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Owusu Obeng et al. J Transl Genet Genom 2021;5:64-79 I http://dx.doi.org/10.20517/jtgg.2020.52 Page 65

Keywords: Pharmacogenomics, pharmacogenetic testing, personalized medicine, precision medicine,
phenoconversion, pharmacogenetics in special populations, pharmacogenetics

INTRODUCTION
Medical professionals have long observed that some individuals respond differently to treatments than
expected. Standard therapeutic doses have been proven to be ineffective and even harmful for some
[1,2]
individuals . Such interpatient variability can be attributed to a wide host of factors, including the
[3]
genetic makeup of the individual . Pharmacogenetics - the study of how genes influence one’s response to
[4]
medications - is an application of personalized medicine with clinically actionable examples . Elements
necessary for clinical implementation of pharmacogenetics such as robust and consistently replicated
scientific evidence, affordable and high-throughput testing technologies, electronic health records
(EHR)-enabled clinical decision support (CDS), and expert-driven resources for pharmacogenetics-
guided therapeutic recommendations, are all available and in use at some academic and non-academic
institutions . However, its widespread adoption is still lagging behind. Efforts from Pharmacogenomic
[4,5]
Knowledgebase (PharmGKB) and professional societies such as the Clinical Pharmacogenetic
Implementation Consortium (CPIC) and the Dutch Pharmacogenetic Working Group (DPWG) are
[4,6]
actively ongoing and helping to overcome the challenge related to evidence and clinical guidance . Early
adopter and implementation programs in collaboration with federal networks/consortia are also working
[7]
to address the remaining challenges and provide process best-practices for this discipline . For instance,
patient education materials and protocol resources have been developed by the Clinical Sequencing
Evidence-Generating Research (CSER) (https://cser-consortium.org/). The Electronic Medical Records and
Genomics (eMERGE) Consortium investigators developed validated electronic phenotyping algorithms
named Phenotype Knowledge Base (https://phekb.org/) and also curated member-sites’ implemented
clinical decision support rules on their Clinical Decision Support Knowledge Base (https://cdskb.org/). In
addition, the Implementing Genomics in Practice (IGNITE) Network has developed the Spark Toolbox
(https://gmkb.org/ignite-toolbox-overview/) which hosts educational and implementation resources for
researchers and clinicians.

[8]
Most clinicians agree that genetics impact response to medications , and are favorable towards
pharmacogenetics in general ; however, their lack of appreciable knowledge of pharmacogenetic utility in
[9]
[9]
practice have led to some resistance and slow uptake .
The purpose of this manuscript is to provide stakeholders, particularly clinicians, who are interested in
pharmacogenetic implementation with pre-implementation preparatory steps and other considerations for
effective clinical adoption. We also discuss available pharmacogenetic resources, how to select genes and
laboratories for testing and how to interpret and utilize the results to guide prescribing decisions [Table 1].
This manuscript is intended to provide a broad overview of pertinent points to consider prior to embarking
on integration of this discipline into routine practice. Several actionable pharmacogenetic drug-gene pairs
associated with psychiatry, pain, cardiology, and others are highlighted throughout the paper as examples
for the various discussion points.

SELECTING THE MEDICATIONS, GENES, AND THE LABORATORY FOR TESTING
The medications
As of August 2020, there are 24 CPIC guidelines covering approximately 46 medications from cardiology to
pain, infectious diseases, oncology, psychiatry, and others. A full list of the guidelines can be accessed via
the CPIC website (https://cpicpgx.org/guidelines/). The United States Food and Drug administration (FDA)
has also included pharmacogenetic information in the drug labeling for 279 drugs . This information is
[10]