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\begin{document}

\title{Ontogeny of drug metabolism and transport: new data derived by
innovative approaches}



\author[1]{Bianca van Groen}%
\author[2]{Karel Allegaert}%
\author[1]{Dick Tibboel}%
\author[3]{Saskia N. de Wildt}%
\affil[1]{Erasmus MC - Sophia Children's Hospital}%
\affil[2]{KU Leuven}%
\affil[3]{Radboud Institute of Health Sciences, Radboud University Medical Center}%


\vspace{-1em}



  
  \date{March 17, 2025}


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\selectlanguage{english}
\begin{abstract}
Drug disposition in children is impacted by developmental changes in
drug absorption, distribution, metabolism and excretion. This mandates
the need for dosing regimens specifically tailored to children. Yet,
there are gaps in the knowledge on these developmental changes, for
example regarding hepatic and renal drug transport and drug metabolism,
putting children at risk for subtherapeutic or toxic drug exposure.
Pediatric drug research is faced with ethical and analytical challenges.
This review addresses how these knowledge gaps can be filled and
challenges can be overcome by using innovative study designs.%
\end{abstract}%



\sloppy


\section*{Main text}

{\label{main-text}}

\subsection*{Introduction}

{\label{introduction}}

Drug disposition in neonates, infants and children is impacted by
development and growth. This includes developmental changes, i.e.
ontogeny, in drug metabolism and drug transport.{[}1{]} Several
information gaps on these developmental changes remain, bringing
children at risk of subtherapeutic or toxic drug exposure. Hence,
pediatric drug research to unravel and understand these developmental
changes is highly needed, but is often considered complex -- and
sometimes even impossible -- because of perceived challenges and
burdens. Studies to delineate the age-related variation in drug
metabolism and transport are often non-therapeutic, which makes these
studies ethically challenging. The small blood and tissue sample volumes
inherent to pediatric studies further pose practical challenges.
Nevertheless, although pediatric drug research is considered complex and
requires specific expertise, it is certainly not impossible.

The aim of this review is to show how innovative approaches (see Figure
1) allowed to elucidate important information gaps in ontogeny of drug
transport and metabolism to illustrate the relevance of both \emph{in
vitro} as well as \emph{in vivo} data, with the ultimate goal to improve
drug therapy in pediatrics.

\subsection*{Membrane transporters}

{\label{membrane-transporters}}

\subsubsection*{How does membrane transporter expression change with
age?}

{\label{how-does-membrane-transporter-expression-change-with-age}}

Transporters are membrane-bound proteins present in the apical and
basolateral membranes of organs such as the liver and kidney.{[}2{]}
Their biological role is the bidirectional trafficking of substrates
across membranes, making them critical determinants of tissue and
cellular substrate disposition. Moreover, they act in concert with drug
metabolizing enzymes to maintain homeostatic balance for endogenous
substrates and to facilitate the detoxification and elimination of
exogenous substrates, such as drugs and food toxins.{[}3{]} As a result
of developmental changes in key transporters and enzymes, levels of
exogenous and endogenous substrates may change as children grow,
impacting normal physiology as well drug disposition and effect.{[}3{]}

In 2015 a review on the human ontogeny of drug transporters, it was
concluded that huge knowledge gaps remain for pediatrics, and that, to
achieve safe and effective drug therapy for children, it is crucial to
elucidate developmental patterns of transporter mRNA and protein
expression.{[}2{]} At that time, the data were mainly available from
mRNA expression studies as there were analytical challenges regarding
the quantification of protein levels. By advances in the use of liquid
Chromatography with tandem mass spectrometry (LC-MS/MS) allowing
quantification of the protein expressions of a variety of drug
transporters in only a small amount of tissue, also pediatric proteomics
data became available.{[}4, 5{]}

For both hepatic and renal transporters, age-related variation in
protein expression of clinically relevant transporters has now been
studied.{[}6-9{]} Transporter protein expression show different
developmental patterns: e.g. increasing, decreasing or stable across the
pediatric age range. These developmental patterns appear to be both
isoform- and organ-dependent. Interestingly, most differences in
transporter expression for hepatic transporters were found between the
fetal and adult age groups, and for renal transporters between infants
\textless{}2 years and adults, indicating that major changes in
transporter protein expression occur in early life. More specifically
for hepatic transporters, the sample size and age ranges in a study
from\emph{Prasad et al} {[}5{]} were complimentary to a study from
\emph{van Groen et al} {[}6{]}. \emph{Prasad et al} included 4 neonates,
19 infants, 32 children, 14 adolescents and 41 adults, whereas \emph{van
Groen et al} mainly included fetuses (n=36), preterm neonates (n=12) and
term neonates (n=10). Both studies found that organic cation transporter
1 (OCT1), multi-drug resistance 1 (MDR1) and multidrug
resistance-associated protein 3 (MRP3) increased with age. In a study
from \emph{Li et al} with 43 renal samples from children, adolescents
and adults no age-related change in transporter protein expression were
found{[}9{]}, in contrast to the findings from \emph{Cheung and van
Groen et al} {[}8{]}. This discrepancy is likely explained by the fact
that the study from \emph{Li et al} had almost no samples from neonates
and infants \textless{}2 years (i.e. 1 sample \textless{}1 year and 12
samples 1-\textless{}12 year), which is the age range in which the most
developmental changes were noticed by \emph{Cheung and van Groen et al}
. Interestingly, in addition to renal samples, \emph{Li et al} also
included 26 paired hepatic samples (a subset from \emph{Prasad et al}
{[}5{]}), and confirmed that the transporter expression ontogeny was
organ dependent.{[}9{]} For other major organs like the intestine,
pediatric protein expression data are lacking to date.{[}10{]}

The observations described above show that the transporter substrates
disposition is subject to age-related changes. This impacts the efficacy
and safety of pediatric drug therapy, which statement is supported by a
study concluding that hepatic OCT1 ontogeny partly explained the lower
clearance of its substrate morphine in neonates and infants compared to
adults.{[}11{]} For renal transporters, however, it is more complex to
delineate, as renal excretion of drugs is not only driven by active
tubular secretion by transporters but also by glomerular filtration,
which also changes with age. As children grow and develop, the
glomerular filtration rate (GFR) matures, reaching 50\% of adult values
by 2 months and 90\% of adult values by 1 year of age.{[}12{]} Thus,
observed age-related changes in pharmacokinetics (PK) of renal
transporter substrates are likely due to a combination of both
maturation in transporter expression and GFR.

\subsubsection*{Potential underlying mechanisms for transporter
ontogeny}

{\label{potential-underlying-mechanisms-for-transporter-ontogeny}}

It has been reported that there are differences in maturational patterns
between mRNA and protein transporter expression.{[}6, 8{]} This
interesting finding may aid to understand the underlying mechanisms of
these patterns. Alternative splicing, a process that increases the
diversity of products (splice variants) from a single gene, appears a
potential underlying mechanism for maturational changes in organic anion
transporter protein B1 (OATP1B1) expression, as the gene expression of a
fair amount of splice variants was associated with age.{[}13{]} The
total gene expression quantified by quantitative reverse transcription
polymerase chain reaction (qRT-PCR) could be derived from a mixture of
different splice variants of the targeted gene, also including splice
variants that may not translate into protein.{[}14{]} Quantitative
proteomics measures the actual expression of the protein of interest,
hence alternative splicing could partly explain the lack of correlation
between mRNA and protein expression, especially when a correlation is
found in adults but not in children or \emph{vice versa} .

More specifically, age-related variation in expression of solute carrier
organic anion transporter 1B1 (SLCO1B1) splice variants may have
implications for children, as most of these splice variants were
predicted to result in truncated isoforms of the OATP1B1
transporter.{[}13{]} The functionality of the transporter is thought to
be partly dependent on the number of transmembrane regions. As the
truncated isoforms have fewer transmembrane regions, this could affect
the functionality of the transporter in children, and with that the
disposition of its endogenous and exogenous substrates. Furthermore, the
localization of a transporter is a critical determinant of activity and
correct functionality, which is partly determined by post-translational
changes, such as glycosylation.{[}15{]} The truncated SLCO1B1 isoforms
may lack one or more of the N-glycosylation sites, which may lead to an
inappropriate localization as non-glycosylated OATP1B1 is retained
within the endoplasmic reticulum, e.g. not being present on the cell
membrane.{[}16{]}

Another interesting observation is that the expressions over age of
several transporters are strongly correlated.{[}6, 8{]} For example, the
expressions of renal transporters OAT1 and OAT3 were strongly
correlated.{[}8{]} As these expressions are impacted by age, the
underlying mechanism for ontogeny may be the same. OAT1 and OAT3 are
located in adjacent regions on chromosome 11.{[}17{]} Moreover, they are
both positively regulated by the transcription factors hepatocyte
nuclear factor (HNF) 1\selectlanguage{greek}α \selectlanguage{english}and 1\selectlanguage{english}\selectlanguage{greek}β.{[}18{]} We could speculate that HNF1\selectlanguage{greek}α
\selectlanguage{english}and 1\selectlanguage{greek}β \selectlanguage{english}are also impacted by age, giving rise to the developmental
patterns of OAT1 and OAT3. However, the relationship between
transcription factors maintaining basal expression level, like the HNF
family, and transporter expression, is still largely unknown, let alone
the impact of age on transcription factors.

\subsubsection*{Methods and study design to study ontogeny of drug
transport}

{\label{methods-and-study-design-to-study-ontogeny-of-drug-transport}}

Currently, there is a lack of harmonized practices in the implementation
of LC-MS/MS based proteomic quantification, leading to variability in
reported abundances of the same proteins.{[}19, 20{]} Using such data to
subsequently simulate PK may introduce unexpected bias, especially
when\emph{in vivo} PK data to validate the simulations are lacking. Not
only the analytical method may present differences, also the data may be
reported in different units. For example, it was found for hepatic
transporters that crude membrane yield per amount of tissue was higher
in fetuses than in term newborns.{[}6{]} Therefore, ontogeny patterns
may not be similar when describing transporter protein expression per
gram crude membrane instead of per gram of tissue. However, in
literature these units are used inconsistently, as consensus is lacking
on how to report proteomic data.

More specifically for renal transporters, sample preparation and data
interpretation are further complicated by the fact that the kidney
consists of cortex and medulla. Renal transporters are primarily located
in the kidney's proximal tubules, which are enriched in the cortex,
whereas the medulla is enriched with distal/collecting tubules. The
relative composition of cortex versus medulla in frozen tissue could
vary depending on sample collection and dissection, resulting in
confounded interpretation of ontogeny data. For adult renal
transporters, normalization to the housekeeping protein aquaporin 1,
located specifically in the proximal tubule, is often used. The
literature data on aquaporin 1 in children is conflicting, as aquaporin
1 displays a developmental pattern with 50\% of adult expression at
birth {[}21, 22{]}, restricting its use as a housekeeping protein in
pediatric studies. Interestingly, \emph{Li et al} recently studied
aquaporin 1 protein expression in 43 human samples from 0.5 to 35 years
of age and could not identify an age-related change in
expression.{[}9{]} They first used the ratio of this protein to
aquaporin 2, which is located in the distal/collecting tubules, to
exclude tissues that were contaminated with medulla (based on the
statistical Grubb's test to identify outliers). Thereafter, they
normalized the renal transporter expression to aquaporin 1 to study
ontogeny patterns.

\subsubsection*{Challenges and future perspectives for the ontogeny of
transporters}

{\label{challenges-and-future-perspectives-for-the-ontogeny-of-transporters}}

While the current studies significantly contributed to our understanding
on age-related changes in transporter expression, there are still future
perspectives to continue this work. First, although the current studies
cover the entire age range, the protein expression of renal transporters
in neonates is understudied, as the two available studies both only
included one neonate.{[}8, 9{]} In addition, the unexplained
interindividual variability in protein expression within age groups for
both renal and hepatic transporters is distinct.{[}6-9{]} In addition to
age, other potential factors, such as the use of co-medications and
inflammation, can influence the transporter expression and thereby
contribute to the expression variability.{[}23, 24{]} The impact of
acute and chronic inflammation on transporter expression and activity is
related to the activity of multiple pro-inflammatory cytokines that can
activate or inhibit involved nuclear receptors or transcription
factors.{[}25{]} The exact mechanism remains unknown. Similarly, certain
medications and environmental toxins could lead to activation of nuclear
receptor pathways, and could, therefore, influence the transporter
expression.{[}25, 26{]} The underlying causes of death of our tissue
donors are heterogeneous and so are the exposures to drugs and
environmental toxins. Lastly, using post-mortem tissue is faced with
challenges as the amounts of degradation in mRNA and protein levels from
death to freezing are not known. Degradation may vary among samples, and
may result in reduced absolute levels and increased variability in
expression level measurements. The interplay of age with these other
co-variates merits further elucidation.

Also genomic variation can impact mRNA and/or protein transporter
expression. For hepatic transporters one study was unable to identify
such a relationship in a cohort mainly focused on fetuses and
newborns{[}6{]}, although the selected genetic variants have been shown
earlier to affect mRNA and/or protein expression in adults or older
children. This finding may be explained by the small sample size, but
could also partly be explained by the interplay between development and
genetics. For example, in a previous study,
\emph{SLC22A1}181C\textgreater{}T in adult samples correlated with OCT1
protein expression{[}27{]}, but this was not confirmed in the pediatric
cohort{[}6{]}. The OCT1 expression was low in fetuses, potentially
obscuring a possible effect of genetic variants. Interestingly, on the
other hand, \emph{SLC22A1} genotype (giving rise to the OCT1
transporter) is related to tramadol disposition in preterm infants,
similar to adults.{[}28{]} This suggests that, although protein levels
are low, the \emph{SLC22A1} genotype can result in significant
differences in protein activity in neonates. In the cohort of \emph{van
Groen et al} OATP1B1 protein expression was stable within
\emph{SLCO1B1}diplotypes.{[}6{]} In contrast, \emph{Prasad et al.}
showed higher protein expression in neonates versus older
children/adults with the\emph{SLCO1B1} *1A/*1A haplotype.{[}5{]} Thus,
it remains important to include genotype when analyzing developmental
patterns. Furthermore, the interaction between genetic variants and age
should be further studied.

This review focuses on the kidney and liver, but transporters are also
abundant in other major organs, such as the gastro-intestinal tract, and
sanctuary sites including the brain.{[}29{]} The same holds for drug
metabolizing enzymes. Developmental patterns of transporter and drug
metabolizing enzyme isoforms appear to be organ-dependent.{[}9{]} Hence,
an understanding of the ontogeny of multiple transporters, drug
metabolizing enzymes and tissues in parallel would provide a more
holistic view on entire human development.{[}30{]}

To cope with the lack of consensus for conducting proteomic studies, a
recently published white paper discussed current practices and provided
recommendations towards harmonization of practices.{[}31{]} More
specifically, recommendations were given on specific proteomic
quantification techniques, sample preparation and quality controls, as
well as documenting of tissue weight and the originating organ section.
For further method development, housekeeping proteins should be further
validated to normalize for technical variability. Hence, to enable
comparison and combining data originating from various laboratories, it
is advised to follow these and future recommendations.

Importantly, the impact of a transporter on the disposition of a
substrate is determined by its functional activity. It is widely
accepted to use mRNA and protein expression data as a surrogate
parameter for the functional activity of a transporter.{[}2{]} However,
there may be discrepancies between expression and activity, as already
introduced before. Therefore, \emph{ex vivo} ontogeny should preferably
be validated by \emph{in vivo} data. Earlier, tazobactam PK data were
used to validate the ontogeny profiles of the renal transporters
OAT1/3.{[}10{]} Another approach to estimate transporter maturation is
to distract age-specific GFR estimations from reported total renal
clearance values of a specific renal tubular transporter
substrate.\emph{Willmann et al} . determined the renal tubular MDR1
maturation by distracting estimated GFR from digoxin total renal
clearance, a MDR1 substrate.{[}32{]} Next, for the MDR1 substrate
rivaroxaban plasma concentrations were simulated over the pediatric age
range. The assumption was made that MDR1 transport is the rate-limiting
factor in the tubular secretion of both digoxin and rivaroxaban. These
examples aid in creating confidence to incorporate the \emph{ex vivo}
data for predicting pediatric PK.

\subsection*{\texorpdfstring{\emph{In vivo} pediatric microdosing and
microtracing
studies}{In vivo pediatric microdosing and microtracing studies}}

{\label{in-vivo-pediatric-microdosing-and-microtracing-studies}}

\subsubsection*{Oral bioavailability}

{\label{oral-bioavailability}}

Validated markers to phenotype a drug metabolism route can be used to
study developmental changes of drug metabolizing enzymes \emph{in vivo}
. Most drugs are administered to children orally{[}33{]}, hence the oral
bioavailability is an important determinant for systemic exposure. The
traditional study design to obtain data on oral bioavailability entails
a cross-over study design where an oral or intravenous (IV) dose of a
drug are administered on two separate occasions in random order in the
same individual, with a wash-out period in between. This design is
ethically and practically challenging, as children need to be exposed
twice to therapeutic doses with extensive blood sampling, without any
benefit for themselves. As international ethical guidelines prohibit
studies that may not provide benefit to children and that pose more than
minimal risk and burden over those daily encountered, alternative
approaches are needed. To overcome challenges faced with such pediatric
PK studies, microdosing/microtracing studies with
{[}\textsuperscript{14}C{]}labelled substrates constitute an interesting
alternative. A microdose is a very small, sub-therapeutic dose of a drug
(\textless{}1/100\textsuperscript{th} of the therapeutic dose or
\textless{}100 \selectlanguage{greek}µ\selectlanguage{english}g), which is unlikely to result in pharmacological
effects or adverse events.{[}34, 35{]} A radioactive label
{[}\textsuperscript{14}C{]} allows ultra-sensitive quantification of
extremely low plasma-concentrations by accelerator mass spectrometry
(AMS), for which only 10-15 \selectlanguage{greek}µ\selectlanguage{english}l plasma is required.{[}36, 37{]} The
radiation dose associated with a {[}\textsuperscript{14}C{]}microdose is
safe as it is below 1 \selectlanguage{greek}μ\selectlanguage{english}Sievert. This is much lower than the yearly
background exposure (2.5 mSievert/year in the Netherlands), a computed
tomography (CT)-scan of the head (1200 \selectlanguage{greek}μ\selectlanguage{english}Sievert), or a chest x-ray (12
\selectlanguage{greek}μ\selectlanguage{english}Sievert).{[}38{]} Given these considerations, microdosing studies are
considered as non-therapeutic trials with minimal burden that carry
minimal risk.{[}39{]}

Interestingly, to assess the oral bioavailability, a microdose can be
concurrently administered with a therapeutic dose, where the microdose
is then called a microtracer. This elegant and innovative study design
enables measurement of both intravenous (IV) and oral disposition,
allowing to determine the oral bioavailability of a drug, limiting the
extensive blood sampling needed for a cross-over study and limiting the
day-to-day variability.{[}40, 41{]} There are two pediatric studies
reported using this study design. First, age related changes in oral
bioavailability of paracetamol (acetaminophen or AAP) as a measure of
glucuronidation and sulfation was studied in stable, critically ill
children, who were receiving IV therapeutic paracetamol for clinical
purposes and had an indwelling arterial catheter enabling blood sampling
in place.{[}40{]} Fifty children (median age 6 months {[}range 3
days--6.9 years{]}) received an oral {[}\textsuperscript{14}C{]}AAP
microtracer (3.3 {[}2.0--3.5{]} ng/kg; 64 {[}41--71{]} Bq/kg). They
showed that, with increasing age, plasma and urinary AAP-glucuronide/AAP
and AAP-glucuronide/AAP-sulfation ratios significantly increased by four
fold, while the AAP-sulfation/AAP ratio significantly decreased. In
other words, this showed that a developmental change of AAP metabolism
from mainly sulfation in neonates to glucuronidation in older
children.{[}40{]} The mean enteral bioavailability of AAP in this
population was 72\% (range, 11--91\%).{[}41{]}

The second study used the benzodiazepine midazolam as a well-validated
cytochrome P450 (CYP) 3A probe to unravel the ontogeny of CYP3A that is
abundant in both the intestine and liver and contributes to the
first-pass metabolism of many orally administered drugs.{[}1, 42, 43{]}
Forty-six stable, critically ill children (median age 9.8 {[}range 0.3
-- 276.4{]} weeks; three quarters of the 46 subjects were 0-6 months
old) received a single oral {[}\textsuperscript{14}C{]}midazolam
microtracer (58 {[}40-67{]} Bq/kg).{[}44{]} The bioavailability of
midazolam was 66\% with a large range of 25-85\% for which no
explanatory covariates could be identified. These findings were in line
with the expected CYP3A ontogeny where older children and adults are
thought to have a higher CYP3A activity and with that a lower oral
bioavailability{[}43{]} than the population in the microtracer study,
and \emph{vice versa} for preterm neonates. The median of 66\%{[}44{]}
was lower than the reported median value of 92\% in preterm
neonates{[}45{]} and higher than the reported median value of 21\% in
older children of 1-18 years.{[}46{]} Also the reported mean\selectlanguage{ngerman}±SD of
28±7\% in adults is lower.{[}47{]} The oral bioavailability was also
highly variable in previous pediatric studies. In preterm neonates, the
oral bioavailability ranged from 67 to 95\%{[}45{]} and from 12 to
100\%{[}48{]}, and in children 1-18 years from 2 to 78\%.{[}46{]} Other
factors than age may also influence the oral bioavailability of
midazolam. A previous study from \emph{Vet and Brussee et al.} found a
significant impact of organ failure on the midazolam clearance{[}23{]},
with the greatest impact in children with \selectlanguage{english}[?]3 failing organs, and
inflammation as reflected by CRP.{[}23, 49{]}

Also, besides the oral bioavailability of both AAP and midazolam is
impacted by drug absorption processes like intestinal surface
area{[}50{]}, permeability{[}51{]}, gastric emptying time, intestinal
transit time, the production of bile fluid{[}52{]} and organ blood flow
to the intestines and liver{[}53{]}. The influences of age and
(critical) illness on these processes should be studied to further
explain the variability in oral bioavailability of AAP and midazolam.

The study design with an oral {[}\textsuperscript{14}C{]}microtracer was
shown successful for safely studying the oral bioavailability of AAP and
midazolam in children. To ultimately improve the safety and efficacy of
pediatric drug therapy, we recommend to consider study designs with
microdoses for minimal risk PK studies and microtracer studies to
elucidate oral bioavailability.

\subsubsection*{Dose linearity of a
microdose}

{\label{dose-linearity-of-a-microdose}}

A prerequisite of extrapolating the PK of microdose directly to a
therapeutic dose, is that the PK of a microdose of a certain
drug/compound is linear to the PK of a therapeutic dose.{[}54, 55{]}
Lack of linearity may occur, for example, when a therapeutic dose
saturates drug metabolism pathways, plasma protein binding and/or active
transporters.{[}55, 56{]} Because of the maturational differences in
drug metabolism and transport, results from adults should not be
extrapolated directly to the pediatric population. A very elegant
approach was taken to study the dose-linearity of a
{[}\textsuperscript{14}C{]}midazolam microdose {[}57{]}; the PK
parameters of an isolated {[}\textsuperscript{14}C{]}microdose were
compared with the PK parameters of a
{[}\textsuperscript{14}C{]}microtracer administered concurrently or even
mixed with a therapeutic drug dose. This study supported dose-linearity
of the PK of the isolated {[}\textsuperscript{14}C{]}midazolam microdose
to the PK of the {[}\textsuperscript{14}C{]}midazolam microtracer.

\subsubsection*{\texorpdfstring{{[}\textsuperscript{14}C{]}microtracer
study to create metabolite profiles of
midazolam}{{[}14C{]}microtracer study to create metabolite profiles of midazolam}}

{\label{cmicrotracer-study-to-create-metabolite-profiles-of-midazolam}}

In children, the rate of a metabolic route may be different from that in
adults due to underdevelopment of a certain pathway, but they may even
require alternative metabolic routes. In the latter case, other
metabolites may be present that are not identified during drug
development in adults. This could lead to unsafe drug therapy as
children may be exposed to a metabolite of which the action and PK are
unknown. Interestingly, by using
{[}\textsuperscript{14}C{]}microtracers, a drug can be concurrently
administrated with a therapeutic dose, and therefore metabolites can be
identified and quantified with a radioactivity exposure of even less
than 0.1 \selectlanguage{greek}µ\selectlanguage{english}Ci.{[}34, 35{]} These analytical advances allow us to overcome
ethical and analytical challenges with regard to radioactivity exposure
in pediatrics.{[}58, 59{]} A proof of concept study with a
{[}\textsuperscript{14}C{]}midazolam microtracer showed that in children
metabolite profiles of midazolam were safely created and the routes of
excretion were safely studied.{[}60{]} This approach is promising for
first-in-child studies to delineate age-related variation in drug
metabolite profiles.

\subsubsection*{Feasibility}

{\label{feasibility}}

Earlier studies have shown that it is feasible to use a
{[}\textsuperscript{14}C{]}labelled substrate to phenotype a certain
drug metabolism pathway {[}40, 41, 60-63{]}; not only were the PK
findings in line with expectations, but importantly, parents allowed
their children to participate in these studies with a consent rate of
around 50\%. This informed consent rate is in agreement with the consent
rate of other non-therapeutic studies in pediatric intensive
care.{[}64{]} Most often, professionals express ethical concerns
regarding the radioactive exposure, including the expectation that
parents will not allow their children to participate in such studies.
The authors state that the experience during conversations with parents
was that consent was not necessarily refused due to the radioactivity
exposure as they had received a good understanding of the negligible
exposure compared with the yearly background exposure. The reason for
refusing consent was more often the burden of procedures in addition to
the clinical care. This is not surprising, as for clinical reasons they
may have to undergo a number of painful and stressful procedures (median
11 {[}IQR = 5--23{]} per day established in a previous study{[}65{]}),
which has impact on the parents as well.

\subsubsection*{Challenges and perspectives for pediatric microdosing and
microtracing
studies}

{\label{challenges-and-perspectives-for-pediatric-microdosing-and-microtracing-studies}}

Over the past decade, regulatory legislations for drug development in
pediatric patients have been passed worldwide. These regulations
dramatically increased the number of drugs tested in children. In
2014-2015, the U.S. Food and Drug Administration (FDA) reviewed 274
pediatric study plans, whereas in 2012-2013 the number was only
20.{[}66{]} This trend was also seen in the EU, with 31 new
drugs/indications authorized for use in children in 2004-2006, versus 86
in 2012-2016.{[}67{]} In spite of this impressive increase, still a
large amount of drugs enter the market without being licensed for
pediatric use. Since the entry of the Pediatric Regulation and up to 31
December 2015, eighty-nine new medicines were centrally authorized for
pediatric use, which was only 26\% out of all 352 new medicines.{[}67{]}
This is partly because many pediatric studies failed{[}68{]}, but also
due to limitations of this regulation: when the prediction of the risk
of toxicity of a new compound is not adequate, the regulation allows
European Medicines Agency (EMA) and U.S. Food and Drug Administration
(FDA) to waive the studies in the youngest children.{[}69{]}~Yet, in
clinical practice, the drug will be~prescribed off-label~with
its~inherent risks. Although off-label is not off-evidence, as pediatric
PK data for existing drugs are increasingly available in literature,
there are still huge knowledge gaps. Especially for drugs under
development, a microdosing study could be of benefit by studying the PK
and the metabolite profile, respectively, of a new compound in children
without risk of toxicity. These studies are considered as
non-therapeutic trials with minimal burden that carry minimal risk,
which is echoed by the Dutch legislation, EU regulation and the U.S.
FDA.{[}70{]}

However, despite positive experiences with microdose studies,
professionals may still resist using this approach in children or other
vulnerable populations. Doses are rather derived from physiologically
based pharmacokinetic (PBPK) models (see paragraph `Using PBPK models to
predict pediatric drug exposure and its challenges') or allometry, but
these approaches are suboptimal as the biological data underlying the
PBPK models for~special populations still show large knowledge gaps
despite the literature data and new data presented in this thesis. This
leaves these populations at a higher risk of toxicity or therapeutic
failure. Therefore, microdosing studies may be of great value in
pediatric drug development and are now suggested in the 2019, FDA
(draft) Guidance: General Clinical Pharmacology Considerations for
Neonatal Studies for Drugs and Biological Products Guidance for
Industry.{[}71{]} Lastly, these approaches would also allow studying the
PK or metabolite profiles in other vulnerable special populations like
pregnant women, critically ill or elderly.

\subsection*{Study design, sample sizes, data
sharing}

{\label{study-design-sample-sizes-data-sharing}}

Combining samples from various laboratories or research groups is
interesting for the pediatric population, as tissues for \emph{ex
vivo}studies are scarce and sample sizes for \emph{in vivo} studies are
small. Moreover, pooling samples beforehand avoids lab-to-lab
variability and likely result in more reliable and complete results,
hence research groups should collaborate rather than compete. Also, to
further accelerate data generation, international biobanks would be of
added value to overcome the scarcity of pediatric tissue.

In addition, similar collaborative efforts should be taken to overcome
challenges in recruitment of patients for \emph{in vivo} studies due to
low number of eligible patients, suboptimal pediatric trial
infrastructure and study design challenges. This often results in a long
recruitment period or even failure to fulfill recruitment targets.
Pediatric trial networks may overcome these challenges by providing a
framework to facilitate collaboration and combine resources and
expertise to conduct and manage studies. Examples are the IMI2 project
to develop a European wide Paediatric Clinical Trial Network,
Conect4Children for Europe and, the Pediatric Clinical Trials Network
and iACT both in the U.S. Existing government-supported research
infrastructures in Europe, such as BBMRI (biobanking), ELIXIR (data),
EATRIS (drug development) and ECRIN (clinical trials), which have
traditionally focused on adults, should collaborate to integrate
pediatrics, with the ethical and practical specificities outlined
throughout this review. An important side note: these pediatric trial
networks and infrastructures may create new challenges, such as
accounting for differences in clinical practice between countries and
hospitals, and the extensive time and discussion needed to reach
consensus about study designs. Pharmaceutical companies, academia and
regulatory institutes work together in these initiatives, but their
scientific goals/aims and procedures for pediatric trials may differ.

In terms of data availability, initiatives for data sharing
platforms{[}20{]} are encouraged in which all raw data of published
articles are made freely available. Currently, certain scientific
journals have made it mandatory for authors to submit the raw dataset
along with the manuscript{[}72{]}, which is a positive advancement.

\subsection*{Predicting pediatric drug exposure and dosing
recommendations}

{\label{predicting-pediatric-drug-exposure-and-dosing-recommendations}}

\subsubsection*{Using PBPK models to predict pediatric drug exposure and
its
challenges}

{\label{using-pbpk-models-to-predict-pediatric-drug-exposure-and-its-challenges}}

Protein and gene expression data on drug metabolism and drug transport
may be leveraged in PBPK models alongside with pediatric physiology.
These models are complex multi-compartment kinetic models that allow
prediction of drug exposure in a specific target population. These
models rely on \emph{in vitro} data and physiological parameters and can
subsequently be explored based on \emph{in vivo} data. PBPK models are
also increasingly used in drug development to optimize clinical trial
design and doses, also for pediatric trials.{[}73{]} Fifteen percent of
all new drug application submissions to the FDA between 2008 and 2017
that included PBPK analyses supported the evaluation of
pediatric-related issues such as initial dose recommendation for
clinical trials.{[}74{]} Several pediatric clinical studies have been
replaced with or informed by PBPK modeling.{[}75, 76{]} For example,
models have been used to set a starting dose in a clinical trial with
eribulin in children and adolescents 6-18 years of age, and to bridge
from immediate to extended release quetiapine formulations in children
and adolescents 10-17 years of age.{[}75, 77{]}

Another area where PBPK modeling could be helpful is for drug-drug
interactions (DDIs). Hospitalized children may be exposed to up to 10
different drugs.{[}78{]} With that, DDIs are inevitable, for which
recommendations from adult DDI studies are often extrapolated to the
pediatric population. In a systematic literature review, the numbers of
interactions for 24 drug pairs were compared between 31 pediatric
studies and 33 adult studies. The number of DDIs differed: in 15 of the
33 cases, the fold number of DDIs were higher (\textgreater{}1.25- fold)
for children than for adults, and in 8 of the 33 cases, the fold number
of DDIs were lower (\textless{}0.8- fold) in children than in adults.
For example, digoxin plus amiodarone and lamotrigine plus valproate
resulted in a 2.18- fold higher and a 0.58- fold lower exposure,
respectively, in children compared with adults due to the underlying
maturation of processes involved in drug disposition. These findings
warn us that simple extrapolation of adult DDI studies to the pediatric
population can under- of overpredict the impact of a DDI with
subtherapeutic or toxic exposure to a drug as a result. PBPK modeling is
a powerful tool to explore and quantitatively predict DDIs, also in a
pediatric population.{[}75{]}

The use of PBPK models to predict pediatric drug exposure comes with
important limitations, though. The gene expression, protein expression
and/or activity of some genes involved in drug disposition are
correlated, and for these cases expression data are used in PBPK models
as a surrogate for determining activity. However, these correlations are
most often shown in adult populations. A previous study found that the
fraction of highly glycosylated OATP1B3 increased with age.{[}79{]}
Because post-transcriptional and post-translational modifications like
glycosylation of transporters{[}79{]} may be subject to age-related
changes, it could well be that gene and protein expression are
correlated in adults but not in children. Hence, these correlations
should not be extrapolated directly from adults to children as this
could lead to inaccurate predictions of drug disposition. This also
accounts for the fraction of a parent drug that is metabolized or
transported by a certain protein{[}80{]}, which often is assumed to be
the same for children and adults. Yet, the maturation of enzymes and
transporters may change their relative contributions in the
disposition.{[}80, 81{]}

In the end, the success of PBPK simulation in children depends highly on
knowledge of the drug disposition pathways and the availability
of\emph{ex vivo/in vitro} data. As knowledge gaps remain next to the new
available data, routine use of PBPK modeling in prediction of pediatric
drug disposition should be done carefully until these knowledge gaps are
filled. Nevertheless, PBPK modeling currently helps us understand drug
metabolism and transport pathways, and the impact of a change in a
certain pathway (e.g. maturation).

\subsubsection*{Dosing recommendations in clinical
practice}

{\label{dosing-recommendations-in-clinical-practice}}

The increase in pediatric research has helped enormously in
understanding how PK of a variety of drugs is different in various age
groups, giving rise to pediatric-specific dosing recommendations. But to
implement this knowledge in clinical practice comes with challenges.
First, prescribers may remain unaware of dosing recommendations
published in scientific literature or lack understanding and trust in PK
studies and their subsequent dosing simulations. The Dutch Knowledge
Center on Pharmacotherapy in Children (NKFK) hereby serves as an example
on how this knowledge translation gap can be bridged, as it has
developed a pediatric drug formulary based on best available evidence
from registration data, investigator-initiated research, professional
guidelines, and clinical experience.{[}82{]} That this approach is
valuable is shown by the recent extension of the formulary to
country-specific editions in Germany, Austria and Norway. Second, the
current pediatric dosing recommendations are often only based on PK data
assuming that the target concentration for effect is the same in
children and adults. Yet, these target concentrations may well be
different in children as PD could also be subject to age-related
changes.{[}83{]} These dosing recommendations should therefore
preferably be validated prospectively, especially when they are only
based on PK data. Depending on the confidence of the dosing
recommendation (e.g. the underlying data), this can be done in clinical
practice with opportunistic PK sampling or with clinical data collection
of PD parameters of specific interest. Or, when the confidence is low,
in a pediatric trial where the former dosing regimen can be compared to
the new dosing regimen based on PK and PD parameters.

Using population PK and PBPK models to simulate and predict the optimal
drug exposure may lead to complex dosing regimens and consequently
logistical problems in clinical practice. In hospital, for example,
nurses are used to administer drugs to patients at fixed times. Also for
patients outside the hospital, therapy compliance may be negatively
affected if they have to take drugs at varying times. Moreover, some
doses are not feasible to administer, for example 1/8 of a tablet is
hard to prepare, and pediatric formulations are missing.{[}84{]}
Furthermore, the electronic health care systems are limited in terms of
integrating complex models where covariates are included to provide the
best dosing regimens, followed by integrating this in clinical
care.{[}85, 86{]} Lastly, clinical staff may have little understanding
of complex population PK models, and may lack trust in these dosing
regimens. Nevertheless, efforts are being made to accelerate
implementation, such as the calculator of the Dutch Pediatric Formulary
that allows clinical staff to include covariates to get the optimal
dose. Furthermore, startups create their own electronic systems to
integrate complex dosing regimens that can be used in clinical care
parallel to the electronic health care system.{[}86{]}

\subsection*{Other innovative
techniques/approaches}

{\label{other-innovative-techniquesapproaches}}

Regarding \emph{ex vivo} studies, opportunities exist for unraveling the
ontogeny of drug disposition on the level of protein/mRNA expression in
`fresh' tissue instead of post-mortem tissue. These include the use of
organoids that represent three-dimensional culture systems in which stem
cells grow and represent the native physiology of the cells \emph{in
vivo.} {[}87{]} When using tissue-derived hepatic, renal or intestinal
stem cells from children, the organoid may reflect the pediatric native
physiology and allow to study the expression of transports or drug
metabolizing enzymes. Organoids can also be used to unravel regulatory
pathways. Two-dimensional models of e.g. intestinal organoids may
provide opportunity to study drug metabolism and transporter activity in
a situation that even closer resembles the \emph{in vivo} situation.
Another innovative approach could be the use of exosomes, which are
circulating extracellular vesicles secreted by organs.{[}88{]} These
exosomes are present in the blood and contain proteins, mRNA and
microRNA derived from the originating organ and can be used to measure
transporter and drug metabolizing enzyme expression.{[}88{]} It is not
known, however, whether organoids and exosomes keep their age-specific
properties outside a human body, which is a prerequisite for studying
age-related changes in expression/activity of transport and metabolism.

Another approach involves \emph{in vivo} assessment of drug metabolizing
enzyme or transport activity through endogenous substrates as
biomarkers, for example 6b-hydroxycortisol or 4b-hydroxycholesterol for
assessment of CYP3A activity.{[}89{]} Interestingly, studies in adults
have identified endogenous substrates as potential markers to phenotype
the activity of transporters \emph{in vivo} , for example thiamine for
OCT1 and dehydroepiandrosterone sulfate (DHEAS) for OATP1B1/3.{[}89{]}
This approach does not require administration of an external marker and
the levels could be measured with a single rather than multiple blood
draws, thereby overcoming one of the challenges in pediatric research.
This approach is not used often yet, as reference values of endogenous
substrate levels in children are lacking. These values cannot be simply
extrapolated from values in adults, as homeostatic levels in children
may differ from those in adults. For example, DHEAS levels at birth are
high and decrease drastically over the first month of life, followed by
a more progressively decrease until the first 6\textsuperscript{th}month
of life.{[}90{]} Hence, specific reference values for these endogenous
substrates for various age groups should be gained first.

Global metabolomics studies can help identify biomarkers for enzymes and
transporters, also in a pediatric population. This approach was taken
by\emph{Tay-Sontheimer et al} who identified a urinary biomarker to
phenotype CYP2D6 activity.{[}91{]} Although that specific biomarker was
not structurally identified, this example shows that metabolomic methods
are useful in revealing biomarkers in children.

\subsection*{Conclusion}

{\label{conclusion}}

To conclude, our understanding of ontogeny of drug metabolism and
transport in children has increased, supported by e.g. the expansion of
knowledge in the ontogeny of transporters, especially those in the liver
and kidney. This increased knowledge has significant implications for
understanding drug disposition of substrates in the younger age groups.
Pediatric {[}\textsuperscript{14}C{]}labeled microdosing and
microtracing studies have been shown useful to study the PK of drugs
used in children, like AAP and midazolam. Moreover, PBPK modeling helps
us understand drug metabolism and transport pathways, and the impact of
a change in a certain pathway but should be done carefully until
knowledge gaps on ontogeny of drug disposition are filled. These results
are of importance for pediatric drug development and current practice
with the ultimate aim to improve pediatric drug therapy.

\section*{Acknowledgements}

{\label{acknowledgements}}

The authors thank K. Hagoort for editorial assistance.

\section*{Conflict of interest
statement}

{\label{conflict-of-interest-statement}}

Nothing to declare.

\section*{Funding information}

{\label{funding-information}}

No funding was received for this review.

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\section*{Figure legends}

{\label{figure-legends}}

Figure 1: An overview of innovative approaches to gather new data on
ontogeny of drug metabolism and transport, and their results and
implementation. These approaches are discussed in this review, and are
supplemented with suggestions for future approaches.

\emph{Footnote: PK=pharmacokinetics, PBPK=physiologically based
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