Pediatric cardiology providers commonly observe variable responses to cardiovascular pharmacotherapy and, as a result, inconsistent or counterintuitive outcomes in congenital and acquired heart disease. The contribution of development and/or genetic variation to the observed variability in cardiovascular drug disposition and response in childhood requires further investigation and clarity. In the absence of data that are applicable to the individual child, providers making clinical decisions must often rely on data generated in adults. However, it is universally appreciated in our specialty that growth and development (ontogeny) can potentially influence the diagnosis and treatment of clinical disease during childhood. Ontogeny is equally important when discussing pediatric drug disposition and response. Therefore, drawing the same conclusions or, potentially worse, extrapolating adult pharmacogenetic and/or pharmacogenomics data to the pediatric cohort must be performed with caution.
Genetic variation is invariably associated with the pioneer efforts to deliver “precision medicine.” However, the provider must recognize that genetic variation remains only one patient-specific factors that may influence drug disposition and response. The physiologic profile of the individual patient (e.g., Fontan circulation), formulation of the drug (e.g., tablet vs. liquid preparation), and/or environmental factors (e.g., diet) must be taken into consideration when providing precision-guided therapy. Notwithstanding, for the purpose of this chapter, the primary emphasis will be on the influence of pharmacogenetics on the treatment and outcome of pediatric cardiovascular disease.
Integral to this discussion is a review of the basic terminology utilized in clinical pharmacology. The following terms are described in greater detail in referenced texts.
Absorption is the process of drug movement from the site of administration or application into systemic circulation. The rate (e.g., time to maximal concentration; tmax) and extent (e.g., bioavailability) of absorption can be impacted by drug factors (e.g., formulation, solubility, protein binding, lipid/water-partition coefficient) and/or patient factors (e.g., protein binding capacity, gastric emptying time, portal venous stasis).
Area under the curve (AUC) is the area under the plasma drug concentration–time curve. In the domain of pharmacokinetics, AUC and maximal plasma drug concentration (Cmax) represent the important parameters by which one can quantitate how much systemic exposure (e.g., dose-exposure relationship) occurs over a given period of time ( Fig. 79.1 ). Time to maximal plasma drug concentration (Tmax) represents the time required for drug absorption after extravascular administration.
Bioavailability is the fraction of a drug, administered by a route other than intravenous, that reaches the systemic circulation. The aforementioned drug and patient factors influence the degree of systemic exposure that occurs after a given dose (e.g., dose-exposure relationship).
Clearance is the pharmacokinetic measurement of the volume of plasma from which a substance is completely removed per unit time.
Disposition refers to the entire process of absorption, distribution, metabolism, and elimination (ADME) that influences the pharmacokinetics of a drug.
Distribution is a pharmacokinetic term that describes the reversible transfer of a drug from one location to another within the body.
Metabolism is the biotransformation process by phase 1 and/or 2 metabolizing enzymes to enhance the excretion of drugs. Of note, metabolism may contribute to the inactivation (e.g., verapamil) or activation (e.g., enalaprilat) of a drug moiety. Phase 1 is composed of oxidation, reduction, hydrolysis, and methylation reactions that collectively serve to increase the polarity of a drug. Phase 1 metabolism is performed primarily by a group of oxidases referred to as cytochrome p450 enzymes (CYPs). Conversely, phase 2 metabolism is responsible for converting drugs into a more water-soluble form for excretion. Several major gene families that are involved in phase II reactions have been identified, including N-acetyltransferases (NAT), uridine diphosphate–glucuronosyl transferases (UGT), glutathione S-transferases (GST), and sulfotransferases (SULT). Analogous to the CYP450 families, these gene families also have individual isoforms, each with its own ontogenic profile.
Pharmacodynamics entails both the qualitative and quantitative description of drug effect. Simply put, this is what a drug and/or metabolite does to the body.
Pharmacokinetics describes the change of drug concentration in the drug product and changes in concentration of a drug and/or its metabolite in the human body following administration. This term represents the dose-exposure relationship of drug administration. Simply put, this is what the body does to a drug and/or metabolite.
Pharmacogenetics describes interindividual variation in DNA sequence related to drug absorption and distribution (PK) or drug action (PD).
Pharmacogenomics is the application of genomic principles and technologies in drug discovery to pharmacologic function, disposition, and therapeutic response.
Pediatric pharmacotherapy, similar to adult pharmacotherapy, is dependent on a clear understanding of the dose-exposure-response relationship of the administered drug to predict a response for a given dose. Providers must recognize that many drug (e.g., formulation, protein binding affinity coefficient, physicochemical properties) and patient factors (e.g., age, Tanner staging, cardiac physiology, altered drug metabolism or transport) contribute to the systemic exposure (e.g., dose-exposure relationship) of a drug for the individual child and thereby influences drug response. For example, poor responders to a particular drug may not have genetic variation of the drug target affecting response but alternatively have insufficient exposure at the drug target to generate an adequate response. Fig. 79.2 illustrates the complexity involved in precision-guided therapy, outlining each level of the drug disposition pathway, absorption through elimination, that must be considered as a potential source for variable drug exposure (e.g., dose-exposure relationship).
Even when the dose-exposure relationship has been optimized, variable drug response can be a product of two mechanisms: (1) abnormal drug-target interaction (e.g., drug target genetic variation) or (2) abnormal drug-target signaling following a normal drug-target interaction. As alluded to earlier, before development and genetic variation at the drug targets and associated signaling pathways (e.g., response pathways) are considered in clinical practice for precision-guided therapy, it is crucial to initially determine if genetic variation contributing to altered systemic exposure is the source of variable response, meaning that the perceived response is not secondary to poor or excessive drug exposure at the drug target.
A recent review of developmental pharmacology described the known development aspects of drug disposition that should be considered for pediatrics. In pediatric pharmacotherapy, extrapolation of adult experiences is complicated by age-associated differences in the pharmacokinetics of several pharmacologic agents used clinically in children. Therefore development (e.g., ontogeny of drug metabolizing enzymes or transporters) is another consideration that must be added when characterizing the dose-exposure relationship in a child. Until we understand and fully characterize the pediatric drug disposition pathways, it will not be feasible to control the exposure at the drug target following a given dose in a manner that will improve clinical trial outcomes.
Genotype-stratified pharmacokinetic studies performed separately in a pediatric cohort, described in the section Practical Applications : Statins , allow for full characterization of the anticipated extremes of the dose-exposure relationship. Subsequently, these initial data assist in the development of pharmacogenomic investigations utilizing individualized dosing to achieve a target exposure and reduce interindividual variability. Collectively, pharmacogenomics can be utilized to enhance pediatric dosing guidelines and provide precision-guided care.
In the absence of more comprehensive pediatric data, however, a systematic approach has been developed to gather more information about certain drugs and identify knowledge gaps to more accurately inform prospective pediatric trials and clinical decision making. The fundamental questions outlined in these referenced manuscripts are as follows. (1) What gene products are quantitatively important in the disposition (absorption, distribution, metabolism, and excretion) of the drug? (2) What known allelic variants in the genes of interest are associated with functional consequence in vivo? (3) What is the developmental profile (ontogeny) of the key pathways involved in the drug’s disposition?
Prior to a more detailed discussion on the practical applications of pharmacogenetics and pharmacogenomics in cardiovascular pharmacotherapy, knowledge of the major biotransformational and transport pathways is required. Detailed herein is a brief summary of the most common enzymes and proteins involved in the disposition of cardiovascular-related drugs (a more comprehensive list of other contributing enzymes and proteins is summarized in Tables 79.1 and 79.2 ). If known, information on allelic variation and ontogeny is included.
|Enzyme/Gene||Location a||Substrates b||Inhibitors b||Inducers b|
|CYP1A2||Liver, colon (rare)||Caffeine, propranolol, verapamil, R-warfarin||Cimetidine, d ciprofloxacin, e fluvoxamine e||Omeprazole, rifampin, tobacco|
|CYP2B6||Liver, lung||Ketamine, methadone, propofol||Clopidogrel||Phenobarbital, phenytoin, rifampin,|
|CYP2C8||Liver||Cerivastatin, torsemide||Gemfibrozil, e trimethoprim f||Rifampin|
|CYP2C9||Liver, small intestine||NSAIDs: diclofenac, ibuprofen, S-naproxen |
ARBs: losartan, irbesartan
Others: fluvastatin, torsemide, S-warfarin
|Amiodarone, f fluconazole, e fluvastatin||Rifampin|
|CYP2C19||Liver, small intestine (rare)||PPIs: esomeprazole, lansoprazole, omeprazole, pantoprazole |
Others: clopidogrel, indomethacin, labetalol, propranolol, R-warfarin
|Cimetidine, esomeprazole, lansoprazole, omeprazole, pantoprazole||Rifampin|
|CYP2D6||Liver, lung (rare), brain (rare), small intestine (rare)||Antiarrhythmics: flecainide, mexiletine, procainamide , propafenone |
β-blockers: atenolol, carvedilol, metoprolol, propranolol, timolol
|Amiodarone, d bupropion, e cimetidine, d fluoxetine, e paroxetine, e quinidine, e sertraline, f terbinafine f|
|CYP3A4/5/7||3A4: Liver, small intestine, lung (rare), stomach (rare), colon (rare) |
3A5: Liver, lung
|Benzodiazepines: alprazolam, midazolam |
Calcium channel blockers: amlodipine, diltiazem, felodipine, nifedipine, verapamil
HMG-CoA reductase inhibitors: atorvastatin, lovastatin, simvastatin
Immunosuppressives: cyclosporine, tacrolimus, sirolimus
Others: erythromycin, midazolam, quinidine, sildenafil, tadalafil
|Cimetidine, d clarithromycin, e diltiazem, f erythromycin, f fluconazole, f grapefruit juice, f ketoconazole, e indinavir, e itraconazole, e ritonavir, g verapamil f||Barbiturates, carbamazepine, efavirenz, nevirapine, phenytoin, rifampin, ritonavir, g St. John’s wort|
|UGT1A1 c||Liver||Bilirubin, carvediol, simvastatin||Atazanavir, indinavir, sorafenib|
|UGT2B7 c||Liver||Codeine, morphine, lorazepam, propranolol|
a CYP location summarized from Zanger UM, Schwab M. Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol Ther. 2013;138(1):103–141 and Paine MF, Hart HL, Ludington SS, et al. The human intestinal cytochrome P450 “pie”. Drug Metab Dispos. 2006;34(5):880–886.
b Data from medicine.iupui.edu/clinpharm/ddis/main-table .
|SLCO||OATP1A2||Brain, kidney, liver||Indomethacin||CNS distribution|
|OATP1B1||Liver||Atorvastatin, bosentan, enalapril, pravastatin, rosuvastatin, simvastatin acid||Hepatic uptake|
|OATP1B3||Liver||Digoxin, olmesartan, rosuvastatin, telmisartan, valsartan||Hepatic uptake|
|OATP2B1||Liver, intestine, placenta||Digoxin, fluvastatin||Hepatic uptake, oral absorption|
|SLC10A1||NTCP||Liver||Atorvastatin, fluvastatin, pitavastatin, rosuvastatin||Hepatic uptake|
|SLC22||OAT1||Kidney, brain||Acyclovir||Renal uptake|
|OAT3||Kidney, brain||Salicylates, valacyclovir||Renal uptake|
|OAT4||Kidney, placenta||Renal secretion|
|OCT1||Liver, brain, small intestine||Quinidine, verapamil||Hepatic and renal uptake|
|OCT2||Kidney, brain, small intestine||Dopamine, norepinephrine||Renal uptake|
|OCT3||Heart, skeletal muscle, liver, adrenal||Epinephrine, norepinephrine|
|SLC47||MATE1||Kidney, liver||Renal secretion, biliary excretion|
|ABCB||BSEP||Liver||Lovastatin, pravastatin, rosuvastatin, simvastatin||Biliary secretion|
|P-gp (MDR1)||Kidney, liver, brain, small intestine||Cyclosporine, digoxin, erythromycin, simvastatin||Oral absorption, biliary excretion, renal secretion, CNS distribution|
|ABCC||MRP1||Liver, small intestine, kidney, brain|
|MRP2||Liver, kidney, small intestine||Pravastatin||Biliary excretion, renal secretion|
|MRP3||Liver, kidney, small intestine|
|ABCG||BCRP||Liver, small intestine, placenta||Pravastatin||Oral absorption, biliary excretion|
The majority of drug biotransformation occurs in the liver. Extrahepatic tissues do contain drug-metabolizing enzymes, although their relative contributions to overall biotransformation are typically not as pronounced as those of the liver. Drug biotransformation reactions are categorized into phase I (e.g., CYP-mediated oxidation) and phase II (e.g., UGT-mediated glucuronidation). The relative contribution of CYP enzymes to drug metabolism is summarized in Fig. 79.3 . The ontogeny of drug metabolism enzymes, summarized by Hines, is characterized to a larger extent compared with drug transporters and is essential in determining the potential genotype-phenotype relationship for the individual child.
CYP2D6 is an important phase 1 drug metabolizing enzyme (DME) that contributes to the metabolism of approximately 20% to 25% of drugs used clinically, despite comprising only 2% to 5% of the total hepatic CYP content. Expression is undetectable in the fetus but rapidly increases postnatally to near adult levels within the first few weeks. CYP2D6 is the predominant pathway for bioactivation or elimination of many cardiovascular medications, including antiarrhythmics and β-blockers. The highly polymorphic CYP2D6 gene resides on chromosome 22q13.1, located close to two nonfunctional genes, CYP2D7 and CYP2D8 . Currently over 100 allelic variants have been identified ( www.cypalleles.ki.se/cyp2d6.htm ), with an extensive range of absent, decreased, normal, or excessive allelic functions resulting in variable interindividual CYP2D6 enzyme activity. Given the array of possible allelic functionality, it is not surprising that hepatic CYP2D6 protein expression varies dramatically among individuals. Additionally, this expression can be highly variable within the world’s population and various ethnic groups.
The poor metabolizer phenotype, comprising individuals carrying no functional alleles (i.e., *3, *4, *5, *6), and the ultrarapid metabolizer phenotype, compromising individuals carrying an increase of functional alleles with a normally functional allele, represent the broad range of possible enzyme activity. A higher risk of adverse events or treatment failure can occur in these phenotypic groups based on the drug involved. For instance, most antiarrhythmic drugs are metabolically deactivated by CYP2D6, thus placing poor metabolizers at higher risk for drug accumulation, leading to adverse events or toxicity. Conversely, in the same example, ultrarapid metabolizer phenotypes would be at risk for extensive metabolism and clearance of the active drug, potentially leading to decreased efficacy. This example is contrasted with codeine, where CYP2D6 metabolically activates the drug to produce morphine. In this scenario, the poor metabolizer phenotype is susceptible to treatment failure or decreased efficacy secondary to decreased morphine production. Conversely, the ultrarapid metabolizer phenotype is at risk for opioid toxicity due to increased plasma concentration of morphine from enhanced CYP2D6-mediated production.
CYP2C9, comprising an average of 20% of the total hepatic CYP content, contributes to the metabolism of approximately 12% to 13% of clinically used drugs. Expression of CYP2C9 is minimal during early fetal life, increasing to levels 10% of adult values in the third trimester. After birth to 5 months of age, protein expression of CYP2C9 is about 25% of adult levels. Of concern, this age group displayed the highest degree of interindividual variability of CYP2C9 expression, with 50% of the infants having no change in expression compared with third-trimester fetal livers; conversely, others demonstrate expression nearly equivalent to adult levels. Collectively, this translates to a nearly 35-fold range in enzyme expression over the first 5 months of life. Less variability is observed from 5 months to 18 years, where CYP2C9 levels were approximately 50% of adult values. However, an absence of adult levels observed in some postpubertal samples by Treluyer et al. suggests that the maturation of CYP2C9 could occur later in childhood. This determination of late CYP2C9 is not consistent with in vivo pharmacokinetic data in children, where metabolism of CYP2C9 substrates was comparable to adults. Clearly these discrepancies warrant further investigation, but they likely result from pharmacogenetic and physiologic changes in liver mass, as summarized further on with warfarin metabolism.
Most of the expression and metabolic activity of CYP2C9 occurs in the liver; however, CYP2C9 is quantitatively the second most common isoform (~15%) found in the human intestine following the CYP3A subfamily (~80%). Despite the human intestinal content being of an order of magnitude less than that of the liver, the large degree of variability of intestinal CYP2C9 content and activity among individuals could result in variable drug exposure secondary to decreased presystemic clearance of CYP2C9 substrates (e.g., fluvastatin, warfarin). Collectively, there is a paucity of data regarding the role of intestinal CYPs and their respective contributions to drug exposure, and nothing related to the ontogeny of CYP2C9 in extrahepatic tissue is known. For the purpose of precision therapeutics, the role of extrahepatic CYP2C9 and CYP3A should be considered in the design of pharmacogenetic and pharamacogenomic trials and clinical decision making.
CYP2C9, located on chromosome 10q23.33, exhibits genetic polymorphism with over 60 allelic variants currently reported ( www.cypalleles.ki.se/cyp2c9.htm ). As opposed to CYP2D6, most CYP2C9 allelic variants result only in a decrease of function. In fact, CYP2C9*2 and *3 , the most investigated CYP2C9 alleles, result in decreased activity, although the degree of change is substrate-specific. Comparatively, the CYP2C9*3 allele results in a greater decrease in enzymatic activity (~70% to 90%). The most common application of CYP2C9 polymorphism is related to warfarin pharmacogenetics, where it accounts for ~15% to 20% of the variation in dose requirements. Further details of warfarin pharmacogenomics are detailed further on, under Practical Applications.
Despite comprising only 1% to 4% of the total hepatic CYP content, CYP2C19 is the second most important DME in the CYP2C subfamily, estimated to contribute to ~7% metabolism of all clinically used drugs. It is almost exclusively expressed in the liver and comprises only 2% of CYP expression at the intestinal tract. Expression of CYP2C19 occurs in the fetus (~10% to 20% of adult levels), and it undergoes considerable change during gestation or immediately postpartum. In contrast to CYP2C9, from birth to 5 months of age there was an increasing trend of CYP2C19 expression, approaching 50% to 75% of adult levels. There is variability in this within this age group, but less compared with CYP2C9. Conversely, from infancy (>5 months) through puberty, a 21-fold range of enzyme expression was observed by Koukouritaki et al. Postpubertal expression was equivalent to adult levels, which is in contrast to the aforementioned findings by Treluyer et al. for CYP2C9.
CYP2C19, also located on chromosome 10q23.33, is highly polymorphic, having over 35 reported allelic variants ( www.cypalleles.ki.se/2cyp2c19.htm ). Two allelic variants, CYP2C19*2 and *3 result in no functional activity, and CYP2C19*17 produces increased enzymatic activity. The variant allelic frequency of CYP2C19*2 fluctuates within different population groups, being nearly 15% in whites and blacks and 30% in Asians. Similar to CYP2D6, CYP2C19 is categorized into phenotype groups: ultrarapid, extensive, intermediate, and poor metabolizers. The ultrarapid metabolizers, accounting for between 5% and 30% of patients, carry two increased-activity alleles (e.g., *17/*17 ) or one functional allele with one increased-activity allele (e.g., *1/*17 ). Extensive metabolizers, accounting between 35% and 50% of patients, carry two functional alleles (e.g., *1/*1 ). Intermediate metabolizers, accounting for 18% and 45% of patients, carry one functional plus one decreased-function allele (e.g., *1/*2, *1/*3 ). Poor metabolizers, accounting for the 2% to 15% of patients, carry two decreased-function alleles (e.g., *2/*2, *2/*3, *3/*3 ). Interpretation of the potential risk for adverse events and evaluation of the efficacy profiles must be performed in the context of the substrate involved, comparable to the descriptions provided previously for CYP2D6. For instance, proton pump inhibitors (e.g., omeprazole, pantoprazole) are substrates for CYP2C19, resulting in deactivation of the compound. Therefore a poor metabolizer of CYP2C19 would have increased AUC (e.g., systemic exposure) of the drug and thus an increased risk of adverse events. Conversely, CYP2C19 is responsible for the activation of the antithrombotic agent clopidogrel. Thus poor metabolizers of CYP2C19 would have decreased AUC and an increased risk of treatment failure. Clopidogrel is discussed in further detail under Practical Applications.
CYP3A is qualitatively and quantitatively the most important subfamily of CYP enzymes, comprising an average of 30% to 35% of the total hepatic CYP content and contributing to the metabolism of 30% to 45% of all drugs. The CYP3A subfamily is composed of four CYP enzymes (3A4, 3A5, 3A7, 3A43). CYP3A43 is minimally expressed and does not participate in drug metabolism. CYP3A7 is the dominant CYP3A enzyme expressed in fetal life, declining steadily over the first year. Conversely, CYP3A4 is minimally expressed in fetal life (~10% to 20% of adult levels), increases to 30% to 60% of adult levels after 1 week of age, and approaches the adult level at 1 year. CYP3A5 expression is highly variable, does not have an age-dependent expression pattern, and is more highly expressed in black than in white and Hispanic populations. In adults, increased clearance of CYP3A4 substrates occurs in females compared with males. However, there is a paucity of data related to the influence of gender on CYP3A4-mediated clearance in children.
As mentioned earlier, in the CYP2C9 category, CYP3A comprises ~80% of the total CYP content in the enterocyte and should be considered in the evaluation of presystemic clearance of CYP3A substrates. Of note, the total CYP content in the liver and intestinal tract can be significantly increased in blacks secondary to a higher allelic frequency of the functional CYP3A5 *1 , which increases CYP3A5 content relative to nonexpressers.
CYP3A, located on chromosome 7q22.1, has an 85% similar sequence identity among the three major enzymes (CYP3A4, CYP3A5, CYP3A7), making substrate specificity and the impact of individual isozyme polymorphism difficult to interpret. Currently over 40 allelic variants are reported ( www.cypalleles.ki.se ), yet they are less well described compared with CYP2D6 . The CYP3A4*22 allele, intron 6 C>T variant, is associated with decreased CYP3A4 expression both in vitro and in vivo. In fact, carriers of the variant T allele taking a statin metabolized by a CYP3A4-mediated pathway (e.g., atorvastatin, lovastatin, simvastatin) require significantly lower statin dosing compared with those carrying the reference genotype (CC) secondary to decrease clearance of the parent drug and active metabolite. Additionally, in the study by Klein et al., variation in the peroxisome proliferator activated receptor alpha (PPARα) influenced CYP3A4 phenotype and accounted for 10% of the variability in atorvastatin hydroxylase activity. CYP3A5*3 , the most common CYP3A5 allelic variant, leads to decrease enzyme expression while the presence of a CYP3A5*1 gain-of-function mutation, leading to an increase in liver and intestinal tract CYP3A5 content. The latter genotype is found in ~60% of blacks and ~30% of whites.
UGTs are most important phase 2 metabolizing superfamily of enzymes responsible for the conjugation of xenobiotics, developing a more polar compound for elimination. UGTs account for ~35% of phase 2 enzymatic metabolism of all drugs. UGTs are subdivided into two gene families, UGT1 ( UGT1A1, 1A3, 1A4, 1A5, 1A6, 1A7, 1A8, 1A9, 1A10 ) located on chromosome 2q37 and UGT2 (2A1, 2B4, 2B7, 2B10, 2B11, 2B15, 2B17) located on chromosome 4q13-13.2. The ontogenic profiles of the UGTs are less characterized compared with CYPs. UGT1A1 is absent in the fetus, increasing immediately after birth and approaching adult levels at 3 to 6 months. UGT1A6 is present at low levels (1% to 10%) during fetal life, slowly increases after birth, and approaches 50% of adult levels after birth. In contrast to UGT1A1, complete maturation of UGT1A6 occurs near puberty. UGT2B7 is detectable in the fetus (~10% to 20% of adult levels), with adult levels achieved at 2 to 3 months of age. Hundreds of UGT polymorphisms are reported ( www.pharmacogenomics.pha.ulaval.ca/cms/ugt_alleles/ ); however, the importance of these genetic variants requires further elucidation.
Although many drugs undergo passive diffusion across the cellular membrane, transport-mediated uptake and efflux serve as another mechanism of drug distribution that has the potential to influence the dose-exposure relationship. Transport proteins are distributed throughout many body organs and tissues (see Fig. 79.2 ), and each of these portals of entry or exit is subject to altered function via drug-drug interaction, genetic variation, and/or development. Recent data suggest that the expression of several transporters may be age dependent. This implies that the dose-exposure relationship may change during growth and development, altering the genotype-phenotype relationship during childhood and thereby limiting the application of adult pharmacogenetic experiences to children and adolescents. The adenosine triphosphate–binding cassette (ABC) and solute carrier (SLC) are the superfamilies of transmembrane proteins contributing to drug transport. The most common drug transporters are noted hereafter; however, additional drug transporters and their respective substrates applicable to our subspecialty are noted in Table 79.2 .
Adenosine Triphosphate–Binding Cassette Superfamily
Most cellular efflux transporters belong to the ABC superfamily and utilize adenosine triphosphate (ATP) to facilitate drug translocation across the cellular membrane. This superfamily includes multidrug-resistance protein 1 (MDR1, also known as P-glycoprotein, ABCB1 ), the bile salt export pump (BSEP, ABCB11 ), multidrug resistance-associated protein (MRP, ABCC ), and the breast cancer resistance protein (BCRP, ABCG ). Expression of these transporters is common in the liver, intestine, kidney, and brain.
Organic Anion Transporting Polypeptides and Organic Cation Transporters
OATPs are a family of glycoprotein transporters within the SLC superfamily that are expressed in a variety of tissues, including the liver, intestine, brain, and kidney. This family includes the OATP1A2 ( SLCO1A2 ), OATP1B1 ( SLCO1B1 ), OATP1B3 ( SLCO1B3 ), and OATP2B1 ( SLCO2B1 ) proteins. The liver-specific transporters OATP1B1 and OATP1B3 are influential in the hepatic uptake of statins and enalapril. In pediatric livers, utilizing proteomic analysis, age-dependency for OATP1B3 expression was demonstrated, and a threefold difference between neonates and adults was noted. The same age-dependency pattern was not observed in OATP1B1 as a whole but was demonstrated in the reference genotype group (e.g., SLCO1B1 *1A/*1A ). OCTs are a family of transporters that primarily facilitate the renal clearance of cationic drugs. OCT1 ( SLC22A1 ) is subject to a fivefold change in expression during development, potentially influencing the renal clearance of antiarrhythmics and calcium channel blockers. Future studies characterizing the influence of these family of transporters are essential to ascertain whether the emerging genotype-phenotype relationships observed in adults has relevance in the developing child.