A widely-held vision arising from the Human Genome Project is to use information on genomic variation to guide preventive and therapeutic decision making in individual patients. One therapeutic area that seems especially ripe for an early test of this concept is pharmacogenomics (PGx) – the idea that variability in response to a wide range of drug therapies includes a genomic component. Variability in response to therapy is an accepted feature of virtually all drug treatments and the adoption of contemporary molecular biologic tools since the 1980s has resulted in the definition of key genes mediating drug metabolism, transport, and targets. Importantly, common variation in these genes is an increasingly well-recognized contributor, sometimes with large effects, to variability in drug responses, and as a result, recommendations for genotype-guided therapy are being disseminated.
The goals of the eMERGE II PGx project include:
- Developing technical and regulatory solutions to integrate genomic information into the Electronic Health Record (EHR) in a useable fashion;
- Assessing physician and patient attitudes towards the value of these data;
- Beginning to develop strategies to educate physicians and patients in the use of genomic data; and
- Expanding our knowledge of clinically-significant genetic variants.
To accomplish these goals, we will genotype patients seen at the Northwestern Medical Group (NMG) General Internal Medicine (GIM) clinic on the PGRN-Seq platform in a CLIA-approved environment, integrate a number of these genetic variants into our EPIC-based EHR, develop decision-support for GIM physicians surrounding these variants, and track outcomes relating to implementation of these genetic test results, including physician actions and patient and physician attitudes and concerns.
The PGx Project builds upon processes already developed for phase II of the eMERGE II parent grant. These include relationships with physician and patient advisory committees, methods to evaluate patient and physician attitudes and expectations for return of genomic results, use of clinical decision support tools for returning results, and methods for storing and parsing the test data.