Physical Address
304 North Cardinal St.
Dorchester Center, MA 02124
Pediatric Pharmacogenetics, Pharmacogenomics, and Pharmacoproteomics
Interindividual variability in the response to similar doses of a given medication is an inherent characteristic of both adult and pediatric populations. Pharmacogenetics , the role of genetic factors in drug disposition and response, has resulted in many examples of how variations in human genes can lead to interindividual differences in pharmacokinetics and drug response at the level of individual patients. Pharmacogenetic variability contributes to the broad range of drug responses observed in children at any given age or developmental stage. Therefore, it is expected that children will benefit from the promise of personalized medicine —identifying the right drug for the right patient at the right time ( Fig. 72.1 ). However, pediatricians are keenly aware that children are not merely small adults. Numerous maturational processes occur from birth through adolescence such that utilization of information resulting from the Human Genome Project and related initiatives must take into account the changing patterns of gene expression that occur over development to improve pharmacotherapeutics in children.
The promise of genomic medicine to human health and disease.
The goal of personalized medicine is to identify subgroups of patients who will respond favorably to a given drug with a minimum of side effects, as well as those who will not respond or who will show excessive toxicity with standard doses. A further benefit of pharmacogenomics is the ability to select the most appropriate alternative drug for patients who cannot be treated successfully with conventional drugs and doses.
(Adapted from Yaffe SJ, Aranda JV: Neonatal and pediatric pharmacology, ed 3, Philadelphia, 2004, Lippincott Williams & Wilkins.)
Definition of Terms
The terms pharmacogenomics and pharmacogenetics tend to be used interchangeably, and precise, consensus definitions are often difficult to determine. Pharmacogenetics classically is defined as the study or clinical testing of genetic variations that give rise to interindividual response to drugs. Examples of pharmacogenetic traits include specific adverse drug reactions, such as unusually prolonged respiratory muscle paralysis due to succinylcholine, hemolysis associated with antimalarial therapy, and isoniazid-induced neurotoxicity, all of which were found to be a consequence of inherited variations in enzyme activity. The importance of pharmacogenetic differences has become better understood and is exemplified by the half-life of several drugs being more similar in monozygotic twins than in dizygotic twins. However, it is important to note that in addition to pharmacogenetic differences, environmental factors (diet, smoking status, concomitant drug or toxicant exposure), physiologic variables (age, sex, disease, pregnancy), and patient adherence all contribute to variations in drug metabolism and response. Likewise, ethnicity is another potential genetic determinant of drug variability. Chinese patients who are HLA-B*1502 positive have an increased risk of carbamazepine-induced Stevens-Johnson syndrome; white patients who are HLA-B*5701 positive have an increased risk of hypersensitivity to abacavir ( Table 72.1 ).
Table 72.1
Examples of Effects of Gene Polymorphisms on Drug Response
| GENE | ENZYME/TARGET | DRUG | CLINICAL RESPONSE |
|---|---|---|---|
| BCHE | Butyrylcholinesterase | Succinylcholine | Prolonged paralysis |
| CYP2C9 | Cytochrome P450 2C9 | Warfarin | Individuals having ≥1 reduced function alleles require lower doses of warfarin for optimal anticoagulation, especially initial anticoagulant control. |
| CYP2C19 | Cytochrome P450 2C19 | Clopidogrel | Individuals having ≥1 loss-of-function alleles have reduced capacity to form pharmacologically active metabolite of clopidogrel and reduced antiplatelet effect. |
| CYP2D6 | Cytochrome P450 2D6 | Codeine | Poor metabolizers—individuals with 2 loss-of-function alleles—do not metabolize codeine to morphine and thus experience no analgesic effect. Ultrarapid metabolizers—individuals with ≥3 functional alleles—may experience morphine toxicity. |
| G6PD | Glucose-6-phosphate dehydrogenase | Primaquine (others) | Hemolysis |
| HLA-A*3101 | Human leukocyte antigen A31 | Carbamazepine | Carriers of HLA-A*3101 allele have increased risk of SJS and TEN from carbamazepine. |
| HLA-B*1502 | Human leukocyte antigen B15 | Allopurinol | Han Chinese carriers of HLA-B*1502 allele have increased risk of SJS and TEN from carbamazepine. |
| HLA-B*5701 | Human leukocyte antigen B57 | Abacavir Flucloxacillin |
Carriers of HLA-B*5701 allele have increased risk of hypersensitivity reactions to abacavir- and flucloxacillin-induced liver injury. |
| HLA-B*5801 | Human leukocyte antigen B58 | Allopurinol | Carriers of HLA-B*5801 allele have increased risk of severe cutaneous adverse reactions to allopurinol, including hypersensitivity reactions, SJS, and TEN. |
| NAT2 | N -Acetyltransferase 2 | Isoniazid, hydralazine | Individuals homozygous for “slow acetylation” polymorphisms are more susceptible to isoniazid toxicity, or hydralazine-induced systemic lupus erythematosus. |
| SLCO1B1 | Organic anion–transporting protein (OATP) 1B1 | Simvastatin | Carriers of the SLCO1B1*5 allele are at increased risk for musculoskeletal side effects from simvastatin. |
| TPMT | Thiopurine S -methyltransferase | Azathioprine 6-Mercaptopurine |
Individuals homozygous for an inactivating mutation have severe toxicity if treated with standard doses of azathioprine or 6-mercaptopurine; rapid metabolism causes undertreatment. |
| UGT1A1 | Uridine diphospho-glucuronosyltransferase 1A1 | Irinotecan | UGT1A1*28 allele is associated with decreased glucuronidation of SN-38, the active metabolite of irinotecan, and increased risk of neutropenia. |
| VKORC1 | Vitamin K oxidoreductase complex 1 | Warfarin | Individuals with a haplotype associated with reduced expression of VKORC1 protein (therapeutic target of warfarin) require lower doses of the drug for stable anticoagulation. |
SJS, Stevens-Johnson syndrome; TEN, toxic epidermal necrolysis.
Pharmacogenomics represents the marriage of pharmacology and genomics and can be defined as the broader application of genome-wide technologies and strategies to identify both disease processes that represent new targets for drug development and factors predictive of efficacy and risk of adverse drug reactions.
Pharmacokinetics describes what the body does to a drug. It is often studied in conjunction with pharmacodynamics , which explores what a drug does to the body. The pharmacokinetic properties of a drug are determined by the genes that control the drug's disposition in the body (absorption, distribution, metabolism, excretion). Drug-metabolizing enzymes and drug transporters play a particularly important role in this process ( Table 72.2 ), and the functional consequences of genetic variations in many drug-metabolizing enzymes have been described between individuals of both similar and different ethnic groups. The most common clinical manifestation of pharmacogenetic variability in drug biotransformation is an increased risk of concentration-dependent toxicity caused by reduced clearance and consequent drug accumulation. However, an equally important manifestation of this variability is lack of efficacy caused by variations in metabolism of prodrugs that require biotransformation to be converted into a pharmacologically active form of a medication. The pharmacogenetics of drug receptors and other target proteins involved in signal transduction or disease pathogenesis can also be expected to contribute significantly to interindividual variability in drug disposition and response.
Table 72.2
Some Important Relationships Between Drugs and Cytochrome P450 (CYP) Enzymes * and P-Glycoprotein Transporter
From Med Lett 2003;45:47.
| ENZYME | DRUG SUBSTRATES | INHIBITORS | INDUCERS |
|---|---|---|---|
| CYP1A2 | Caffeine, clomipramine ( Anafranil † ), clozapine ( Clozaril † ), theophylline | Cimetidine ( Tagamet † ) | Omeprazole ( Prilosec † ) |
| Fluvoxamine ( Luvox † ) | Tobacco | ||
| Ciprofloxacin ( Cipro ) | |||
| CYP2C9 | Diclofenac ( Voltaren † ), ibuprofen ( Motrin † ), piroxicam ( Feldene † ), Losartan ( Cozaar ), irbesartan ( Avapro ), celecoxib ( Celebrex ), tolbutamide ( Orinase † ), warfarin ( Coumadin † ), phenytoin ( Dilantin ) | Fluconazole ( Diflucan ) | Rifampin ( Rifadin † ) |
| Fluvastatin ( Lescol ) | |||
| Amiodarone ( Cordarone ) | |||
| Zafirlukast ( Accolate ) | |||
| CYP2C19 | Omeprazole, lansoprazole ( Prevacid ), pantoprazole ( Protonix ), (S)-mephenytoin, (S) -citalopram ( Lexapro ); nelfinavir ( Viracept ), diazepam ( Valium † ), voriconazole ( Vfend ) | Cimetidine | Rifampin |
| Fluvoxamine | |||
| CYP2D6 | CNS-active agents: Atomoxetine ( Strattera ), amitriptyline ( Elavil † ), desipramine ( Norpramin † ), imipramine ( Tofranil † ), paroxetine ( Paxil ), haloperidol ( Haldol † ), risperidone ( Risperdal ), thioridazine ( Mellaril † ) | Fluoxetine ( Prozac † ) Paroxetine ( Paxil ) |
|
| Antiarrhythmic agents: Mexiletine ( Mexitil ), propafenone ( Rythmol ) | Amiodarone ( Cordarone † ) Quinidine ( Quinidex † ) |
||
| β-Blockers: Propranolol ( Inderal † ), metoprolol ( Lopressor † ), timolol ( Blocadren † ) | Terbinafine | ||
| Narcotics: Codeine, dextromethorphan, hydrocodone ( Vicodin † ) | |||
| Others: Tamoxifen ( Nolvadex ) | Cimetidine Ritonavir |
||
| CYP3A4 | Calcium channel blockers: Diltiazem ( Cardizem † ), felodipine ( Plendil ), nimodipine ( Nimotop ), nifedipine ( Adalat † ), nisoldipine ( Sular ), nitrendipine, verapamil ( Calan † ) | Amiodarone | Barbiturates |
| Carbamazepine ( Tegretol † ) Phenytoin ( Dilantin † ) |
|||
| Immunosuppressive agents: Cyclosporine A ( Sandimmune , Neoral † ), tacrolimus ( Prograf ) | Efavirenz ( Sustiva ) | ||
| Corticosteroids: Budesonide ( Pulmicort ), cortisol, 17β-estradiol, progesterone, testosterone | Fluconazole Ketoconazole ( Nizoral † ) Itraconazole ( Sporanox ) |
Nevirapine ( Viramune ) | |
| Macrolide antibiotics: Clarithromycin ( Biaxin ), erythromycin ( Erythrocin † ), troleandomycin ( TAO ) | Clarithromycin Erythromycin Troleandomycin |
||
| Anticancer agents: Cyclophosphamide ( Cytoxan † ), gefitinib ( Iressa ), ifosfamide ( Ifex ), tamoxifen, vincristine ( Oncovin † ), vinblastine ( Velban † ), | Imatinib | Rifampin | |
| Ritonavir ‡ | |||
| Benzodiazepines: Alprazolam ( Xanax † ), midazolam ( Versed † ), triazolam ( Halcion † ) | St. John's wort | ||
| Opioids: Alfentanil ( Alfenta † ), fentanyl ( Sublimaze † ), sufentanil ( Sufenta † ) | |||
| HMG-CoA reductase inhibitors: Lovastatin ( Mevacor ) † , simvastatin ( Zocor ), atorvastatin ( Lipitor ) | |||
| HIV protease inhibitors: Indinavir ( Crixivan ), nelfinavir, ritonavir ( Norvir ), saquinavir ( Invirase, Fortovase ), amprenavir ( Agenerase ) | Ritonavir ‡ Indinavir |
||
| Others: Quinidine ( Quinidex † ), sildenafil ( Viagra ), eletriptan ( Relpax ), ziprasidone ( Geodon ) | Grapefruit juice Nefazodone ( Serzone ) |
||
| P-glycoprotein | Aldosterone, amprenavir, atorvastatin, cyclosporine, dexamethasone ( Decadron † ), digoxin ( Lanoxin † ), diltiazem, domperidone ( Motilium ), doxorubicin ( Adriamycin † ), erythromycin, etoposide ( VePesid ), fexofenadine ( Allegra ), hydrocortisone, indinavir, ivermectin ( Stromectol ), lovastatin, loperamide ( Imodium † ), nelfinavir, ondansetron ( Zofran ), paclitaxel ( Taxol ), quinidine, saquinavir, simvastatin, verapamil, vinblastine, vincristine | Amiodarone | Amprenavir |
| Carvedilol ( Coreg ) | Clotrimazole ( Mycelex † ) | ||
| Clarithromycin | Phenothiazine | ||
| Cyclosporine | Rifampin | ||
| Erythromycin | Ritonavir ‡ | ||
| Itraconazole | St. John's wort | ||
| Ketoconazole | |||
| Quinidine | |||
| Ritonavir ‡ | |||
| Tamoxifen | |||
| Verapamil |
CNS, Central nervous system.
Therapeutic drug monitoring (TDM) programs recognize that all patients are unique and that the serum concentration-time data for an individual patient theoretically can be used to optimize pharmacotherapy. TDM programs have been the earliest application of personalized medicine; however, routine TDM does not necessarily translate to improved patient outcome in all situations.
The concept of personalized medicine is based on the premise that the information explosion accompanying the application of genomic technologies to patient-related problems will allow (1) stratification of patient populations according to their response to a particular medication (e.g., lack of drug efficacy or excessive toxicity) and (2) stratification of diseases into specific subtypes that are categorized according to genomic criteria and by response to particular treatments. Personalized medicine has become supplanted by individualized medicine , which takes into consideration the vast amount of information that can be collected from an individual patient and applied to inform decisions for that patient. Precision medicine is an emerging approach for disease treatment and prevention that considers individual variability in genes, environment, and lifestyle for each person; it reflects the progression in delivery of care for more accurately diagnosing or treating a patient at an individual level. As the amount of data specific to an individual patient increases (e.g., genomic data, electronic health records), precision medicine can be further divided into precision diagnosis and precision therapeutics ; pharmacokinetics, pharmacodynamics, and pharmacogenomics all represent tools that can be applied to implement precision therapeutics for children.
Genetic polymorphisms ( variations ) result when copies of a specific gene present within a population do not have identical nucleotide sequences. The term allele refers to one of a series of alternative DNA sequences for a particular gene. In humans, there are 2 copies of every gene. An individual's genotype for a given gene is determined by the set of alleles that the individual possesses. The most common form of genetic variation involves a single base change at a given location, referred to as a single nucleotide polymorphism (SNP) (see Chapter 95 ). At the other end of the spectrum are copy number variations (CNVs) , which refer to the deletion or duplication of identical or near-identical DNA sequences that may be thousands to millions of bases in size. CNVs occur less frequently than SNPs, but may constitute 0.5–1% of an individual's genome and thereby contribute significantly to phenotypic variation. Haplotypes are collections of SNPs and other allelic variations that are located close to each other; when inherited together, these create a catalog of haplotypes, or HapMap . When the alleles at a particular gene locus on both chromosomes are identical, a homozygous state exists, whereas the term heterozygous refers to the situation in which different alleles are present at the same gene locus. The term genotype refers to an individual's genetic constitution, whereas the observable characteristics or physical manifestations constitute the phenotype , which is the net consequence of genetic and environmental effects (see Chapter 94, Chapter 95, Chapter 96, Chapter 97, Chapter 98, Chapter 99, Chapter 100, Chapter 101 ).
Pharmacogenetics focuses on the phenotypical consequences of allelic variation in single genes. Pharmacogenetic polymorphisms are monogenic traits that are functionally relevant to drug disposition and action and are caused by the presence (within one population) of >1 allele (at the same gene locus) and >1 phenotype with regard to drug interaction with the organism. The key elements of pharmacogenetic polymorphisms are heritability, the involvement of a single gene locus, functional relevance, and the fact that distinct phenotypes are observed within the population only after drug challenge.
Developmental or Pediatric Pharmacogenetics and Pharmacogenomics
Our current understanding of pharmacogenetic principles involves enzymes responsible for drug biotransformation . Individuals are classified as being “fast,” “rapid,” or “extensive” metabolizers at one end and “slow” or “poor” metabolizers at the other end of the continuum. This may or may not also include an “intermediate” metabolizer group, depending on the particular enzyme. With regard to biotransformation, children are more complex than adults; fetuses and newborns may be phenotypically “slow” or “poor” metabolizers for certain drug-metabolizing pathways because of their stage of development and may acquire a phenotype consistent with their genotype at some point later in the developmental process as they mature. Examples of drug-metabolizing pathways that are significantly affected by ontogeny include glucuronidation and some of the cytochrome P450 (CYP) activities. It is also apparent that not all infants acquire drug metabolism activity at the same rate, a result of interactions between genetics and environmental factors. Interindividual variability in the trajectory (i.e., rate and extent) of acquired drug biotransformation capacity may be considered a developmental phenotype ( Fig. 72.2 ). This helps to explain the considerable variability in some CYP activities observed immediately after birth.
In contrast to pharmacogenetic studies that typically target single genes, pharmacogenomic analyses are considerably broader in scope and focus on complex and highly variable drug-related phenotypes with targeting of many genes. Genome-wide genotyping technologies and massively parallel “next-generation” sequencing platforms for genomic analyses continue to evolve and allow evaluation of genetic variation at more than 1 million sites throughout an individual genome for SNP and CNV analyses. Genome-wide association studies (GWAS) have been conducted in several pediatric settings, in part to identify novel genes involved in disease pathogenesis that can lead to new therapeutic targets. GWAS are also being applied to identify genetic associations with response to drugs, such as warfarin and clopidogrel, and risk for drug-induced toxicity, including statin-induced myopathy and flucloxacillin hepatotoxicity. The “Manhattan plot,” a form of data presentation for GWAS, is becoming more common in many medical journals ( Fig. 72.3 A ). Whole genome and exome sequencing have been applied in a diagnostic setting to identify disease-causing genetic variation, usually in the context of rare, undiagnosed diseases that would otherwise require a “diagnostic odyssey” lasting several years before a definitive diagnosis is made (and thereby delaying therapeutic intervention). Contained within this genome sequence is the pharmacogenome , and an area of intense interest is the development of bioinformatics tools to determine a patient's drug metabolism and response genotype from whole genome sequence data.
Investigating differential gene expression before and after drug exposure has the potential to correlate gene expression with variable drug responses and uncover the mechanisms of tissue-specific drug toxicities. These types of studies use microarray technology to monitor global changes in expression of thousands of genes (the transcriptome ) simultaneously. Genomic sequencing technologies can also be applied to RNA (RNA-Seq) and result in a more complete and quantitative assessment of the transcriptome. Gene expression profiling data from microarrays or RNA-Seq analyses are used to improve disease classification and risk stratification and are common in oncology. This approach has been widely used to address treatment resistance in acute lymphoblastic leukemia and has provided clinically relevant insights into the mechanistic basis of drug resistance and the genomic basis of interindividual variability in drug response. Subsets of transcripts, or gene expression “signatures,” are being investigated as potential prognostic indicators for identifying patients at risk for treatment failure ( Fig. 72.3 B ).
Pharmacoproteomic and Metabolomic Tools
Proteomic studies use many different techniques to detect, quantify, and identify proteins in a sample (expression proteomics) and to characterize protein function in terms of activity and protein-protein or protein–nucleic acid interactions ( functional proteomics ). Mass spectrometry–based analyses are able to provide quantitative data regarding protein abundance, and several studies have been applied to pediatric liver samples, for example, to generate more accurate developmental trajectories for several drug-metabolizing enzymes and transporters.
Metabolomics and metabonomics utilize sophisticated analytical platforms, such as nuclear magnetic resonance (NMR) spectroscopy and liquid or gas chromatography coupled with mass spectral detection, to measure the concentrations of all small molecules present in a sample. Metabolomics refers to the complete set of low-molecular-weight molecules (metabolites) present in a living system (cell, tissue, organ or organism) at a particular developmental or pathologic state. Metabonomics is defined as the study of how the metabolic profile of biologic systems change in response to alterations caused by pathophysiologic stimuli, toxic exposures, or dietary changes. Pharmacometabonomics involves prediction of the outcome, efficacy, or toxicity of a drug or xenobiotic intervention in an individual patient based on a mathematical model of preintervention metabolite signatures.
Drug Biotransformation: Applications to Pediatric Therapy
The major consequence of pharmacogenetic polymorphisms in drug-metabolizing enzymes is concentration-dependent toxicity caused by impaired drug clearance. In certain cases, reduced conversion of prodrug to therapeutically active compounds is also of clinical importance (see Table 72.2 ). Chemical modification of drugs by biotransformation reactions generally results in termination of biologic activity through decreased affinity for receptors or other cellular targets as well as more rapid elimination from the body. The process of drug biotransformation can be very complex but is characterized by 3 important features: (1) the concept of broad substrate specificity , in which a single isozyme may metabolize a large variety of chemically diverse compounds; (2) many different enzymes may be involved in the biotransformation of a single drug ( enzyme multiplicity ); and (3) a given drug may undergo several different types of reactions. One example of this product multiplicity occurs with racemic warfarin, in which at least 7 different hydroxylated metabolites are produced by different CYP isoforms.
Drug biotransformation reactions are conveniently classified into 2 main types, which occur sequentially and serve to terminate biologic activity and enhance elimination (see Chapter 73 ). Phase I reactions introduce or reveal (through oxidation, reduction, or hydrolysis) a functional group within the substrate drug molecule that serves as a site for a phase II conjugation reaction. Phase II reactions involve conjugation with endogenous substrates, such as acetate, glucuronic acid, glutathione, glycine, and sulfate. These reactions further increase the polarity of an intermediate metabolite, make the compound more water soluble, and thereby enhance its renal excretion. Interindividual variability in drug biotransformation activity (for both phase I and phase II reactions) is a consequence of the complex interplay among genetic (genotype, sex, race or ethnic background) and environmental (diet, disease, concurrent medication, other xenobiotic exposure) factors. The pathway and rate of a given compound's biotransformation are a function of each individual's unique phenotype with respect to the forms and amounts of drug-metabolizing enzymes expressed.
The CYP enzymes (CYPs) are quantitatively the most important of the phase I enzymes . These heme-containing proteins catalyze the metabolism of many lipophilic endogenous substances (steroids, fatty acids, fat-soluble vitamins, prostaglandins, leukotrienes, thromboxanes) as well as exogenous compounds, including a multitude of drugs and environment toxins. CYP nomenclature is based on evolutionary considerations and uses the root symbol CYP for cytochrome P450. CYPs that share at least 40% homology are grouped into families denoted by an Arabic number after the CYP root. Subfamilies, designated by a letter, appear to represent clusters of highly related genes. Members of the human CYP2 family, for example, have >67% amino acid sequence homology. Individual P450s in a subfamily are numbered sequentially (e.g., CYP3A4, CYP3A5). CYPs that have been identified as being important in human drug metabolism are predominantly found in the CYP1, CYP2, and CYP3 gene families. Importantly, enzyme activity may be induced or inhibited by various agents (see Table 72.2 ).
Phase II enzymes include arylamine N -acetyltransferases (NAT1, NAT2), uridine diphospho-glucuronosyltransferases (UGTs), epoxide hydrolase, glutathione S -transferases (GSTs), sulfotransferases (SULTs), and methyltransferases (catechol O -methyltransferase, thiopurine S -methyltransferase, several N -methyltransferases). As with the CYPs, UGTs, SULTs, and GSTs are gene families with multiple individual isoforms, each having its own preferred substrates, mode of regulation, and tissue-specific pattern of expression.
For most CYPs, genotype-phenotype relationships are influenced by development in that fetal expression is limited (with the exception of CYP3A7) and functional activity is acquired postnatally in isoform-specific patterns. Clearance of some compounds appears to be greater in children relative to adults, and the correlation between genotype and phenotype in neonatal life through adolescence may be obscured.
CYP2D6
The CYP2D6 gene locus is highly polymorphic, with >110 allelic variants identified to date ( http://www.imm.ki.se/CYPalleles/cyp2d6.htm ; see Table 72.2 ). Individual alleles are designated by the gene name (CYP2D6) followed by an asterisk, and an Arabic number. By convention, CYP2D6*1 designates the fully functional wild-type allele. Allelic variants are the consequence of point mutations, single–base pair deletions or additions, gene rearrangements, or deletion of the entire gene, resulting in a reduction or complete loss of activity. Inheritance of 2 recessive, nonfunctional or “null' alleles results in the poor-metabolizer (PM) phenotype , which is found in approximately 5–10% of whites and approximately 1–2% of Asians. In whites the *3, *4, *5, and *6 alleles are the most common loss-of-function alleles and account for approximately 98% of PM phenotypes. In contrast, CYP2D6 activity on a population basis tends to be lower in Asian and African American populations because of a lower frequency of nonfunctional alleles (*3, *4, *5, and *6) and a relatively high frequency of population-selective alleles associated with decreased activity (“reduced function” alleles) relative to the wild-type CYP2D6*1 allele. The CYP2D6*10 allele occurs at a frequency of approximately 50% in Asians, whereas CYP2D6*17 and CYP2D6*29 occur at relatively high frequencies in persons of black African origin.
In addition to nonfunctional and partial-function alleles, the presence of gene duplication and multiplication events (≥3 copies of CYP2D6 gene in tandem on a single chromosome) further complicates the prediction of phenotype from genotype information. The concept of “activity score” has been developed to simplify translation of CYP2D6 genotype information into a predicted phenotype of CYP2D6 activity for a particular patient. Fully functional alleles ( *1, *2, *35, etc.) are assigned a value of “1”, reduced-function alleles ( *9, *10, *17, *29 ) are assigned a value of “0.5”, and nonfunctional alleles ( *3-*6, etc. ) are assigned a value of “0”; for duplications/multiplication events, the allele score is multiplied by the number of copies detected (*10 × 2 = 0.5 × 2 = “1”). The activity score for an individual is the sum of the scores for each chromosome, with poor metabolizers (PMs) defined by a score of “0”, whereas a score of “0.5” indicates an intermediate-metabolizer (IM) phenotype , and a score >2 indicating an ultrarapid-metabolizer (UM) phenotype ; scores of 1 to 2 are referred to as extensive metabolizers (EMs) . The activity score classification system has been adopted by the Clinical Pharmacogenetics Implementation Consortium (CPIC; see below). In the past, individuals with an activity score of “1” have been referred to as “IMs,” and any reference to IM status in literature before 2012 likely refers to a genotype with the equivalent of 1 functional allele, in contrast to the current definition (0.5).
CYP2D6 is involved in the biotransformation of >40 therapeutic entities, including several β-receptor antagonists, antiarrhythmics, antidepressants, antipsychotics, and morphine derivatives †
† For an updated list, see http://www.mayomedicallaboratories.com/it-mmfiles/Pharmacogenomic_Associations_Tables.pdf .
(see Table 72.2 ). CYP2D6 substrates commonly encountered in pediatrics include selective serotonin reuptake inhibitors (SSRIs; fluoxetine, paroxetine), risperidone, atomoxetine, promethazine, tramadol, and codeine. Furthermore, over-the-counter cold remedies (e.g., dextromethorphan, diphenhydramine, chlorpheniramine) are also CYP2D6 substrates. An analysis of CYP2D6 ontogeny in vitro that utilized a relatively large number of samples revealed that CYP2D6 protein and activity remain relatively constant after 1 wk of age up to 18 yr. Similarly, results from an in vivo longitudinal phenotyping study involving >100 infants over the 1st year of life demonstrated considerable interindividual variability in CYP2D6 activity, but no relationship between CYP2D6 activity and postnatal age between 2 wk and 12 mo. Furthermore, a cross-sectional study involving 586 children reported that the distribution of CYP2D6 phenotypes in children was comparable to that observed in adults by at least 10 yr of age. Thus, both available in vitro and in vivo data, although based on phenotype data rather than information on drug clearance from pharmacokinetic studies, imply that genetic variation is more important than developmental factors as a determinant of CYP2D6 variability in children.
One consequence of CYP2D6 developmental pharmacogenetics may be the syndrome of irritability, tachypnea, tremors, jitteriness, increased muscle tone, and temperature instability in neonates born to mothers receiving SSRIs during pregnancy. Controversy exists as to whether these symptoms reflect a neonatal withdrawal (hyposerotonergic) state or represent manifestations of serotonin toxicity analogous to the hyperserotonergic state associated with the SSRI-induced serotonin syndrome in adults. Delayed expression of CYP2D6 (and CYP3A4) in the 1st few weeks of life is consistent with a hyperserotonergic state caused by delayed clearance of paroxetine and fluoxetine (CYP2D6) or sertraline (CYP3A4) in neonates exposed to these compounds during pregnancy. Furthermore, decreases in plasma SSRI concentrations and resolution of symptoms would be expected with increasing postnatal age and maturation of these pathways. Given that treatment of a “withdrawal” reaction may include administration of an SSRI, there is considerable potential for increased toxicity in affected neonates. Resolution of the question whether symptoms are caused by withdrawal vs a hyperserotonergic state is essential for appropriate management of SSRI-induced neonatal adaptation syndromes. Until further data are available, it would be prudent to consider newborns and infants <28 days of age as CYP2D6 PMs.
In older children, drug accumulation and resultant concentration-dependent toxicities in CYP2D6 genotypic poor metabolizers should be anticipated in the same way that they are in adults due to the risk of significant morbidity and mortality. Although a fluoxetine-related death has been reported in a 9 yr old child with a CYP2D6 PM genotype, experience with paroxetine indicates that the risk of drug accumulation may also occur, under certain conditions, in individuals at the opposite end of the activity spectrum. For example, chronic dosing of paroxetine may lead to greater-than-anticipated drug accumulation in children classified as CYP2D6 EMs. In fact, the largest decreases in paroxetine clearance observed with ascending doses are seen in patients who have the greatest clearance at the initial dose level (10 mg/day) and are predicted to have the greatest CYP2D6 activity based on CYP2D6 genotype. This seemingly paradoxical effect appears to involve oxidation of paroxetine within the CYP2D6 active site to form a reactive intermediate that is associated with irreversible modification of the CYP2D6 protein in or near the active site and loss of enzyme activity. As a consequence, CYP2D6 activity progressively declines such that drug accumulation may occur over time, placing CYP2D6 EM patients also at increased risk of concentration-dependent toxicity.
Theoretically, younger children may experience decreased efficacy or therapeutic failure with drugs such as codeine and tramadol that are dependent on functional CYP2D6 activity for conversion to the pharmacologically active species. CYP2D6 catalyzes the O -demethylation of codeine to morphine. Infants and children appear capable of converting codeine to morphine and achieving morphine:codeine ratios comparable to those of adults. However, in one study, morphine and its metabolites were not detected in 36% of children receiving codeine making the level of analgesia from codeine unreliable in the studied pediatric population. Interestingly, levels of morphine and its metabolites were not related to CYP2D6 phenotype. Finally, ultrarapid CYP2D6 metabolism of codeine may result in opiate intoxication, including maternal ultrarapid metabolism of codeine, which can result in high serum and breast milk concentrations of morphine and may have adverse effects in the breastfed neonate.
Rapid metabolism and clearance of CYP2D6 substrates may also contribute to poor therapeutic response because of an inability to achieve adequate plasma concentrations, even when medications are dosed at the maximum approved dose level. The product label for atomoxetine (Strattera) indicates that CYP2D6 PMs have a systemic exposure to the drug (e.g., amount of drug in body over time as determined by area under plasma concentration-time curve) that is 10 times greater than in typical individuals (EMs), and yet the same starting dose of 0.5 mg/kg is recommended for all patients. A genotype-stratified pharmacokinetic study of atomoxetine in children and adolescents with attention-deficit/hyperactivity disorder (ADHD) confirmed an 11-14-fold difference in average systemic exposure between PM and EM groups. However, the most informative finding was the 50-fold range in exposure (30-fold, if exposure corrected for actual mg dose administered) between the PM participant with the highest exposure and the UM participant (3 functional alleles) with the lowest exposure. Using the results of this single-dose study to simulate atomoxetine exposure at steady state for each study participant revealed that even at the maximum recommended dose of atomoxetine, exposure was likely to be subtherapeutic for the majority of patients with ≥1 functional CYP2D6 alleles.
Avoiding ineffective treatment at one end of the spectrum and excessive toxicity at the other are potential benefits of individualizing doses based on genomic information for medications dependent on a polymorphic clearance pathway, such as CYP2D6. The CPIC has published several guidelines that include CYP2D6 substrates, such as the CPIC guideline for codeine, *
* https://cpicpgx.org/guidelines/guideline-for-codeine-and-cyp2d6/ .
SSRIs, †
and tricyclic antidepressants. ‡
‡ https://cpicpgx.org/guidelines/guideline-for-tricyclic-antidepressants-and-cyp2d6-and-cyp2c19/ .
Although pediatric data are sparse, these links serve as valuable sources of information regarding the effect of genotype on the dose-exposure relationship for several CYP2D6 substrates.
You're Reading a Preview
Become a Clinical Tree membership for Full access and enjoy Unlimited articles
If you are a member. Log in here