infrastructure and in particular to identify for prioritized long-term support a set of Global Core Biodata Resources that are crucial for sustaining the broader biodata infrastructure.” After a rigorous two-stage process evaluating scientific quality and impact, 37 resources were selected in the first list of GCBRs. One key feature of the GCBRs is that the data from these resources are available openly and can be accessed and used without restriction by researchers worldwide. PharmGKB is honored to be recognized as a Global Core Biodata Resource and we fully support GBC's mission to stabilize support for the global biodata infrastructure.

We would like to take this opportunity to thank all the present and past members of PharmGKB, our funding agencies, scientific advisors and collaborators, and especially our users, for their continued support and contribution to build this vital resource. PharmGKB serves both basic science investigators as well as clinicians and laboratories. Sustainable long-term support is critically important for us to provide stable, comprehensive, and dependable pharmacogenomic information to our users across the globe.

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 feedback@pharmgkb.org. We also encourage users who are interested in collaborating on a drug pathway to contact us.

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?