The PharmGKB Blog has transitioned to the ClinPGx Blog

 

We have a brand new blog platform: the ClinPGx Blog.

PharmGKB, CPIC and PharmCAT are transitioning to a unified resource under the ClinPGx umbrella and will subsequently publish news to the ClinPGx Blog. This is the last post made on this blog site; all future posts will be found on https://blog.clinpgx.org. If you subscribe to announcements from the PharmGKB Blog, your subscription will be migrated to the new system automatically. You’ll also get the opportunity to sign up for announcements from other ClinPGx projects. More details are available on the ClinPGx Blog.

New blog posts will only appear on the ClinPGx Blog and previous PharmGKB Blog posts are also accessible there, so all posts can be obtained from one place. The PharmGKB Blog will remain online (but not updated) for as long as Blogger will allow. That means existing links to PharmGKB Blog posts will be maintained.

Thank you for your continued interest in PharmGKB, CPIC, PharmCAT and all the other projects we have written about on this blog. We are excited to keep sharing our work with you on the new ClinPGx Blog!

PharmGKB data now available in OpenTargets

Data from PharmGKB clinical annotations are now integrated into OpenTargets; a free online tool which brings together data from publicly available datasets to help prioritize potential drug targets.

The Pharmacogenetics section of the profile for both drugs and targets (genes) now shows information from PharmGKB clinical annotations, including the phenotype category and the Level of Evidence. By hovering over the question mark next to the phenotype, the full text of one phenotype description from the clinical annotation can be displayed, as in these images for OPRM1.

Information from toxicity-related clinical annotations is now also displayed in the Safety section for targets, as shown for HLA-A below.

We’re excited to be involved with OpenTargets’ work and hope to be able to integrate more data from PharmGKB in the future. For more information, visit the OpenTargets blog.

Update to the CPIC Guideline for Thiopurines Regarding Dual TPMT and NUDT15 Intermediate Metabolizers

CPIC has updated guidance for patients who are both TPMT and NUDT15 Intermediate Metabolizers from "Clinical Pharmacogenetics Implementation Consortium Guideline for thiopurine dosing based on TPMT and NUDT15 genotypes: 2018 Update". From the CPIC website:

"At the time of guideline publication, the extent of reduction for thiopurines recommended for patients with intermediate metabolism for both TPMT and NUDT15 was unclear. A recent publication (PMID: 38230823) found that these individuals need a substantial dose reduction to mitigate toxicity in TPMT/NUDT15 IM/IM patients. The recommendation for a TPMT intermediate metabolizer/NUDT15 intermediate metabolizer has been updated for all thiopurines to recommend a starting dose at 20%-50% of normal dosages, depending on the starting dose. See here for updated recommendation tables (azathioprine, mercaptopurine, thioguanine). The pre- and post-test alert tables have been updated accordingly."

Please find the new wording and tables on the PharmGKB guideline annotations for azathioprine, mercaptopurine and thioguanine.  The CPIC database and website have also been updated.

PharmCAT Tutorial Videos Now Available on YouTube

Pharmacogenomics Clinical Annotation Tool (PharmCAT) tutorial videos are now available on the PharmGKB YouTube channel. The tutorial videos provide clear, step-by-step instructions for running PharmCAT from command lines. By providing genotype-based drug prescribing recommendations, we hope to engage the wider genomics community and create a standard for the use in precision medicine.

The first two videos cover (1) the introduction to PharmCAT, modules, and reports and (2) a hands-on example that walks you through the setup and commands for running PharmCAT.

Future videos will cover how to supply external pharmacogenomic calls to PharmCAT, how to use ‘Research’ modes, how to use the PharmCAT functionalities for biobank-scale analyses, and more.

 
More information about PharmCAT can be found at https://pharmcat.org/. To follow PharmCAT, subscribe to the PharmGKB YouTube channel for new videos or the PharmCAT mailing list for the latest PharmCAT updates.

If you have questions regarding PharmCAT, please contact us at pharmcat@pharmgkb.org.

NAT2 now released on PharmVar

PharmVar and PharmGKB are excited to share that NAT2 has been transitioned into the PharmVar database and updated accordingly on PharmGKB. NAT2 metabolizes several pharmaceutical substrates, including isoniazid, hydralazine, amifampridine, procainamide and sulfonamides, as well as some highly carcinogenic arylamines. NAT2 enzymatic activity varies considerably between individuals, due to polymorphisms in NAT2 coding sequence that may be found more commonly in some populations than others. NAT2 alleles usually contain more than one single nucleotide variation (SNV), with specific variants defining different allelic groups. Given the complex haplotypic nature of NAT2 alleles, phasing of SNVs to determine diplotype from genotype can be difficult. It is therefore important to maintain up-to-date information regarding sequence variation and star allele (haplotype) definitions to facilitate accurate genetic testing, genotype interpretation and phenotype prediction in the research and clinical settings.

Some drastic changes have been made to NAT2 nomenclature during transition from the original Database of Arylamine N-Acetyltransferases (NATs) to the new “star” allele definitions on PharmVar:

The NAT2 reference allele has now changed: The NG_012246.1 RefSeq differs from X14672.1 (which was used in the past to define star alleles) at the position that corresponds to c.803 (rs1208), where X14672.1 has “A” while NG_012246.1 has “G”. Thus, depending on which reference sequence is utilized, this position is reported as either reference or variant. The transition of allele definitions to the NG_012246.1 RefSeq caused “variant switching”, meaning that all star alleles which were previously described as having c.803A>G lost this variant, as “G” is now considered reference, while all other star alleles gained c.803G>A, as “A” is now considered variant. 

 

The new reference allele is now catalogued as NAT2*1: The sequence of NG_012246.1 RefSeq corresponds to the allele that was formerly described as NAT2*12A. Its renaming as NAT2*1.001 facilitates the application of PharmVar rules to NAT2 allelic nomenclature, while it also enables the use of NAT2*1 name to describe the reference allele, in line with the star allele nomenclature of other pharmacogenes. To avoid confusion during the transition from the legacy to the new nomenclature, *12 is now retired. Having a NAT2*1 allele grouping in place also facilitates NGS analyses using GRCh38 as a reference.

 

NAT2*4 is no longer considered as the reference allele: Alleles carrying c.803G>A as their only amino acid changing SNV will be called as *4 according to the new nomenclature. This includes the former NAT2*4 reference allele of sequence X14672.1, now considered a variant and listed as NAT2*4.001. Although this will no longer be used as a reference, it is considered to be functionally equivalent to NAT2*1.001 (formerly NAT2*12A) and may still be used to compare the enzymatic or structural properties of polymorphic NAT2 proteins. 

 

Star alleles have been renamed: Several NAT2 star alleles have been renamed to conform to PharmVar rules during the transition from the legacy nomenclature to the PharmVar database.

 

Not all previously defined star alleles have been transferred: In the past, it has not always been clear how a NAT2 haplotype was determined. Many have been inferred via computational phase analysis and were not verified experimentally. In the case of haplotypes where the available evidence from the literature was deemed insufficient to support confident allele definition, those haplotypes were not transferred to PharmVar, but they will remain posted on the original Database of Arylamine N-Acetyltransferases (NATs).

 

PharmGKB-annotated NAT2 alleles that are not transitioned into PharmVar will remain on PharmGKB with the original Database of Arylamine N-Acetyltransferases (NATs) name (e.g., NAT2*6J). PharmVar-transitioned NAT2 alleles are indicated on PharmGKB NAT2 allele pages with the new "PharmVar Allele" tag (e.g., NAT2*4).

 

The requirements for new allele definition have changed: In the past, reporting only the SNVs in the NAT2 coding region was sufficient to define new star alleles. According to PharmVar rules, new NAT2 alleles must cover SNVs within defined coordinates that enclose the 5’ untranslated region (including the untranslated first exon), the coding exon, the exon/intron junctions, and the entire 3’ untranslated region relative to genomic reference sequence NG_012246.1. Former alleles covering only the coding region have been annotated with a limited evidence level, as they may have additional variants that were not captured when the allele was first defined.

 

During the course of updating NAT2 nomenclature, we have been able to confirm several of the former haplotypes, while also identifying new ones. PharmVar encourages submissions of novel haplotypes for star allele definition, as well as for existing definitions to either raise their evidence levels from Limited or Moderate to Definitive, or to solidify their status of Definitive. 

Additional information has been summarized in the Read Me and Change Log documents available on the PharmVar NAT2 page. Also please consult the “NAT2 Look-up” table available under “More Documents” on the same page for an up-to-date record of alleles transferred and a quick reference of PharmVar and legacy star allele names. 

We would like to thank all members of the PharmVar NAT2 Gene Expert Panel for their massive contribution to this project, as well as the NAT Gene Nomenclature Committee for their services to the NAT community since 1998.

ClinPGx 2024 Registration and Abstract Submission Open

 

REGISTRATION OPEN

In collaboration with CPIC, PharmGKB, PharmCAT and PharmVar, the Penn Institute for Biomedical Informatics will be hosting the ClinPGx 2024: Knowledge, Implementation, Education meeting on June 20th and 21st, 2024 in Philadelphia, PA. This meeting will provide educational content to cover all aspects of PGx implementation, including knowledgebases, implementation strategy, informatics, use of AI in precision medicine, clinical laboratory insights, and more.

Participants are invited to submit abstracts for the poster sessions. You must be registered for the meeting before your abstract will be accepted.

Please note breakfast & lunch will be provided all conference days.

Registration and detailed agenda

REMINDERS / DEADLINES:

March 22, 2024: Deadline for Abstract Submission. Abstract submission form link here.

April 30, 2024: Deadline for $350 Registration Fee (Starting May 1st, registration fee will be $450)

May 20, 2024: Deadline for Hotel Reservations. Click HERE for hotels offering special rates.

June 1, 2024: Deadline to register for the event.

Release of Additional PharmGKB Pediatric Drug Summaries

A new round of PharmGKB's pediatric drug summaries is now available on PharmGKB under the Pediatric Focus.

To view the site with the focus active, please select the "Focus" button in the upper right-hand corner and toggle the "Pediatric Focus" setting.

Users can also access the Pediatric Focus on the Focus landing page, or by typing pediatric.pharmgkb.org. 

These pediatric drug summaries contain key information relevant to prenatal, postnatal, and pediatric populations, manually curated from PharmGKB annotations, pathways, CPIC guidelines, and FDA-approved drug labels.

Drugs with newly available summaries include:

• Busulfan
• Cisplatin
• Cladribine
• Dapsone
• Doxorubicin
• Etoposide
• Fluindione
• Fluticasone propionate
• Gemcitabine
• Gemtuzumab ozogamicin
• Imatinib
• Irinotecan
• Lamivudine
  

PharmGKB continues to add to the list of drugs with pediatric summaries. To browse all drugs with available pediatric summaries, please check out the PharmGKB Pediatric Dashboard.

 

Updates to Drug Label Annotation Tags and Criteria for PGx levels

PharmGKB has made some changes in the Drug Label Annotation process to clarify the pharmacogenomic (PGx) relationships of biomarkers. PharmGKB curates all drug labels on the FDA’s Table of Pharmacogenomic Biomarkers in Drug Labeling and previously attempted to classify all labels with PharmGKB-created “PGx levels” . However many of the relationships do not fit standard pharmacogenomic definitions. It is clear from over a decade of label curation that many labels, including many on the FDA Biomarker list, mention genes, variants, chromosomal rearrangements, testing, drug-drug interactions and other incidental information but do not explicitly provide pharmacogenomic clinical guidance or information. Accordingly, we have altered the PGx level definition for “Informative PGx”, and created a new PGx level “Criteria Not Met” and a new drug label annotation tag "Indication”.

PharmGKB also attempts to identify and curate drug labels from other regulatory agencies for drugs with annotated FDA labels. Labels from other regulatory agencies may or may have similar information to the FDA-approved label, but sometimes the label cannot be found after a search. Previously, if a label was not found, the entry on the Drug Label Annotations Landing page was left blank. However, if a search has not yet been done, the entry is also blank, which led to ambiguity. PharmGKB now states when a label is not found.  

The main changes we are implementing are:

1. to define where the testing requirement is part of the indication of the drug.

The Indication tag is defined as “The gene or variant is an Indication for the drug, or the drug is for use in a specific population defined by the gene or variant(s), according to the label.” There are many anti-cancer drug examples of this including adagrasib and afatinib, and currently 128 out of 182 indication-tagged drug labels regard variants and genes in the cancer genome. Additional non-cancer examples are alglucosidase alfa, a hydrolytic lysosomal glycogen-specific enzyme indicated for patients with Pompe disease, and desmopressin, a synthetic vasopressin analog that is FDA approved to increase plasma levels of factor VIII activity in patients with hemophilia A (F8 deficiency) and von Willebrand’s disease Type I. 

2. to update the criteria for "informative PGx" level and to indicate where the information on the label does not meet our criteria for pharmacogenomics with tag "Criteria not met". Previously, these would have been lumped into “Testing required” and “Informative PGx” tags, respectively.

The Informative PGx level is now defined as “The label contains information stating that particular gene/protein/chromosomal variants or metabolizer phenotypes do NOT affect a drug’s efficacy, dosage, metabolism or toxicity. Or, the label states that particular variants or phenotypes affect a drug’s efficacy, dosage, metabolism or toxicity, but this effect is NOT “clinically” significant”.

The Criteria not met level is defined as "the labels appear or appeared on the FDA Biomarker List but do not currently meet the requirements to be assigned as “Testing required”, “Testing recommended”, “Actionable PGx” or “Informative PGx”.  PharmGKB annotates every label that appears on the FDA Biomarker list, regardless of whether we would otherwise annotate the label. PharmGKB also checks European Medicines Agency (EMA), and Health Canada/Santé Canada (HCSC) databases for the same drug label and annotates them if they contain pharmacogenomics information and applies the PGx level tags as appropriate.”

An example that changed from "informative PGx" to "criteria not met" is blinatumomab, which is listed on the FDA table of biomarkers with BCR-ABL and CD19. The mechanism of action is via CD19 but the label does not have information on variants of CD19 and any changes in efficacy or toxicity. The label includes information on clinical studies that included patients with Philadelphia chromosome negative Refractory B-cell Precursor ALL.

Example where labels language is different across the different agencies:

Lomitapide (https://www.pharmgkb.org/chemical/PA166114922/labelAnnotation) - FDA and HCSC criteria not met, EMA testing required.

Mavacamten (https://www.pharmgkb.org/chemical/PA166272922/labelAnnotation) -  FDA and HCSC label for Mavacamten has actionable PGx whereas the EMA label requires CYP2C19 genotyping. 

3. To show when a search was performed for the label but the curator was unable to find the document at the stated repository. 

Entries in the Drug Label Annotations landing page now show “No document available” if a label could not be found. See an example screen shot from the landing page below.

4. To enable accession to the label source directly for FDA and EMA labels.

At the bottom of the drug label annotation page, there is a section titled “Source” that has a link to download the drug label that is the source of the information in the annotation. This label is typically also available in the body of the annotation. The downloadable PDF file is usually highlighted with relevant PGx and drug-drug interaction information. See an example screen shot from the label annotation for abacavir below.

When we began annotating FDA-approved drug labels over a decade ago, we did not anticipate how widespread the use of our label annotation tags would become. Many groups have published papers that discuss PGx and FDA labels using the relative numbers of our tags (e.g. PMIDs 26693845, 25317785, 25521333, 29205206, 29722046, 34412102, 28073152). In 2012 we had 113 label annotations from the FDA; we now have over a thousand label annotations across global sources. As stated in our blog from 2013, “Curators have tried to define clearly how they interpret the level of PGx information on labels, resulting in definitions that are really a rather long list of criteria. These definitions may change over time as new label wording comes up and definitions need to adjust for these new cases.” Indeed, the PGx level definitions have been tweaked over the years to accommodate the unique nuances of labels encountered during the curation process, and we continually work to apply the changes across labels from the FDA and then cross-check against other international regulatory bodies, such as the EMA and HCSC. We are pleased that the community had found our label annotations useful and strive to improve and expand the annotations for future use. We hope these updates make the drug label annotations more consistent and evidence more transparent. Please contact us at feedback@pharmgkb.org with questions.

CPIC Guideline for Potent Volatile Anesthetic Agents and Succinylcholine - RYR1 variant update

The original guideline, supplement, and the supporting gene and drug files are available on the CPIC website. Annotations of the guideline, including an interactive genotype picker tool are available on the PharmGKB website.

Subsequent to the publication of the CPIC Guideline for the Use of Potent Volatile Anesthetic Agents and Succinylcholine in the Context of RYR1 or CACNA1S Genotypes (PMID 30499100), the ClinGen malignant hyperthermia susceptibility (MHS) variant curation expert panel (VCEP) developed and published recommendations for RYR1 pathogenicity classifications in malignant hyperthermia susceptibility (PMID 35849058). CPIC has added an additional 291 variants and updated the RYR1 allele definition, frequency, and functionality tables accordingly (additional details can be found in the notes tab of the RYR1 allele functionality table). 

Pathogenic and likely pathogenic variants are assigned a CPIC function of Malignant Hyperthermia associated, variants of uncertain significance (VUS) are mapped to uncertain function, and benign and likely benign variants are assigned normal function. 

Additionally, CPIC has created a RYR1 diplotype to phenotype table. RYR1 phenotypes are determined based on the function combinations of two RYR1 variants. The RYR1 diplotype to phenotype table contains all possible combinations of two RYR1 variants included in the RYR1 allele functionality file. In case >2 variants are found, the variants with Malignant Hyperthermia associated function should be used first. If 2 Malignant Hyperthermia associated variants are found, those are assumed on different chromosomes.

New Fluoropyrimidine Toxicity Variants Reported in DPYS and PPARD

A recent paper highlights new variants and genes associated with severe fluoropyrimidine-related toxicity in patients who were genotyped as negative for the four DPYD variants the European Medicines Agency recommends testing for.

Online ahead of print in the Human Genomics journal is De Mattia et al "The burden of rare variants in DPYS gene is a novel predictor of the risk of developing severe fluoropyrimidine-related toxicity" [PMID: 37946254].

The study sequenced 120 patients with fluoropyrimidine induced grade 3-5 toxicity confirming they lacked DPYD*2A, DPYD*13, c.2846A > T, c.1236G > A-HapB3. The paper reports rare and common variants including DPYS rs143004875-T and PPARD rs2016520-T which were associated with increased risk of severe toxicity.

Save the Date: ClinPGx 2024 Meeting

In collaboration with CPIC, PharmGKB, PharmCAT and PharmVar,  the University of Pennsylvania, Penn Center for Precision Medicine will be hosting the ClinPGx 2024: Knowledge, Implementation, Education meeting June 20^th and 21^st, 2024 in Philadelphia, PA. The meeting will provide educational content to cover all aspects of PGx implementation including knowledgebases, implementation strategies, informatics, use of AI in precision medicine, clinical laboratory insights, and more. Additional details to soon.

Frequencies of Pharmacogenomic Alleles across UK Biobank Biogeographic Groups Published

We are happy to announce the publication of our latest research article in the American Journal of Human Genetics (AJHG), titled “Frequencies of Pharmacogenomic Alleles across Biogeographic Groups in a Large-Scale Biobank.” The paper is now available online on the AJHG website.

Genetic biobanks provide rich data sets to investigate population-specific pharmacogenomic (PGx) allele frequencies and the implications of equitable and inclusive implementation. Using an integrated UK Biobank 200K genetic dataset (N = 200,044), we estimated the pharmacogenomic (PGx) allele frequencies for seventeen (17) pharmacogenes in five (5) biogeographic groups. 

• Pharmacogenes included ABCG2, CACNA1S, CYP2B6, CYP2C19, CYP2C9, CYP3A5, CYP4F2, DPYD, G6PD, IFNL3, NUDT15, RYR1, SLCO1B1, TPMT, UGT1A1, and VKORC1. CFTR and the CYP2C cluster variant (rs12777823) were also investigated. CYP2D6 alleles that could be determined from VCF, and therefore not including structural variants, were also predicted.
• The five (5) biogeographic groups are Europeans (n = 187,660), Central/South Asians (n = 3,460), East Asians (n = 637), African Americans/Afro-Caribbeans (n = 1,926), and Sub-Saharan Africans (n = 1,235)
• Frequencies of PGx alleles, diplotypes, phenotypes, and/or activity scores, if applicable, were reported for each gene in each biogeographic group.

We found that 100% of the UK Biobank participants harbored at least one genetic variant found in known PGx haplotypes. 

The main takeaways messages are:

1. UK Biobank PGx frequencies complemented the CPIC frequency tables.

2. This study reported frequencies for a nontrivial number of PGx alleles that are rare or seldom tested, especially in the non-European group.

3. There are uncataloged PGx alleles.

PGx frequencies estimated from this study will be disseminated via PharmGKB. Biobank-derived allele frequencies can provide guidance for future PGx studies and clinical genetic test panel design, and better serve individuals from wider biogeographic backgrounds.

Disulfiram Pathway published in Pharmacogenetics and Genomics

Substance abuse disorders are a significant public health cost [PMID: 34453125]. Prescribers have limited choices for pharmaceutical therapies with only three FDA-approved choices for alcohol use disorder and zero for cocaine use disorder. Disulfiram is an FDA-approved treatment for alcohol use disorder which is also used for cocaine use disorder. There are some preliminary studies on the PGx of disulfiram but little replication.

PharmGKB together with Dr Aneysis De Las Mercedes Gonzalez (Stanford University School of Medicine), have published PharmGKB summary: disulfiram pathway in Pharmacogenetics and Genomics, 2023 Sep 21. doi: 10.1097/FPC.0000000000000509. Online ahead of print. PMID: 37728645. It summarizes the candidate genes involved in disulfiram PGx in pharmacokinetics and action in dopaminergic, seratonergic and noradrenergic neurons and highlights the knowledge gaps.

As always interactive versions of the diagrams with underlying linked evidence, are available on PharmGKB:

Disulfiram Pathway, Pharmacokinetics,

Disulfiram Pathway, Pharmacodynamics (Cocaine and Ethanol PK), 

Disulfiram Pathway, Pharmacodynamics (Dopaminergic neuron), 

Disulfiram Pathway, Pharmacodynamics (Serotonergic neuron), 

Sympathetic Nerve Pathway (Neuroeffector Junction)

Please Take This Survey If Your Site Conducts DPYD Genetic Testing Prior to Fluoropyrimidine Chemotherapy

Dan Hertz (DLHertz@med.umich.edu) and the DPYD Implementation Team are collecting information from sites and clinicians in the USA that conduct DPYD genetic testing prior to fluoropyrimidine chemotherapy treatment. If this applies to you, please complete this brief (<5 minutes) survey on behalf of your site before mid-September. This information will be used to develop best practice guidelines for pre-treatment DPYD testing. 

https://umich.qualtrics.com/jfe/form/SV_9Fjv2HdyQ6K6MU6

 

Thanks for your participation!

New AMP testing recommendations for alleles in CYP3A4 and CYP3A5

The Association for Molecular Pathology, Clinical Pharmacogenetics Implementation Consortium, College of American Pathologists, Dutch Pharmacogenetics Working Group of the Royal Dutch Pharmacists Association, European Society for Pharmacogenomics and Personalized Therapy, and Pharmacogenomics Knowledgebase have jointly published recommendations on what constitutes the minimum panel of variant alleles (tier 1) and an extended panel of variant alleles (tier 2) for CYP3A4 and CYP3A5 [PMID:337419245].

Tier 1 CYP3A4*22, CYP3A5*3, CYP3A5*6, CYP3A5*7

Tier 2 CYP3A4*20

PharmGKB maintains the lists for these alleles and the tier 1 and tier 2 alleles previously published for CYP2C19, CYP2C9, VKORC1, TPMT. NUDT15 at https://www.pharmgkb.org/ampAllelesToTest

When on an allele page there is a tag to show if this allele is part of the recommended test set:

When on a gene page there is a tag to show the gene has an AMP set of test alleles plus there is a link to the AMP page from the VIP summary. 

Insights on CYP2C19 and phenoconversion

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 (see blog from October 2022). Historically much of the discussion on phenoconversion has focused on CYP2D6.

A new paper in Frontiers in Pharmacology investigates the phenoconversion effects of different CYP2C19 inhibitors [PMID:37361233]. 

Forty donor liver samples were genotyped for CYP2C19 *2, *3 and *17 and the metabolizer phenotypes predicted. Microsomes were assayed with the probe drug s-mephenytoin and then in the presence of strong CYP2C19 inhibitor fluvoxamine, moderate inhibitors omeprazole and voriconazole and weak inhibitor pantoprazole to look at changes in metabolizer status. 

Excerpts from paper:

“Our results demonstrate that the outcome of a DDI is dictated by both inhibitor strength and CYP2C19 activity, which is in turn dependent on genotype and non-genetic factors including comorbidities. … 

Fluvoxamine, a strong inhibitor of CYP2C19, caused 86% of *1/*17 donors to become phenotypically IM, whereas most of genetically-predicted IMs were converted to a PM phenotype (57%). In accordance with unaltered CYP2C19 activity in patients with gastroesophageal reflux disease taking pantoprazole, weak inhibition by pantoprazole did not induce phenoconversion…

However, the outcomes of DDIs with moderate inhibitors (omeprazole/voriconazole) matched less well to the proposed phenoconversion model by Mostafa et al, which predicted that NMs/IMs convert to a PM phenotype upon moderate inhibition of CYP2C19. In our study, voriconazole, which acts as a moderate CYP2C19 inhibitor, significantly reduced the drug metabolizing capabilities of CYP2C19 by approximately one level (i.e., from a phenotypic NM to a IM). As a result, 40% of the donors (12/30) were converted into IM or PM phenotypes by voriconazole. Though, none of the NMs were converted into PMs, except for one donor who already exhibited impaired CYP2C19 activity in the absence of voriconazole treatment (basal phenoconversion). For omeprazole, phenoconversion into IM or PM phenotypes was even less frequently seen, in only 10% of the donors …

Altogether, our data suggest that CYP2C19 inhibition by moderate inhibitors can result in phenoconversion, but it seems unlikely to result into a PM phenotype for wild-type *1/*1 genotypes.”

There are a number of interesting results and discussion points:

• There is phenoconversion from disease phenotype - namely diabetes. 
• The initial concordance for genotype to phenotype with s-mephenytoin was only 40% and the two CYP2C19*17/*17 did not have ultra-rapid UM phenotype (with Vmax in the low normal NM range). The discussion mentions “other (rare) genetic variants within CYP2C19 could also have influenced the mismatch between predicted and observed activities in our study” but it would have been useful to have ruled out *4. The *17/*17 did produce functional mRNA but the *4 is in the start codon and its impact is on translation not transcription [PMID: 9435198]. 
• There were two *1/*1 outliers with very high UM phenotype that would be interesting to see further genetic analysis of especially given the escitalopram UM CYP2C-haplotype defined by rs2860840T and rs11188059G in [PMID: 33759177]. 

Overall, this paper shows that while phenoconversion exists for CYP2C19 based on disease status and DDIs, the impact is not a simple downgrading of phenotype (e.g. from IM to PM) that can be applied in a consistent manner across subjects. The authors show that even for strong inhibitors, phenoconversion happens in 40%-86% of subjects with no clear way to predict which subjects would experience phenoconversion and which wouldn’t. More research on how DDIs alter patient-predicted genotype to phenotype  is needed to enable better prediction of patient phenotype for PGx drug dosing recommendations.