Wednesday, April 20, 2016
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
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 http://www.rxlist.com/script/main/hp.asp ) and 16 (Pharmacy student study guide http://www.pharmacy-tech-study.com/memorize-the-top-200-drugs.html) 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 cpicpgx.org. 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
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 guideline gene 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 email@example.com. Thank you!