OBJECTIVES

After completing this chapter, the reader should be able to

  • Define pharmacogenetics

  • Differentiate germline and somatic mutations

  • Describe the use of molecular testing in pharmacogenetics/pharmacogenomics as tools for personalizing therapy

  • Describe the difference between empirical pharmacotherapy and genotype-guided pharmacotherapy

  • Describe how pharmacogenetics can enhance therapeutic drug monitoring

  • Assess the utility of genotype in addition to other patient-specific factors for specific medications in the provision of pharmaceutical care

  • Discuss the role of laboratory medicine in pharmacogenetics in terms of turnaround time, interpretative reporting, and assay performance

PHARMACOGENETICS

As early as the 1950s, the heritable nature of drug response was noted for agents such as succinylcholine, isoniazid, and primaquine.1-3 Later, twin studies confirmed this heritability by showing that the half-lives of some drugs were tightly correlated in monozygotic twins and had little correlation in dizygotic twins.4 Since that time, the fields of pharmacogenetics and pharmacogenomics have taken off, and the genetic basis for variability in drug metabolism, transport, and pharmacodynamic effect is increasingly being appreciated. In fact, pharmacogenetic and molecular tests are routinely used in therapeutic areas such as hematology/oncology and infectious disease, and their usefulness is being explored in every major therapeutic drug class.5,6

Pharmacogenetics/pharmacogenomics is the translational science of correlating interindividual genetic variation with variability in drug response, including both drug efficacy and safety (Table 6-1). Historically and practically, the terms pharmacogenetics and pharmacogenomics have been used interchangeably (as in this chapter). However, definitions may vary depending on the context. For example, pharmacogenetics can be seen as the study of variants in a handful of candidate genes. On the contrary, because of our expanding technological ability to simultaneously investigate millions of variants across the human genome using genome-wide genotyping arrays or high-throughput sequencing, pharmacogenomics may refer to genome-wide investigation of drug response variability.

TABLE 6-1.

Key Terms Related to Genomics and Pharmacogenomics

KEY TERMS

DEFINITION

Allele

A variant form of a gene or locus; an individual inherits two alleles for each gene or locus, one from each parent

Diplotype

The combination of two haplotypes or star alleles in a single individual

Gene

The basic physical unit of inheritance that is passed from parents to offspring and contains the information needed to specify traits

Genotype

The combination of alleles in an individual at a particular locus

Germline variant

Heritable DNA variant occurring in gametes and present in all nucleated cells

Haplotype

Combination of genetic variants inherited together on a single chromosome

Pharmacogenetics/Pharmacogenomics

The translational science of correlating interindividual genetic variation with variability in drug response, including both drug efficacy and safety

Phenotype

The observable characteristics or traits of an organism that are produced by the interaction of the genotype and the environment

Preemptive PGx test

PGx test done before a medication order or adverse reaction

Reactive PGx test

PGx test done after a medication order or adverse reaction

Somatic variant

DNA variant occurring in nongermline cells (not heritable) and occurring during the lifespan of an individual

Single nucleotide polymorphism (SNP)

Variation at a single position in a DNA sequence among individuals

Star allele

Combination of genetic variants inherited together, defined by PharmVar

PGx = pharmacogenetic.

Pharmacogenetics seeks to avoid adverse drug reactions and improve clinical efficacy, providing personalized medicine to patients, much the same way therapeutic drug monitoring by serum drug concentrations customizes certain medication regimens for individual patients. One goal of pharmacogenetics is to refine the current empirical approach to drug therapy management so that it is less “trial-and-error” in nature. There are often many drug classes available to treat a given condition and several drugs within each of those classes that a clinician may opt to use. This large armamentarium of drug therapy choices can lead to an inefficient, time-consuming management strategy in which the therapeutic decision is based on little more than clinician preference. Another goal of pharmacogenetics is to provide the appropriate dose to individual patients so that the “one dose fits all” strategy is avoided. Incorporating the results of genetic tests along with nongenetic factors (eg, age, sex, smoking status, kidney and liver function, drug–drug interactions) into pharmacotherapy decision making may help streamline this process such that the likelihood for response is maximized while the chance of toxicity is minimized.7,8

Understanding the results of molecular tests that are used in the application of pharmacogenetics is of critical importance to healthcare providers if this form of personalized medicine is going to improve patient care. Many institutions are attempting to implement preemptive genotyping so that results will be in the electronic medical record before a particular drug with a clinically actionable genetic test is prescribed. Furthermore, direct-to-consumer genetic tests are already available to patients, regardless of whether significant published evidence is available supporting that the test improves clinical care. Despite the great promise of personalized medicine, the field is changing rapidly, and exactly how and when tests should be applied clinically is still very much a work in progress. Therefore, this chapter focuses on pharmacogenetic laboratory tests that are used commonly in clinical practice or are most likely to be incorporated into clinical practice in the near future.

While some organizations, such as the National Academy of Clinical Biochemistry (NACB), have established practice guidelines for the application of pharmacogenetics, clinical guidance for pharmacogenetic tests is often provided in disease-specific guidelines on a case-by-case basis.9-11 The U.S. Food and Drug Administration (FDA) has released both a Table of Pharmacogenetic Associations (https://www.fda.gov/medical-devices/precision-medicine/table-pharmacogenetic-associations) and a Table of Pharmacogenomic Biomarkers in Drug Labeling (https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling). Clinical pharmacology groups such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) have published practice guidelines for specific drug/gene pairs with clinical importance as data become available (Table 6-2; https://cpicpgx.org/guidelines).12 CPIC guidelines are regularly updated, and the CPIC website should be checked for the most up-to-date information. CPIC guidelines have been endorsed by several professional societies, including the Association for Molecular Pathology, the American Society for Clinical Pharmacology and Therapeutics, and American Society of Health-System Pharmacists, and provide a concise summary of clinical recommendations and supporting evidence for pharmacogenetic tests. An important consideration for CPIC guidelines is that they do not give recommendations on when to administer a pharmacogenetic test but give recommendations for clinical interventions in the event that a test result is already known. Taken together, guidelines from these organizations and others will likely be useful in bringing together the fields of laboratory medicine and clinical pharmacology in the application of pharmacogenetics.

TABLE 6-2.

Drugs with Actionable Dosing/Patient Selection Guidelines Based on Germline Variation According to the CPICa

DRUG(S)

GENE(S)

CPIC GUIDELINE RECOMMENDATIONS SUMMARY

Abacavir

HLA-B

Alternative agent in carriers of HLA-B*57:01113

Allopurinol

HLA-B

Alternative agent in carriers of HLA-B*58:01118

Aminoglycosides

MT-RNR1

Drug: aminoglycosides*, Gene: MT-RNR1, summary: Avoid aminoglycosides in patients susceptible to aminoglycoside-induced hearing loss based on MT-RNR1 genotype.130

Atazanavir

UGT1A1

Alternative agent in UGT1A1 poor metabolizers64

Atomoxetine

CYP2D6

Modified dosing based on CYP2D6 metabolizer status27

Carbamazepine

Oxcarbazepine

HLA-B

HLA-A

Alternative agent in carriers of HLA-B*15:02 and HLA-A*31:01118

Clopidogrel

CYP2C19

Alternative agent in CYP2C19 poor and intermediate metabolizers31

Codeine

CYP2D6

Alternative agent in CYP2D6 ultrarapid and poor metabolizers23

Efavirenz

CYP2B6

Decreased dose in CYP2B6 intermediate and poor metabolizers47

Fluorouracil

Capecitabine

Tegafur

DPYD

Reduced dose in DPYD intermediate metabolizers and avoid agent in DPYD poor metabolizers61

Ivacaftor

CFTR

Use only with specific CFTR genotypes125

NSAIDsb

CYP2C9

Reduced dose or alternative drug depending on CYP2C9 metabolizer status and specific NSAID44

Ondansetron

Tropisetron

CYP2D6

Alternative agent in CYP2D6 ultrarapid metabolizers25

Phenytoin

CYP2C9

HLA-B

Reduced dose in CYP2C9 intermediate and poor metabolizers; alternative agent in carriers of HLA-B*15:0243

Peginterferon alfa-2a

Peginterferon alfa-2b

Ribavirin

IFNL3

Consider alternative agent in patients with unfavorable response genotype for IFNL3126

Proton pump inhibitors

CYP2C19

Drug: proton pump inhibitors*, Gene: CYP2C19, summary: Increased dose for CYP2C19 rapid and ultrarapid metabolizers, reduced dose for chronic therapy in poor and intermediate metabolizers.131

Rasburicase

G6PD

Contraindicated for deficient WHO class II or III G6PD variants66

Simvastatin

SLCO1B1

Decreased dose or alternative agent in low or intermediate SLCO1B1 function127

SSRIsc

CYP2D6

Reduced dose or alternative drug depending on CYP2D6 metabolizer status and specific SSRI29

CYP2C19

Reduced dose or alternative drug depending on CYP2C19 metabolizer status and specific SSRI29

Tacrolimus

CYP3A5

Increased dose for CYP3A5 normal and intermediate metabolizers46

Tamoxifen

CYP2D6

Alternative agent in CYP2D6 poor and some intermediate metabolizers26

Mercaptopurine

Azathioprine

Thioguanine

TPMT

NUDT15

Reduced dose for TPMT and/or NUDT15 intermediate or poor metabolizers depending on patient’s condition128

Tricyclic antidepressantsd

CYP2D6

Decreased dose or alternative agent depending on CYP2D6 and CYP2C19 metabolizer status and specific tricyclic antidepressant28

CYP2C19

Decreased dose or alternative agent depending on CYP2C19 metabolizer status and specific tricyclic antidepressant28

Volatile anesthetic agentse

Succinylcholine

RYR1

CACNA1S

Alternative agent in patients susceptible to malignant hyperthermia based on RYR1 and CACNA1S genotypes129

Voriconazole

CYP2C19

Alternative agent in CYP2C19 ultrarapid, rapid, and poor metabolizers; dose decrease for poor metabolizers in pediatric patients39

Warfarin

CYP2C9

VKORC1

CYP4F2

Use of warfarin dosing algorithms with CYP2C9, VKORC1, and CYP4F2 polymorphisms depending on patient race40

NSAID = nonsteroidal anti-inflammatory drug.

aBased on a table from https://cpicpgx.org/guidelines, which is regularly updated; not necessarily an all-inclusive list.

bGuidance for NSAIDs includes celecoxib, flurbiprofen, lornoxicam, ibuprofen, meloxicam, piroxicam, and tenoxicam.

cGuidance for SSRIs includes citalopram, escitalopram, fluvoxamine, paroxetine, and sertraline.

dGuidance for tricyclic antidepressants includes amitriptyline, clomipramine, desipramine, doxepin, imipramine, nortriptyline, and trimipramine.

eGuidance for volatile anesthetic agents include desflurane, enflurane, halothane, isoflurane, methoxyflurane, sevoflurane, and succinylcholine.

*Guidance for aminoglycosides includes amikacin, gentamicin, kanamycin, paromomycin, plazomicin, streptomycin, tobramycin.

*Guidance for proton pump inhibitors includes omeprazole, lansoprazole, pantoprazole, and dexlansoprazole

Pharmacogenetic Testing Versus Disease Genetic Testing

Laboratory testing for pharmacogenetic and genetic polymorphisms yields the same general types of results (ie, a patient’s genotype[s] or diplotype[s]). However, the target populations and the use of test results for pharmacogenetic testing versus disease genetic testing may be quite different. Clinically used pharmacogenetic tests provide information that may aid in selection or dosing of medications. Therefore, individuals receiving pharmacogenetic tests typically are candidates for a particular therapeutic agent, which may or may not be administered. Individuals receiving disease genetic tests, on the other hand, are likely considered at risk for developing or suspected of having a particular disease or condition without regard to drug treatment. Because no drug exposure is necessary to consider a patient a risk and the presence or risk of disease may not be modifiable, implications of disease genetic tests are generally more severe.

Historically, pharmacogenetic testing has been considered to have fewer ethical issues surrounding it than disease genetic testing.13 However, while this is still generally considered to be the case, the risks of pharmacogenetic testing also have been outlined and a framework created to ensure appropriate delivery of pharmacogenetic information in the healthcare system.14 This framework outlines three major considerations regarding whether a particular pharmacogenetic test raises ethical issues: whether the genetic variant is inherited or acquired, whether the goal of testing is to address a specific clinical question or to provide information for future clinical care, and whether the test reveals ancillary clinical information (eg, disease risk).14

Pharmacogenetics and Personalized/Precision Medicine

Pharmacogenetics offers one piece to the puzzle of personalized or precision medicine. Personalized medicine seeks to tailor medical therapy to individual characteristics of patients. It can include genomic information, as in pharmacogenetics, or any other molecular analyses (eg, metabolomics, proteomics) in combination with commonly used clinical parameters such as age, weight, and renal function. Personalized medicine may also tailor therapy based on characteristics of tumors or pathogens. This chapter focuses on pharmacogenetic testing of patient DNA as a means of providing personalized medicine.

Resources for Pharmacogenetics

The National Institutes of Health (NIH) has supported several pharmacogenetic resources. The Pharmacogenomics Knowledgebase (PharmGKB) collects, curates, and disseminates knowledge about clinically actionable gene–drug associations and genotype–phenotype relationships. This includes prescribing recommendations written by CPIC, the Dutch Pharmacogenetics Working Group, the Canadian Pharmacogenomics Network for Drug Safety, and American, Canadian, European, Swiss, and Japanese medication labels with pharmacogenomic information (Table 6-3). PharmGKB curates pathways of pharmacokinetics and pharmacodynamics. They catalog publications detailing the effects of individual variants in each gene, summarize gene–drug pairs, and give each gene–drug pair a strength of evidence rating. The PharmGKB website includes all the CPIC guideline recommendations, allowing the user to select a diplotype to get the CPIC recommendations for a specific drug. Some clinicians use this tool when they have a pharmacogenetic test that does not use CPIC’s recommendations or come with an interpretation. CPIC’s website contains all the guidelines published and assigns levels of evidence of gene–drug pairs nominated for guidelines. The guidelines are all peer reviewed and published as open access. Each guideline contains a large supplemental file with population frequencies of the alleles, diplotypes, and phenotypes as well as a rating of each publication reviewed during the guideline development (Table 6-3).

TABLE 6-3.

Commonly Used Online Resources for Pharmacogenomic Information

RESOURCE

ACRONYM/ABBREVIATION

WEBSITE

Clinical Pharmacogenetic Implementation Consortium

CPIC

cpicpgx.org

Canadian Pharmacogenomics Network for Drug Safety

CPNDS

cpnds.ubc.ca

Database of Single Nucleotide Polymorphisms

dbSNP

ncbi.nlm.nih.gov/snp

Electronic Medical Records and Genomics Network

eMERGE

emerge-network.org

Implementing GeNomics In pracTicE

IGNITE

gmkb.org

Pharmacogenomics Research Network

PGRN

pgrn.org

Pharmacogenomics Knowledgebase

PharmGKB

pharmgkb.org

Pharmacogene Variation Consortium

PharmVar

pharmvar.org

Ubiquitous Pharmacogenomics

U-PGx

upgx.eu

The combinations of specific polymorphisms on a single chromosome or haplotypes leading to these phenotypes are typically described using star (*) nomenclature as defined by the Pharmacogene Variation Consortium (Pharmvar; https://www.pharmvar.org).15 The combination of haplotypes is referred to as a diplotype, such that each patient inherits two star alleles for a given gene (eg, CYP2C9*1/*1). For most genes, the *1 genotype is considered the common or normal, fully functioning form of the gene. It is important to note that the *1 allele is reported if all other alleles tested for are absent, but this is not usually accurate. If gene sequencing has not been performed and rare loss-of-function alleles have not been interrogated, a *1/*1 genotype may erroneously be reported. The Association for Molecular Pathology PGx Working Group has defined minimal alleles to test prior to designating *1 for CYP2C19, CYP2D6, CYP2C9, and VKORC1.16,17,18 Star alleles contain one or more variants and can confer no function, decreased function, normal function, or increased function based on the functional consequences of the variant for the gene’s protein product. The terms for allele function and phenotype are standardized by CPIC.19 In PharmVar, the variants defining each star allele for each gene are annotated, with the function and references to the reports describing the function.15

Three large networks are studying pharmacogenetic implementation and incorporation into electronic medical records. The Electronic Medical Records and Genomics (eMERGE) Network combines DNA biorepositories with electronic medical record (EMR) systems for large scale, high-throughput genetic research in support of implementing genomic medicine (Table 6-3). This network has implemented pharmacogenetic testing and incorporation into EMRs. The Implementing GeNomics In pracTicE (IGNITE) Pragmatic Clinical Trials Network is dedicated to supporting the implementation of genomics in healthcare, with trials implementing pharmacogenetics in the treatment of depression and pain. The Ubiquitous Pharmacogenomics Consortium (U-PGx) is investigating whether pre-emptive genotyping of an entire panel of important pharmacogenetic markers is cost-effective and results in a better outcome for patients across several European countries.

DRUG DISPOSITION-RELATED MOLECULAR TESTS

Pharmacokinetics is concerned with the fate of drugs or other substances once administered and studies the rate and extent of absorption, distribution, metabolism, and excretion. A great deal of interpatient variability exists in the pharmacokinetics of many drugs and a common source of interpatient variability occurs in drug metabolism. Drug disposition reactions can be divided into phase I, phase II, and phase III reactions. Phase I reactions typically involve processes such as oxidation, reduction, and hydrolysis of compounds and are typified by hepatic cytochrome P450 (CYP) drug metabolism. Phase II reactions include conjugation or synthetic reactions such as glucuronidation, sulfation, methylation, acetylation, and others. The purpose of phase II metabolism is to make compounds more water-soluble and facilitate excretion. Finally, phase III reactions are characterized by transport protein-mediated cellular efflux of drugs, usually at the level of the gut, liver, kidney, and highly sequestered tissues. While genetic variability occurs in each of the previously listed phases of drug disposition, many examples of actionable pharmacogenetic associations are found in phase I metabolism. For example, genes encoding many phase I enzymes (eg, CYP2C9, CYP2D6, CYP2C19) contain common genetic variation with a high impact on enzymatic function. When a drug is primarily metabolized through one of these enzymes, such variation has a large impact on drug concentration and thus drug response. Fewer phase II/III genes have actionable variants (eg, UGT1A1, NAT2, ABCB1).

Cytochrome P450 System

Although many of the genes encoding CYP enzymes are highly polymorphic, CYP2D6, CYP2C9, and CYP2C19 have genetic variation (polymorphisms), which can describe fairly predictable distributions of drug concentrations, making them clinically relevant for some pharmacogenetic tests. Metabolizer status can be described as normal, intermediate, poor, rapid, or ultrarapid based on the presence or absence of gene variations.19 This genotype–phenotype relationship could help identify poor metabolizers likely to experience side effects (or therapeutic failure in the case of prodrugs requiring activation) to usual doses of CYP450-metabolized drugs or ultrarapid metabolizers more prone to therapeutic failure (or toxicity in the case of prodrugs). With knowledge of genotype, drug doses in these individuals with altered metabolism could then be increased or decreased as appropriate or alternative drugs may be chosen. This ability of genotype to predict drug metabolizing phenotype may be especially important for drugs with narrow therapeutic indices and less important for wide therapeutic index drugs.20

CYP2D6

The CYP2D6 gene contains more than 120 alleles, which can lead to a normal function, reduced function, or no function. Increased CYP2D6 function is also observed when there are multiple copies of the gene present. The most common no-function alleles are *3 (rs35742686), *4 (rs1065852, rs3892097, and rs1135840), *5 (gene deletion), and *6 (rs5030655). These single nucleotide polymorphism (SNP) reference numbers (rs numbers) are curated reference numbers for specific variants established by the National Center for Biotechnology Information (NCBI) SNP database (Database of Single Nucleotide Polymorphisms [dbSNP] at www.ncbi.nlm.nih.gov/snp). Multiple copies of the gene are designated by the allele number, the letter x, the the number of copies detected (e.g., *1x2). Due to the number of CYP2D6 alleles and their disparate functional consequences, the conversion of diplotypes to standardized and clinically interpretable metabolizer phenotypes becomes particularly important.19,21,22 An activity score system has been devised in which a numeric value is given to each allele, scores are summed for both alleles, and the resulting numeric value is converted into a CYP2D6 metabolizer phenotype. Each functional group is assigned an activity value ranging from 0 to 1 (eg, 0 for no function, 0.5 for decreased function, and 1 for normal function). If multiple copies of the CYP2D6 gene are detected, the activity score is multiplied by the number of copies of each allele present. Therefore, a normal metabolizer with an activity score of 1.5 will have less enzymatic activity than a normal metabolizer with an activity score of 2. CYP2D6 metabolizer phenotypes include CYP2D6 ultrarapid metabolizer (activity score >2.25), normal metabolizer (activity score is 1.25 to 2.25), intermediate metabolizer (activity score is 0.25 to 1), and poor metabolizer (activity score is 0). CYP2D6 poor metabolizers are more common among individuals of European ancestry and CYP2D6 ultrarapid metabolizers are more common in individuals with Oceania and Near East ancestry.23

One of the most reproducible associations between CYP2D6 genotype and a drug response occurs with codeine. Codeine is a prodrug requiring metabolism by CYP2D6 into its active form morphine for analgesic effect. Therefore, those individuals who are CYP2D6 poor metabolizers are at risk for therapeutic failure and those who are ultrarapid metabolizers are at risk for toxicity. Alternative analgesic therapy is recommended in both groups of patients (Minicase 1).23,24 In addition to codeine, several guidelines have been published with recommendations for CYP2D6 genetic testing interpretation and suggested clinical action for the test results. These include guidelines for CYP2D6 genotype and use of ondansetron/tropisetron, atomoxetine, and tamoxifen.25-27 Additional guidelines are also available for use of CYP2D6 genotype in combination with other CYP enzyme pharmacogenetic test results for tricyclic antidepressants and selective serotonin reuptake inhibitors (SSRIs).28,29

CYP2C19

The CYP2C19 gene contains more than 35 alleles leading to normal, decreased, no, or increased function. The most common no-function alleles are *2 (rs4244285) and *3 (rs4986893), which account for 85% of no function alleles in white and black individuals and 99% of no function alleles in Asians. The other decreased or no function alleles, *4 (rs28399504), *5 (rs56337013), *6 (rs72552267), *7 (rs72558186), *8 (rs41291556), and *10 (rs6413438) are less common. The *17 (rs12248560) allele is an increased function allele and has a frequency of 3% to 20% depending on ethnicity.30,31 The Association for Molecular Pathology has determined the minimum set of alleles for testing in CYP2C19 are *2, *3, and *17 based on their relatively high frequencies and functional consequences for the CYP2C19 enzyme.32 Thus, guidelines typically reserve clinical recommendations for these alleles, and pharmacogenetic testing platforms often interrogate only these three CYP2C19 loci.

One of the most extensively studied associations between CYP2C19 polymorphisms and a drug response is with clopidogrel. Clopidogrel is a prodrug requiring activation by two CYP450-dependent steps, both of which involve CYP2C19. Individuals carrying no function CYP2C19 alleles have been shown to have lower active metabolite concentrations, reduced inhibition of platelet aggregation, and increased risk of adverse cardiovascular outcomes when treated with clopidogrel at standard doses compared with those without reduced function alleles.33-38 The FDA has updated the clopidogrel label to indicate that alternative treatment or treatment strategies be considered in individuals with two no function CYP2C19 alleles. Guidelines have been published with treatment recommendations for clopidogrel based on CYP2C19 genotype (Minicase 2).30,31 Other guidelines use CYP2C19 genotype for drug selection and dosing, including guidelines for voriconazole, tricyclic antidepressants, and SSRIs.28,29,39

CYP2C9

CYP2C9 contains more than 60 alleles, more than 30 of which are known to lead to decreased or no function.40 The most common variants in white individuals in CYP2C9 are the *2 (rs1799853) and *3 (rs1057910) alleles, whereas the *5 (rs28371686), *6 (rs9332131), *8 (rs7900194), and *11 (rs28371685) alleles are more prevalent in black persons. The *2 allele has a frequency of approximately 13% in white persons, 0% in Asians, and 3% in black persons. The *3 allele has a frequency of approximately 7% in white persons, 4% in Asians, and 2% in black persons. The Association for Molecular Pathology has determined the minimum set of alleles for testing in CYP2C9 are *2, *3, *5, *6, *8, and *11.41 One of the most well-documented associations with CYP2C9 is with warfarin dose requirements (Figure 6-1; discussed in detail later).40,42 Warfarin is a particularly interesting example because CPIC recommendations for pharmacogenetics are different based on a patient’s age and self-reported race. These race-based recommendations for warfarin pharmacogenetics underscore the importance of patient race and its influence on the probability that a patient carries specific polymorphisms in relevant pharmacogenetic genes, which can have dramatically different frequencies between race groups. Other guidelines are available to support the use of CYP2C9 genetic testing to guide treatment with nonsteroidal antiinflammatory drugs and phenytoin.43,44

FIGURE 6-1.
FIGURE 6-1.

Visual representation of genes involved in warfarin metabolism and warfarin mechanism of action. Warfarin is administered via a racemic mixture of the R- and S-stereoisomers. The S-warfarin is markedly more potent than the R-warfarin and is metabolized predominantly by CYP2C9. Warfarin’s mechanism of action involves inhibiting vitamin K epoxide reductase, encoded by VKORC1, and this limits the availability of reduced vitamin K, leading to decreased formation of functionally active clotting factors. CYP4F2 functionally removes vitamin K from the vitamin K cycle by metabolizing reduced vitamin K to hydroxyl-vitamin K1.

Codeine Pharmacogenetics

J.P. is a 30-year-old woman who had a caesarean section and delivered a healthy baby boy 12 days ago. She was given codeine in the hospital for pain control and was given a codeine prescription upon discharge. She presents with her baby to the emergency department because her baby has been exhibiting extreme sleepiness, poor feeding, and trouble breathing. She is genotyped and found to be CYP2D6*1/*1X2.

QUESTION: Which gene(s) impact response to codeine?

DISCUSSION: Codeine is a prodrug that is activated to morphine by CYP2D6. Other genes that may impact codeine metabolism and response include UGT2B7, which is involved in the formation of morphine-6-glucuronide, the ABCB1 transporter gene, and the opioid receptor µ1 gene OPRM1.

QUESTION: Genotypes for CYP2D6 are represented by what four phenotypes?

DISCUSSION: (1) Normal (also known as extensive) metabolizers. This phenotype achieves the expected concentrations of morphine. (2) Intermediate metabolizer. This phenotype has intermediate enzyme activity and reduced morphine formation. (3) Poor metabolizers. This phenotype lacks CYP2D6 enzyme activity and has greatly reduced morphine formation, leading to insufficient pain relief when given codeine. (4) Ultrarapid metabolizers. This phenotype has increased enzyme activity and increased morphine formation, leading to an increased risk of toxicity.

QUESTION: What is an activity score, and which score goes with each phenotype from the previous question?

DISCUSSION: Activity score is used in addition to the traditional drug metabolizer phenotypes because of the large number of alleles present in CYP2D6 and the wide range of enzyme activity, even within phenotypic groups. To determine an activity score, the combination of alleles is used to determine diplotype. Each functional group is then assigned an activity value ranging from 0 to 1 (eg, 0 for no function, 0.5 for decreased function, and 1 for normal function). If multiple copies of the CYP2D6 gene are detected, the activity score is multiplied by the number of copies of each allele present. Therefore, a normal metabolizer with an activity score of 1.5 will have less enzyme activity than a normal metabolizer with an activity score of 2.

  • Normal metabolizers: activity score is 1.25 to 2.25.

  • Intermediate metabolizer: activity score is 0.25 to 1.

  • Poor metabolizers: activity score is 0.

  • Ultrarapid metabolizers: activity score is >2.25.

QUESTION: What do you suspect is going on with this patient’s baby, and what treatment management decisions would you recommend based on her genotype?

DISCUSSION: Per the CPIC guidelines, this genotype is an ultrarapid metabolizer with increased enzyme activity (~1% to 2% of patients). CYP2D6 ultrarapid metabolizers treated with codeine have rapid intoxication, even with low doses, due to the increased formation of morphine. Codeine is excreted into the breastmilk; therefore, suspect that the baby is receiving toxic levels of morphine. Codeine should be avoided in ultrarapid metabolizers and alternative analgesics should be considered.

QUESTION: What analgesics are not impacted by CYP2D6? What other medications might variants in this gene affect?

DISCUSSION: Analgesics not impacted by CYP2D6 include morphine and nonopioids. Tramadol, hydrocodone, and, to a lesser extent, oxycodone all have metabolism impacted by CYP2D6.

CYP3A5

CYP3A5 has predictable associations between polymorphisms and expression of CYP3A5 enzyme. The CYP3A5*3 allele is associated with slower metabolism compared with *1 and tacrolimus pharmacokinetics; patients carrying the *1 allele (CYP3A5 expressers) have significantly lower trough (predose) concentrations of tacrolimus for a given dose than nonexpressers. Approximately 10% to 20% of white persons, 85% of black persons, 60% of Hispanic persons, and 50% of East Asians are considered CYP3A5 expressers.45 Consequently, these individuals may require dose modifications of CYP3A5-metabolized drugs, as normal doses are based on nonexpressers. CYP3A5 genetic variants have been implicated in variable drug responses for many drugs, including statins, antiepileptics, calcineurin inhibitors, and tacrolimus. However, dosing guidelines based on genotype exist only for tacrolimus (Minicase 3).46

Other CYP450s

Similar to CYP2C9, polymorphisms in the gene CYP4F2 have been associated with warfarin dose requirements.40 However, the impact of CYP4F2 variation is not due to CYP4F2-mediated warfarin metabolism but to CYP4F2-mediated metabolism of reduced vitamin K, which affects a patient’s ability to produce functioning clotting factors and thus a patient’s stable warfarin dose (discussed in detail in the following section). A relatively recent example of CYP enzyme pharmacogenetics is CYP2B6. This enzyme is highly polymorphic with more than 35 known variant alleles.47 Different race groups have substantial differences in the frequencies of CYP2B6 alleles, which include decreased function (eg, CYP2B6*6 and *9), no function (eg, CYP2B6*18), and increased function (eg, CYP2B6*4) alleles. These alleles can be used to guide dosing of efavirenz, a nonnucleoside reverse transcriptase inhibitor used to treat individuals with human immunodeficiency virus (HIV) infection. Although many other CYP enzymes are involved in drug metabolism, many do not have a marked impact on drug metabolism. CYP3A4 contains more than 30 reported polymorphisms, but this variation is either rare or does not have major functional consequences for enzyme function. CYP3A4 polymorphisms thereby result in a unimodal distribution of drug clearance, likely due to phenotypic variation influenced by food or other environmental factors. Thus, they are not typically used in the clinical setting. Unlike CYP2D6, CYP2C19, or CYP2C9, only a small number of rare variations cause loss of CYP3A4 activity.48

Cardiovascular Pharmacogenetics

J.K. is a 58-year-old 109 kg (240 lbs), 1.7 m (5′7″), white man who was admitted to the emergency department after experiencing an episode of sustained, substernal chest pain. He is diagnosed with an ST elevation myocardial infarction and sent immediately for coronary angiography and subsequent percutaneous coronary intervention. The patient is started on clopidogrel 75 mg daily, aspirin 325 mg daily, and metoprolol succinate 25 mg daily in addition to his current medication list. You are consulted for pharmacogenomics of clopidogrel and simvastatin.

  • Allergies: no known drug allergies

  • Past medical history: uncontrolled hypertension, hyperlipidemia, type 2 diabetes

  • Vital signs: blood pressure 155/92 mm Hg, pulse 55 beats/min, respiration rate 17 breaths/min, temperature 98.9°F

  • Medications prior to admission: lisinopril 20 mg daily, glipizide XL 10 mg daily, simvastatin 40 mg daily, omeprazole 20 mg daily

  • Pharmacogenetic test results: CYP2D6 *3/*3 (poor metabolizer); CYP2C19*2/*3 (poor metabolizer); SLCO1B1 *1a/*1a (normal function); VKORC1-1639 G/A; CYP2C9*1/*3 (CYP2C9*5, *6, *8, *11 not tested); CYP4F2 *1/*1

QUESTION: Based on this patient’s clinical and genotype information, what recommendations do you have for antiplatelet therapy (ie, clopidogrel treatment)?

DISCUSSION: According to CPIC guidelines, clinical recommendations for clopidogrel will depend on CYP2C19 genotype results. If he is an ultrarapid (CYP2C19*17 carrier) or normal metabolizer (*1/*1), then he would be considered to have an adequate response to clopidogrel. If he is an intermediate metabolizer (*2 or *3 carrier) or poor metabolizer (*2/*2, *2/*3, *3/*3), then a switch to an alternative antiplatelet, such as prasugrel or ticagrelor, could be considered. Because this patient is a poor metabolizer, antiplatelet treatment with clopidogrel should be avoided and either prasugrel or ticagrelor should be started to avoid an inadequate antiplatelet treatment and a resulting stent thrombosis.

QUESTION: If this patient had a CYP2C19*1/*17 genotype, what would be the best long-term antiplatelet therapy for him?

DISCUSSION: Because the CYP2C19*1/*17 diplotype would indicate that he is an ultrarapid metabolizer, J.K.’s functional copies of CYP2C19 would result in efficient production of the active clopidogrel metabolite. So, clopidogrel is an effective option for J.K. to prevent stent thrombosis.

QUESTION: Does this patient’s statin therapy need to be changed?

DISCUSSION: According to CPIC guidelines, minimization of rhabdomyolysis risk with simvastatin should be directed by SLCO1B1 rs4149056 genotype. The patient’s diplotype is SLCO1B1 *1a/*1a, indicating that his genotype is rs4149056 TT. The TT genotype indicates a normal risk for muscle toxicity with the drug. The treatment does not need to be changed unless additional cholesterol lowering is needed, in which case simvastatin should be switched to an alternative such as a higher potency statin. This is because simvastatin 80 mg is not recommended by the FDA due to increased incidence of rhabdomyolysis.

QUESTION: Before discharge, this patient develops atrial fibrillation. If warfarin is used for anticoagulation (baseline INR is 1.01 and target INR is 2.5), what initial dose of warfarin would you use?

DISCUSSION: Because the patient is white, the CPIC guidelines recommend use of a pharmacogenetic algorithm that includes VKORC1-1639G>A and CYP2C9*2 and *3 variants. Additional variants such as CYP2C9*5, *6, *8, *11 can be included in the dosing algorithm or used to decrease the warfarin dose by 15% to 30% in variant carriers. The CYP4F2*3 allele can also be included in the dosing algorithm or used to increase warfarin dose 5% to 10%.

J.K.’s genotype results, discussed previously, are VKORC1-1639 G/A, CYP2C9*1/*3, and CYP4F2 *1/*1, but the CYP2C9*5, *6, *8, *11 alleles have not been tested. If the patient reported African ancestry, then no genotype-guided algorithm should be used. Because the patient is white, then a dosing algorithm can be used, several of which are found at WarfarinDosing.org (http://warfarindosing.org/Source/Home.aspx). Using J.K.’s information on WarfarinDosing.org, a 3.4 mg/day therapeutic dose according to the Gauge algorithm and 4.1 mg/day according to the International Warfarin Pharmacogenetics Consortium algorithm are recommended. These therapeutic doses may be rounded to the nearest mg dose that is clinically available. INR monitoring should still be performed as per clinical recommendations. A starting dose of 5 mg is not recommended given genomic data are available.

Solute Carrier Organic Anion Transporter Family Member 1B1

Solute carrier organic anion transporter family member 1B1 (SLCO1B1) encodes a transporter in the liver that facilitates the uptake of many endogenous compounds and drugs, including statins. Several alleles with decreased function have been described, but the CPIC guideline for simvastatin focuses on the alleles that harbor the rs4149056 C allele (*5, *15, *46, and *47).49 People with these alleles are at higher risk for simvastatin-induced myopathy, so reduced doses or alternative lipid-lowering agents are recommended (pravastatin or rosuvastatin).50 The frequency of having decreased or poor function varies significantly by race group, from 6% in Africans to 43% in Europeans. Although other drugs are substrates of SLCO1B1/OATP1B1, there are not currently any recommendations for adjusting doses based on SLCO1B1 genotype.

Immunosuppressant Pharmacogenetics

D.L. is a 2-year-old African American girl who has renal dysplasia and recently received a kidney transplant. After the transplant, she was started on the usual dose of 0.1 mg/kg of tacrolimus twice a day. After a tacrolimus trough concentration was low, her dose was doubled. Two days later, a trough concentration was high. Her trough concentrations continued to vacillate above and below the target range as the dose was adjusted several times. After a dose of 0.3 mg/kg, D.L. suffered a seizure. The tacrolimus was held, and she developed acute kidney injury and rejection. CYP3A5 testing was ordered at this time, with a genotype result of *1/*3 (expresser). In addition, azathioprine was started due to mycophenolic acid-related diarrhea. She became neutropenic, so TPMT phenotyping was ordered. (The TPMT phenotyping assay detects the TPMT enzyme activity in red blood cells.) After the results came back as TPMT normal metabolizer, the pharmacogenetics team was consulted, and it was discovered she had a blood transfusion prior to the TPMT phenotyping sample being drawn. The TPMT genotype test was ordered, resulting in a *1/*3A intermediate metabolizer. She developed a fever and was started on fluconazole. She was subsequently trialed on tacrolimus again, and the dose was adjusted due to the drug–drug interaction between fluconazole and tacrolimus.

QUESTION: Which pharmacogene(s) influence tacrolimus and azathioprine?

DISCUSSION: Up to 50% of the interpatient variability in tacrolimus pharmacokinetics is explained by variants in CYP3A5. This patient is an expresser and likely needs a higher-than-standard starting dose of tacrolimus. Myelosuppression related to azathioprine is influenced by TPMT and NUDT15. This patient is a TPMT intermediate metabolizer and may require reduced doses of thiopurines.

QUESTION: Why were the TPMT phenotyping and genotyping inconsistent?

DISCUSSION: The TPMT phenotype is testing with an assay for the enzyme’s activity in red blood cells. Given the patient had a blood transfusion recently, the enzyme activity was for the transfused red blood cells, indicating normal activity. However, the genotyping was subsequently performed on the patient’s white blood cells, where the genotype indicated she was an intermediate metabolizer.

QUESTION: What contributed to the seizures this patient experienced?

DISCUSSION: In a CYP3A5 expresser, the tacrolimus trough concentrations are lower than expected. When the dose is increased, the trough concentration increases, as does the maximal concentration. This patient likely experienced a seizure because of the high maximal concentration of tacrolimus. The clinical team was comfortable initiating tacrolimus again when fluconazole was also part of the medication regimen because it inhibits CYP3A4, which slows the metabolism of tacrolimus, and lower doses are needed to receive the same exposure and trough concentrations. Some studies have demonstrated an association with the CYP3A4*22 allele and tacrolimus pharmacokinetics; however, it is not included in the CPIC guideline.

Thiopurine Methyltransferase and Nudix Hydrolase 15

Thiopurine methyltransferase (TPMT) is the enzyme responsible for the conversion of thiopurines azathioprine, thioguanine, and 6-mercaptopurine into inactive metabolites. Genetic variants in the TPMT gene can result in deficient or absent TPMT activity, leading to severe hematologic adverse effects with azathioprine or 6-mercaptopurine (6-MP) treatment. The most common variants in TPMT are referred to as TPMT*2 (rs1800460), *3A (rs1800460 and rs1142345), and *3C (rs1142345). Individuals with one copy of a variant allele make up approximately 10% of the white population and require dose reductions of approximately 50%.51,52 Individuals with two no-function alleles require dose reductions of thiopurines like 6-MP on the magnitude of 90% to avoid hematologic toxicity.

More recently, variants in nudix hydrolase 15 (NUDT15) were discovered to influence thiopurine tolerability in patients with leukemia and inflammatory bowel disease.53,54 This enzyme inactivates thioguanine nucleotides; reduced or absent function is associated with more active metabolites of thiopurines. The *2 and *3 alleles confer no enzymatic function, resulting in high concentrations of active metabolites and an increased risk for myelosuppression. CPIC recommends reduced dosing in carriers of these alleles.55 These alleles are most common in Asians and Hispanics, in whom TPMT no function alleles are less common.

Thiopurines are used in the treatment of childhood acute lymphoblastic leukemia and rheumatoid arthritis, the prevention of renal allograft rejection, and the management of inflammatory bowel disease. The FDA-approved drug label for mercaptopurine states that patients with severe myelosuppression should be evaluated for TPMT or NUDT15 deficiency while taking the drug and that patients with homozygous TPMT or NUDT15 deficiency require substantial dose reductions.56 Many major academic medical centers routinely perform TPMT genotyping or activity testing prior to thiopurine dosing for leukemia, although the frequency of testing is lower in other specialties (eg, gastroenterology and rheumatology).57

Dihydropyrimidine Dehydrogenase

Dihydropyrimidine dehydrogenase (DPD) metabolizes fluoropyrimidine agents (5-fluorouracil, capecitabine, and tegafur) commonly used in the treatment of solid organ tumors. In the mid-1980s, it was recognized that deficiencies in DPD were heritable and associated with severe 5-fluorouracil toxicity.58,59 Many polymorphisms in the DPYD gene, encoding DPD, have been found. The most widely studied polymorphism is in an intron, IVS14 +1 G>A (DPYD*2A, rs3918290), which results in a splicing defect rendering DPYD inactive, have been the most widely studied. Because of the number and complexity of DPYD variants, an activity score system has been implemented similar to CYP2D6. CPIC guidelines have been published with dosing recommendations based on genotype.60,61 As with TPMT testing, DPD deficiency can be tested for genetically or with enzymatic testing.62,63

Uridine Diphosphate Glucuronosyltransferase

Uridine diphosphate glucuronosyltransferases mediate phase II conjugation of glucuronic acid of drugs and endogenous substrates.64 The major UGT1A subfamily enzyme, UGT1A1, is critical for elimination of bilirubin, the main byproduct of heme catabolism. The UGT1A1*28 allele was originally identified as a causative genetic variant of Gilbert syndrome, a form of mild unconjugated hyperbilirubinemia.65 Since then, multiple UGT1A1 alleles have been identified that reduce the transcription of UGT1A1, making patients more susceptible to drugs that inhibit UGT1A1-mediated glucuronidation of bilirubin. Such drugs include atazanavir, which is a protease inhibitor used to treat HIV. To avoid hyperbilirubinemia associated with atazanavir, guidelines recommend avoiding atazanavir in UGT1A1 poor metabolizers. It is important to note that while UGT1A1 (and G6PD described later) have guidelines to support clinical intervention based on pharmacogenetic testing, these enzymes have no direct role in the metabolism of their associated drugs. In these cases, drug therapy increases the risk for adverse reactions; therefore, guidelines recommend that these drugs be avoided in persons with enzymatic deficiencies.64,66

Glucose-6-Phosphate Dehydrogenase

Glucose-6-phosphate dehydrogenase (G6PD) is an enzyme that produces reduced nicotinamide adenine dinucleotide phosphate (NADPH) from NADPH.66,67 G6PD is particularly important in erythrocytes because in these cells G6PD is one of the only available sources of NADPH, which is required to protect erythrocytes from oxidative stress. Individuals who are deficient in G6PD have erythrocytes with a much-reduced capacity for NADPH production and a compromised ability to handle oxidative stress. G6PD-deficient individuals are more susceptible to hemolytic anemia resulting from drug-induced lysis from drugs such as rasburicase, which is used to treat hyperuricemia. Multiple polymorphisms in G6PD are known to cause G6PD deficiency, and a World Health Organization (WHO) class system is available to classify individuals who are G6PD deficient.67 Accordingly, a guideline is available to guide the use of G6PD genotyping results to avoid rasburicase in G6PD deficient individuals. An interesting aspect of G6PD genotyping is that the gene is present on the X chromosome; thus, WHO classifications are primarily based on assessments in males, who have only one copy of the gene. Tests for G6PD deficiency can thereby result in incidental disease findings, including diagnosis of Klinefelter’s syndrome, a rare genetic disease in male patients who have an extra X chromosome.

Determination of Clinical Significance

When deciding whether drug metabolism polymorphisms might be clinically significant for particular drugs, it is helpful to consider three main factors. First, is the drug metabolism enzyme of interest an important route of elimination for the drug in question? If not, even functional polymorphisms in this gene may not have a great impact on the pharmacokinetics of the drug. Second, does the medication of interest have a narrow therapeutic index or steep exposure/response relationship? If not, changes in plasma concentrations may not be great enough to influence the dose-response relationship. Third, are other therapeutic alternatives available to the medication in question? If so, these alternatives may have other routes of metabolism that are not polymorphic, and the variability in pharmacokinetics could be avoided altogether. Determination of clinical significance for pharmacodynamic and immune-related genes requires different considerations, such as whether alternative agents are available and the associated risks (or cost) of alternative agents.

DRUG TARGET–RELATED MOLECULAR TESTS

Germline Variation and Genotype-Guided Therapy

Although pharmacokinetics is concerned with absorption, distribution, metabolism, and excretion, pharmacodynamics is concerned with drug effects on target molecules, tissues, and physiologic processes. This section discusses examples of how variation in genes that encode drug targets can affect drug disposition. There are illustrative examples in cardiology, oncology, infectious diseases, and others in which consideration of genetic variation affecting pharmacodynamics may improve drug therapy. This section focuses on germline mutations, which are heritable mutations present in all nucleated cells, rather than somatic mutations, which occur during the lifespan of an individual and are typically associated with cancer. Warfarin is the main example discussed and includes both pharmacokinetic and pharmacodynamic genes that can be used to improve dose prediction. The next section focuses on illustrative examples of drug-target related molecular tests for somatic mutations in oncology.

Warfarin Pharmacogenetics

Warfarin is one of the most commonly prescribed anticoagulants for the treatment and prevention of thrombosis. Warfarin has a narrow therapeutic range (as measured by the international normalized ratio [INR]) below which thrombosis risk is increased and above which bleeding risk is increased. Although patient-specific factors such as age, sex, race, and diet partially explain variability in warfarin response, these factors do not reliably predict the likelihood of efficacy or bleeding risk. As such, investigators have studied the role of genetic variants in an enzyme responsible for warfarin’s metabolism (CYP2C9) and vitamin K epoxide reductase complex subunit 1 (VKORC1) on warfarin responses (Figure 6-1). These studies have investigated the impact of genetic and nongenetic factors on endpoints related to INR, bleeding, and clinical efficacy.

VKORC1 catalyzes the conversion of vitamin K-epoxide to vitamin K, which is the rate-limiting step in vitamin K recycling.68 Warfarin exerts its anticoagulant effect through inhibition of VKORC1, which in turn limits availability of reduced vitamin K, leading to decreased formation of functionally active clotting factors.40 Pharmacogenetic studies have interrogated VKORC1 polymorphisms and determined that a common variant upstream of VKORC1 (c.-1639G>A, rs9923231) is significantly associated with warfarin sensitivity, and patients with one or two c.-1639G>A alleles require progressively lower warfarin doses than –1639G/G homozygotes. CYP2C9 is the major metabolic route for the more potent warfarin enantiomer S-warfarin. The CYP2C9*1 allele is associated with full metabolic capacity, while the well-studied *2 (rs1799853) and *3 (rs1057910) alleles are associated with decreased metabolic activity, diminished warfarin clearance, and lower warfarin dose requirements.69-74 By extension, these variant carriers exhibit longer than normal time to achieve target INR and are at increased risk for bleeding.75 Another CYP enzyme, CYP4F2, plays no direct role in the metabolism of warfarin, but CYP4F2 polymorphisms affect stable warfarin dose because CYP4F2 removes reduced vitamin K from the vitamin K cycle. Taken together, CYP2C9, CYP4F2, and VKORC1 polymorphisms, when considered with clinical correlates of warfarin dose, explain approximately 50% of the variability in warfarin dose requirements.76,77 Dosing algorithms, including pharmacogenetic variation and clinical information, are continually being developed and tested, and the FDA updated the warfarin label with estimated doses by genotype.78-80 Guidelines also have been published with recommendations for clinical interpretation of warfarin pharmacogenetics data.40,80

The CYP2C9*2 and *3 variants are common in individuals with European ancestry and were preferentially incorporated into genotype-guided warfarin dosing algorithms due in part to the fact that most of the evidence was generated in individuals with European ancestry.81 Other CYP2C9 variants are associated with warfarin dose requirements in African Americans include CYP2C9*5, CYP2C9*6, CYP2C9*8, and CYP2C9*11.).82-84 Another SNP in the CYP2C cluster of genes, rs12777823, has also been observed to have an important impact on warfarin dose in African Americans.85 Early warfarin dosing guidelines did not differentiate recommendations based on race, but subsequent guidelines advise against the use of warfarin dosing algorithms in African Americans if CYP2C9*5, *6, *8, *11 have not been interrogated. For patients who do not report West African ancestry, CPIC guidelines recommend the use of a pharmacogenetic algorithm that includes VKORC1-1639G>A and CYP2C9*2 and *3 variants.86 The additional variants CYP2C9*5, *6, *8, *11 can be included in the dosing algorithm or used to decrease the warfarin dose by 15% to 30% in variant carriers. The CYP4F2*3 allele can also be included in the dosing algorithm or used to increase warfarin dose 5% to 10%. Warfarin pharmacogenetics have been complicated by contradictory prospective clinical trials assessing pharmacogenetic-guided versus traditional dosing.87-90 The U.S.-based study was called Clarification of Optimal Anticoagulation Through Genetics (COAG),88 and the two European studies were called European Pharmacogenetics of Anticoagulant Therapy.89,90 All of the studies had the same primary endpoint: time in the therapeutic range. To briefly summarize the trials’ findings related to the primary outcome, pharmacogenetic-guided dosing was superior to empirical dosing; however, pharmacogenetic-guided dosing was not superior to a clinical dosing algorithm.87-90 Further complicating the issue is that in COAG, African Americans fared significantly worse with a pharmacogenetic dosing algorithm compared with the clinical algorithm.88 The pharmacogenetic algorithm used in these studies did not include variants that are associated with warfarin dose requirements in African Americans (CYP2C9*5, CYP2C9*6, CYP2C9*8, CYP2C9*11, and rs12777823).82-84 The exclusion of these variants from the algorithm may have overestimated the dose in African American study subjects.82 Although time in the therapeutic range (primary endpoint) did not differ between the groups, major bleeding was more common in the clinically guided dosing arm of the COAG trial compared with the genotype-guided dosing arm (HR 0.36; 95% CI 0.15-0.86; P = .021).88 Other randomized controlled trials have since been published that support implementation of genotype-guided warfarin dosing, including the Genetic Informatics Trial conducted primarily in white persons.91,92

Tumor Molecular Testing to Guide Therapy Choices

In oncology, the advances in genomic technologies and the realization that many tumors can be subdivided in molecular subsets defined by specific genomic alterations have fueled the development of therapeutic agents targeting these molecular alterations. Therefore, genomic testing to identify patients to be considered for a specific therapy based on the patient’s tumor molecular classification (and germline variation, for some drugs) is becoming the standard of care. Also, in addition to germline (inherited) variations, tumor cells also can exhibit acquired (somatic) variations only present in the tumor tissue. This adds a layer of complexity to molecular testing, involving the acquisition and processing of tumor tissue with adequate quality and quantity to accommodate the test of interest.

It is important to keep in mind that the biological understanding of the molecular landscape of tumors is constantly evolving, as are the genomic technologies used to assess molecular alterations in tumor samples, circulating tumor cells or cell-free tumor DNA. This section deals with two illustrative examples of molecular alterations in tumors at the gene and protein level used to guide therapy: (1) the epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) erlotinib, gefitinib, and afatinib in EGFR mutation-positive non–small-cell lung cancer (NSCLC); and (2) the serine/threonine-protein kinase B-raf (BRAF) inhibitors vemurafenib and dabrafenib in BRAF mutation-positive melanoma.

Epidermal Growth Factor Receptor Mutation-Positive Non–Small-Cell Lung Cancer and Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors

Epidermal growth factor receptor aberrant signaling is associated with development and prognosis of certain cancers. EGFR mutation-positive NSCLC depends on EGFR signaling for proliferation and survival. Therefore, EGFR therapeutic inhibition results in blockage of important processes in the pathogenesis of lung cancers.93

In NSCLC, the presence of certain EGFR-activating mutations in the tumor (mostly in exons 18 to 21 of the EGFR gene) defines a molecular subset of lung cancer associated with increased sensitivity to EGFR TKIs such as erlotinib, gefitinib, and afatinib in the metastatic setting. The best documented EGFR TKI-sensitizing mutations are exon 19 deletions and L858R in exon 21, representing approximately 90% of reported EGFR mutations in NSCLC. The remaining 10% of EGFR mutations represent a heterogeneous, less common, and less characterized group of mutations. EGFR mutations are most common in East Asians (35% versus 10% in white persons), female never-smokers, and patients with adenocarcinoma histology.94,95 In a 2004 landmark study, Lynch and colleagues identified mutations in the EGFR gene in tumors of patients with NSCLC who were responsive to gefitinib.96 Sensitivity and specificity were 89% and 100%, respectively; positive and negative predictive values were 100% and 88%. Since this publication, several clinical trials have prospectively tested the impact of EGFR mutations on clinical response to EGFR TKIs among patients with lung cancer. Results from these studies have underscored the importance of tumor molecular profiling, and EGFR mutation testing is currently a standard procedure in guiding therapy choices for advanced NSCLC.97-99 First-generation (gefitinib, erlotinib) and second-generation (afatinib) EGFR TKIs are FDA approved for the first-line treatment of patients with metastatic NSCLC whose tumors have EGFR exon 19 deletions or exon 21 (L858R) substitution mutations as detected by an FDA-approved test. These drugs were approved in conjunction with polymerase chain reaction (PCR)-based companion diagnostic tests designed to detect defined EGFR mutations in patients considered for therapy with any of these drugs.

BRAF Mutation-Positive Melanoma and BRAF Pathway Inhibitors

The presence of certain BRAF mutations defines a molecular subset of melanoma especially sensitive to inhibition by BRAF inhibitors as single agents or in combination with MEK inhibitors. Vemurafenib and dabrafenib are BRAF inhibitors indicated for the treatment of patients with unresectable or metastatic melanoma with BRAF V600E mutation as detected by an FDA-approved test. Mutated BRAF proteins often have elevated kinase activity leading to aberrant activation of survival and antiapoptotic signaling pathways in the tumor cells.100 Approximately 40% to 60% of cutaneous melanomas are positive for BRAF V600 mutations. Among these, the V600E mutations constitute 80% to 90% of reported V600 BRAF mutations, but other less common BRAF mutations, such as V600K and V600D, also occur. Their role in conferring sensitivity to BRAF inhibitors, however, is not as well documented.101

As with other therapeutic agents targeting specific tumor molecular alterations, vemurafenib and dabrafenib also were developed along with PCR-based companion diagnostic tests designed to identify specific BRAF V600 mutations.102 Currently, BRAF inhibitors are not indicated for patients with wild-type BRAF melanoma because of the potential of paradoxical activation of the mitogen-activated protein kinase pathway and tumor promotion in certain cellular contexts, which highlights the importance of understanding test performance for determining therapy eligibility and the consequences of false-positive and false-negative results.103

Resistance to Targeted Therapy

Only a percentage of patients respond to targeted therapies, and despite initial clinical benefit, responders often develop resistance. Elucidating underlying mechanisms of primary or acquired resistance at a molecular level is an intense area of research in oncology. For example, somatic point mutations in the KRAS gene, most commonly found in codons 12 and 13, have been strongly associated with primary resistance to the anti-EGFR monoclonal antibodies panitumumab and cetuximab in colorectal cancer. K-ras is downstream of EGFR signaling, so activating mutations in KRAS bypass the EGFR inhibition. Therefore, these antibodies are not indicated for colorectal cancer patients with tumors positive for these mutations. Of note, different KRAS mutations may not predict to the same extent resistance to anti-EGFR antibodies.104 Point mutations, gene amplifications, changes in protein expression, and activation of alternate pathways are among the mechanisms implicated in resistance.105 Molecular assays to identify these alterations and evaluate their clinical significance are in different stages of development. This is illustrated by osimertinib, which is a third-generation irreversible EGFR TKI first approved in conjunction with a PCR-based companion diagnostic assay for patients with metastatic EGFR T790M mutation-positive NSCLC who have progressed on or after EGFR TKI therapy. The EGFR T790M second-site mutation accounts for approximately 50% of the reported cases of acquired resistance to the reversible EGFR TKIs erlotinib and gefitinib. It was subsequently approved as first-line therapy for metastatic NSCLC to anticipate and avoid the development of the EGFR T790M mutation. Many other mechanisms of resistance to EGFR TKIs involving different molecular alterations have been reported in NSCLC.106

IMMUNE-RELATED ADVERSE REACTIONS AND MOLECULAR TESTS

Variants in immune-related genes are increasingly being associated with drug-induced adverse events. In particular, human leukocyte antigen (HLA) screening has emerged as a preventive strategy for immune-mediated adverse drug reactions (IM-ADRs). HLA alleles are highly polymorphic and, within the genome-wide association study catalogue, more phenotype associations have been identified in the HLA region than any other region of the genome.107,108 Immune response is modulated by the HLA system, and amino acid sequences of each HLA molecule determine peptide binding and antigen presentation to T-lymphocytes.109 Variation in HLA is critical to prevent allograft rejection and plays a key role in determining susceptibility to infection and autoimmune disease.

One of the earliest examples of HLA-associated IM-ADRs was with abacavir hypersensitivity reaction (HSR), for which a strong association was observed with HLA-B*57:01.110,111 Immune-mediated hypersensitivity reactions occur in 5% to 8% of patients treated with abacavir, usually within the first 6 weeks of treatment. The presence of at least one HLA-B*57:01 allele is necessary for the HSR reaction to occur, although the HLA-B*57:01 allele is not sufficient to predict HSR. Prospective randomized-controlled trials demonstrated that screening for the HLA-B*5701 allele eliminated immunologically confirmed hypersensitivity reactions with a negative predictive value of 100% and a positive predictive value of 47.9%.111 Current HIV treatment guidelines recommend screening for HLA-B*5701 before initiation of an abacavir-containing treatment regimen.10,112,113

Subsequently, HLA-B*15:02 and HLA-A*31:01 were observed to be strongly associated with carbamazepine-induced Stevens-Johnson syndrome,114-116 which is a severe, cutaneous immune-mediated reaction to several classes of drugs, including antiepileptics, antibiotics, and antivirals. HLA-B*15:02 has also been associated with IM-ADRs to other anticonvulsants, including oxcarbazepine, phenytoin, and lamotrigine.107 Similarly, IM-ADRs caused by allopurinol, a drug used to treat gout and hyperuricemia, are associated with HLA-B*58:01.117

Multiple guidelines are available that support the use of HLA allelic information to guide drug selection (Table 6-2). In these guidelines, if a patient carries an associated HLA allele, then the recommendation is to avoid that agent.43,113,118,119 This is true whether the patient carries one or two copies of the HLA allele. Importantly, no dose adjustments are recommended based on HLA alleles given these alleles have no role in the metabolism or pharmacologic action of these drugs. In most cases, these alleles have a high negative predictive value (usually near 100%) but low positive predictive value for the IM-ADR. This indicates that the HLA allele is generally necessary to elicit the reaction in the presence of the drug, but drug treatment and the presence of an HLA allele are not deterministic for the IM-ADR.

Furthermore, allele frequencies vary dramatically by race. In the case of HLA-B*15:02 and HLA-B*57:01, carriage of these alleles is essentially nonexistent in many African populations but is as high as 20% in some Asian populations. This implies that the use of HLA screening to prevent IM-ADRs may only be cost-effective in populations that have increased frequencies of the associated HLA alleles.

GENOTYPING PLATFORMS

Many commercial genetic tests are available for pharmacogenetic-related genes or gene panels. The AmpliChip (Roche Molecular Diagnostics, Basel, Switzerland) microarray is one such example that is FDA-cleared. It provides analysis for CYP2D6 and CYP2C19 genotypes to predict enzymatic activities. The assay tests for up to 33 CYP2D6 alleles, including gene duplications and three CYP2C19 variants, and includes software to predict the drug metabolism phenotype based on the combination of alleles present (eg, normal, intermediate, poor, and ultrarapid metabolizers). Luminex xTAG CYP2D6 Kit (Luminex Corporation, Austin, TX) is another FDA-cleared test that tests for 15 CYP2D6 alleles as well as gene duplications. Because commercially available genetic testing options change frequently, we do not discuss all available tests. However, the Genetic Testing Registry provides a central location for voluntary submission of genetic test information by providers (www.ncbi.nlm.nih.gov/gtr/). Most pharmacogenomic testing panels are ordered by a provider rather than a patient, and many have not received FDA approval because they are considered laboratory-developed tests. The first and only pharmacogenetic test given FDA authorization for direct-to-consumer testing (rather than being ordered by a provider) is provided by 23andMe (Sunnyvale, CA).

Pharmacogenetic genotyping assays do have two limitations with which the clinician should be familiar: (1) New alleles that alter function are constantly being discovered, so there are patients who will not be perfectly assigned to a phenotype group or could be inappropriately assigned the *1/*1 genotype by default because these alleles are untested. (2) Because genotyping does not directly measure metabolic activity or drug concentrations, the effect of drug interactions on the drug metabolizing phenotype is not captured by the test. In other words, a person may genotypically be a normal metabolizer but phenotypically be a poor metabolizer because the person is taking a drug that inhibits the particular CYP450 enzyme. This limitation highlights the importance of proper patient-specific interpretation of CYP genotyping results in clinical practice.

To accommodate the growing number of genetic variants of (potential) clinical significance, clinical testing is moving from single variants to multiplexed panels that can simultaneously interrogate a limited number of variants (polymorphisms and hotspot mutations). Next-generation sequencing (NGS) technologies, although not yet implemented in routine practice, provide even more comprehensive genomic analysis, including genome copy number changes and structural rearrangements, which are not captured by multiplexed panels. NGS technologies can perform whole-genome sequencing, targeted sequencing, including whole-exome sequencing, transcriptome analysis, and epigenetic profiling. NGS-based approaches have been used to uncover underlying disease biology and to identify markers influencing response to therapies. In the context of clinical oncology, high-throughput gene sequencing has the potential to change patient care from diagnosis to disease management. However, the integration and accurate biological interpretation of the large genomic data generated by these platforms are still major challenges for clinical implementation.120-122

The cost of pharmacogenetic testing varies depending on the number of alleles being tested. Although multigene arrays are more cost-effective than genotyping individual SNPs, they currently do not have current procedural terminology codes for reimbursement; thus, genotyping for clinical care is often done as individual tests.123 The turnaround time also varies, but it is usually 24 to 96 hours. In institutions in which the clinical laboratory is onsite, turnaround could be as fast as 4 hours depending on the assay. Generally, genotyping results that are to be used in the clinical care of patients need to be generated from certified laboratories (such as those approved or certified by the Clinical Laboratory Improvements Amendments [CLIA] or the College of Pathologists [CAP]). Germline variation is stable throughout a person’s lifetime; therefore, in general, it only needs to be done once. However, it is important to note which alleles are tested because additional alleles may have been discovered, which will require additional testing to gain information on those alleles.

Clinical Implementation of Pharmacogenomics

Perhaps the biggest barrier to the application of pharmacogenetics is the inability to apply genotype results. This application would require integration into the electronic health record and adequate clinical decision support to guide practitioners in the use of pharmacogenetic data. In this regard, the pharmacogenetics testing process could be interdisciplinary, involving physicians, translational scientists, clinical pharmacists, and others. Additionally, accreditation standards such as those put forth by The Joint Commission, CLIA, CAP and others will have to be addressed when formally incorporating pharmacogenetic testing into institution-based practice. Multiple medical centers, including primarily large academic medical centers, have addressed many of these issues and are implementing pharmacogenomics in clinical care. Developing this infrastructure is a key component of some NIH-supported resources such as eMERGE and IGNITE.

As mentioned elsewhere in the chapter, organizations, including the NACB and CPIC, are working in a multidisciplinary fashion to address issues related to pharmacogenetic test methodology, standardization and quality control/assurance of tests, selection of appropriate test panels, reporting and interpretation of results, and other issues related to testing applied in clinical practice.9,12,19,22,124 Although expansive in scope, the NACB guidelines specifically highlight the role of the clinical laboratory in the development of genotyping strategies that maximize test performance (ie, sensitivity and specificity) for clinical application. Furthermore, guideline recommendations developed the following criteria for a pharmacogenetic test to be clinically useful: (1) analytical reliability (consistent measurement of the genotype/allele tested); (2) operational implementation (operational characteristics should not be beyond the complexity level certified by CLIA for reference laboratories); (3) clinical predictive power (specificity and sensitivity consistent with other diagnostics in use); and (4) compatibility with therapeutic management (interpretation of genotype results should inform clinical decision-making). Interestingly, model examples outlined by the guidelines for drugs in which pharmacogenetics can be implemented include warfarin (CYP2C9 and VKORC1) and irinotecan (UGT1A1). CPIC has taken the approach of publishing clinical practice guidelines for specific drug/gene pairs as enough data becomes available to warrant clinical action based on genotype. A sample of drug/gene pairs that contain pharmacogenetic information, which can inform dosing or patient selection, is shown in Table 6-2.

SUMMARY

Pharmacogenetics is currently being used most widely in hematology/oncology as well as infectious disease and holds the promise of improving patient care by adding another dimension to therapeutic drug monitoring in other diseases. As the evidence for pharmacogenetic relationships increases and the cost of genotyping decreases, the use of genetic information will likely be applied to chronic drug therapy for agents with narrow therapeutic indices, with critical pathways of metabolism and bioactivation, and with severe adverse drug reactions. The field of pharmacogenetics is evolving rapidly. Consequently, specific information regarding molecular tests and labeling information is likely to constantly change. Basic skills in interpreting genetic information will serve as an important foundation for laboratory medicine and drug therapy as more clinical applications of pharmacogenetics emerge.

For pharmacogenetics to translate to practice, the research and clinical communities jointly must create a meaningful level of evidence in support of pharmacogenetics-enhanced, therapeutic decision-making. Because of their unique training and position in the healthcare sector, pharmacists can foresee the forefront of pharmacogenetics research and application. Pharmacists will likely be called upon to synthesize evidence-based practices for incorporating genetic information into treatment algorithms. Once a genetic biomarker is validated (eg, thiopurine pharmacogenetics), clinicians (including pharmacists) will be responsible for the appropriate use and interpretation of the genetic test. The pharmacist’s drug and disease expertise, coupled with an understanding of pharmacogenetic principles, may lead to a revolutionary treatment paradigm with enhanced patient outcome as the ultimate goal.

LEARNING POINTS

1. What types of genes could impact drug response?

ANSWER: Genes that influence the pharmacokinetics (eg, drug metabolism enzymes or drug transporters) and pharmacodynamics (eg, drug targets) of a drug or immune response to a drug could impact drug response. Genetic variation that impacts pharmacogenetics could be germline (ie, host DNA) or somatic (eg, tumor DNA).

2. How might pharmacogenomics enhance therapeutic drug monitoring and management?

ANSWER: Traditional patient-specific factors such as age, sex, renal function, hepatic function, and body weight are frequently used to determine the appropriateness of a particular drug or dose for an individual. However, these factors only partially account for the likelihood of efficacy or toxicity. As our knowledge of how genetic variability impacts drug response is solidified, we can begin to incorporate pharmacogenetic information into algorithms for optimizing pharmacotherapy for individual patients and move toward personalized medicine.

3. Who is likely to be involved in incorporating pharmacogenomics into clinical decision-making?

ANSWER: The incorporation of pharmacogenomics requires an interdisciplinary team of healthcare providers with knowledge of the specific pharmacological properties of individual drugs, molecular biology, genetics, laboratory medicine, clinical medicine, genetic counseling, and economics. In addition, patients and consumers will likely be active participants and drivers of the use of genetic tests in clinical practice.

ACKNOWLEDGMENTS

The authors would like to acknowledge the contributions of Dr. Amber L. Beitelshees and Dr. Rosane Charlab, who authored this chapter in previous editions of this textbook. The authors would also like to acknowledge Ms. Hayley Patterson for her graphic design assistance and administrative assistance with this chapter.

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