Tuesday, November 10, 2020
In the sections pharmacogenomic mechanisms, pharmacogenomic evidence and guidelines for psychiatry, and pharmacogenomic testing in psychiatry the review provides an up-to-date summary of recent developments that clinicians should know when considering PGx testing for their patients. It summarizes and discusses the evidence that was considered by the International Society of Psychiatric Genetics (ISPG) for a statement about “Genetic testing and psychiatric disorders” updated in 2019.
The author group concludes that PGx testing should be viewed as part of the decision supporting measures to assist implementation of good clinical care, highlighting CYP2D6, CYP2C19, HLA-A, and HLA-B. For further details read the review [PMID: 33147643].
Thursday, November 5, 2020
Clinical Pharmacogenetics Implementation Consortium (CPIC) has released the first version of its database and API to the public. The CPIC database is a relational database containing data from CPIC's guidelines in structured formats. These data include the guideline manuscript recommendations and information from all of the supporting guideline tables, including gene variant and allele frequencies, function assignments, definitions, diplotype-phenotype mappings and example CDS (clinical decision support) language, as well as example drug-based pre- and post-test alerts and CDS flow charts. Mappings of CPIC gene and drug names to multiple vocabularies/terminologies are also available. The CPIC database can be accessed through the CPIC API or via whole database exports. The API is a RESTful interface and allows access to all parts of the currently defined data model. Documentation for how to use the API with examples can be found in the database documentation. Versioned, whole-database exports can be found in each release on GitHub.
We encourage users to please read the extensive documentation about the data models and formats. Different guideline gene-drug pairs require slightly different models which is explained in the documentation. An understanding of these differences is critical to use the data appropriately. Additionally, each guideline has unique caveats and nuances that can only be fully appreciated by reading the guideline itself, so we encourage users to read the guidelines when accessing and using data from the CPIC database.
We anticipate and encourage user feedback from the community. Please contact us at email@example.com with questions and comments.