Preemptive clinical pharmacogenetics implementation: current programs in five US medical centers

Abstract

Although the field of pharmacogenetics has existed for decades, practioners have been slow to implement pharmacogenetic testing in clinical care. Numerous publications describe the barriers to clinical implementation of pharmacogenetics. Recently, several freely available resources have been developed to help address these barriers. In this review, we discuss current programs that use preemptive genotyping to optimize the pharmacotherapy of patients. Array-based preemptive testing includes a large number of relevant pharmacogenes that impact multiple high-risk drugs. Using a preemptive approach allows genotyping results to be available prior to any prescribing decision so that genomic variation may be considered as an inherent patient characteristic in the planning of therapy. This review describes the common elements among programs that have implemented preemptive genotyping and highlights key processes for implementation, including clinical decision support.

Keywords: clinical decision support; individualized medicine; personalized medicine; pharmacogenomics; precision medicine; prediction in pharmacology.

Figure 1

Percentage of individuals expected to have a high-risk diplotype for 12 genes identified by the Clinical Pharmacogenetics Implementation Consortium (CPIC) to have at least one known actionable inherited variant plotted by self-reported race category [white (red bars) or black (blue bars)]. For the genes CYP2C9, CYP2C19, CYP2D6, CYP3A5, SLCO1B1, TPMT, UGT1A1, and VKORC1, diplotype frequencies were obtained from the St. Jude Children's Research Hospital PG4KDS study, based on data from 624 black patients and 732 white patients. Due to small sample size in other race categories, other race categories were omitted from the figure. For the genes for which validated diplotype data were not yet available from the PG4KDS study, (DPYD, G6PD, HLA-B, and IFNL3), high-risk diplotype frequencies were estimated using published allele frequency data (7; 8; 13; 15; 48). High risk diplotypes were considered as follows: for CYP2C19, diplotypes containing a *2 , *3 or *17 allele; for CYP2C9, diplotypes containing a *2 or *3 allele; for CYP2D6, diplotypes resulting in activity scores of < 1 or > 2; for CYP3A5, diplotypes containing a *1 allele; for DPYD, diplotypes containing *2A, *3, or the rs67376798 A variant; for G6PD, those with G6PD deficient phenotypes; for HLA-B, diplotypes containing a *5701 or *5801 allele; for IFNL3, diplotypes containing an rs12979860 T allele; for SLCO1B1, diplotypes containing a *5 or *15 allele; for TPMT, diplotypes containing a *2, *3A, *3B, *3C, *4 or *8 allele; for UGT1A1, diplotypes containing two copies of the *28 allele; and for VKORC1, diplotypes containing an rs9923231 A allele. High-risk diplotypes for blacks were not calculated for VKORC1 due to the low predictive value of genotype-driven warfarin dosing in this population.

Figure 2

Number of outpatient prescriptions dispensed in the United States for the calendar year 2013 for top 30 drugs with high pharmacogenetic risk plotted by drug and diplotype risk category. The dark grey portion of each bar represents prescriptions potentially prescribed to blacks or whites with a high-risk diplotype for the applicable gene(s); the light grey portion represents those prescribed to those without a high-risk diplotype. Total number of prescriptions for each drug was collected from the IMS Health (IMS) National Prescription Audit proprietary prescription database (32). This database contains all retail prescriptions filled from a representative sample of 35,000 (73% of the approximately 50,000) U.S.-based retail pharmacies. IMS then proportionately extrapolates their data on the basis of populations served by the included pharmacies to provide weekly estimates of all prescriptions filled in the United States for these drugs. The National Prescription Audit database does not track prescriptions filled by in-hospital pharmacies. The number of prescriptions potentially prescribed to black or white patients with a high-risk diplotype per drug was calculated as follows: (total number of prescriptions for a drug) * (percent of Americans with Caucasian ancestry (74.8%)) * (percent of high-risk diplotypes in whites for each corresponding gene as shown in Figure 1) + (total number of prescriptions for a drug) * (percent of Americans with African American ancestry (12.6%)) * (percent of high-risk diplotypes in blacks for each corresponding gene as shown in Figure 1), where the percent of Caucasians and African Americans was derived from the 2010 U.S. census (http://www.census.gov/2010census/). For warfarin, only whites with a high-risk diplotype were used in the calculation. If the drug is affected by two genes, the presence of a high-risk diplotype for either gene was considered as the presence of a high-risk diplotype for that drug.

Figure 3

Steps required to implement preemptive pharmacogenetics. CDS= clinical decision support

Figure 4

Steps needed to translate a genotype result into a clinically useful action