Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Pharmacogenomics. Author manuscript; available in PMC 2011 September 1.
Published in final edited form as:
PMCID: PMC3044438

Identifying genomic and developmental causes of adverse drug reactions in children


Adverse drug reactions are a concern for all clinicians who utilize medications to treat adults and children; however, the frequency of adult and pediatric adverse drug reactions is likely to be under-reported. In this age of genomics and personalized medicine, identifying genetic variation that results in differences in drug biotransformation and response has contributed to significant advances in the utilization of several commonly used medications in adults. In order to better understand the variability of drug response in children however, we must not only consider differences in genotype, but also variation in gene expression during growth and development, namely ontogeny. In this article, recommendations for systematically approaching pharmacogenomic studies in children are discussed, and several examples of studies that investigate the genomic and developmental contribution to adverse drug reactions in children are reviewed.

Keywords: adverse drug reactions, genetic variants, methotrexate, pediatrics, pharmacogenomics

Adverse drug reactions (ADRs) remain an ever present concern for all clinicians who utilize medications in adults and children. ADRs have been defined by the WHO as “a response to a drug that is noxious and unintended, and occurs at doses normally used in man for the prophylaxis, diagnosis or therapy of disease, or for modification of physiological function” [1]. Edwards and Aronson have broadened this definition to “an appreciably harmful or unpleasant reaction resulting from an intervention related to the use of a medicinal product, which predicts hazard from future administration and warrants prevention or specific treatment, or alteration of the dosage regimen, or withdrawal of the product” [2].

There is no question that ADRs represent a major burden to patients, the healthcare system and the drug-development process. The magnitude of the problem has been the subject of many publications, particularly over the past 10 years. A widely cited (and criticized) meta-analysis suggested that severe ADRs ranked between the fourth and sixth leading cause of death in the USA in 1994, affecting an estimated 2 million hospitalized patients [3]. A more recent study from Sweden implicated ADRs as the seventh-most common cause of death [4], supporting the unexpectedly high findings of the earlier study. In addition, ADRs can occur in patients once admitted to hospital, incurring increased costs owing to additional interventions and prolongation of hospital stays [5]. Despite the limited data available, in this nonsystematic article, we would like to highlight the importance of ADRs specifically in children, focusing on the contribution of development and genetic variation related to ADRs. We will discuss approaches to pharmacogenomic studies in children and utilize examples from the literature and our center (Children’s Mercy Hospitals & Clinics, MO, USA) to illustrate these points.

ADR reporting in children: general considerations & challenges

Adverse drug reactions have not been as thoroughly studied in children as they have in adults, despite the significance of ADRs in children having been recognized for more than 25 years [6,7]. Of the 30 studies included in the original meta-analysis conducted by Lazarou et al., only four contained pediatric data [3]. Several factors contribute to this phenomenon. Most drugs used in children have never been formally studied in the pediatric population, nor have they been approved by the US FDA for use in pediatric populations of different ages or developmental stages. Thus, there is little pediatric ‘experience’ from which knowledge of incidence and risk factors can be derived. Furthermore, the youngest of children may be at an increased risk owing to an inability to effectively communicate to caregivers the subjective symptoms attributable to a medication.

Nevertheless, ADRs in children represent a significant public health concern, and there is some literature that reports the frequency of ADRs in children. ADRs have been reported to be responsible for 4.3% of admissions to a pediatric hospital in children less than 2 years of age. This same group conducted a survey of ADRs in already-hospitalized children, reporting an incidence of 16.6%, with most reactions classified as mild to moderate [8]. Similar results have been reported by others, noting a rate of ADRs of 13.7% found in a prospective study in hospitalized children in Chile [9]. The risk of ADRs is even higher for infants in an intensive care setting, who are often critically ill, suffering from multiple organ system dysfunction and who require multiple drugs [10]. A meta-analysis of 17 published reports of pediatric ADRs [11] concluded that 2.1% of admissions to children’s hospitals were due to ADRs, with 39% of those ADRs being life-threatening; and in children already hospitalized, the incidence of ADRs was higher at 9.5%, with 12.3% of those reactions being classified as severe.

These estimates almost certainly underestimate the true incidence of ADRs in children as there is no standard reporting system, and available primary data is derived from assessments conducted in individual pediatric hospitals. Furthermore, the under-reporting of ADRs is more likely to occur when data is collected for more general reasons in the first place, such as the need to meet the requirements of regulatory bodies. Concerns related to the ‘discoverability’ of ADR data may also limit the extent to which events are identified and recorded [12]. It is not surprising then that ADR detection strategies utilizing targeted trigger tools elicit higher reporting rates compared with strategies relying on voluntary or verbally solicited reports from hospital staff or unfocused retrospective chart reviews [13]. Finally, an overview of ADRs reported to the FDA’s Adverse Event Reporting System revealed that the number of pediatric reports to the Adverse Event Reporting System over the past 5 years has remained steady (Figure 1), while reports for adults have increased fivefold over the same time period. The authors concluded that improvement in the passive surveillance system is needed from a pediatric perspective, and recommended that investments be made in active surveillance systems to better capture risk information in children [14].

Figure 1
The number of prescriptions dispensed by US retail pharmacies and crude counts of US Adverse Event Reporting System reports for pediatric (<18 year) and adult (18 year and older) subjects, by year

ADRs in children: the impact of age on ADRs

Untoward events occurring in children have been pivotal in history for the subsequent development of the FDA and legislation aimed at protecting the public [4,15]. Clinical experience has revealed that children differ from adults in terms of risk for particular ADRs. For example, delayed maturation of drug-metabolizing enzymes may contribute to concentration-dependent toxicities, particularly in newborns where drug biotransformation capacity is most limited [16]. Furthermore, compelling data has been presented indicating that several severe idiosyncratic ADRs, such as valproic acid (VPA) hepatotoxicity [1719], activation cluster adverse events with fluvoxamine [20] and cutaneous toxicity associated with lamotrigine, [21,22] occur more frequently in children than in adults. ADRs occurring early in life can additionally be accompanied by life-long morbidity with furosemide-induced ototoxicity in newborns and inappropriate weight gain due to atypical anti-psychotics, being just two examples. Similarly, there are situations where drug exposures or chronic drug therapy initiated early in life may be associated with consequences that do not become apparent until later on in life. Examples include the development of secondary malignancies following treatment for various childhood cancers [23,24], and the effect of steroid therapy on linear growth [25].

Several severe idiosyncratic ADRs, such as aspirin and Reye’s syndrome [26], and cefaclor serum-sickness-like reactions [27] occur more frequently in children than in adults. The mechanisms of toxicity are poorly understood at best, and as a result, explanations for the apparent increased risk of these events in children is unknown. In vivo pharmacokinetic and therapeutic drug monitoring data implies that some drug biotransformation pathways exceed adult capacity, at least when dosage requirements/drug clearance between children and adults are compared and expressed relative to bodyweight. This issue is far from resolved, but developmentally increased CYP activity during childhood could conceivably result in increased formation of reactive, potentially toxic metabolites. If combined with pharmacogenetic variation in detoxification pathways (e.g., glucuronosyl transferases and sulfotransferases of glutathione transferases), net increases in reactive metabolite formation could be a significant determinant of risk for the development of idiosyncratic ADRs in young children. For example, experimental evidence suggests that chemically reactive metabolites generated from the potentially toxic metabolite 4-ene-VPA, such as 2,4-diene-VPA, have the potential to deplete mitochondrial glutathione pools [28], and through formation of conjugates with coenzyme A [29], inhibit enzymes in the β-oxidation pathway [30,31].

The phenomenon of behavioral adverse events termed ‘activation cluster adverse event’ (AC-AEs), manifested as an increased activity level, impulsivity, disinhibition and insomnia, have been associated uniquely in children receiving certain medications, specifically serotonin selective reuptake inhibitors (SSRIs) [20,32]. Reinblatt and colleagues reported AC-AEs in 45% of children receiving fluvoxamine compared with 4% in the placebo group as part of a retrospective analysis of a double-blind placebo-controlled trial. Elevated mean blood levels of fluvoxamine in patients who exhibited these behaviors and an apparent resolution of symptoms with dose reduction suggested that dose and metabolism may play an important role [20]. A pharmacokinetic study by Labellarte and colleagues has demonstrated a higher exposure to fluvoxamine in children than in adolescents and adults [33]. Fluvoxamine is metabolized by the cytochrome P450 enzymes, primarily CYP1A2. Delayed acquisition of catalytic activity may account for the differences in activation seen between age groups, as CYP1A2 is one of the last CYPs to be expressed postnatally. However, one cannot exclude the possibility that susceptibility of younger children to AC-AEs is owing to developmental differences in the expression of the drug target, the serotonin reuptake pump (SLC6A4), or the presence of ‘off-target’ interactions that do not occur at older ages. Unfortunately, few, if any data characterizing the ontogeny of drug targets and downstream signal transduction pathways are presently available.

Ontogeny & ADRs in children

The developmental continuum between birth and adolescence is a very dynamic, complex period of life. The effects of development can be applied to all steps of drug disposition and response. These effects range from differences in gastric pH [34,35] and gastric emptying (affecting the absorption of compounds) [36], to changes in circulating plasma proteins with age, potentially affecting drug distribution [37]. Developmental changes in phase I drug biotransformation and phase II conjugating enzyme expression have the potential to alter drug metabolism [38]. In addition, developmental differences in glomerular filtration rates will affect drug excretion in children [39]. Common drug biotransformation pathways are also known to be shared with endogenous compounds involved in growth and development, a nonexhaustive list including: testosterone, progesterone, prostaglandins, cortisol and vitamin D3 [16]. Therefore, it may not be surprising that some of these drug biotrans-formation pathways may be affected by rapid growth and maturation, for example, during infancy and puberty. The developmental expression of these pathways at different rates may also lead to further variability in drug disposition and response.

Age-dependent predisposition to ADRs may also be a function of developmental changes in the expression of drug targets, including transporters, ion channels, receptors and downstream signal transduction pathways [40]. For example, the serotonergic system plays an important role in postnatal brain development, a period of considerable plasticity. However, very little research has actually been conducted in the area of ontogeny of the response to medications targeting the serotonergic pathway. In fact, the ontogeny of many important drug targets for medications widely used in human adults, such as warfarin (vitamin K oxido-reductase complex 1 [VKORC1]), HMG Co-A reductase inhibitors, angiotensin-converting enzyme inhibitors and atypical antipsychotics (dopaminergic pathway) remains virtually unknown.

Based on available in vitro data and in vivo pediatric pharmacokinetic studies, drug clearance pathways are known to undergo dramatic changes throughout the maturation process [37,38]. The activities of many enzymes involved in drug biotransformation are absent or very limited at birth, raising the possibility that there may be periods of relatively increased vulnerability to concentration-dependent drug toxicity. Cardiovascular collapse from chloramphenicol, associated with delayed maturation of glucuronidation and accumulation of the parent drug, is a classic example of this phenomenon [4143]. The specific UGT isoform responsible for glucuronidation of chloramphenicol has now been identified as UGT2B7 [44]. The ontogeny of UGT2B7, both in vitro and in vivo, is reasonably well understood owing to the considerable number of studies on the ontogeny of morphine, a CYP2B7 substrate, in newborns, infants and young children [45]. Thus, delayed development of chloramphenicol glucuronidation is consistent with the ontogeny of UGT2B7 as inferred from the morphine data. However, genetic variation also contributes to variability in UGT2B7 activity and morphine glucuronidation [46], which may account for the apparent dose-dependent toxicity of chloramphenicol reported in adults [47].

Another example of the potential role of ontogeny in pediatric ADRs is the syndrome of irritability, tachypnea, tremors, jitteriness, increased muscle tone and temperature instability in neonates born to mothers receiving SSRIs during pregnancy. Controversy currently exists as to whether these symptoms reflect a neonatal withdrawal (hyposerotonergic) state [48], or whether they represent manifestations of serotonin toxicity [49,50] analogous to the hyperserotonergic state attributed to SSRI-induced serotonin syndrome in adults [51]. Currently available data reveals that CYP2D6 and CYP3A4 are acquired in the first weeks of life. They support a hyperserotonergic state owing to delayed clearance of paroxetine and fluoxetine (CYP2D6), or sertraline (CYP3A4) in neonates exposed to these compounds in utero. Furthermore, decreases in plasma SSRI concentrations and resolution of symptoms would be expected to be present with increasing postnatal age and maturation of these pathways. In addition, genetic variation, especially in CYP2D6, may contribute to susceptibility to these reactions [52]. Given that treatment of a ‘withdrawal’ reaction may include administration of an SSRI, there is considerable potential for increased toxicity in affected neonates if this course of action is taken when they are at risk for delayed clearance. However, the relative contribution of ontogeny and genetic variation in genes involved in serotonin biosynthesis, catabolism, transport and response for risk of SSRI-induced neonatal adaptation syndrome and its appropriate management is less well understood. Initial data from a study investigating the role of genetic variation in the serotonin reuptake pump, SLC6A4, implies a complex interaction between genotype and adverse neonatal outcomes following maternal SSRI exposure [53].

Genotyping an individual for variations that affect function is an important step in understanding variability in outcomes. However, knowing if and when that gene is expressed at a given point in the developmental continuum is a concept specific to genotype–phenotype relationships in children [54,55]. An approach to investigating hypotheses related to drug outcomes in children can be guided by the following questions [54]:

  • What gene products are quantitatively important in the disposition (absorption, distribution, metabolism and excretion) of the drug in question?
  • For each gene product, what is the ontogeny for the acquisition of functional activities?
  • Is allelic variation in the gene(s) of interest associated with any functional consequences in vivo?
  • Does allelic variation affect the ontogeny of the drug disposition phenotype?
  • What is the developmental context in which the gene(s) of interest is/are operating?

It may be impossible to address all of these questions simultaneously, and there may be several unknown areas requiring further investigation to fill a specific knowledge deficit. However, by keeping these questions in mind, one can systematically investigate many of the potential sources of variability in drug responses experienced in children. From this, additional new research opportunities may arise. The entire process results in a more global approach to investigating outcomes, such as drug response and toxicity. Several examples of investigative approaches evaluating drug responses in children in various stages of development are discussed in the next section.

Examples of genotype–phenotype correlations in children: dextromethorphan & CYP2D6

Several medications commonly used in pediatrics are dependent upon CYP2D6 activity for their elimination from the body, including dextromethorphan (DM). DM has been an area of investigation at our center for several years. Subjects participating in these studies range from 2 weeks postnatal age to adolescents (16 years of age). The youngest subjects participated in a longitudinal study in which phenotyping was conducted at time points coinciding with well-baby visits at 2 weeks, 1 month, 2 months, 4 months, 6 months and 12 months of age [56]. In this study, a total of 193 male and female newborns (more than 32 weeks gestation and birthweight of more than 1500 g) received an oral dose (0.3 mg/kg) of DM 1 h after their evening feeding at each time point (882 total visits). Urine was expressed from diapers collected overnight for up to 12 h after dosing and analyzed for DM and its metabolites; dextrorphan, 3-methoxymorphinan and 3-hydroxymorphinan, by HPLC. Results from this study revealed that CYP2D6 phenotype was consistent with genotype from as young as 2 weeks of age, and remained consistent throughout the first year of life. Thus, genetic variation in CYP2D6 is a more important determinant of variability in CYP2D6 activity than ontogeny over the first year of life (Figure 2). These in vivo phenotyping data are consistent with a recent in vitro investigation involving a larger number (>220) of fetal and postnatal liver microsomal samples. This in vitro investigation revealed that CYP2D6 protein and activity were similar between third trimester samples and those from infants in the first week of life. Furthermore, both protein and activity remained relatively constant after 1 week of age up to 18 years [57]. A greater role for genetic variation over developmental changes in CYP2D6 activity is supported by studies of tramadol pharmacokinetics in newborns [58]. An important secondary observation from the longitudinal DM phenotyping study was the increasing proportion of N-demethylated metabolites (predominantly 3-hydroxymorphinan) in the total DM and metabolites recovered in the urine collections, increasing from approximately 20% at 2 weeks of age to approximately 50–60% at 12 months, higher than the approximately 30% observed in adults [59,60].

Figure 2
Variability in CYP2D6 activity over the first year of life

This information has direct relevance to investigating the several advisories issued over the past 5 years concerning the use of several substrates in childhood (e.g., fatal respiratory depression and promethazine in children less than 2 years of age; DM and other over the counter cough and cold remedies in children less than 2 years of age; SSRIs and the risk of self-harm in adolescents). Given the data indicating that genetic variability is a greater source of variability in observed CYP2D6 activity than ontogeny, pharmacokinetic studies conducted in a relatively small number of children drawn from the population extremes – children with no functional alleles or genotypic poor metabolizers at one extreme and those with two or more functional alleles or genotypic ultrarapid metabolizers at the other – would be expected to efficiently capture the full range of drug exposures anticipated in the target population following a fixed dose of drug [55]. Such knowledge would be extremely helpful in predicting the risk of concentration-dependent toxicity involving drugs that are CYP2D6 substrates in both children and adults.

6-mercaptopurine & thiopurine S-methyltransferase

The proposed paradigm can also be applied to assess the relative roles of ontogeny and genetic variation as determinants of toxicity to 6-mercaptopurine (6-MP), azathioprine and 6-thioguanine. Thiopurine S-methyltransferase (TPMT) is quantitatively important in the disposition of 6-MP and 6-thioguanine. TPMT activity is usually measured in erythrocytes, which is thought to reflect activity found in other tissues, including liver and leukemic blasts. In newborn infants, peripheral blood TPMT activity is reported to be 50% greater than in race-matched adults and shows a distribution of activity that is consistent with the polymorphism characterized in adults. Ganiere-Monteil et al. reported that TPMT activity was lower in children than adults, but was not likely to be clinically relevant [61]. Similarly, TPMT activities have been reported to be comparable to previously reported adult values in a population of Korean schoolchildren aged 7–9 years [62].

Thiopurine S-methyltransferase is also subject to genetic variation. Although approximately 89% of Caucasians and African–Americans have high TPMT activity, and 11% have intermediate activity, 1 in 300 individuals inherit TPMT deficiency as an autosomal recessive trait. Three mutations have been identified in the TPMT gene (*2, *3A and *3C), which account for 98% of Caucasian subjects with low activity [63]. In patients with intermediate or low activity, more drug is shunted toward production of cytotoxic thioguanine nucleotides. TPMT can also methylate 6-thioinosine 5′-monophosphate to generate a methylated metabolite that is capable of inhibiting de novo purine synthesis. In the 0.3% of treated patients who are homozygous for variant (extremely low activity) TPMT alleles, severe and potentially life-threatening myelo-suppression can develop in those receiving standard doses of thiopurine. Subsequently, starting doses must be reduced to 6–10% of the normal dose. In the treatment of acute lymphoblastic leukemia, genetic variation in TPMT appears to be a more important determinant of 6-MP toxicity than ontogeny.

Likewise, it is probable that genetic variation is more relevant than ontogeny as a determinant of interindividual variability in TPMT, contributing to the clinical manifestations of hematopoietic toxicity in patients who utilize 6-MP and azathioprine for diseases such as inflammatory bowel disease and systemic lupus erythematosus [64,65]. Relling and Evans have recently demonstrated that genetic variation in additional enzymes in the mercaptopurine metabolic pathway, specifically inosine triphosphate pyrophosphatase, also contribute to mercaptopurine metabolism and clinical neutropenic toxicity, even when doses are adjusted based on TMPT genotype [65]. This illustrates that drug metabolism genotype–phenotype relationships are likely to be even more complex than currently appreciated, and that a systems-based approach will be necessary to further unravel the complexity of these relationships.

Balance between bioactivation & detoxification pathways: VPA

Valproic acid is a broad-spectrum antiepileptic drug used widely in children to treat idiopathic generalized epilepsy, absence epilepsies, primary generalized tonic-clonic seizures and juvenile myoclonic epilepsy [66,67]. More recently, its use has been expanded to include bipolar disorder and migraine prophylaxis in children and adolescents [68,69]. VPA hepatotoxicity may occur at any age, but epidemiologic studies have revealed that the risk of fatal hepatotoxicity is highest (~1:600) in children less than 2 years of age receiving concurrent anticonvulsant therapy. This is a 20-fold increase in risk relative to children of the same age on VPA monotherapy (1:13,000) [1719]. Other studies have confirmed polytherapy as a risk factor but found little difference in risk occurring between younger (<3 years of age) and older children, 3–6 years of age [7072].

A terminal olefin metabolite of VPA, 2-n-propyl-4-pentenoic acid (4-ene-VPA), is thought to be responsible for the observed toxicity owing to its structural similarity to known hepatotoxins, such as 4-pentenoic acid [73]. In mitochondria, 4-ene-VPA-CoA undergoes β-oxidation to (E)-2-propyl-2,4-pentadienoic acid-CoA (2,4-diene-VPA-CoA), a chemically reactive metabolite that is subject to glutathione conjugation [28,29]. Identification of various N-acetylcysteine conjugates in the urine of VPA-treated patients indicates that this bioactivation-detoxification pathway occurs in vivo in humans [28,29,74,75], but its role in the pathogenesis of VPA hepatotoxicity is unknown.

To identify a VPA metabolite signature predictive of increased risk of hepatotoxicity, we have applied a longitudinal phenotyping study design to characterize VPA metabolite patterns at steady state in a pediatric patient population without hepatotoxicity. Preliminary data from this study suggests that considerable variability in VPA metabolite patterns exists among VPA-treated patients [MANUSCRIPT IN PREPARATION]. However, novel relationships between metabolites reflecting bioactivation and detoxification pathways may serve as future biomarkers of susceptibility. These will provide novel phenotypes for further investigation of pharmacogenetic determinants of susceptibility, warranting further investigation for predicting the risk of hepatotoxicity in this patient population.

Searching for appropriate outcome predictors of methotrexate in juvenile idiopathic arthritis

Methotrexate (MTX) is the most common second-line therapeutic agent used to treat juvenile idiopathic arthritis (JIA) worldwide. Interestingly, there is significant interpatient variability in the therapeutic response and toxicity observable with MTX in rheumatoid arthritis and JIA, with no clear predictors of efficacy and toxicity to the drug existing in either condition. Gastrointestinal toxicity is reported in up to 15% of JIA patients [76], and specifically liver toxicity in 4–17% [77]. Toxicity frequently results in dose adjustment, interruption and at times, termination of therapy.

Genetic variability resulting in increased or decreased function of key enzymes within the folate pathway (Figure 3) potentially plays a role in the biotransformation of MTX and therapeutic response to the drug. Although pharmacogenomic studies in adults with rheumatoid arthritis have been conducted over the last decade, results are variable and at times contradictory [78]. How genetic variation in the folate pathway alone, or by affecting intracellular concentrations of MTX or patterns of MTX polyglutamate concentrations, results in clinical efficacy or toxicity has not been adequately illustrated in JIA.

Figure 3
Cellular folate pathway with both folate and methotrexate represented

Considerable interpatient variability was observed in MTX polyglutamate concentrations (MTXglun) and patterns in approximately 100 JIA patients studied at our center. Concentrations of MTXglu1–7 were measured both individually and as a percentage of each patient’s total MTX polyglutamation concentration. Among clinical variables investigated, route of administration and dose of medication per kg of bodyweight were significantly associated with variability in MTXglun subtypes. After correcting for dose of drug administered, higher concentrations of MTXglu1+2 were observed in patients receiving oral doses of MTX, whereas higher concentrations of MTXglu3–5 were observed in patients receiving subcutaneous doses of MTX (p < 0.0001) (Figure 4). An explanation for these apparently route-specific patterns of MTXglun is not obvious at this point. However, saturability of oral absorption of MTX has been well reported with increasing doses of MTX [79]. If bypassing gastrointestinal absorption played the sole part in our findings, we would expect all glutamate moieties to be uniformly higher in the subcutaneous group, which was not observed. One possibility is that subcutaneous dosing may allow for higher and more consistent peak plasma concentrations after each dose, which could induce a more pronounced folate-depleted state in the cell relative to that observed following oral dosing [76]. In cultured cells, growth in folate-depleted medium results in upregulation of polyglutamation and increased cellular retention of polyglutamated folates [80]. As a folate analog, the cellular response to a MTX-mediated folate-depleted state could be to enhance polyglutamation in an attempt to retain as much folate as possible in the cell [76]. The intracellular stability of long chain MTXglu3–5 allows for preferential retention and therefore accumulation of the longer chain glutamates. When we evaluated the correlation between mean corpuscular volume (used as a potential surrogate of folate deficiency) and total MTX polyglutamation concentrationstratified by route, an association with the subcutaneous route (p = 0.04), but no association in the oral group (p = 0.44), was observed [81]. Until the measurement of intracellular folate concentrations and polyglutamate patterns are completed, we will not have any further support for this hypothesis. If higher concentrations of long chain MTXglu3–5 are truly associated with improvement in disease, as some authors have suggested [8285], these data may advocate for the use of subcutaneous dosing in more patients. However, prospective studies evaluating the effect of route of MTX administration on cellular folate status will be necessary.

Figure 4
Comparison of MTXglu subgroups between per os and subcutaneous routes

Evaluation of the genetic contribution to observed variability in MTXglun concentrations and patterns of polyglutamation, and the association of MTXglun and genetic variation in folate pathway genes with outcomes in JIA, are currently being investigated. At this point, it appears that several SNP combinations in the folate pathway, including the de novo purine synthesis pathway as well as the mitochondrial folate pathway, may contribute to polyglutamate patterns and outcomes in JIA [86]. Further prospective analysis incorporating genotype, intra-cellular folate status and MTXglu patterns with clinical response, may be useful in better predicting how an individual in a current folate status may respond to perturbation with antifolate compounds such as MTX.


By 5 years of age, 95% of children have been prescribed a medication, with an average of 8.5 prescriptions and 5.5 different medications per child [85]. Despite the paucity of data investigating the safety and efficacy of drugs in children, the risks for ADRs are real and may pose significant risks for morbidity and mortality. Now, more than ever, in the era of genomics and personalized medicine, we can strive to better comprehend drug biotransformation and therapeutic response in the pediatric population. An essential first step is the recognition and characterization of the inherent differences between children and adults as manifested by differences in disease expression, drug biotransformation and utilization, and specific drug responses that may be unique to pediatric patients. In conjunction with the improved reporting of ADRs in children and, potentially, a comprehensive active surveillance system, we can systematically approach personalized therapeutics in children.

Future perspective

Serious ADRs occur in children, and yet represent an understudied area of investigation. The purpose of this article was to present a systematic approach to assess the relative contribution of ontogeny and genetic variability as predisposing factors to ADRs in children. However, future investigation of ADRs in children will require improved surveillance tools that can be used to capture unexpected drug outcomes in children. Multicenter active surveillance networks, such as the Canadian Pharmacogenomics Network for Drug Safety [87], need to be established. These networks are well-equipped to identify ADRs in children, and also to capture the clinical and genomic data that can then be used to investigate the etiology of these drug responses in children. The value of comprehensive, multicenter networks exemplified by the Canadian Pharmacogenomics Network for Drug Safety is best illustrated by the recent publication describing the association between cisplatin-induced hearing loss in children and genetic variation in the TPMT and COMT genes [88]. The passive surveillance systems currently in place are unlikely to provide a full understanding of the frequency and severity of ADRs in children, and will certainly not allow us to fully clinically characterize the reactions, nor obtain important genomic data, that ultimately may contribute to improved risk assessment.

During this era of genomic medicine, we have established methods capable of determining genotype and genetic variation in the patients that we treat. However, comprehending how genotype contributes to risk of disease or differences in drug response will be the challenge in the years ahead. For investigators, this requires a systematic approach for future research studies, including an assessment of the ontogeny of gene expression and drug metabolism when approaching these issues in the pediatric population. For clinicians, this requires an understanding of how to incorporate this new data into everyday practice when treating individual patients. Together, with the common goal of better understanding the variability in drug response in children, clinicians and researchers need first to recognize what makes children different from adults, and then prioritize and plan future studies to specifically include the pediatric population at every stage of drug development.


For reprint orders, please contact: moc.enicidemerutuf@stnirper

Financial & competing interests disclosure

The methotrexate work presented in this review was supported by the Katherine B. Richardson Grant, the Children’s Mercy Hospital Young Investigator Award, The Kansas City Area Life Sciences Institute Grant, the PhRMA Foundation Award, and the Paul Henson Immunology Research Award. The CYP2D6 longitudinal study was supported by grant HD36783, and the VPA study by grant HD044239 from NICHD. Both studies were supported by the NICHD-funded Network of Pediatric Pharmacology Research Units (PPRU). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.


Papers of special note have been highlighted as:

[filled square] of interest

[filled square][filled square] of considerable interest

1. WHO. Tech Rep Serv WHO, No 498. Geneva, Switzerland: 1972. International drug monitoring: the role of national centres.
2. Edwards IR, Aronson JK. Adverse drug reactions: definitions, diagnosis, and management. Lancet. 2000;356:1255–1259. [PubMed]
3[filled square][filled square]. Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions in hospitalized patients. J Am Med Assoc. 1998;279:1200–1205. Landmark meta-analysis raising awareness of adverse drug reactions (ADRs) as a serious public health problem. [PubMed]
4. Wester K, Jonnson AK, Sigset O, Druid H, Hagg S. Incidence of fatal adverse drug reactions: a population base study. Br J Clin Pharmacol. 2008;65:573–579. [PMC free article] [PubMed]
5. Davies EC, Green CF, Tayloe S, Williamson PR, Mottram DR, Pirmohamed M. Adverse drug reactions in hospital in-patients: a prospective analysis of 3695 patient-episodes. PLoS ONE. 2009;4:e4439. [PMC free article] [PubMed]
6. Aranda JV. Factors associated with adverse drug reactions in the newborn. Ped Pharmacol. 1983;3:245–249. [PubMed]
7. Mitchell AA, Lacouture PG, Sheehan JE, Kauffman RE, Shapiro S. Adverse drug reactions in children leading to hospital admission. Pediatrics. 1988;82:24–29. [PubMed]
8. Martínez-Mir I, García-López M, Palop V, et al. A prospective study of adverse drug reactions in hospitalized children. Br J Clin Pharmacol. 1999;47:681–688. [PMC free article] [PubMed]
9. Gonzalez-Martin G, Caroca CM, Paris E. Adverse drug reactions (ADRs) in hospitalized pediatric patients: a prospective study. Int J Clin Pharmacol Ther. 1998;36:530–533. [PubMed]
10. Kaushal R, Bates DW, Landrigan C, et al. Medication errors and adverse drug events in pediatric inpatients. JAMA. 2001;285:2114–2120. [PubMed]
11. Impicciatore P, Choonara I, Clarkson A, Provasi D, Pandolfini C, Bonati M. Incidence of adverse drug reactions in pediatric in/out-patients: a systematic review and meta-analysis of prospective studies. Br J Clin Pharmacol. 2001;52:77–83. [PMC free article] [PubMed]
12. Carleton B, Poole R, Milton J, Travis J, Grinder D. The pediatric adverse drug reaction reporting system. J Pediatr Pharm Pract. 1999;4:284–307.
13. Takata GS, Mason W, Taketomo C, Logsdon T, Sharek PJ. Development, testing, and findings of a pediatric-focused trigger tool to identify medication-related harm in US children’s hospitals. Pediatrics. 2008;121:e927–e935. [PubMed]
14. Johann-Liang R, Wyeth J, Chen M, Cope JU. Pediatric drug surveillance and the food and drug administration’s adverse event reporting system: an overview of reports, 2003–2007. Pharmacoepidemiol Drug Saf. 2009;18:24–27. [PubMed]
15. Rodriguez W, Roberts R, Murphy D. Adverse drug events in children: the US Food and Drug Administration persepctive. Curr Ther Res. 2001;62:711–723.
16. Leeder JS, Pearce RE, Gaedigk A, Modak A, Rosen DI. Evaluation of a [13C]-dextromethorphan breath test to assess CYP2D6 phenotype. J Clin Pharmacol. 2008;48(9):1041–1051. [PubMed]
17. Dreifuss FE, Santilli RN, Langer DH, Sweeney KP, Moline KA, Menander KB. Valproic acid hepatic fatalaties: a retrospective review. Neurology. 1987;37:379–385. [PubMed]
18. Dreifuss FE, Langer DH, Moline KA, Maxwell JE. Valproic acid hepatic fatalities: II. US experience since 1984. Neurology. 1989;39:201–207. [PubMed]
19. Bryant AE, Dreifuss FE. Valproic acid hepatic fatalities: III. U.S experience since 1986. Neurology. 1996;46:465–469. [PubMed]
20. Reinblatt SP, DosReis S, Walkup JT, Riddle MA. Activation adverse events induced by the selective serotonin reuptake inhibitor fluvoxamine in children and adolescents. J Child Adolesc Psychopharmacol. 2009;19(2):119–126. [PMC free article] [PubMed]
21. Guberman AH, Besag FMC, Brodie MJ, et al. Lamotrigine-induced rash: risk/benefit considerations in adults and children. Epilepsia. 1999;40:985–991. [PubMed]
22. Messenheimer JA. Rash in adult and pediatric patients treated with lamotrigine. Can J Neurol Sci. 1998;25:S14–S18. [PubMed]
23. Relling MV, Rubnitz JE, Rivera GK, et al. High incidence of secondary brain tumours after radiotherapy and antimetabolites. Lancet. 1999;354:34–39. [PubMed]
24. Relling MV, Boyett JM, Blanco JG, et al. Granulocyte colony-stimulating factor and the risk of secondary myeloid malignancy after etoposide treatment. Blood. 2003;101:3862–3867. [PubMed]
25. Wagner–Weiner L. Pediatric rheumatology for the adult rheumatologist. J Clin Rheumatol. 2008;14:109–119. [PubMed]
26. Belay ED, Bresee JS, Holman RC, Khan AS, Shahriari A, Schonberger LB. Reye’s syndrome in the United States from 1981 through 1997. N Engl J Med. 1999;340:1377–1382. [PubMed]
27. Kearns GL, Wheeler JG, Childress SH, Letzig LG. Serum sickness-like reactions to cefaclor: Role of hepatic metabolism and individual susceptibility. J Pediatr. 1994;125:805–811. [PubMed]
28. Kassahun K, Farrell K, Abbott FS. Identification and characterization of the glutathione and N-acetylcysteine conjugates of (E)-2-propyl-2,4-pentadienoic acid, a toxic metabolite of valproic acid, in rats and humans. Drug Metab Disp. 1991;19:525–535. [PubMed]
29. Kassahun K, Abbott FS. In vivo formation of the thiol conjugates of reactive metabolites of 4-ene-VPA and its analogue 4-pentenoic acid. Drug Metab Disp. 1993;21:1098–1106. [PubMed]
30. Rettenmeier AW, Prickett KS, Gordon WP, et al. Studies on the biotransformation in the perfused rat liver of 2-n-propyl-4-pentenoic acid, a metabolite of the antiepileptic drug valproic acid: Evidence for the formation of chemically reactive intermediates. Drug Metab Disp. 1985;13:81–96. [PubMed]
31. Baillie TA. Metabolic activation of valproic acid and drug-mediated hepatotoxicity: role of the terminal olefin, 2-n-propyl-4-pentenoic acid. Chem Res Toxicol. 1988;1:195–199. [PubMed]
32. Safer DJ, Zito JM. Treatment-emergent adverse events from selective serotonin reuptake inhibitors by age group: children versus adolescents. J Child Adolesc Psychopharmacol. 2006;16(1–2):159–169. [PubMed]
33. Labellarte M, Biederman J, Emslie G, et al. Multiple-dose pharmacokinetics of fluvoxamine in children and adolescents. J Am Acad Child Adolesc Psychiatry. 2004;43(12):1497–1505. [PubMed]
34. Agunod M, Yamaguchi N, Lopez R, Luhby AL, Glass GB. Correlative study of hydrochloric acid, pepsin, and intrinsic factor secretion in newborns and infants. Am J Dig Dis. 1969;14(6):400–414. [PubMed]
35. Moazam F, Kirby WJ, Rodgers BM, McGuigan JE. Physiology of serum gastrin production in neonates and infants. Ann Surg. 1984;199(4):389–392. [PubMed]
36. Gupta M, Brans YW. Gastric retention in neonates. Pediatrics. 1978;62(1):26–29. [PubMed]
37[filled square]. Kearns GL, Abdel-Rahman SM, Alander SW, Blowey DL, Leeder JS, Kauffman RE. Developmental pharmacology – drug disposition, action, and therapy in infants and children. N Engl J Med. 2003;349(12):1157–1167. Authoritative review of developmental changes in drug disposition (absorption, distribution, metabolism and excretion) in children. [PubMed]
38[filled square][filled square]. Hines RN. The ontogeny of drug metabolism enzymes and implications for adverse drug events. Pharmacol Ther. 2008;118(2):250–267. Most comprehensive review of phase I and phase II drug biotransformation processes in fetal and postnatal life: classifies developmental patterns of expression into three general groups. [PubMed]
39. Papadopoulou ZL, Leticia TU, Sandler P, Jose PA, Calcagno PL. In: Children Are Different: Developmental Physiology. 2. Johnston TR, MaJJ W, editors. Ross Laboratories; OH, USA: 1978. p. 99.
40. Mulla H. Understanding developmental pharmacodynamics: importance for drug development and clinical practice. Paediatr Drugs. 2010;12(4):223–233. Recent review of developmental pharmacodynamics. [PubMed]
41. Burns LE, Hodgman JE, Cass AB. Fatal circulatory collapse in premature infants receiving chloramphenicol. N Engl J Med. 1959;261:1318–1321. [PubMed]
42. Lambdin MA, Waddell WW, Birdsong M. Chloramphenicol toxicity in the premature infant. Pediatrics. 1960;25:935–940. [PubMed]
43. Weiss CF, Glazko AJ, Weston JK. Chloramphenicol in the newborn infant: a physiologic explanation of its toxicity when given in excessive doses. N Engl J Med. 1960;262:787–794. [PubMed]
44. Chen M, LeDuc B, Kerr S, Howe D, Williams DA. Identification of human UGT2B7 as the major isoform involved in the O-glucuronidation of chloramphenicol. Drug Metab Dispos. 2010;38(3):368–375. [PubMed]
45. de Wildt SN, Kearns GL, Leeder JS, van den Anker JN. Glucuronidation in humans: pharmacogenetic and developmental aspects. Clin Pharmacokinet. 1999;36(6):439–452. [PubMed]
46. Innocenti F, Liu W, Fackenthal D, et al. Single nucleotide polymorphism discovery and functional assessment of variation in the UDP-glucuronosyltransferase 2B7 gene. Pharmacogenet Genomics. 2008;18(8):683–697. [PMC free article] [PubMed]
47. Lancaster T, Swart AM, Jick H. Risk of serious haematological toxicity with use of chloramphenicol eye drops in a British general practice database. BMJ. 1998;316(7132):667. [PMC free article] [PubMed]
48. Stiskal JA, Kulin N, Koren G, Ho T, Ito S. Neonatal paroxetine withdrawal syndrome. Arch Dis Child Fetal Neonatal Ed. 2001;84:F134–F135. [PMC free article] [PubMed]
49. Mhanna MJ, Bennet JB, Izatt SD. Potential fluoxetine chloride (Prozac) toxicity in a newborn. Pediatrics. 1997;100:158–159. [PubMed]
50. Spencer MJ. Fluoxetine hydrochloride (Prozac) toxicity in a neonate. Pediatrics. 1993;92:721–722. [PubMed]
51. Lane R, Baldwin D. Selective serotonin reuptake inhibitor-induced serotonin syndrome: review. Clin Psychopharmacol. 1997;17:208–221. [PubMed]
52. Laine K, Kytölä J, Bertilsson L. Severe adverse effects in a newborn with two defective CYP2D6 alleles after exposure to paroxetine during late pregnancy. Ther Drug Monit. 2004;26:685–687. [PubMed]
53. Oberlander TF, Bonaguro RJ, Misri S, Papsdorf M, Ross CJ, Simpson EM. Infant serotonin transporter (SLC6A4) promoter genotype is associated with adverse neonatal outcomes after prenatal exposure to serotonin reuptake inhibitor medications. Mol Psychiatry. 2008;13(1):65–73. [PubMed]
54. Leeder JS. Developmental pharmacogenetics: a general paradigm for application to neonatal pharmacology and toxicology. Clin Pharmacol Ther. 2009;86(6):678–682. [PubMed]
55[filled square][filled square]. Leeder JS, Kearns GL, Spielberg SP, van den Anker J. Understanding the relative roles of pharmacogenetics and ontogeny in pediatric drug development and regulatory science. J Clin Pharmacol. 2010;50(12):1377–1387. Proposes an information gathering process that can be employed to systemmatically approach variability in drug disposition and response in children. [PubMed]
56[filled square]. Blake MJ, Gaedigk A, Pearce RE, et al. Ontogeny of dextromethorphan O- and N-demethylation in the first year of life. Clin Pharmacol Ther. 2007;81(4):510–516. First example of a longitudinal phenotyping study in children to investigate the relative contributions of ontogeny and genetic variation for a polymorphically expressed drug biotransformation gene. [PubMed]
57. Stevens JC, Marsh SA, Zaya MJ, et al. Developmental changes in human liver CYP2D6 expression. Drug Metab Dispos. 2008;36(8):1587–1593. [PubMed]
58. Allegaert K, Van den Anker JN, Verbesselt R, et al. O-demethylation of tramadol in the first months of life. Eur J Clin Pharmacol. 2005;61(11):837–842. [PubMed]
59. Chládek J, Zimová G, Martínková J, Tuma I. Intra-individual variability and influence of urine collection period on dextromethorphan metabolic ratios in healthy subjects. Fund Clin Pharmacol. 1999;13:508–511. [PubMed]
60. Jones DR, Gorski JC, Hamman MA, Hall SD. Quantification of dextromethorphan and metabolites: a dual phenotypic marker for cytochrome P450 3A4/5 and 2D6 activity. J Chromatogr B. 1996;678:105–111. [PubMed]
61. Ganiere-Monteil C, Medard Y, Lejus C, et al. Phenotype and genotype for thiopurine methyltransferase activity in the French Caucasian population: impact of age. Eur J Clin Pharmacol. 2004;60(2):89–96. [PubMed]
62. Park-Hah JO, Klemetsdal B, Lysaa R, Choi KH, Aarbakke J. Thiopurine methyltransferase activity in a Korean population sample of children. Clin Pharmacol Ther. 1996;60(1):68–74. [PubMed]
63. Relling MV, Hancock ML, Rivera GK, et al. Mercaptopurine therapy intolerance and heterozygosity at the thiopurine S-methyltransferase gene locus. J Natl Cancer Inst. 1999;91(23):2001–2008. [PubMed]
64. Ooi CY, Bohane TD, Lee D, Naidoo D, Day AS. Thiopurine metabolite monitoring in paediatric inflammatory bowel disease. Aliment Pharmacol Ther. 2007;25(8):941–947. [PubMed]
65. Stocco G, Cheok MH, Crews KR, et al. Genetic polymorphism of inosine triphosphate pyrophosphatase is a determinant of mercaptopurine metabolism and toxicity during treatment for acute lymphoblastic leukemia. Clin Pharmacol Ther. 2009;85(2):164–172. [PMC free article] [PubMed]
66. Aldenkamp A, Vigevano F, Arzimanoglou A, Covanis A. Role of valproate across the ages. Treatment of epilepsy in children. Acta Neurol Scand. 2006;114(Suppl 184):1–13. [PubMed]
67. Welty TE. Juvenile myoclonic epilepsy: epidemiology, pathophysiology and management. Paediatr Drugs. 2006;8:303–310. [PubMed]
68. Azorin JM, Findling RL. Valproate use in children and adolescents with bipolar disorder. CNS Drugs. 2007;21:1019–1033. [PubMed]
69. Eiland LS, Jenkins LS, Durham SH. Pediatric migraine: pharmacologic agents for prophylaxis. Ann Pharmacother. 2007;41:1181–1190. [PubMed]
70. Koenig SA, Buesing D, Longin E, et al. Valproic acid-induced hepatopathy: nine new fatalities in Germany from 1994–2003. Epilepsia. 2006;47:2027–2031. [PubMed]
71. König SA, Siemes H, Bläker F, et al. Severe hepatotoxicity during valproate therapy: an update and report of eight new fatalities. Epilepsia. 1994;35:1005–1015. [PubMed]
72. Scheffner D, König S, Rauterberg-Ruland I, Kochen W, Hofmann WJ, Unkelbach S. Fatal liver failure in 16 children with valproate therapy. Epilepsia. 1988;29:530–542. [PubMed]
73. Zimmerman HJ, Ishak KG. Valproate-induced hepatic injury: analysis of 23 fatal cases. Hepatology. 1982;2:591–597. [PubMed]
74. Gopaul SV, Farrell K, Abbott FS. Gas chromatography/negative ion chemical ionization mass spectrometry and liquid chromatography/electrospray ionization tandem mass spectrometry quantitative profiling of N-acetylcysteine conjugates of valproic acid in urine: application in drug metabolism studies in humans. J Mass Spectrom. 2000;35:698–704. [PubMed]
75. Gopaul SV, Farrell K, Abbott FS. Identification and characterization of N-acetylcysteine conjugates of valproic acid in humans and animals. Drug Metab Disp. 2000;28:823–832. [PubMed]
76. Cassidy P, Lindsley C, Laxer R, editors. Textbook of Pediatric Rheumatology. Saunders, USA: 2005.
77. Becker MLRC, Cron RQ, Sherry DD, Bilker WB, Lautenbach E. Effectiveness and toxicity of methotrexate in juvenile idiopathic athritis: comparison of two initial dosing regimens. J Rheumatol. 2010;37(4):870–875. [PMC free article] [PubMed]
78. Ranganathan P. An update on pharmacogenomics in rheumatoid arthritis with a focus on TNF-blocking agents. Curr Opin Mol Ther. 2008;10(6):562–567. [PubMed]
79. Everens WE, Schentag JJ, Jusko WJ, Harrison H, editors. Applied Therapeutics. Lippincott Williams & Wilkins; NY, USA: 1986. Applied Pharmacokinetics: Principles Of Therapeutic Drug Monitoring.
80. Ifergan I, Shafran A, Jansen G, Hooijberg JH, Scheffer GL, Assaraf YG. Folate deprivation results in the loss of breast cancer resistance protein (BCRP/ABCG2) expression. A role for BCRP in cellular folate homeostasis. J Biol Chem. 2004;279(24):25527–25534. [PubMed]
81. Becker ML, van Haandel L, Gaedigk R, et al. Analysis of intracellular methotrexate polyglutamates in juvenile idiopathic arthritis: effect of route of administration upon intracellular methotrexate polyglutamate variability. Arthritis Rheum. 2010;62(6):1803–1812. [PubMed]
82. Dervieux T, Kremer J, Lein DO, et al. Contribution of common polymorphisms in reduced folate carrier and γ-glutamylhydrolase to methotrexate polyglutamate levels in patients with rheumatoid arthritis. Pharmacogenetics. 2004;14(11):733–739. [PubMed]
83. Dervieux T, Furst D, Lein DO, et al. Pharmacogenetic and metabolite measurements are associated with clinical status in patients with rheumatoid arthritis treated with methotrexate: results of a multicentred cross sectional observational study. Ann Rheum Dis. 2005;64(8):1180–1185. [PMC free article] [PubMed]
84. Dervieux T, Greenstein N, Kremer J. Pharmacogenomic and metabolic biomarkers in the folate pathway and their association with methotrexate effects during dosage escalation in rheumatoid arthritis. Arthritis Rheum. 2006;54(10):3095–3103. [PubMed]
85. Loebstein R, Koren G. Clinical pharmacology and therapeutic drug monitoring in neonates and children. Pediatr Rev. 1998;19(12):423–428. [PubMed]
86. Becker LM, Gaedigk R, van Haandel L, et al. The effect of genotype on methotrexate polyglutamate variability in juvenile idiopathic arthritis and association with drug response. Arthritis Rheum. 2010 doi: 10.1002/art.30080. (Epub ahead of print) [PubMed] [Cross Ref]
87[filled square][filled square]. Carleton BC, Poole RL, Smith MA, et al. Adverse drug reaction active surveillance: developing a national network in Canada’s children’s hospitals. Pharmacoepidemiol Drug Safety. 2009;18:718–721. Example of establishing a multicenter active surveillance network to investigate serious ADRs in children. [PubMed]
88[filled square][filled square]. Ross CJ, Katzov-Eckert H, Dube MP, et al. Genetic variants in TPMT and COMT are associated with hearing loss in children receiving cisplatin chemotherapy. Nat Genet. 2009;41(12):1345–1349. Example of the potential value of a multicenter active surveillance network to investigate serious ADRs in children. [PubMed]