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B*15:02 allele and is phenytoin naïve.
263
The HLA-A*31:01 allele is found in
most populations worldwide and may also be a risk factor for SJS/TEN,
although it seems to be more strongly associated with other carbamazepineinduced reactions, such as DRESS and maculopapular eruptions. As such, CPIC
guidelines recommend carbamazepine not be used in carbamazepine-naïve
patients who are positive for HLA-B*15:02 or any HLA-A*31:01 genotype.
264
Alternative antiseizure medications other than oxcarbazepine or phenytoin (or
its prodrug fosphenytoin) should be used for any HLA-B*15:02 carrier.
264
The
presence of the HLA-B*58:01 allele is associated with allopurinol-induced
severe cutaneous adverse reactions, and CPIC guidelines recommend carriers
of the allele not receive the drug.
265
Additional HLA-B variants have been
implicated in cutaneous adverse reactions to nevirapine (HLA-B*35:05),
266,267
dapsone (HLA-B*13:01),
268,269
and methazolamide (HLA-B*59:01)
270,271
; the
FDA has not issued guidelines for pharmacogenetic screening for these drugs at
this time. Although most data for HLA associations with idiosyncratic drug
reactions have been based on data from adults, the guidelines should be
considered applicable to pediatric patients as well.
IMPROVING UPON ROUTINE THERAPEUTIC
MONITORING WITH “-OMICS” DATA
The introduction of routine TDM has improved patient safety and efficacy for a
number of drugs, and application of “-omics” data, such as pharmacogenomics,
has the potential to lead to more precise therapy. For example, the relative
activities of multiple contributing metabolic pathways, through measurement of
metabolites in addition to the parent drug, are not typically captured in routine
TDM. When one or more of these pathways is polymorphic, or the competing
pathways have differing developmental trajectories, an understanding of the
relationship between pharmacogenomics and metabolite-to-parent ratios can
help in the translation of measurements taken during routine TDM into relatively
more clinically meaningful numbers. Furthermore, understanding the
relationship between pharmacogenomics and the contribution of individual
pathways can provide insight into individual’s unique sensitivity for drug–drug
interactions (DDIs). PBPK models, leveraging data on the relationship between
genotype and phenotype and known trends in the ontogeny of drug-metabolizing
enzymes and transporters, provides a potential means to estimate full
concentration–time course of drugs in the pediatric population. Similarly,
metabolomics offers the prospect of identifying metabolomic signatures or
discrete urinary biomarkers that could aid in the prediction of patient-specific
pharmacokinetics and adverse event profile of a drug in a given patient.
INDIVIDUALIZED DRUG–DRUG INTERACTION RISK
Drugs are typically cleared from the body via multiple metabolic and excretory
pathways that may or may not be dependent upon the effects of genetic
polymorphisms. There is often a generally recognized major clearance pathway,
but the specific contribution of each pathway will vary from individual to
individual based on their relative expression levels for the participating
enzymes. The risks of inappropriate drug exposure presented by this underlying
interindividual variability can be somewhat mitigated by the practice of
adjusting the dose to achieve a particular effect, such as is done with warfarin,
or with the implementation of routine TDM. Nevertheless, the magnitude of
change in drug exposure upon coadministration of an interacting medication will
vary greatly from patient to patient, even among patients who have previously
been dosed to the same systemic exposure or pharmacologic effect. When a
clear genotype–phenotype relationship exists, pharmacogenomics can offer
insight into an individual’s unique propensity to experience a particular DDI.
Furthermore, enzyme expression within a given genotype may vary considerably
as a function of age and environmental factors. Here, identification of
endogenous biomarkers of activity through metabolomic screening offers an
attractive avenue for determining the compliment of drug-metabolizing enzymes
in a given person at a given point in time, and to predict an individualized foldchange in drug as exposure with the addition of a concomitant medication.
While the use multiple concomitant medications has traditionally been
associated with the adult population, the rate of polypharmacy in pediatric
population has been progressively expanding. In recent years, our understanding
of the interplay between pharmacogenetics and DDIs has been leveraged to
quantitatively predict changes in relative drug exposure. However, refinement
and clinical implementation of these predictive models, in the form of decision
support tools for dose adjustment, is still outstanding. Furthermore, the
incorporation of parameters reflecting the ontogeny of drug-metabolizing
enzymes and transporters will be required to produce accurate estimates in the
pediatric population.
PEDIATRIC PHARMACOGENETICS:
CHALLENGES FOR THE FUTURE
Challenges for pediatric pharmacogenetics in the future and considerations for
the design of studies to improve the quality of pharmacogenomic data in
children are well exemplified by the progress in warfarin pharmacogenetics
over the past 10 years. Warfarin is a 4-hydroxycoumarin anticoagulant that
exerts its pharmacologic actions via the antagonism of vitamin K epoxide
reductase (VKOR), the enzyme responsible for the bioactivation of vitamin K
and downstream synthesis of vitamin K–dependent clotting factors II, VII, IX,
and X. Warfarin is well known to be a CYP2C9 substrate, and historically,
prepubertal children have been reported to require larger weight-based doses
of warfarin to achieve the same target international normalized ratio (INR),
137
as older children and adults.
272
This observation is now attributed to age-
dependent changes in the ratio of liver mass to total body mass,
273
but hints that
the pharmacodynamic response to warfarin may differ between younger
children and adults were also present. Studies of warfarin pharmacogenetics in
children have lagged behind those of adults, but the results of at least seven
studies consistently have found that genetic variation in CYP2C9 and the target
of warfarin action, vitamin K oxidoreductase complex 1 (VKORC1), are the
most important determinants of warfarin dose in studied populations. Although
these studies consistently reveal that children with VKORC1 -1639AA
genotypes require significantly lower doses of warfarin to achieve the same
target INR as children with -1639GG genotypes, a major inconsistency among
the various studies is the relative importance of genetic (primarily VKORC1
and CYP2C9 genotype) and “nongenetic/developmental” factors as determinants
of variability in the warfarin dose required to achieve a stable INR target
therapeutic goal. For example, age was reported to account for 28.3% of dose
variability, and genetic factors contributed approximately 4% (3.7% for
VKORC1 and 0.4% for CYP2C9) in one study, whereas in six subsequent
studies, the genetic contribution (predominantly VKORC1 genotype) was larger
(11.9% to 52% of dose variability), but in four of these studies, the
“developmental” component still exceeded the genetic contribution (reviewed
in reference
274
). It has been proposed that the composition of individual study
cohorts may contribute to the discrepancy between relative contributions of
genetic and nongenetic factors.
274
For example, in a subgroup analysis, patients
receiving warfarin after a Fontan surgery, genetic factors accounted for
approximately 50% of variability in dose and developmental factors were
<10%, whereas for patients receiving warfarin for a thromboembolic disorder,
developmental factors were most important, accounting for approximately 60%
of variability, and genetic factors were negligible.
274
This case of pediatric warfarin pharmacogenetics provides important
insights that should be considered in the design of future pharmacogenetic
studies in children. First, treatment with warfarin was the primary inclusion
criterion for the pediatric warfarin studies to ensure adequate cohorts for
analysis. In fact, the practice of enrolling all pediatric patients based primarily
on use of a specific medication, without consideration of underlying disease
process is a fairly common practice in pediatrics where the numbers of affected
patients generally are much smaller than adult populations. Including patients
who are receiving a drug for different indications—for primary prevention of a
thromboembolic event or for prophylaxis to prevent recurrent thromboembolic
events, both of which may be cardiac or noncardiac in origin, and post-Fontan
procedure—risks confounding the analysis when different disease mechanisms
may be operative. Second, the warfarin case illustrates the importance of
improved mechanistic insights into the effects of increasing age—specifically,
potential differences in the influence of the processes involved in growth (e.g.,
changes in height, weight, and body composition) and development, such as
progression through the various Tanner stages to achieve full sexual maturity—
on drug disposition and response.
274
SUMMARY AND CONCLUSIONS
The postgenomic era represents an unprecedented opportunity to translate the
increasing volume of untapped genomic, transcriptomic, proteomic, and
metabonomic data into discoveries that favorably impact the care and treatment
of children. Many diseases have their onset during childhood, and effective
early intervention may have unforeseen benefits later in life. On the other hand,
pharmacologic management of disease or unintended exposure to environmental
toxins at critical stages of development may have consequences that are not
immediately apparent due to the profound changes that occur as a fetus
develops, and as newborn infants mature through childhood to adolescence and,
ultimately, adulthood. Given the complexity of human development, a focus on
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the influence of a single gene or gene product is likely to be of limited value in
terms of understanding the consequences of small molecule interactions with a
dynamic developmental environment. Rather, the developmental process should
be perceived, at a minimum, as networks of interacting genes and different
networks being operative at different developmental stages. Furthermore, the
repertoire of genes operative within a given network may vary at different
developmental stages, and the phenotypic manifestations of gene variants may
not manifest until much later in the process of maturation. In the context of
identifying new target genes or gene networks for therapeutic intervention, the
most compelling challenge to pediatric pharmacogenomic research will be to
identify the essential network or pathway (knowing where to look) at the
appropriate developmental stage (knowing when to look). There is reason to be
optimistic that new strategies and technologies will help unravel the
complexities of pediatric disorders since this new knowledge is essential for
children to benefit as much as adults from new treatment modalities.
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