Imagine a scenario by which a doctor can access a genetic profile of a patient that would guide selection of drugs and drug doses that would be optimally therapeutic for the patient’s conditions and without side effects. This scenario describes the ideality of clinical pharmacogenetics for precision medicine. How close are we to this ideality?

The association between variants in genes coding for drug-metabolizing enzymes and drug response is now well-understood for hundreds of gene-drug pairs. Evidence-based clinical guidelines have been published for over 30 of those pairs by the Clinical Pharmacogenetics Implementation Consortium (CPIC) (1). Widely-accepted gene-drug pairs include HLA-B–abacavir in the treatment of HIV, TPMT–thiopurines, such as the immunosuppressant drug mercaptopurine, and CYP2C19–clopidogrel in anti-platelet therapy. Genotyping patients for indicated genetic variants can determine the likely degree of drug metabolism, which is classified as poor, intermediate, normal, rapid, or ultrarapid. From this classification, prescribers can identify the potential need for altered drug doses or deviations to other therapies in order to prevent ineffective treatment or adverse drug reactions.

Pharmacogenetic testing may be ordered reactively (a targeted testing approach at the time of a specific drug prescription) or preemptively (a broader testing approach before it is known that a particular drug may be needed). With a reactive approach, the patient’s therapy may be delayed, or possibly improperly prescribed, as the prescriber awaits test results. With advances in molecular multiplexing approaches, preemptive testing is increasingly available. Preemptive testing is advantageous in that it allows mutation status to be available in the medical chart at the time it is decided therapy is needed, and it can guide dosing decisions from the start. Several academic medical institutions have now described the implementation of preemptive pharmacogenotyping initiatives and their benefits. Our recent review article summarizes many of these initiatives (2).

Will the preemptive genotyping approach gain widespread clinical practice? It is worth noting that many of these programs to date have been supported by research funding, and broad preemptive genotyping panels may not be reimbursed or affordable for the average patient outside of these environments. Further, preemptive genotyping for precision medicine requires advanced clinical decision support (CDS) tools within the electronic health record (EHR) to maintain the variant statuses and prompt their consideration when an impacted drug is prescribed. While these tools are not currently available by default across all EHR vendors, solutions are in development. Hicks and colleagues described the incorporation of relevant clinical decision alerts, including a pop-up alert for poor CYP2C19 metabolizers in the setting of voriconazole prescribing at St. Jude Children’s Research Hospital (3). Finally, questions remain surrounding the standardization of pharmacogenetic testing across different laboratories, as there exist inconsistencies in which variants are tested for and the naming notation for these variants. With the recent release of new CPIC guidelines for proposed consensus terms for standard pharmacogenetic test resulting, standardized CDS tools may became more widely adopted (4). Thus, the ideality of clinical pharmacogenetics for precision medicine may be closer than we think.


  1. Clinical Pharmacogenetics Implementation Consortium. Guidelines.
  2. Chambliss AB and Marzinke MA. Clinical Pharmacogenetics for Precision Medicine: Successes and Setbacks. The Journal of Applied Laboratory Medicine 2018, 3(3): 474-486.
  3. Hicks JK, Dunnenberger HM, Gumpper KF, Haidar CE, Hoffman JM. Integrating pharmacogenomics into electronic health records with clinical decision support. Am J Health Syst Pharm 2016;73:1967–76.
  4. Caudle KE, Dunnenberger HM, Freimuth RR, Peterson JF, Burlison JD, Whirl-Carrillo M, et al. Standardizing terms for clinical pharmacogenetic test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC). Genet Med 2017;19:215–23.