Jesse J. Swen

Pharmacogenetics: from bench to byte--an update of guidelines.

Currently, there are very few guidelines linking the results of pharmacogenetic tests to specific therapeutic recommendations. Therefore, the Royal Dutch Association for the Advancement of Pharmacy established the Pharmacogenetics Working Group with the objective of developing pharmacogenetics‐based therapeutic (dose) recommendations. After systematic review of the literature, recommendations were developed for 53 drugs associated with genes coding for CYP2D6, CYP2C19, CYP2C9, thiopurine‐S‐methyltransferase (TPMT), dihydropyrimidine dehydrogenase (DPD), vitamin K epoxide reductase (VKORC1), uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1), HLA‐B44, HLA‐B*5701, CYP3A5, and factor V Leiden (FVL).

Clinical Pharmacogenetics Implementation Consortium Guideline for CYP2D6 and CYP2C19 Genotypes and Dosing of Tricyclic Antidepressants

Polymorphisms in CYP2D6 and CYP2C19 affect the efficacy and safety of tricyclics, with some drugs being affected by CYP2D6 only, and others by both polymorphic enzymes. Amitriptyline, clomipramine, doxepin, imipramine, and trimipramine are demethylated by CYP2C19 to pharmacologically active metabolites. These drugs and their metabolites, along with desipramine and nortriptyline, undergo hydroxylation by CYP2D6 to less active metabolites. Evidence from published literature is presented for CYP2D6 and CYP2C19 genotype–directed dosing of tricyclic antidepressants.

Clinical Pharmacogenetics Implementation Consortium Guidelines for Dihydropyrimidine Dehydrogenase Genotype and Fluoropyrimidine Dosing

The fluoropyrimidines are the mainstay chemotherapeutic agents for the treatment of many types of cancers. Detoxifying metabolism of fluoropyrimidines requires dihydropyrimidine dehydrogenase (DPD, encoded by the DPYD gene), and reduced or absent activity of this enzyme can result in severe, and sometimes fatal, toxicity. We summarize evidence from the published literature supporting this association and provide dosing recommendations for fluoropyrimidines based on DPYD genotype (updates at http://www.pharmgkb.org).

Incorporation of pharmacogenomics into routine clinical practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline development process.

The Clinical Pharmacogenetics Implementation Consortium (CPIC) publishes genotype-based drug guidelines to help clinicians understand how available genetic test results could be used to optimize drug therapy. CPIC has focused initially on well-known examples of pharmacogenomic associations that have been implemented in selected clinical settings, publishing nine to date. Each CPIC guideline adheres to a standardized format and includes a standard system for grading levels of evidence linking genotypes to phenotypes and assigning a level of strength to each prescribing recommendation. CPIC guidelines contain the necessary information to help clinicians translate patient-specific diplotypes for each gene into clinical phenotypes or drug dosing groups. This paper reviews the development process of the CPIC guidelines and compares this process to the Institute of Medicine’s Standards for Developing Trustworthy Clinical Practice Guidelines.

Translating Pharmacogenomics: Challenges on the Road to the Clinic

Pharmacogenomics is one of the first clinical applications of the postgenomic era. It promises personalized medicine rather than the established “one size fits all” approach to drugs and dosages. The expected reduction in trial and error should ultimately lead to more efficient and safer drug therapy. In recent years, commercially available pharmacogenomic tests have been approved by the Food and Drug Administration (FDA), but their application in patient care remains very limited. More generally, the implementation of pharmacogenomics in routine clinical practice presents significant challenges. This article presents specific clinical examples of such challenges and discusses how obstacles to implementation of pharmacogenomic testing can be addressed.

Pharmacogenetics: From Bench to Byte

Despite initial enthusiasm, 1 , 2 , 3 the use of pharmacogenetics has remained limited to investigation in only a few clinical fields such as oncology and psychiatry. 4 , 5 , 6 , 7 , 8 The main reason is the paucity of scientific evidence to show that pharmacogenetic testing leads to improved clinical outcomes. 9 , 10 Moreover, for most pharmacogenetic tests (such as tests for genetic variants of cytochrome P450 enzymes) a detailed knowledge of pharmacology is a prerequisite for application in clinical practice, and both physicians and pharmacists might find it difficult to interpret the clinical value of pharmacogenetic test results. Guidelines that link the result of a pharmacogenetic test to therapeutic recommendations might help to overcome these problems, but such guidelines are only sparsely available. In 2001, an early step was taken to develop such guidelines for the therapeutic use of antidepressants, and these included CYP2D6‐related dose recommendations drawn from pharmacokinetic study data. 11 However, the use of such recommendations in routine clinical practice remains difficult, because they are currently outside the ambit of the clinical environment and are not accessible during the decision‐making process by physicians and pharmacists, namely the prescription and dispensing of drugs.

Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP3A5 Genotype and Tacrolimus Dosing

Tacrolimus is the mainstay immunosuppressant drug used after solid organ and hematopoietic stem cell transplantation. Individuals who express CYP3A5 (extensive and intermediate metabolizers) generally have decreased dose‐adjusted trough concentrations of tacrolimus as compared with those who are CYP3A5 nonexpressers (poor metabolizers), possibly delaying achievement of target blood concentrations. We summarize evidence from the published literature supporting this association and provide dosing recommendations for tacrolimus based on CYP3A5 genotype when known (updates at www.pharmgkb.org).

Challenges in CYP2D6 Phenotype Assignment from Genotype Data: A Critical Assessment and Call for Standardization

The cytochrome P450 2D6 (CYP2D6) enzyme contributes to the metabolism and/or bioactivation of approximately 25% of clinically used drugs. The CYP2D6 gene locus is highly polymorphic and complex, and variants within this gene locus affect CYP2D6 enzymatic function resulting in a wide range of metabolic activity from little to no activity to ultrarapid metabolism. For many of the drugs metabolized by CYP2D6, the variation in metabolic activity is one of the most important factors responsible for interindividual drug response. Therefore, determining an individuals CYP2D6 phenotype, or metabolic status, will help identify individuals that may benefit from a change in drug or drug dosage. Genotype analysis has become the method of choice to predict a persons metabolic status. Numerous reference laboratories now offer CYP2D6 genotyping; however, there can be substantial differences in the number of genetic variants interrogated as well as test interpretation. Furthermore, there is no standardized process of how a CYP2D6 genotype result is translated into a phenotype assignment. This review summarizes the complexity of CYP2D6 genotyping and highlights the major challenges for phenotype classification. We call for the implementation of a universally accepted system for CYP2D6 phenotype assignment to promote consistency of test interpretation among reference laboratories and medical institutions. We propose a system that utilizes the CYP2D6 activity score system to place individuals into a continuum of activity scores - rather than using the traditional poor, intermediate, extensive and ultra-rapid metabolizer categorizations - and directly translating activity scores into clinically actionable recommendations.

Effect of CYP3A4*22, CYP3A5*3, and CYP3A Combined Genotypes on Cyclosporine, Everolimus, and Tacrolimus Pharmacokinetics in Renal Transplantation

Cyclosporine, everolimus, and tacrolimus are the cornerstone of immunosuppressive therapy in renal transplantation. These drugs are characterized by narrow therapeutic windows, highly variable pharmacokinetics (PK), and metabolism by CYP3A enzymes. Recently, the decreased activity allele, CYP3A4*22, was described as a potential predictive marker for CYP3A4 activity. This study investigated the effect of CYP3A4*22, CYP3A5*3, and CYP3A combined genotypes on cyclosporine, everolimus, and tacrolimus PK in renal transplant patients. CYP3A4*22 carriers showed a significant lower clearance for cyclosporine (−15%), and a trend was observed for everolimus (−7%) and tacrolimus (−16%). Patients carrying at least one CYP3A5*1 allele had 1.5‐fold higher tacrolimus clearance compared with noncarriers; however, CYP3A5*3 appeared to be nonpredictive for everolimus and cyclosporine. CYP3A combined genotype did not significantly improve prediction of clearance compared with CYP3A5*3 or CYP3A4*22 alone. These data suggest that dose individualization of cyclosporine, everolimus, or tacrolimus therapy based on CYP3A4*22 is not indicated.

Association Analysis of Genetic Polymorphisms in Genes Related to Sunitinib Pharmacokinetics, Specifically Clearance of Sunitinib and SU12662

Interpatient variability in the pharmacokinetics (PK) of sunitinib is high. Single nucleotide polymorphisms (SNPs) in PK candidate genes have been associated with the efficacy and toxicity of sunitinib, but whether these SNPs truly affect the PK of sunitinib remains to be elucidated. This multicenter study involving 114 patients investigated whether these SNPs and haplotypes in genes encoding metabolizing enzymes or efflux transporters are associated with the clearance of sunitinib and its active metabolite SU12662. SNPs were tested as covariates in a population PK model. From univariate analysis, we found that the SNPs in CYP3A4, CYP3A5, and ABCB1 were associated with the clearance of both sunitinib and SU12662. In multivariate analysis, CYP3A4*22 was found to be eliminated last with an effect size of −22.5% on clearance. Observed effect sizes are below the interindividual variability in clearance and are therefore too limited to directly guide individual dosing of sunitinib.