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Pharmacogenetics is the study of how genetic differences among individuals cause varied responses to a drug. Clinicians can tap into a substantial knowledge base about how certain genetic variations are predictors for how a patient will respond to a wide variety of very commonly prescribed medications for immediately relevant clinical applications today. [1] For example, if the same drug is given to 4 different people, the rate at which they are able to metabolize the drug will vary. Similarly, patients with the same diagnosis receiving similar prescriptions will not only have varying efficacy when taking the drug but also may have adverse reactions independent of how efficacious the drug may be.

Impact of Adverse Drug Reactions

It’s estimated that 40% of the total costs of non-hospital related adverse drug events (ADE’s) are preventable. The total cost of adverse drug events in healthcare is about $3.5B/year in the US. [1] Estimates also suggest that if medication issues were classified as a disease, they would be the 4th largest cause of death in the United States. [2] The rate of adverse drug events requiring hospitalization is nearly seven times greater for patients over the age of 65. About 2/3rds of nursing home patients experience ADE’s and 1 in 7 require hospitalization. Furthermore, adverse drug reactions are a cost leader contributing to the continued escalation in the expense for malpractice. [1]

Precision Medicine Initiative

The Precision Medicine Initiative is a research project created by Obama in 2015 with $215 million in funding that aims to make advances in tailoring medical care to the individual. [3] The project aims to collect genetic and health data from one million subjects. The initiative was announced during the 2015 state of the union address. One specific subset of the broader context of precision medicine is Pharmacogenomics (PGx) in which the ultimate goal is to reduce adverse effects to medications, lower the cost of therapies, and improve efficacy. It’s about getting the right treatment, for the right person, at the right time and in the right dose.

Barriers to Precision Medicine [8]

1. Limited genetic proficiency of clinicians

2. Limited available genetics experts

3. Growth of genetic knowledge base

The current practice is a “one size fits all” approach that ends up being a trial and error medicine where the focus is on maintenance therapy rather than cure or prevention. [4] The by product is enormous waste because treatment is only effective half the time. The hope of personalized or precision medicine is to be able to identify those who are predisposed to a disease allowing for earlier intervention where the focus is now on prevention and leveraging this data to allow for more targeted therapies. [4]

Pharmacogenomics Historical Milestones

510 BC: Pythagoras, a greek philosopher, noted that ingestion of fava beans resulted in hemolytic anemia in certain individuals [5]

1906: English physiologist Archibald Garrod was the first to propose that genetic variants might account for varying drug responses. He also came up with the concept of “chemical individuality” of man. His experiment tested patient’s ability to taste a foreign chemical. Garrod was testing for the inability to taste (taste blindness) to phenylthiocarbamide (PTC). It was found that taste blindness is inherited as an autosomal-recessive trait [5]

1957: Friedrich Vogel coined the term "pharmacogenetics" to describe the genetic-based differences in drug response [5]

1959: Several WWII soldiers who were given the anti-malarial drug Primaquine developed anemia which led to the discovery of G6PD deficiency [5]

2003: The human genome is sequenced [5]

2005: First FDA approval of a pharmacogenetic test (CYP2D6 and CYP2C19) [5]

Pharmacogenomics Knowledge Bases (PharmGKB)

PharmGKB is a pharmacogenomics (PGx) knowledge resource that encompasses clinical information including dosing guidelines and drug labels giving potentially clinically actionable gene-drug associations and genotype-phenotype relationships. [6] The PharmGKB collaborates with groups carrying out PGx research as well as those who are bringing PGx into the clinic.

Clinical Pharmacogenetic Implementation Consortium (CPIC)

The Clinical Pharmacogenetic Implementation Consortium or CPIC, formed in 2009, writes drug dosing guidelines based on an individual's genotype. These drug dosing guidelines are then found on pharmGKB along with those published by other working groups. (i.e., Dutch Pharmacogenetics Working Group (DPWG)). [7] Clinical Pharmacogenetic Implementation Consortium (CPIC) is also responsible for the creation and dissemination of peer-reviewed, freely available genotype-based drug-dosing guidelines for clinicians. The overall goal was to accelerate the implementation of research discoveries in pharmacogenomics into the clinic. CPIC guidelines are designed to help clinicians understand how available genetic test results should be used to optimize therapy. This is based on the assumption that you will start to see patients getting preemptive genotype testing earlier in life and healthcare providers will be faced with how to handle all these results. [9] The CPIC informatics working group was created in 2013 to focus on the application of CPIC guidelines in electronic health records (EHR) and clinical decisions support (CDS). [10] The goal is to support the adoption of CPIC guidelines by identifying and resolving where possible potential technical barriers to implementation of the guidelines within a clinical electronic environment. [10] EHR vendors do not yet provide a standard set of CDS functions for pharmacogenetics, so the working group set out to develop and systematically incorporate a set of EHR agnostic implementation resources into all CPIC guidelines so they may be used at the point of care. [14]

Other National PGx Knowledge Resources

Other national efforts linked with CPIC are also making strides toward developing a repository of pharmacogenomic knowledge. The Clinical Genome Resource known as ClinGen is an emerging NIH National Human Genome Research Institute (NHGRI)–funded project dedicated to building an authoritative central repository that defines the clinical relevance of genomic variants for use in precision medicine and research. [11] [14] The Institute of Medicine has convened a group of stakeholders in a collaborative effort dubbed “DIGITizE: Displaying and Integrating Genetic Information Through the EHR” to work on scalability, privacy, security and storage issues. [12] The DIGITizE Action Collaborative is focused on increasing support for clinical genomics within the EHR. It approached this goal by initially focusing on creating support for a very specific set of use cases. These use cases are being used to bring relevant stakeholders together to produce functionality that improves clinical care. [14] 2 NIH funded groups the Emerge and Ignite network are collaborating to develop a new resource the CDS knowledge Base to catalog and share CDS implementation artifacts and design considerations for genomic medicine programs from a broad community. [13]

Key Technology and Information Gaps

1. Genomic data is huge and the meaning of data might change as the knowledge base changes. There must be support for long term reinterpretation of results over time; new genomic knowledge may prompt new recommendations for previously reported variants that either contradict or supersede previous recommendations and there needs to be a procedure to not only reconcile these differences but to disseminate this knowledge [14]

2. Also, the data standards themselves are immature and interpretation is often difficult and requires expertise

3. Interoperability between different care platforms continues to be a challenge

4. As far as the data and information sources for clinical decision support, one needs to understand what the needs are for and availability of data and information

5. A set of standardized terminology and data exchange standards will need to be developed. There will need to be a list of common semantics among disparate systems allowing for seamless exchange of information; key data elements include which variants were interrogated, phenotype terms, and medications involved in the gene drug interaction [14]

6. It is important to establish an “integrated knowledge environment” to connect structured with text based database repositories

7. Knowledge resources must be able to rate level of evidence of each variant as well as for the overall recommendation. With the transition to next gen sequencing the number of identified variants relevant to drug metabolism will grow exponentially making it even more of a critical step to report level of evidence at each variant level [14]

8. It must be able to integrate with other knowledge at the point of care: to allow multiple recommendations to be integrated at the point of care [14]

Infrastructure challenges

1. Preserving this data for future reanalysis and automatic this reanalysis if and when new findings are published

2. Delivering clinical guidance to the right person and place. The biggest challenge is having a consistent interpretation to all caregivers across what now can be a fragmented health care delivery system that has limited interoperability [14]

3. Secure communication to not only patients but also their families









8. Welch & Kawamoto et al. JAMIA, 2012. Figure 1 Retrieved from:






14. Developing knowledge resources to support precision medicine: principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC). James M. Hoffman; Henry M Dunnenberger; J Kevin Hicks; Kelly E Caudle; Michelle Whirl Carrillo; Robert R Freimuth; Marc S Williams; Teri E Klein; Josh F Peterson. Journal of the American Medical Informatics Association 2016; doi: 10.1093/jamia/ocw027

Submitted by Gargi Schneider