Wednesday, April 20, 2016

Introducing the new PharmGKB Cancer PGx Portal

PharmGKB has collected a number of resources for Cancer PGx into one easy location. There are tables with direct links to genes important for cancer drug response both for PD and PK, to cancer drug pathways, particular cancers that have PGx data, types of toxicities common to cancer drugs, and external resources.

Eight new VIP gene pages give a short text based summary of important genes for cancer drug response. These are for the genes ALK, ABL1, BCR, BRAF, ERBB2 (HER2), KIT, KRAS and NRAS. Anyone with expertise in the genes who wishes to develop these with us for publication in PG&G, please contact feedback.

There is a shortlist of drug labels for cancer drugs with biomarker PGx.

We currently have 34 anti-cancer agent drug pathways with 8 new pathways in development. The portal gives shortcuts to a selection.

PharmGKB currently uses a flat ontology for diseases, which means that the Neoplasms disease page does not link to the many different cancers we have data for. The cancer portal has direct links to the cancers for which there is the most PGx information in the knowledgebase, such as pediatric ALL, CML, colorectal, breast, renal and non-small cell lung cancers. The portal also has links to the common types of toxicities with PGx data.

Finally there is a collection of external links that are useful for Cancer PGx.

Tuesday, April 19, 2016

Top 100 Prescribed Drugs with CPIC Guidelines

Pharmacogenetics has important consequences for some of the most commonly prescribed medications. Depending on the source and structure of the list, the 100 most commonly prescribed drugs include between 3 (RxList from the Medscape report of August 2014 ) and 16 (Pharmacy student study guide drugs with CPIC recommendations.  CPIC guidelines offer recommendations for changes to drug choice or drug dose based on patient genotype, with important implications for efficacy and toxicity, and can be found at  In the RxList, the drugs with CPIC guidelines are rosuvastatin, simvastatin, and atorvastatin. In the pharmacy study list, the drugs with CPIC guidelines are citalopram, escitalopram, warfarin, clopidogrel, amitriptyline, paroxetine, sertraline, oxycodon, codeine, allopurinol, and the 5 statins simvastatin, rosuvastatin, pravastatin, atorvastatin, and lovastatin. In addition, the illustrated pharmacokinetic and pharmacodynamics pathways developed and freely available on the PharmGKB website contain 37 of the drugs found on the pharmacy study list.

Friday, April 15, 2016

Pharmacogenomics Clinical Annotation Tool (PharmCAT)

An active area of genomic medicine implementation at many health care organizations and academic medical centers includes development of decision support and return of results around pharmacogenomics.  The Clinical Pharmacogenetics Implementation Consortium (CPIC) has established guidelines surrounding gene-drug pairs that can and should lead to treatment modifications based on genetic variants.  One of the challenges in implementing pharmacogenomics is the representation of the information in the CPIC guidelines (including star-alleles) and extracting these variants and haplotypes from genetic datasets.  In a collaboration between the PGRN Statistical Analysis Resource (P-STAR), the Pharmacogenomics Knowledgebase (PharmGKB), the Clinical Genome Resource (ClinGen), and CPIC, we are developing a software tool to extract all CPIC level-A variants from a genetic dataset (represented as a vcf), interpret the variant alleles, and generate a report.  The CPIC pipeline report can then be used to make future treatment decisions. 

We assembled a focus group of thought leaders in pharmacogenomics to brainstorm and design the software pipeline.  We hosted a one-week Hackathon at the PharmGKB at Stanford University to bring together computer programmers with scientific curators to implement version one of this tool.  We will host a meeting to summarize and evaluate next steps in mid-May including the development of a manuscript and dissemination plan for the tool.  This software pipeline will be made available in a Creative Commons license and disseminated in GitHub later this spring for all in the scientific community to test, explore, improve and to give us feedback.  Be a part of this project!  As many of our institutions are building implementation workflows for pharmacogenomics, our ability to automate some of the extraction of genes/variants of interest would be enormously helpful.

We welcome scientific input from our colleagues.  If you would like to become involved in this effort, we ask that you contact  Thank you!

Thursday, March 31, 2016

CPIC Informatics Working Group publishes article in JAMIA

An article from the Clinical Pharmacogenetics Implementation Consortium (CPIC) Informatics Working Group has now been published ahead-of-print on the Journal of the American Medical Informatics Association (JAMIA) website. Within the paper, the authors discuss principles that will support the implementation of precision medicine, particularly pharmacogenomics, into routine clinical care.                                                                                

Hoffman et al. write that successful adoption of pharmacogenomics in the clinic requires a database of knowledge that can be used in an electronic health record (EHR) with Clinical Decision Support (CDS). To this end, the CPIC Informatics Working group has been developing and incorporating EHR vendor-agnostic resources into CPIC guidelines. Based on this experience, the working group outlines five principles that can apply to future knowledge resources that implement precision medicine. Though the principles are mainly in reference to pharmacogenomics, they are applicable any type of precision medicine initiative. The five principles cover the limitations of current genetic testing methods, the importance of levels of evidence, integration of different types of medical knowledge and handling of new knowledge, and the use of standardized terminology. By laying out these principles, the CPIC Informatics Working Group provides a reference for the design and implementation of future, national-level precision medicine resources.

CPIC is a shared project between PharmGKB and the Pharmacogenomics Research Network. Read more about CPIC and the Informatics Working Group at

Read the paper on JAMIA:

Developing knowledge resources to support precision medicine: principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC). James M. Hoffman; Henry M Dunnenberger; J Kevin Hicks; Kelly E Caudle; Michelle Whirl Carrillo; Robert R Freimuth; Marc S Williams; Teri E Klein; Josh F Peterson. Journal of the American Medical Informatics Association 2016; doi: 10.1093/jamia/ocw027.

Friday, March 25, 2016

DPYD testing saves money as well as prevents fluoropyrimidine toxicity

A recent article [PMID: 26573078] and editorial [PMID: 26644533] in The Journal of Clinical Oncology looked at prospective testing for DPYD *2A and prevention of grade 3 or higher toxicity. The study examined a large number of patients (n=2,038) and identified 22 carriers of DPYD *2A who then received a reduced dose of fluoropyrimidines or alternative drug regimen, as per CPIC and DPWG guidelines. The FDA and EMA drug labels for fluorouricil and capecitabine do not currently recommend DPYD testing prior to fluoropyrimidine therapy.

Deenen et al stated that “the risk of grade ≥ 3 toxicity was thereby significantly reduced from 73% (95% CI, 58% to 85%) in historical controls (n = 48) to 28% (95% CI, 10% to 53%) by genotype-guided dosing (P <0.001); drug-induced death was reduced from 10% to 0%.”

The authors found when average total treatment costs were examined, “the cost per patient was lower for screening (€2,772 [$3,767]) than for nonscreening (€2,817 [$3,828]), outweighing screening costs. “

Although several DPYD variants have been reported to decrease DPD activity the frequency of each are very low, (in the range of 1%, see supplementary table 3 from the CPIC guideline) which has been considered a barrier to testing.  As the editorial states “The per-genotype cost of genotypic tests is continually decreasing, and studies of pre-emptive genotyping have suggested that large-scale, multitarget tests could further drive down that cost in the future”

A follow up study from the same authors is already underway and will look at DPYD*2A, rs67376798, rs56038477/HapB3 and DPYD*13

The full dosing guidelines, including details for all functional variants of DPYD, and drug labels regarding DPYD and fluoropyrimidines can be found on PharmGKB.

Friday, March 18, 2016

The Wall Street Journal Discusses PGx

An article in the Wall Street Journal this week by Health Editor Melinda Beck called “Is Your Medicine Right for Your Metabolism?” discusses the current state of pharmacogenetics and obstacles to implementation of pharmacogenetics (PGx) in routine clinical care. The article and an accompanying video explain that variations in enzymes can drastically alter the metabolism of many commonly prescribed medications and may affect drug response, efficacy, and safety. The article goes on to discuss how some professional medical groups have hesitated to implement PGx testing because of insufficient evidence from large randomized controlled trials, but it also highlights medical research centers such as Vanderbilt University, the University of Pittsburgh, the Mayo Clinic, and St. Jude’s Children’s Research Hospital that are currently implementing PGx testing into care. 

Information about FDA approved drug labels mentioned in the article as well as detailed information about all of the genes and drugs discussed in the article can be found by searching PharmGKB. In addition, some of the cited gene-drug pair interactions such as CYP2D6 and codeine, clopidogrel and CYP2C19, warfarin and CYP2C9, and statins and SLCO1B1, are also the subjects of Clinical Pharmacogenetic Implementation Consortium (CPIC) dosing guidelines, which are available on the CPIC and PharmGKB websites.  For more information about research institutions currently implementing pharmacogenomics, see the lists on the PharmGKB and CPIC websites.

Integrating PGx throughout the Drug Development Process

Nelson, et al. argue for integrating more pharmacogenomic research into drug screening and the pre-approval process, with benefits for patient outcomes and drug development. Their article  “The genetics of drug efficacy: opportunities and challenges,” published this week in Nature Reviews Genetics reviews currently known associations of genetic variants with drug efficacy. In addition to using other sources, they selected genes from the list of CPIC guidelines. Clinical annotations describing the relationships and evidence for the variants that appear on their list of robust associations (Table 1 of the review), such as for the association of CYP2D6 and codeine efficacy, can be found on the PharmGKB website.

With nearly all pharmacogenetic relationships discovered post-approval, dozens of drugs currently on the market and in development are likely to benefit from clinical application of pharmacogenetics. Many relevant studies simply haven’t been done. Additionally, with genetic testing platforms becoming progressively cheaper, now at $1K for whole genome sequencing, it is possible to interrogate rare variation in addition to common variation, which until now has limited the discoveries of pharmacogenetic relationships to common variants with large effect sizes. 

Even while the majority of drugs may not have clinically useful genetic predictors of efficacy, Nelson, et al. have calculated that somewhere between 2 and 20% of drugs likely do, with important implications for clinical outcomes. We would like to add that beyond their statistic for drug efficacy, genetic variation can have important implications for drug safety, including risk of toxicity and hypersensitivity reactions. By integrating pharmacogenetic research earlier and more comprehensively into the drug development process, the benefit of personalized medicine related to efficacy AND safety can achieved more quickly and more completely, with additional benefits for understanding the biological mechanism of drug activity and targeted development of future drugs.