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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5195_Библиотеки_им_академика_М_И_Перельмана.pdf
Figure 5.1 The promise of genomic medicine to human health and disease. The goal of personalized
medicine will be achieved by identifying subgroups of patients who will respond favorably to a given drug
with a minimum of side effects, as well as those who will not respond or who will show excessive toxicity
with standard doses. A further benefit of pharmacogenomics will be the ability to select the most
appropriate alternative drug for those patients who fail treatment with conventional drugs and doses.
BASIC CONCEPTS AND DEFINITIONS
Genetic variability results from gene mutation and the exchange of genetic
information between chromosomes that occurs during meiosis. With the
exception of sex-linked genes (genes occurring on the X or Y chromosomes),
every individual carries two copies of each gene they possess. All copies of a
specific gene present within a population may not have identical nucleotide
sequences, and these genetic polymorphisms contribute to the variability
observed in that population. The presence of different nucleotides at a given
position within a gene is called a single-nucleotide polymorphism (SNP), and
SNPs are rapidly becoming an important component of the genomics lexicon.
More recently, focus has shifted to characterizing haplotypes, collections of
SNPs and other allelic variations that are located close to each other and
inherited together; creating a catalog of haplotypes is also a goal of the Human
Genome Project, referred to as the HapMap Project.26 In genes where
polymorphisms have been detected, alternative forms of the gene are called
alleles. Individual alleles are designated by the italicized gene name (e.g.,
CYP2D6) followed by an asterisk and an Arabic number, with *1 typically
designating, by convention, the fully functional wild-type allele. When the
alleles at a particular gene locus are identical, a homozygous state exists,
whereas the term heterozygous refers to the situation in which different alleles
are present at the same gene locus. The term genotype refers to an individual’s
genetic constitution, while the observable characteristics or physical
manifestations constitute the phenotype, which is the net consequence of genetic
and environmental effects.
Human genetic variation can take many forms, but is broadly divided into
two general classes of variation: single-nucleotide variations and structural
variations. SNPs are the most prevalent class of genetic variation, and it has
been estimated that there are approximately 11 million SNPs in the human
genome. Structural variations encompass all differences in DNA sequence that
involve more than one nucleotide. Insertions–deletions variants (indels) occur
when a contiguous set of one or more nucleotides is absent in some individuals
and present in others. Block substitutions involve variation of a string of
contiguous nucleotides (or “block”) that differs between two genomes.
Sequence inversions occur when the order of an entire block of nucleotides is
reversed in a specific region of the genome. Finally, copy number variations
(CNVs) refer to the deletion or duplication of identical or near-identical DNA
sequences that may be thousands to millions of bases in size. Although they
occur less frequently than SNPs, structural variations may constitute 0.5% to
1% of an individual’s genome and thus are the subject of intensive investigation
for their contribution to phenotypic variation.
27,28
Pharmacogenetics, the study of the role of genetic factors in drug
disposition, response, and toxicity, essentially relates allelic variation in human
genes to variability in drug responses at the level of the individual patient. In
other words, the promise of pharmacogenetics is to identify the right drug at the
right dose for the right patient. The field of pharmacogenetics classically has
focused on the phenotypic consequences of allelic variation in single genes, but
often in the past, there was confusion between genotypic and phenotypic
definitions of “polymorphism” and thus a need to clarify the relationship
between genetic concepts and the clinical relevance of a given phenotype. In
1991, Meyer proposed that pharmacogenetic polymorphism be defined as a
monogenic trait caused by the presence in the same population of more than one
allele at the same locus and more than one phenotype with regard to drug
interaction with the organism. The frequency of the least common allele should
be at least 1%.29 According to this definition, the key elements of
pharmacogenetic polymorphisms are heritability, the involvement of a singlegene locus, and the fact that distinct phenotypes are observed within the
population only after drug challenge.
The vast majority of our current understanding of pharmacogenetic
polymorphisms involves enzymes responsible for drug biotransformation.
Clinically, individuals are classified as being “fast,” “rapid,” or “extensive”
metabolizers, at one end of the spectrum, and “slow” or “poor” metabolizers, at
the other end of a continuum, that may, depending on the specific enzyme, also
include an intermediate-metabolizer group. Pediatric pharmacogenetics
involves an added measure of complexity since fetuses and newborns may be
phenotypically “slow” or “poor” metabolizers for certain drug-metabolizing
pathways, acquiring a phenotype traditionally consistent with their genotype at
some point later in the developmental process as those pathways mature (e.g.,
glucuronidation, some cytochrome P450 activities).
30–32
Although some authors use the terms pharmacogenetics and
pharmacogenomics interchangeably, the latter term represents the marriage of
pharmacology with genomics and is, therefore, considerably broader in scope.
Pharmacogenomics can be defined as the study of the genome-wide response to
small molecular weight compounds administered with therapeutic intent—
finding the right drug for the right disease. Proteomics represents the systematic
investigation of qualitative and quantitative changes in protein expression in a
cell or tissue in response to disease or disease treatment. In this context,
pharmacoproteomics involves characterizing the response of the proteome to
therapeutic agents. Similarly, toxicogenomics and toxicoproteomics investigate
the analogous response to environmental contaminants and other toxicants.
33,34
In
contrast to the focus of “pharmacogenetics” on single-gene events,
“pharmacogenomics” involves understanding how interacting systems or
networks of genes influence drug responses.35 This definition is particularly
appealing to pediatric health and disease since the concept of many genes acting
in concert captures the essence of the developmental processes that characterize
maturation from the time of birth through adulthood while retaining a focus on
the individual.
It is safe to say that application of pharmacogenomic principles to pediatric
medicine has received far less attention than its application to diseases affecting
adults, and the scope of the field remains to be completely defined. However,
developmental and pediatric pharmacogenomics necessarily must take into
consideration the dynamic changes in gene expression that accompany
maturation from embryonic life through fetal development, the neonatal period,
infancy, childhood, and adolescence (e.g., during organogenesis, as receptor
systems and neural networks become established, and functional drug
biotransformation capacity is acquired, among others). In other words, patterns
of gene expression and the nature of the gene interactions that contribute to the
pathogenesis of pediatric diseases (and thereby serve as potential targets for
pharmacologic intervention) may only be discernable or relevant at specific
critical points in the developmental continuum. Furthermore, variability in drug
disposition (i.e., pharmacokinetics) and action (i.e., pharmacodynamics) that
ultimately impact drug response in pediatric patients can also be expected to
change as children grow and develop. Finally, developmental and pediatric
pharmacogenomic investigations can be distinguished from similar studies
conducted in adults by the fact that drug or toxicant exposure at critical points in
development may disrupt or alter the normal patterns of development—a
genome-wide response to drug/toxicant exposure. This may have immediate,
observable consequences, for example, fetal demise or major structural
abnormalities such as those associated with retinoids36 or other human
teratogens. Of equal concern, however, is the possibility that drug exposure, or
lack of effective drug treatment,37 may have unintended consequences on
cognitive or behavior development that do not manifest until much later in
maturation. The remainder of this chapter highlights examples of how
pharmacogenomic approaches are currently improving pediatric
pharmacotherapy and presents several opportunities for future application.
PHARMACOGENETIC, PHARMACOGENOMIC,
PHARMACOPROTEOMIC, AND
METABOLOMIC TOOLS
Completion of the Human Genome Project was facilitated by several
technological advances, and the demands of pharmacoproteomic, metabolomic,
pharmacogenetic, and pharmacogenomic analyses have driven the development
of an industry dedicated to the discovery and refinement of technologies
capable of generating large data sets of information derived from DNA, RNA,
proteins, and small molecules that are present in the body from endogenous
sources. These tools are used widely to investigate disease pathogenesis but are
equally applicable to investigations of variability in drug disposition and
response.
PHENOTYPING TOOLS
Historically, pharmacogenetic analyses have been dependent upon phenotyping
studies to estimate enzyme activity in vivo at a specific time point as well as
genotyping strategies to identify and characterize SNPs and other forms of
genetic variation. Phenotyping studies are best conducted with a probe
compound carefully selected to ensure that its biotransformation is primarily
dependent upon a single target enzyme and varies quantitatively with the level
of protein expression.38 An ideal probe should involve noninvasive sampling
strategies, such as collection of urine or expired air rather than blood samples,
especially when phenotyping studies are to be conducted in children. Finally,
candidate phenotyping probes should be widely available (nonprescription
status, preferably) and have a wide margin of safety. For pediatric studies, the
phenotyping probe should be selected from compounds that are likely to be
administered to children and perceived as safe by parents, caregivers, and
ethics committees (e.g., dextromethorphan as opposed to debrisoquine or
sparteine for CYP2D6).
The advantages and disadvantages of phenotyping probes commonly used in
adult studies have been comprehensively and critically evaluated by Streetman
et al.39 Dextromethorphan and caffeine are commonly used in pediatric
phenotyping studies with nontherapeutic intent.
40–43
However, other accepted
phenotyping probes, such as midazolam for CYP3A4 and omeprazole for
CYP2C19, may be utilized in selected patient populations where their use is
required for therapeutic purposes. Because of the rigorous demands for
enzymatic specificity, safety, and clinical feasibility, very few drugs have been
identified that meet the criteria of a useful probe compound, particularly for
non-P450 drug-metabolizing enzymes. Fortunately, recent advances in
technologies for metabolomic analysis have shifted focus toward the
measurement of endogenous biomarkers for phenotyping studies. Including
endogenous biomarkers among the phenotyping tools at our disposal may greatly
expand the breadth of drug disposition pathways we can evaluate; some drugmetabolizing enzymes or transporters exist for which no currently marketed drug
is a sufficiently specific probe. Furthermore, endogenous biomarkers offer a
number of advantages to exogenously administered probe substrates. They do
not require a potentially invasive administration and do not subject a patient to
any compounds that are not already present in circulation at the time of
phenotyping. Their minimal invasiveness makes them better suited for
phenotyping studies in children.
GENOTYPING TOOLS
The genotyping component of pharmacogenetic studies has undergone
tremendous change over the past 30 years. Historically, studies were conducted
at the level of individual genes using rather insensitive DNA hybridization
techniques44 to detect differences in the patterns of DNA fragments generated
following digestion of genomic DNA with restriction endonucleases (enzymes
that cleave DNA molecules at specific nucleotide sequences). The restriction
fragment length polymorphism (RFLP) technique was later coupled with
polymerase chain reaction (PCR) (PCR-RFLP) to allow a specific region
surrounding the SNP of interest to be amplified from small amounts of genomic
DNA followed by endonuclease digestion to identify the allelic variant(s)
present.45 PCR-RFLP techniques have been widely used to study cytochrome
P450 polymorphisms,46 among others, but they are too labor-intensive for
routine use in genomic applications, such as fine-mapping of disease loci or
candidate gene association studies, which involve the analysis of multiple SNPs
in thousands of genes. Instead, microarray or gene-chip technology was
developed for the purpose. These whole-genome genotyping technologies now
make it possible to interrogate genetic variation at more than a million sites
throughout an individual genome for SNP and CNV analyses using a single
“chip.” Most genome-wide association studies (GWASs) have been conducted
with “SNP chips” utilizing one of two commercial platforms, and the approach
has been applied to several pediatric diseases. A study of Kawasaki disease
identified a set of functionally related genes potentially related to inflammation,
apoptosis, and cardiovascular pathology.47 The results of the study provide
novel insights into the pathogenesis of the disorder and lead to the possibility of
identifying new targets for therapeutic intervention. Similarly, GWASs in
patients with early-onset asthma48 and pediatric inflammatory bowel disease
49
have been implemented as a new strategy to identify novel genes in disease
pathogenesis. GWASs are also being applied to identify genetic associations
with drug dosing, response, and efficacy, as reported for warfarin50 and
clopidogrel51 and risk for drug-induced toxicity, as has been described for
statin-induced myopathy.52 A defining feature of GWAS is the use of Manhattan
plots, and an example is presented in Figure 5.2. Chips targeting the
pharmacogenome are now in use by several institutions, with a focus on genes
broadly involved in drug disposition and response (various cytochromes P450,
transporters, and targets of drug action, such as neurotransmitter receptors or
reuptake pumps).
Figure 5.2 Example of a Manhattan Plot from a genome-wide association study. This type of plot gains
its name from the similarity of such a plot to the Manhattan skyline and presents the genome-wide
significance of several hundred thousand single-nucleotide polymorphisms (SNPs) distributed throughout the
genome with the trait or phenotype of interest. Along the x-axis, each SNP is plotted according to its
chromosomal coordinate, with each shade representing an individual chromosome from chromosome 1 to
the X chromosome. The y-axis represents the inverse log10 of the p value for the association. SNPs
exceeding a particular threshold are subject to further verification and validation. (From Schizophrenia
Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophreniaassociated genetic loci. Nature 2014;511(7510):421–427.
The most recent addition to the genomic toolbox is next-generation
sequencing technology.53 The major difference between next-generation
sequencing methods and the older, more established Sanger capillary
sequencing method is the vast amount of sequencing data that can be generated
by the next-generation technologies, which utilize massively parallel and shortread strategies to sequence DNA and RNA templates. Whereas it has been
estimated that 500 days of runtime were required to generate one gigabase (one
billion nucleotides) of data, the newer next-generation sequencers can produce
these same amount of data in half a day at one-hundredth the cost. However, the
short 50 to 75 bp read lengths provide computational challenges related to
assembly of the short sequence reads to produce an accurate contiguous
genomic sequence. Despite the computational challenges, genotyping strategies
utilizing next-generation sequencing show considerable promise for routine
application, even for complex genes like CYP2D6.
54
Recommendations and requirements for pharmacogenetics testing are now a
regular part of the Food and Drug Administration (FDA) drug approval process,
and international groups of experts like the Clinical Pharmacogenetics
Implementation Consortium (CPIC) have been established to help identify
further “actionable” polymorphic genes. Health care providers, who serve as
gatekeepers, use this pharmacogenetics information to lead discussions with
patients and guide evidence-based drug and dosage selection with the patient.
However, continuing innovations that expand the accessibility of at home
genetic testing and the gradual loosening of regulations related to what
information can be provided directly to patients may upend this long-standing
model. In 2018, a major change occurred within the regulatory landscape of
pharmacogenetic testing. For the first time, the FDA approved direct-toconsumer at home pharmacogenetic testing for drug biotransformation
pathways.55 It is critical now more than ever before that health care
practitioners be informed about the utility and limitations of pharmacogenetics
testing as patients become increasingly more likely to approach practitioners
with personal pharmacogenetic testing results already in hand. Important ethical
questions regarding who owns patient data, what level of data interpretation and
reporting by vendors could be described as practicing medicine, what legal
protections should be afforded patients from discrimination by medical
insurance or health care providers, and what respective roles should children
and parents have in providing consent to testing in the absence of trained
intermediary health care providers must also be addressed.
PROTEOMIC TOOLS
Genetic mutations in genes are like atypical features in a relatively static
blueprint, and the penetrance is the proportion of individuals with a given
genetic mutation who exhibit a phenotypic change. While some mutations, such
as those contributing to complete loss of CYP2D6 activity, have high
penetrance, functional differences for many mutations may never come to bear
or they may manifest differentially over the course of years, weeks, or even
across the span of a single day. Furthermore, polymorphic genes interact with
environmental conditions or other genes, often in complex and unanticipated
ways. It is with this understanding that considerable research has been
undertaken in the related fields of transcriptomics, proteomics, and
metabolomics. Each of these fields represents a successive step toward a more
accurate “snapshot” of true functional differences in a given individual at a
given time, and each offer unique opportunities and limitations in the realm of
personalized medicine. Following in the wake of advancements in technologies
for studying genomics, great investments have been made to improve other “omics” technologies. The field of pharmacoproteomics has benefited
considerably with improvements across the entire workflow from sample
preparation to analysis, but perhaps the most enabling advancements have been
the development of more sensitive and accurate mass spectrometers.56 This is
because, unlike with sequences of nucleotides, no amplification method
analogous to PCR exists to increase the quantity of protein from small amounts
of starting material. Regardless of specific requirements for sample processing,
whether whole proteins (i.e., top-down proteomics) or proteolytically digested
protein fragments (i.e., bottom-up proteomics) are being assessed, highly
sensitive mass spectrometers are crucial for the identification and quantification
of proteins in human tissues. While it is important to note that traditional
antibody-based methods such as Western blots and enzyme-linked
immunosorbent assays (ELISA) do still offer liquid chromatography–mass
spectrometry (LC/MS)–independent means to detect and quantify amounts of
proteins in human tissues, these techniques are increasingly falling out of favor
due to their generally reduced throughput, specificity, sensitivity, and severely
limited capacity for simultaneous measurement of multiple proteins (i.e.,
multiplexing) when compared to techniques using mass spectrometers.
57,58
Furthermore, neither ELISA nor Western blots are capable of identifying or
detecting proteins beyond those for which the assays have been designed to
measure. As such, they cannot be used to truly characterize novel proteins or to
agnostically evaluate the contribution of proteins that are not designated a priori
to any sort of outcome, such as drug responsiveness. Conversely, mass
spectrometer–based proteomics can be used to detect a set of proteins that are
defined a priori (i.e., targeted proteomics) and those that are not (i.e., global
proteomics). The incorporation of calibration curves consisting of labeled
proteins or peptides, in conjunction with targeted proteomic analysis, further
permits relative or absolute quantification of proteins in human tissues.
The use of quantitatively targeted proteomics has proved to be of
considerable value to the field pediatric clinical pharmacology, in that it has
provided important information regarding the ontogeny of proteins important in
drug disposition. Most recently, quantitative proteomic data are making
significant contributions in the area of modeling and simulation as protein
abundance of drug-metabolizing enzymes and transporters is crucial information
for the construction of physiologically based pharmacokinetic (PBPK) models
that can estimate an exposure range for a given drug as function of age and other
patient-specific variables. The coupling of accurate proteomic data to PBPK
models has been used to estimate doses for first-in-children studies of drugs
seeking pediatric dosing indications and even to simulate the results of clinical
pharmacokinetic studies where difficulties in enrollment prohibit conducting
properly powered pediatric clinical studies. Notably, the FDA approval of both
valganciclovir dosing for infants under 4 months and nilotinib dosing in
children over 2 years of age was based, in part, on results from PBPK
simulations incorporating targeted proteomic data relating to the ontogeny of
protein expression of drug-metabolizing enzymes and transporters.
59,60
However, much of the current data for the maturation of drug-metabolizing
enzymes and transporters span a very wide age range and are not sufficiently
descriptive of the early years where the most dramatic changes in the
expression of these proteins often occur. More detailed, pediatric-focused,
targeted proteomic studies into the ontogeny of proteins important in drug
disposition and response are warranted to adequately refine these pediatric
PBPK models.
There is also considerable interest in using pediatric PBPK models as the
basis for clinical decision support tools for the precision dosing of medications
in children.61 The measurement of protein abundance in organs of interest often
requires obtaining biopsies, which are typically acquired from large tissue
repositories available to scientific researchers. However, it is neither ethical
nor feasible to obtain tissue biopsies in the clinic for the sole purposes of
generating a tailored PBPK model. Even biopsies acquired as a part of routine
care would not be of great utility for this purpose as enzyme and transporters
expression change with age and environmental exposures. One approach is to
rely on more readily accessible tissues, such as the blood, and use global
proteomics approaches to characterize signature protein abundance patterns
reflective the protein expression of various drug-metabolizing enzymes and
transporters in other tissues like the liver. The application of other targeted