Curators' Favorite Papers

The first paper comes from the journal Pharmacogenomics (Clinical pharmacogenetics: how do we ensure a favorable future for patients?) and it discusses the factors that have impeded the implementation of pharmacogenomic (PGx) testing into routine clinical care. Randomized clinical trials (RCT) are the current gold-standard for clinical research for new drug approvals, but the nature of PGx studies is ill-suited to the RCT format. The authors propose alternatives to RCTs such as demonstration of non-inferiority to standard of care, N-of-1 trials for individuals or “hybrid effectiveness-implementation” clinical trials (trials that blend design components investigating clinical effectiveness with implementation research). They also emphasize that implementation of PGx data would require comprehensive, pre-emptive testing, population specific PGx considerations, and storage of results in electronic medical records (EMR) with adequate clinical decision support (CDS) tools. Finally, they make the case for testing for genetic variants with robust evidence of a PGx association, and specifically cite the Clinical Pharmacogenenomics Implementation Consortium (CPIC) as a “promising place to start” in selecting PGx genes to test as well as PharmGKB as a resource for information on gene-drug PGx associations.

The second paper, authored by the International Society for Biocuration (Biocuration: Distilling data into knowledge), comes from the journal PLoS Biology. It explains the specific role that biocurators play within teams that manage biological information resources and databases. Beginning with the premise that data is an asset whose value increases each time it is shared, the authors argue that biocurators maximize value by assuring the “accuracy, comprehensiveness, integration, accessibility, and reuse” of data through the process of extracting knowledge (such as data) from unstructured forms (usually publications) into structured and machine readable forms to enhance its usability and sharing. The authors note that there is encouraging development with regard to data reporting tools, an increase in demand and support for data standards and a growth in the use of biocuration tools by researchers, and all of these are expected to facilitate data curation, data sharing and ultimately, scientific progress.

The Clobazam Pathway on the Cover of Pharmacogenetics and Genomics

One of our newest pathways, the Clobazam Pathway, Pharmacokinetics, is featured on the cover of the April issue of the journal Pharmacogenetics and Genomics. PharmGKB Scientific Curator Dr. Rachel Huddart is the first author on this pathway. Clobazam is a benzodiazepine and is used primarily as an anti-epileptic.  The primary metabolizing enzyme is CYP2C19, but additional metabolizing enzymes include CYP3A4, CYP2B6 and CYP2C18. You can now view the interactive Clobazam Pathway, Pharmacokinetics on PharmGKB.

Curator's Favorite Papers

Genetics has been shown to have a profound effect on response to treatment in patients with major depressive disorder (MDD) and may account for as much as 42% of the variability in treatment response, according to some studies. In addition, personality traits, as defined by “the Big-Five" (openness, conscientiousness, extraversion, agreeableness, and neuroticism), which are likely to also be influenced by genetics, may also associate with response to anti-depression treatment. A new study (Association of the Polygenic Scores for Personality Traits and Response to Selective Serotonin Reuptake Inhibitors in Patients with Major Depressive Disorder) intended to uncover gene variants associated with the cross-trait associations between “the Big-Five” and treatment response using data from the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (N = 529) and the International SSRI Pharmacogenomics Consortium (N = 865). Evidence points to an overlap in association between specific personality traits and SSRI treatment outcomes and that the association is partly due to genetics. Specifically, the study evaluated the combined effect of multiple genes (polygenic score, PGS) and their association with one of the five personality traits, as well as response and remission in patients with MDD who are prescribed SSRIs. The PGS for openness personality with treatment response was statistically significant in the ISPC cohort and statistically significant with remission in the PGRN-AMPS sample. PGS for conscientiousness was associated with response, but not remission. Cross-trait meta-analysis of GWAS uncovered eight overlapping genetic loci with previously reported associations with response to SSRIs as well as certain personality traits, particularly neuroticism.  Of note, several PharmGKB members were co-authors on this study: Katrin Sangkuhl, Scientific Curator, Ryan Whaley, Technical Lead, Russ Altman, Co-PI of PharmGKB and Teri Klein, Director and Co-PI of PharmGKB. 

You can find more information about pharmacogenetic guided dosing for SSRIs on cpicpgx.org as well as on the PharmGKB annotation of the CPIC Guideline for SSRIs and CYP2D6 and CYP2C19.

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.

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.

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.