Wednesday, March 21, 2018

PharmVar Launches CYP2D6, CYP2C9 and CYP2C19 Interactive DB

PharmVar has launched its interactive database for CYP2D6, CYP2C9 and CYP2C19. One cool feature is the ability to now easily find the position of each SNP on different reference sequences. The user can now also choose to count from the beginning of a sequence or the ATG start codon. And by clicking on a SNP of interest  a new page will display on which alleles a SNP is located, its respective positions on each reference sequence and the link to dbSNP. Before getting started, the user is advised to read the STANDARDS Document under the GENES tab describing the conventions used for storing and displaying allelic data. Once on the genes page users are encouraged to check out the READ ME and CHANGE LOG documents – these provide important information for each gene and list the changes that have been made as each gene was transferred into the database. These documents can be found on top of the bar showing the reference sequences.  PharmVar is keeping busy adding more CYP genes into the database and developing additional features. 

PharmGKB and CPIC have updated the Allele Definition Tables for CYP2C9 and CYP2C19, part of the PGx Gene Specific Information Tables, to harmonize with PharmVar.  The CYP2D6 Allele Definition Table will be updated soon.

PharmGKB FAQs: How do I read a clinical annotation?

Clinical annotations are written to summarize associations between variants and drug response and are based on annotations of published literature. As an example, please refer to this clinical annotation for rs4149056 in the gene SLCO1B1 and the drug simvastatin.

Clinical annotations consist of 3 main parts:

1. Genotype/diplotype-based summaries describe the pharmacogenetic associations with specific genotypes or diplotypes (or in some cases, haplotypes) and the association for any given genotype or diplotype is reported relative to the other genotypes and diplotypes. Clinical annotations can include evidence from individual or multiple papers. 

In the example clinical annotation the summaries are for the following genotypes at rs4149056: CC, CT, and TT. Notice that the summaries are written relative to one another.

2. Evidence is based on variant annotations, which report the pharmacogenetic association between a variant (eg. SNPs, indels, repeats, haplotypes, etc.) and a pharmacogenetic phenotype from a single publication. The variant annotations support the assertions in the clinical annotation summaries and are listed underneath. 

In our example, there are eight variant annotations under the summaries that support the assertions of the genotype summaries.

3. Level of Evidence (LoE) is assigned for each clinical annotation and is based on the strength of evidence when assessing all of the variant annotations. LoE ranges from 1- 4, with level 1 meeting the highest criteria, meaning the strongest evidence, and level 4 meeting minimal criteria, meaning the weakest level of evidence. LoE 1 and 2 have two additional categories denoted by letters, A and B so that LoE 1A is higher than 1B. LoE is based on multiple criteria including replication, statistical significance and study size.

The example clinical annotation is a level 1A, the highest evidence. The criteria used for scoring LoE is available here.

Wednesday, March 14, 2018

RIP Stephen Hawking

"However difficult life may seem, there is always something you can do and succeed at. It matters that you don't just give up." - Stephen Hawking
Rest in peace.

Tuesday, March 13, 2018

NIH Requests Input on Draft Strategic Plan for Data Science

The National Institutes of Health (NIH) is drafting a Strategic Plan for Data Science and is requesting feedback from the community.  The amount of data in the biomedical field continues to grow exponentially, leading to "Big Data" and the increasing role of data science to process and analyze the data. The NIH would like to "capitalize on the opportunities presented by advances in data science" in the biomedical field and has drafted a plan that includes strategies, goals and implementation tactics for modernizing the biomedical data science ecosystem funded by the NIH.  The draft of the plan is available online, along with a Request for Information (RFI).  Responses need to be submitted by April 2, 2018 through an online form.

Monday, March 5, 2018

Curators' Favorite Papers

The first paper, by A. Ahmed et al. in the journal Clinical Pharmacology and Therapeutics (Benefits of and Barriers to Pharmacogenomics-Guided Treatment for Major Depressive Disorder), discusses pharmacogenetic (PGx) testing in the context of depression. In a pilot study, 1,002 out of 1,013 (99%) subjects had an actionable variant in CYP2D6, CYP2C9, CYP2C19, SLCO1B1 or VKORC1. With regards to anti-depressants, 79% of subjects had actionable variants in CYP2D6 (metabolizes fluoxetine, paroxetine, nortriptyline, and desipramine), 60% in CYP2C19 (metabolizes citalopram and escitalopram) and 36% in CYP2C9 (metabolizes fluoxetine). The authors posit that wider implementation will still require several steps going forward. In addition to making PGx testing pre-emptive, so that genotype information is already present in patient electronic health records (EHRs), physicians will need to be convinced of the clinical utility of PGx testing for patients with mood disorders.  The authors believe this means generating data from larger studies “conducted by disinterested groups”, studies in adolescents, studies investigating multiple genes and the inclusion of metabolomics to inform genomics.

The second paper by Maciel et al. in the journal Neuropsychiatric Disease Treatment (Estimating Cost Savings of Pharmacogenetic Testing for Depression in Real-World Clinical Settings) describes potential cost-savings associated with pharmacogenetic (PGx) testing in patients with depression. The researchers estimated a one-time cost of USD $2,000 for PGx testing and used a published cost-calculator to estimate cost-savings. Data were from a published double-blind, multi-center, randomized clinical trial of 685 adults who had been diagnosed with depression or anxiety. Patients were randomized to PGx-guided treatment or standard of care (control). The study found that those subjects randomized to PGx-guided dosing and prescribing had significant improvements in clinical outcomes as compared to the control group. Using these data, the study calculated that the cost savings for PGx-testing vs standard of care for patients with depression or anxiety totaled USD $3,962 per year after accounting for the one-time cost of PGx testing.

A new meta-analysis of 522 studies that was recently published in The Lancet concluded that at minimum, short-term treatment with anti-depressants was more effective than placebos in treating acute depression. The study made headlines in major media-outlets and found that some anti-depressants worked better than others, which may lead to more interest in PGx-guided dosing.

You can find PGx-guided drug dosing guidelines from the Clinical Pharmacogenetic Implementation Consortium (CPIC) for tricyclic anti-depressants and selective serotonin re-uptake inhibitors on You may also find more information about the following genes and drugs on PharmGKB:



Friday, March 2, 2018

PharmGKB featured in Stanford Engineering Magazine

PharmGKB was the focus of Dr. Russ Altman’s talk at the 2018 Beckman Symposium held at Stanford in February and is now featured in the latest issue of the Stanford Engineering magazine.

Attendees at the symposium, which focused on technology, innovation and the human genome, were given a guided tour of the PharmGKB website and the different types of information stored in the knowledgebase. PharmGKB currently has information covering around 5,500 genetic variants and over 600 drugs in addition to annotated clinical guidelines, evidence-based drug pathways and integrated information from drug labels from the FDA and other regulatory bodies. Dr. Altman also explained how he uses information from PharmGKB to personalize a patient’s drug therapy in his pharmacogenomics clinic.