Objectives Cytochrome P450 enzymes play a dominant role in drug elimination and variation in these genes is a major source of interindividual differences in drug response. Little is known, however, about pharmacogenetic variation in American Indian and Alaska Native (AI/AN) populations. We have developed a partnership with the Confederated Salish and Kootenai Tribes (CSKT) in northwestern Montana to address this knowledge gap. Methods We resequenced CYP2D6 in 187 CSKT individuals and CYP3A4, CYP3A5, and CYP2C9 in 94 CSKT individuals. Results We identified 67 variants in CYP2D6, 15 in CYP3A4, 10 in CYP3A5, and 41 in CYP2C9. The most common CYP2D6 alleles were CYP2D6*4 and *41 (20.86 and 11.23%, respectively). CYP2D6*3, *5, *6, *9, *10, *17, *28, *33, *35, *49, *1xN, *2xN, and *4xN frequencies were less than 2%. CYP3A5*3, CYP3A4*1G, and *1B were detected with frequencies of 92.47, 26.81, and 2.20%, respectively. Allelic variation in CYP2C9 was low: CYP2C9*2 (5.17%) and *3 (2.69%). In general, allele frequencies in CYP2D6, CYP2C9, and CYP3A5 were similar to those observed in European Americans. There was, however, a marked divergence in CYP3A4 for the CYP3A4*1G allele. We also observed low levels of linkage between CYP3A4*1G and CYP3A5*1 in the CSKT. The combination of nonfunctional CYP3A5*3 and putative reduced function CYP3A4*1G alleles may predict diminished clearance of CYP3A substrates. Conclusion These results highlight the importance of carrying out pharmacogenomic research in AI/AN populations and show that extrapolation from other populations is not appropriate. This information could help optimize drug therapy for the CSKT population.

The cytochrome P450 2D6 (CYP2D6) gene locus is challenging to accurately genotype due to numerous single nucleotide variants and complex structural variation. Our goal was to determine whether the CYP2D6 genotype‐phenotype correlation is improved when diplotype assignments incorporate structural variation, identified by the bioinformatics tool Stargazer, with next‐generation sequencing data. Using CYP2D6 activity measured with substrates dextromethorphan and metoprolol, activity score explained 40% and 34% of variability in metabolite formation rates, respectively, when diplotype calls incorporated structural variation, increasing from 36% and 31%, respectively, when diplotypes did not incorporate structural variation. We also investigated whether the revised Clinical Pharmacogenetics Implementation Consortium (CPIC) recommendations for translating genotype to phenotype improve CYP2D6 activity predictions over the current system. Although the revised recommendations do not improve the correlation between activity score and CYP2D6 activity, perhaps because of low frequency of the CYP2D6*10 allele, the correlation with metabolizer phenotype group was significantly improved for both substrates. We also measured the function of seven rare coding variants: one (A449D) exhibited decreased (44%) and another (R474Q) increased (127%) activity compared with reference CYP2D6.1 protein. Allele‐specific analysis found that A449D is part of a novel CYP2D6*4 suballele, CYP2D6*4.028. The novel haplotype containing R474Q was designated CYP2D6*138 by PharmVar; another novel haplotype containing R365H was designated CYP2D6*139. Accuracy of CYP2D6 phenotype prediction is improved when the CYP2D6 gene locus is interrogated using next‐generation sequencing coupled with structural variation analysis. Additionally, revised CPIC genotype to phenotype translation recommendations provides an improvement in assigning CYP2D6 activity.

Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene whose protein product metabolizes more than 20% of clinically used drugs. Genetic variations inCYP2D6are responsible for interindividual heterogeneity in drug response that can lead to drug toxicity and ineffective treatment, makingCYP2D6one of the most important pharmacogenes. Prediction of CYP2D6 phenotype relies on curation of literature-derived functional studies to assign a functional status toCYP2D6haplotypes. As the number of large-scale sequencing efforts grows, new haplotypes continue to be discovered, and assignment of function is challenging to maintain. To address this challenge, we have trained a convolutional neural network to predict functional status ofCYP2D6haplotypes, called Hubble.2D6. Hubble.2D6 predicts haplotype function from sequence data and was trained using two pre-training steps with a combination of real and simulated data. We find that Hubble.2D6 predictsCYP2D6haplotype functional status with 88% accuracy in a held-out test set and explains 47.5% of the variance inin vitrofunctional data among star alleles with unknown function. Hubble.2D6 may be a useful tool for assigning function to haplotypes with uncurated function, and used for screening individuals who are at risk of being poor metabolizers.

Despite evidence that pharmacogenetics can improve tamoxifen pharmacotherapy, there are few studies with American Indian and Alaska Native (AIAN) people. We examined variation in cytochrome P450 (CYP) genes (CYP2D6, CYP3A4, CYP3A5, and CYP2C9) and tamoxifen biotransformation in AIAN patients with breast cancer (n = 42) from the Southcentral Foundation in Alaska and the Confederated Salish and Kootenai Tribes in Montana. We tested for associations between CYP diplotypes and plasma concentrations of tamoxifen and metabolites. Only the CYP2D6 variation was significantly associated with concentrations of endoxifen (P = 0.0008) and 4-hydroxytamoxifen (P = 0.0074), tamoxifen's principal active metabolites, as well as key metabolic ratios. The CYP2D6 was also the most significant predictor of active metabolites and metabolic ratios in a multivariate regression model, including all four genes as predictors, with minor roles for other CYP genes. In AIAN populations, CYP2D6 is the largest contributor to tamoxifen bioactivation, illustrating the importance of validating pharmacogenetic testing for therapy optimization in an understudied population.

Pharmacogenetics is a subset of personalized medicine that applies knowledge about genetic variation in gene–drug pairs to help guide optimal dosing. There is a lack of data, however, about pharmac...

Barriers to incorporating pharmacogenetics into routine clinical practice in the United States are well documented. Initial surveys by the Clinical Pharmacogenetics Implementation Consortium (CPIC) in 2009 and 2010 identified barriers across four key domains that have hindered the widespread adoption of clinical pharmacogenetic testing. These are presented verbatim as: (i) absence of a definition of the processes required to interpret genotype information and to translate genetic information into clinical actions; (ii) need for recommended drug/gene pairs to implement clinically now; (iii) clinician resistance to consider pharmacogenetic information at the bedside; and (iv) concerns about test costs and reimbursement. Over time, many of these challenges have been overcome, and clinical pharmacogenetic testing has subsequently reached broader implementation. Despite this progress, several barriers remain that block further adoption. This narrative review used authors' expertise and experience to identify and describe current barriers to pharmacogenetic implementation across seven domains in the United States: equity and inclusion; guidelines and supporting evidence; regulatory agency oversight; payer coverage and insurance; availability of quality pharmacogenetic tests; electronic health records; and provider and patient education. Within each domain, it revisits past successes and challenges and explores remaining barriers. We also propose solutions to address ongoing challenges across these domains, including further expansion of recommendations beyond pharmacogenetic‐specific guidelines, standards for designing clinical decision support tools, and broader pharmacogenetics education. Addressing these remaining obstacles directs work to enable broader adoption of clinical pharmacogenetic implementation to ultimately improve patient outcomes.