Content uploaded by Bernd Meibohm
Author content
All content in this area was uploaded by Bernd Meibohm on Sep 04, 2014
Content may be subject to copyright.
state art
nature publishing group
40 VOLUME 92 NUMBER 1 | JULY 2012 | www.nature.com/cpt
Pediatric drug development and pharmacotherapy represent new
frontiers for clinical pharmacology. In the past decade, pediatric
drug development, which occurs in the scenarios summarized in
Tabl e 1, has received much attention and support through regu-
latory incentives and requirements in the United States and the
European Union, as well as from an increasing societal aware-
ness of the necessity of performing clinical research in pediatric
patients. Nevertheless, drug research involving pediatric indica-
tions and drug dosing optimization in pediatric clinical practice
remains challenging because of the very nature of conducting
research in pediatric patients, especially in infants and children,
who are considered a vulnerable population.
Although there are many opportunities for applying physiologi-
cally based pharmacokinetic (PBPK) modeling in pediatric clini-
cal pharmacology, there is a lingering concern and skepticism in
the pediatric community about the robustness and reliability of
PBPK-based prediction of drug exposure and ultimately about
ecacy and toxicity, in pediatric patients. is article not only
reviews some of the potential benets and utility of PBPK mod-
eling in pediatrics but also critically discusses current limitations
and deciencies that have so far dampened the condence in this
approach and impeded its more widespread application.
WHY IS PBPK MODELING SUITED TO THE CURRENT
NEEDS IN PEDIATRIC DRUG DEVELOPMENT AND
PHARMACOTHERAPY?
Clinical research in pediatric patients is hampered by inter-
related logistical as well as ethical constraints, for example,
the limited number, extent, and invasiveness of study-related
interventions that can be performed if they are not a part of a
routine therapeutic plan. us, clinical studies in pediatrics are
oen limited with respect to the number of enrolled patients and
treatment arms and in the availability of appropriate primary
and secondary outcome measures.
1
Given that it is inherently more dicult to conduct clinical
studies in infants and children than in adults, pediatric doses of
medicinal products have traditionally been scaled from adult
doses, using functions related to body weight, height, or age.
Such simple allometric approaches may be questionable when
complex absorption and disposition processes are encountered
and can fail to predict exposure accurately, particularly in the
very young.
2
When the adult dose is normalized by body weight
(i.e., mg/kg), under the assumption of a linear relationship
between weight and dose, it indicates that the dose required
will double with a twofold increase in the weight of a child.
When dose adjustment is based on age (i.e., preterm newborns,
term newborns, infants, toddlers, children, and adolescents),
the rapid changes in rates of organ maturation, blood ow, body
composition, and ontogeny of drug elimination and transport
mechanisms occurring in developing children within each age
group are not taken into account.
3
Scaling by body surface
area has been reported to lead to overdosing in neonates and
infants, because of either the inaccuracy of the formula or the
unpredictability of low drug metabolizing enzyme activity at
birth.
2
Moreover, the change in PK parameters across pediat-
ric age strata does not change proportionally with body surface
The last three authors contributed equally to this work.
1
Department of Pediatrics, Division of Clinical Pharmacology & Therapeutics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA;
2
Scientific
Direction, Clinical Pharmacology and Clinical Trials Unit, IRCCS Giannina Gaslini Institute, The Children’s Hospital of Genoa, Genoa, Italy;
3
Department of Clinical
Pharmacy and Pharmacotherapy, Heinrich-Heine University, Düsseldorf, Germany;
4
Department of Pharmaceutical Sciences, College of Pharmacy, University of
Tennessee Health Science Center, Memphis, Tennessee, USA. Correspondence: O Della Casa Alberighi (ornelladellacasa@ospedale-gaslini.ge.it)
Received 18 January 2012; accepted 9 April 2012; advance online publication 6 June 2012. doi:10.1038/clpt.2012.64
Physiologically Based Pharmacokinetic (PBPK)
Modeling in Children
JS Barrett
1
, O Della Casa Alberighi
2
, S Läer
3
and B Meibohm
4
This review summarizes the present status of physiologically based pharmacokinetic (PBPK) modeling and simulation
(M&S) and its application in support of pediatric drug research. We address the reasons that PBPK is suited to the current
needs of pediatric drug development and pharmacotherapy in light of the evolution in pediatric PBPK methodologies
and approaches, which were originally developed for the purpose of toxicologic evaluation. Also discussed is the current
degree of confidence in using PBPK to support pediatric drug development and registration and the key factors essential
for robust results and broader adoption of pediatric PBPK M&S.
state artstate art
CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 92 NUMBER 1 | JULY 2012 41
area because it is not a descriptor of metabolic function.
4
On
the other hand, no dierences in drug metabolism may exist
between adolescents and adults.
5
To overcome some of these limitations, the increased use of
pharmacokinetic (PK) and pharmacodynamic (PD) modeling
and simulation (M&S) techniques in pediatric drug develop-
ment and applied pharmacotherapy has been promoted by vari-
ous groups in academia,
6
industr y,
7
and regulatory agencies.
8–10
Two basic areas of M&S application have emerged in recent years
in pediatric clinical pharmacology research. One is an a priori
application of M&S techniques to optimize trial designs, select
dose level and dosing regimens to be studied, develop sampling
schemes, and select outcome measures. is application of M&S
is frequently based on adult data and uses various extrapola-
tion approaches that incorporate PK and PD data from preclini-
cal species, in vitro experiments, and pediatric data from pilot
studies or studies in older pediatric groups.
6,11,12
e second
emerging areas is an a posteriori application of M&S techniques
that has been applied even more frequently to analyze PK and
PK–PD data from pediatric studies, especially using population-
based approaches to derive maximum information content from
the limited data collected in pediatric studies and to establish
a consistent framework that allows the exploration of model-
based dosing recommendations.
13,14
PBPK and PBPK–PD M&S have recently gained increasing
popularity in drug development.
15,16
is has been driven by
major advances in the tools available for performing PBPK
analyses and by a substantial increase in the level of sophis-
tication and accuracy in the underlying physiologic data that
are now available.
16
PBPK and PBPK–PD modeling exemplify
“bottom-up” modeling in systems biology in that specic avail-
able observations, elements, and patterns are combined to form
larger subsystems, which then are linked at many levels to form
a complete multiscale model (see schema in Figure 1).
16
ese
subsystems integrate patient-specic parameters related to
anatomy, physiology, and pathophysiology with drug-specic
properties, such as physicochemical characteristics, metabolic
proles, and pharmacogenomic data. For pediatrics, PBPK mod-
eling also oers a unique opportunity to integrate known matu-
ration trajectories (functions that describe the time course of
organ development until adult physiological status is achieved)
for processes relevant to drug disposition in a consistent frame-
work to better predict drug behavior in various age groups.
17
Such an approach is direly needed—especially in neonates, who
are being delivered at an ever-increasing stage of prematurity
and for whom there is a particular scarcity of drug disposition
data that has been forcing pediatric practitioners to base phar-
macotherapy on empiricism rather than on a scientic ration-
ale.
6,18
Although these PBPK models integrate a wide variety of
biological factors to predict drug PK and thus provide a rational
support for pediatric dose nding, the challenge is to identify
and characterize the ontogeny of the relevant PK and PD proc-
esses that will enable the behavior of a drug to be predicted
across the wide age and physiological development spectrum
of neonates, infants, and children.
19
CONSTRUCTION OF PEDIATRIC PBPK MODELS
As with the traditional scaling approach based on adult data,
the PBPK model provides a computational engine for predict-
ing dose–exposure relationships in children but with a more
physiologically based model.
20
Table 2 provides a template
for the inputs and outputs needed for a typical PBPK model.
Schematically, a PBPK model is a multicompartment model
in which the compartments represent actual organs and other
physiological spaces (Figure 1). Mass balance equations for
each organ describe drug appearance in the organ from arte-
rial blood and its exit into venous blood. e PBPK model is
also constructed to incorporate relevant physiological, phar-
macogenetic, biochemical, and thermodynamic parameters in
a way that organizes much of the knowledge of the drug–body
system.
16
us, PBPK models are more comprehensive than
the empirical models used to analyze routine PK data because
they not only incorporate drug properties but also are built on
a system-specic structure that is independent of the drug.
16,21
e model parameters need to include physiological and drug-
specic parameters, in vitro predictions for distribution and
elimination, and, perhaps, results from in vivo studies in adult
animals.
22,23
e common approach in developing a pediatric
PBPK model is to modify a PBPK model that has been validated
with adult PK data and then to incorporate the dierences in
growth and maturation that can aect all relevant aspects of
drug disposition and PD.
Unique aspects of pediatric PBPK modeling
Human development is characterized by maturation and growth
that aect a child’s morphology, physiology, pathology, and psy-
chology. Maturation and growth oen seem to be a continuum,
but they consist of highly complex sequences of a variety of
concurrent maturation processes from all body compartments,
organs, and interconnected molecular networks that all follow
dierent temporal developmental trajectories. To build a physi-
ologically based model describing PK processes, all developmen-
tal changes aecting drug absorption, distribution, metabolism,
and elimination (ADME) need to be addressed (Tabl e 3). All
of these factors need to be integrated into and qualitatively and
quantitatively assessed in pediatric PBPK models.
Table 1 Scenarios for pediatric drug development
1. Drug has established efficacy and safety in adults, and indication/
disease is similar in adults and children
2. Drug has established efficacy and safety in adults, but indication/
disease has different features or mainly affects children
3. Drug is newly developed for indication/disease that only occurs in
children
4. Drug is newly developed for similar indication/disease in both children
and adults
Scenario 1 could be coupled with scenario 2. Taking the example of epilepsy as a
disease, it includes the following indications: partial-onset seizures and Lennox-
Gastaut syndrome, which are similar in adults and children (scenario 1), allowing
treatment effects to be extrapolated from adults to children, and the infantile
epilepsies, which are specific to children, in which it is not possible to extrapolate
treatment effects including adverse events and PK/PD from adults to children, but
there is a possible model-based extrapolation for PK (scenario 2).
PD, pharmacodynamic; PK, pharmacokinetic.
state artstate art
42 VOLUME 92 NUMBER 1 | JULY 2012 | www.nature.com/cpt
Several groups have developed PBPK models that integrate
maturation and growth processes.
11,12,20,25–27
ese research-
ers then used data sets from the literature or from their own
investigations to validate the performance of their models across
dierent age groups. Model evaluation was based on a compari-
son between predicted and observed drug clearances, volumes of
distribution, and half-lives. Elimination processes investigated
include cytochrome P450s, glucuronidation, sulfonation, and
biliary clearance. e spectrum of drugs investigated include
alfentanil, theophylline, morphine, levetiratectam, levooxacin,
gentamicin, midazolam, carbamazepine, paracetamol, levo-
oxacin, ropivacaine, lorazepam, and fentanyl.
e majority of these studies were based on data sets obtained
aer intravenous (i.v.) drug administration. In each of the PBPK
models, the predictions of drug clearance, distribution volume,
and elimination half-life were within the expected range and
adequately predicted the parameters for the dierent age groups.
Edginton and Willmann
28
also extended their physiologically
based model by including the different pathophysiological
changes that accompany liver cirrhosis. ey adjusted organ
blood ow, cardiac index, plasma binding, hematocrit, func-
tional liver volume, hepatic enzymatic activity, and the glomeru-
lar ltration rate according to the disease state and the age of
pediatric patients. e comparison of predicted and observed
plasma concentrations, as well as clearance values, aer i.v. alfen-
tanil, lidocaine, theophylline, and levetiracetam administration
revealed adequate predictions for the four drugs, thus demon-
strating that PBPK models might be useful in predicting PK of
i.v.
p.o.
Dose
Stomach
Small intestine
Large intestine
Pancreas
Spleen
Liver
Kidney
Testes
Heart
Brain
Fat
Bone
Skin
Muscle
Lung
HO
H
N
O
Portal vein
Gall bladder
V
e
n
o
u
s
b
l
o
o
d
A
r
t
e
r
i
a
l
b
l
o
o
d
Figure 1 Physiologically based pharmacokinetic (PBPK) modeling in a bridging schema using a typical whole-blood PBPK model, in which the tissues and
organs of the body are arranged anatomically and connected via the vascular system. The following extrapolations are considered in pediatrics: (i) The
physiochemical bridge should be based on in vitro experiments and data that need to be correlated with understanding of in vivo drug disposition and transport
across physiologic barriers. (ii) For the animal–human bridge, the need for and timing of juvenile animal studies should be justified, and the target pediatric
age and primary pharmacodynamics in organs/tissues with significant postnatal development (nervous, reproductive, pulmonary, immune, renal, and skeletal
systems; biotransformation enzymes) need to be considered. (iii) For the adult–pediatric bridge, size, ontogeny, and maturation should be accommodated;
some physiologic spaces are still difficult to extrapolate across all age groups (e.g., bone marrow, brain).
state artstate art
CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 92 NUMBER 1 | JULY 2012 43
some drugs aer their i.v. administration to pediatric patients
with a variety of diseases. However, the ability of PBPK models
to reliably predict plasma concentrations and PK parameters
aer oral drug administration to patients in various pediatric
age groups is still in question. is is expected because Poulin
et al.
15
also showed in adults that currently available PBPK
models have a substantially lower eectiveness in simulating
adult plasma concentration–time proles aer oral than aer i.v.
administration. erefore, further research is necessary before
oral administration models can be relied on to predict oral PK
for children.
Role of animal studies in pediatric PBPK development
Current guidelines applied to pharmaceutical drug develop-
ment in pediatrics indicate a need for juvenile animal studies
only when previous animal and human safety data are judged
insucient.
29–31
Situations that would justify toxicity studies
in juvenile animals include, but are not limited to, ndings in
nonclinical studies that indicate target organ or systemic toxicity
relevant for developing systems, possible eects on growth and/
or development in the intended age group, or a pharmacological
eect of the test compound that would aect one or more devel-
oping organs. e need for proper selection of species and
appropriate times of testing is exemplied by a PBPK model
developed recently for the neuroaminidase inhibitor oseltamivir
and its active metabolite in infants and neonates with inuenza
to predict exposures aer i.v. dosing.
32
is model addressed
the suitability of using young and adult marmoset monkeys to
represent age-dependent changes in human PK, the sensitiv-
ity of the tested drug and metabolite concentrations in vivo to
age-dependent changes in metabolism and renal function, and
the suitability of data aer oral administration to represent i.v.
disposition. In a stepwise modeling strategy, simulated data were
veried by comparison with data obtained in a juvenile animal
study; prediction in neonates and infants was then made using
a PBPK model accounting for the age dependencies in humans,
which was informed by renements developed for adult humans
and juvenile animals. However, there are still only a few pub-
lished studies about juvenile animals.
33
Of concern is that, despite the growing knowledge of func-
tional dierences between juvenile and adult animals used in
toxicology studies, there are still areas in which there is a lack of
Table 2 Hierarchy of PBPK model inputs and outputs based on intended use in supporting pediatric research and development
Intended use Model inputs
a
Outputs
Candidate screening (CS) • Drug properties: MW, lipophilicity, solubility, protein
binding, pKa
• In vitro metabolism data (V
max
, K
M
, etc.) and experimental
details
• Pediatric physiology: organ weight, blood flow, CL
ontogeny, fu ontogeny (usually from software tool
database)
• Study population (healthy volunteers); clearance pathways
(CL
R
and CL
H
)
• Dosing (usually single dose but can vary to incorporate
simple phase I designs)
• DDI potential (magnitude and shift of toxicity profiles
relative to a standard (e.g., single agent relative to
combination)
• Dose–exposure relation; evaluation of profiles against
target product profile expectations
FTIP dose finding • CS inputs
• Pediatric-specific demographics (age, body weight, height,
etc.) of study population; clearance pathways
• Dosing (various algorithms and “rules” can be evaluated)
• Dose–exposure relation; evaluation of profiles and PK
metrics across age/developmental strata and relative to
adult exposures
• Comparison of exposure from fixed vs. weight-adjusted
dosing
Target organ exposures (TOEs) • FTIP inputs
• Species (if comparing animal biodistribution to pediatric
predictions)
• Target organs identified (with data/measured
concentrations), if available
• Overlays of observed vs. predicted exposures for animal
studies
• In pediatrics, predicted exposures in target organs;
correlation with toxicity or PD measures
Trial design evaluation • FTIP inputs
• Design features (e.g., parallel vs. crossover), sampling
scheme, sample size/strata, population, etc.
• Replication details
• Probability of success for scenarios or trial designs to
achieve clinical milestones or study objectives
Real-time PK safety • FTIP inputs
• Measured concentrations (sampling/observations)
• Response measures (SAEs, ADRs, PD, etc.)
• Overlays of observed vs. predicted exposures with
comparisons across age strata and relative to adult data
Targeted drug delivery • TOE inputs
• Delivery inputs (e.g., extravascular route, input rate,
duration)
• Cellular constituent targets
• Imaging data (if relevant)
• Overlays of observed vs. predicted exposures (or
equivalent metrics) with comparisons across age strata
and relative to adult data
ADRs, adverse drug reactions; CL, clearance; CL
H
, hepatic clearance; CL
R
, renal clearance; DDI, drug–drug interaction; FTIP, first-time-in-pediatrics; fu, unbound fraction; K
M
,
Michaelis–Menten constant-CYP enzyme affinity; MW, molecular weight; PD, pharmacodynamic; PK, pharmacokinetic; pKa, ionization constant; SAEs, serious adverse events;
V
max
, maximum enzyme activity.
a
Derived parameters include partition coefficients and permeabilities.
state artstate art
44 VOLUME 92 NUMBER 1 | JULY 2012 | www.nature.com/cpt
robust knowledge of cross-species functional and PK dierences
among juveniles. is means that extrapolation of any toxico-
logical study nding to an immature human may not be easy or
even relevant (e.g., in central nervous system development for
which no species provides a direct correlation to human neu-
rological development). So, at a fundamental level, the devel-
opment of PBPK models in relevant animal species or even in
xenogra models, provides an important means of assessing
the portability of organ-based exposures between these animal
models and patients.
34,35
is is needed to permit a more critical
assessment of why animal models do or do not predict what is
observed clinically.
36,37
An important consideration is the avail-
ability of actual biodistribution data to ne-tune PBPK models,
as the absence of such data forces reliance on predicted tissue
partition coecients, which may be less accurate. In addition,
PBPK assessment of nonsystemic biodistribution targets can
support targeted delivery strategies and oers a tool for assess-
ing druggability during preclinical drug development.
Integration with pediatric pharmacodynamics
An extension of the PBPK concept includes the additional pre-
diction of drug exposure–response relationships in PBPK–PD
models. PBPK–PD modeling, which is aimed at the prediction
of exposure–response relationships in humans, including their
inherent intra- and interindividual variability, oers a unique
opportunity to integrate age- and size-dependent changes in
prediction of drug eects. Exposure is expressed as the time
course of the drug concentration in plasma, whereas response
is expressed as the time course of the intensity of the drug
eect. PBPK–PD models contain specic expressions to quan-
titatively characterize processes on the causal path between
plasma concentration and eect, such as drug target–site dis-
tribution, drug target binding and activation, and transduction
mechanisms. Ultimately, PBPK–PD models can also character-
ize the interaction of drug eect with disease processes.
38
e
distinction between drug-specic and biological system-specic
parameters is crucial for accurately predicting drug eects in
children. Such a distinction needs to consider growth and matu-
ration, as well as the availability and use of child-specic disease
outcomes.
PBPK–PD models have been extended to model risk from
chemical exposure in juvenile animals and in children.
39
However, only a few full PBPK–PD models have been devel-
oped so far for therapeutic drugs,
40
although several groups
have coupled PBPK models with classical compartmental PD
models.
41,42
Modeling the relationship between drug biophase
concentration and response can be tenuous when an a priori
approach is used. In an oncologic example, it was assumed suc-
cessfully that the response in children is superimposable on
that of adults.
43
However, this may not always be the case, as
illustrated by a PBPK–PD study of sotalol in pediatric patients
with supraventricular tachycardia, in which it was found that
exposure–response relationships may dier from adults and
may be age-variant,
14
and by a study of etanercept in pediatric
patients with juvenile idiopathic arthritis.
44
Extension of pediat-
ric PBPK modeling to include a PD component (e.g., covariates
associated with drug–receptor interaction) remains an area of
much-needed future research.
Application of PBPK modeling in pediatric drug development
e initial foray into pediatric PBPK modeling began in the
mid-1980s with studies in which PBPK models were used to
assess the risk of exposure to toxic chemicals, with the ultimate
goal of predicting organ-specic toxicity. Environmental toxi-
cology models have been applied to assess the risks of inadvert-
ent exposure to chemical compounds in unborn and breastfed
infants
45
and in children
46,47
, as well as to environmental agents
such as caeine and theophylline in neonates
27
; such models
have incorporated an early-life-stage renal clearance model for
the risk assessment of chemicals cleared primarily by the kid-
neys.
48
Currently, chemical-specic PBPK models are increas-
ingly being used in risk assessment to account for inter- and
intraspecies dierences in PK and to replace default uncertainty
factors in the derivation of reference values.
48
Workow practices for common pediatric PBPK applications
are shown in Figure 2. e most common implementations
of PBPK modeling in the setting of pediatric drug develop-
ment include rst-time-in-pediatrics (FTIP) dose selection,
49
simulation-based trial design,
12
correlation with target organ
toxicities, real-time assessment of PK–safety relationships, and
assessment of nonsystemic biodistribution targets. PBPK models
can support risk assessment by investigating possible drug–drug
interactions in pediatric populations and the eect of impaired
organ function.
50
FTIP dose selection is a critical milestone and decision point
in pediatric drug development.
48,51
Historically, these estimates
have come from an assessment of the adult therapeutic window
Table 3 Developmental changes affecting drug
pharmacokinetics
Changes in drug absorption
5
due to
Changes in gastric emptying and intestinal transit time
pH changes in different intestinal segments
Changes in intestinal transporters and in enzymes causing first-pass
metabolism
Changes in drug distribution
5
due to
Changes in body fluid compartments (e.g., total body water is 78% in
neonates vs. 55% in adults, extracellular fluid space also is relatively
greater, intracellular fluid space is relatively smaller)
Relative percentage of body fat is lower in children than in adults
Protein binding of drugs is less in infants and children than in adults
Blood–brain barrier is more permeable in infants and children than in
adults
Changes in hepatic metabolism
24,25
Liver size is relatively greater in infants and children than in adults
26
Drug metabolizing enzymes undergo age-specific changes (e.g.,
glucuronidation and sulfation are immature in neonates)
17
Changes in renal excretion of drugs and drug metabolites
24,25
Glomerular filtration rate is less in infants and children than in adults
Renal tubular absorption and secretion are less in infants and children
than in adults
state artstate art
CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 92 NUMBER 1 | JULY 2012 45
(if known) and the collective safety/toxicology experience with
a drug molecule and class. Given that timelines are oen com-
pressed and the current regulatory requirement of providing
a Pediatric Investigation Plan, comprehensive adult PK and/
or clinical data are not necessarily available when this decision
point is reached. is necessitates greater reliance on in silico
techniques and preclinical experience to guide FTIP dose selec-
tion. e historical approach in adults was to choose starting
doses as fractions of toxicity indexes
52
(e.g., no adverse eect
level, NOAEL), but more recent criteria are based on drug action
(e.g., minimal anticipated biologic eect level, MABEL).
53
In
any case, these approaches still implicitly rely on extrapolation
of PD relationships observed in animal models
54
or on clinical
experience in adult populations.
55
Currently, PBPK method-
ology and workow for pediatric FTIP prediction
49
is reliant
on drug properties (e.g., physiochemical data, protein binding,
clearance) and is scaled to pediatric organ sizes and ow rates,
even accommodating maturational and ontogeny eects as a
function of the changes in physiologic parameters (e.g., func-
tional enzyme expression). Still, a key element of appropriately
“tuning” these models is the need to adjust experimental or pre-
dicted organ uptake on the basis of available actual organ/tissue
biodistribution data in adults and experimental animals,
34,56
preferably aer i.v. administration to avoid any confounding
with absorption-related processes. Likewise, other drug physi-
ochemical and physiologic and drug-specic PK/ADME proper-
ties that serve as model inputs (e.g., ionization constant, protein
binding, cardiac output) may represent additional factors that
greatly inuence projected exposures. As a result, sensitivity
analyses are essential to identify key dependencies and to appro-
priately assess PBPK simulation results.
Clinical trial design can be aided by PBPK-based simulations
that provide the ability to evaluate dose selection, sample size,
and sampling scheme and aid in selecting design constructs
with the greatest potential to yield meaningful results.
13,57,58
By
shiing the scaling from size and age to function (e.g., a con-
cept that relies on inferences from PK parameter distributions
in children) to predict drug exposure in dierent pediatric age
groups, pediatric PBPK models have been suggested as a method
of choice in neonates and infants to guide optimization of the
dosing schedule and sampling times.
19
When incorporated in a
trial design model with appropriate replication, the PBPK model
also provides a means to generate virtual patients that mimic the
intended target population, and it provides a probability-based
Candidate screening
Define physiochemical
inputs from QSPK
relationships−calculated
based on availability of
preclinical experiments
and data
based on availability of
adult PK, toxicology and
margin of safety
based on availability of
adult PK, and preclinical
distribution data
based on availability of
adult PK, PD and
therapeutic window
Model refinement: Model refinement:
Model refinement: developmental and ontogeny factors reflecting target pediatric
population characteristics
“Optimize” parameters
(e.g., permeability/
partitioning) via animal
distribution: consider
regional effects of
transporters (e.g., brain)
Incorporate proposed
disease progression,
design constructs and
sampling schemes
Screen/rank compound
candidates based on
ability to achieve clinical
exposure targets
Compare exposure
metrics across age strata
for various dose
multiples relative to
adult exposures
(available or targeted)
Simulate exposure
metrics in target tissues
for intended pediatric
population: evaluate
against activity/tox goals
Simulate trial
outcomes for each
design schema and/
or scenario
Model refinement:
Model refinement: Model refinement:
Model refinement:
Define physiochemical inputs from in vitro experiments−measured/estimated
parameters or calculated parameters
First-time-in-pediatrics
dose finding
Target organ
exposures
Tr ial design
evaluation
Figure 2 Simplified workflows for common pediatric PBPK applications. PBPK, physiologically based pharmacokinetic; QSPK, quantum sufficit (adjusted
accordingly) PK.
state artstate art
46 VOLUME 92 NUMBER 1 | JULY 2012 | www.nature.com/cpt
assessment of study outcomes.
12
Recent advances in soware
development also permit easier and more convenient identi-
cation of “virtual twins”: simulated patients with demographic
characteristics similar to those of the target population. As the
underlying data for these tools replaces algorithm-based simula-
tions with actual pediatric patient populations for resampling,
this approach should become even more relevant.
Once PK in children can be described and predicted, assump-
tions about the exposure–response relationships, including the
anticipated therapeutic and potentially toxic eects, can further
rene relationships between expected clinical response(s) and
the administered dose. Depending on the clinical needs and
the nature of the eect, either a threshold model (to simulate
PK proles and to design a dosing regimen to achieve target
drug concentrations above the expected threshold of ecacy but
below an anticipated threshold for an adverse event)
59
or PK–PD
models (to provide guidance on the timing of the response, by
integrating PK and PD parameters to simulate response-time
proles for given dosing regimens) can be considered. PBPK–PD
models also can be used to explore the sensitivity of these target
levels to changes in the dierent model system parameters. For
example, a frequent question concerning the inuence of pedi-
atric formulation variables (such as dierent dissolution-rate salt
forms) on clinical biomarkers can be explored in silico through
appropriately parameterized PBPK–PD models.
60
As more studies have been completed in children since the
Best Pharmaceuticals in Children Act and Product Research and
Equity Act have become law, it is increasingly evident that adult
safety proles cannot be transferred directly to children even
when the disease processes are the same.
61
As drug developers
and the pediatric prescribing community seek reasons for these
dierences, exposure-based explanations for target organ toxici-
ties will need to be considered; PBPK metrics oer an approach
to do this, even if the PBPK framework is ultimately used only to
derive cumulative organ exposure or intensity-based metrics.
Finally, one of the main concerns with pediatric drug devel-
opment is that long-term adverse events may not be identied
for many years.
1
is reality can be mitigated by using real-time
evaluation of PBPK results during ongoing pediatric trials to
guide the review of potential PK-mediated safety relationships
and to examine drug safety in the context of the achieved tar-
get exposures (systemic or nonsystemic). In this setting, PBPK
modeling and simulation output can provide inputs to the Data
Safety Monitoring Board to accompany any observed toxicity
or other adverse events.
Examples of successful application of PBPK modeling in
pediatrics
Pediatric PBPK models have been used successfully to predict
dierences in PK between adults and children for several drugs,
such as the disposition of theophylline and midazolam in infants
and children,
11
the maturation of midazolam clearance,
62
and
the prediction of clearance and its variability for 11 drugs
(midazolam—oral and i.v., caeine, carbamazepine, cisapride,
theophylline, diclofenac, omeprazole, S-warfarin, phenytoin,
gentamicin, and vancomycin) in children and infants.
25
ese
models predict the large dierences between half-life and clear-
ance in neonates, infants, and adults.
Edginton et al.
20
extended existing PBPK models for
adults,
63,64
in conjunction with a previously developed age-
specic clearance model,
17
to develop a generic PBPK model
for children that reected age-related physiological changes in
children from birth to 18 years of age. ese authors showed
that this model predicted pediatric plasma PK proles of aceta-
minophen, alfentanil, morphine, theophylline, and levooxacin
reasonably well aer i.v. administration, with appropriate age-as-
sociated trends. Physiological scaling in this model was based on
extensive literature data and took into consideration the fact that
cardiac output, portal vein ow, extracellular water, total body
water, and lipid and protein values are all highly age-dependent.
Although preterm neonates had plasma concentrations greater
than those in adults and older children had concentrations lower
than those in adults (83%, 97%, and 87%, respectively, of the
predicted plasma concentrations), volumes of distribution and
elimination half-lives were within 50% of the study-reported
values. In addition, there was no age-dependent bias in the pre-
diction of distribution volumes or elimination half-lives across
ages ranging from term neonates to 18 years.
20
More recently, a mechanistic pediatric PBPK model that
incorporated interindividual parameter variability was used to
replicate pediatric clinical studies by performing them in silico.
50
Emphasis was placed on drugs used in pediatric anesthesia, and
the PBPK model parameters, which changed in a number of
“what if ” simulations, were used to explore the likely underly-
ing reasons for observed PK proles of drugs such as mida-
zolam, ibuprofen, zolpidem, itraconazole, theophylline, and
caeine. is exemplies the potential application of PBPK
models in pediatric drug clinical investigation (e.g., in assessing
plasma:brain drug concentrations of diclofenac, theophylline,
sildenal, and dextromethorphan) and practice (e.g., in critically
ill children on multiple therapies that present a high potential
for drug–drug interactions).
Extensions of the PBPK model also make it possible to cor-
relate drug disposition with target organ toxicity, an appli-
cation that obviously has great potential for oncology drug
development.
8,65,66
Whereas the time course and severity of
toxicity are oen poorly characterized in a conventional drug
development paradigm, PBPK models have the potential to pro-
vide a more mechanistic understanding of the temporal eects
and the cascade of events that elicit adverse drug reactions and
ultimately toxicity. Accordingly, toxicity-based PD models
driven by target-organ drug exposures would be an excellent
and much-needed extension of current PBPK applications.
67,68
Medical surveillance has also increased the epidemiologic con-
sideration of the benet-to-risk ratio of chronic drug exposures
overlaid on patient disease trajectories.
69,70
Current limitations of pediatric PBPK modeling
Although the list of successful applications of PBPK in pediat-
rics is broad and probably expanding, there are varying degrees
of condence in this approach because its reliability has been
evaluated in only a limited number of published examples or
state artstate art
CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 92 NUMBER 1 | JULY 2012 47
unpublished experience within individual companies. Hence,
there remains a clear need to generate reliable experimental data
that can reduce uncertainty in predictions and provide a more
complete understanding of the mechanistic basis for all ADME
processes.
21
Particularly problematic has been the ability of PBPK models
to reliably predict plasma concentrations and PK parameters
aer oral drug administration to patients in various pediatric
age groups. Poulin et al.
15
has shown in adults that currently
available PBPK models have a substantially lower eectiveness
in simulating adult plasma concentration–time proles aer oral
than aer i.v. administration when the models were developed
in the absence of human PK data. ese authors found that up to
69% of the simulations in adults demonstrated a medium to high
degree of accuracy for predicting plasma concentrations aer
i.v. drug administration, whereas this number decreased to 23%
aer oral administration. PBPK prediction of oral absorption
in pediatric patients is further complicated by the facts that the
gastrointestinal tract undergoes distinct developmental changes
and that data about the ontogeny of drug transporters in the gas-
trointestinal tract are sparse.
71
us, PBPK model-predicted bio-
availability of drugs in pediatric patients may be highly biased,
with the consequence that the model-predicted dosage might
result in an inappropriate drug exposure in certain age groups.
erefore, thorough validation of PBPK simulated pediatric
oral doses is necessary before oral administration models can
be relied on to predict dosing regimens for children.
Finally, although PBPK models can be useful in optimizing
the design of pediatric drug studies and in minimizing the par-
ticipation of actual pediatric patients in experimental protocols,
these models are not intended to replace the key clinical studies
of new drugs when it is feasible to carry them out.
8,72
The impact of regulatory authorities on pediatric PBPK
modeling
e increasing use of PBPK modeling in pediatric research and
development is probably a result, in part, of the requirement
to develop a Pediatric Investigation Plan early in the course of
drug development before there has been extensive adult clini-
cal experience.
55
Consequently, it has become more common-
place to use PBPK simulations for predicting proposed pediatric
dose regimens, and sponsors and investigators familiar with the
approach are more comfortable with the results and their inter-
pretation than in the past.
50,73
More generally, the US Food and
Drug Administration has used PBPK M&S to assist in decisions
relating to the need to consider specic clinical pharmacol-
ogy studies, to evaluate proposed study design, and to include
appropriate language in drug labels.
74
It appears that the Food
and Drug Administration’s revised protocol for question-based
review will also include questions reecting best practices in the
use of PBPK modeling applications.
74,75
FUTURE DIRECTIONS
As more studies have been completed in children since passage
of the Best Pharmaceuticals in Children Act and the Product
Research and Equity Act, it is increasingly evident that adult
safety proles cannot be transferred directly to children even
when the disease processes are the same.
63
In addition, as drug
developers and the pediatric prescribing community seek rea-
sons for these dierences, exposure-based explanations for
target-organ toxicities will need to be considered, and PBPK
metrics oer an approach to do this even if the PBPK framework
is ultimately used only to derive cumulative organ exposure or
intensity-based metrics. With time it is also possible that these
models will provide insights into (i) pediatric patient popula-
tions dened by altered physiologic states, (ii) nonsystemic
administration in which peripheral exposures are correlated
with outcomes, and (iii) drug dosing requirements appropriate
for chronic administration and relative to dynamic changes in
disease status and progression.
ere is a particular need for reliable experimental data to be
generated in the pediatric population that can reduce uncertainty
in predictions and provide a more complete understanding of
the mechanistic basis for all ADME processes.
21
As microdosing
becomes a viable option for initial clinical testing in children,
this approach may help to ll gaps in the ontogenic information
that is needed and also provide an important quality control
check on the results of PBPK M&S eorts. Extension of these
models to include a PD component that includes covariates
associated with drug–receptor interactions and their maturation
remains an area needing much future research. However, for
PBPK modeling to represent any kind of meaningful alternative
to conventional and better-understood (at least with respect to
the operational characteristics) methods, it must be rigorously
evaluated and reported in a transparent and reproducible man-
ner with agreed-on performance criteria. It will be essential that
PBPK models transparently specify their underlying assump-
tions so that identication of the relevant physiological processes
is clearly dened and guide the development of increasingly reli-
able pediatric physiologically based ADME and PD models to
achieve their potential.
It is apparent that the use of PBPK modeling to support vari-
ous aspects of pediatric research and development is highly
attractive and will probably increase. But for PBPK modeling
to move beyond its application for early-development screen-
ing, users must assess the condence with which the approach
can be used for these applications and determine what is needed
in the future to make these techniques even more robust and
generalizable. It is pertinent that the criteria used in such evalu-
ations be proposed by scientists familiar with pediatric clinical
pharmacology, methodological approaches and alternatives,
computational algorithms, and the assumptions underlying
PBPK models. ese criteria should not be decided by soware
vendors, although their knowledge and development expertise
are important considerations in the evaluation and further evo-
lution of current tools. A further consideration is the necessity
for open disclosure of the details of model construction and
renement when PBPK results are reported and published. Not
unlike an analytical method, the application of PBPK modeling
in all settings must be transparent and reproducible from the
standpoint of the ability of an independent assessor to re-create
modeling outcomes. is is essential for both the integrity of
state artstate art
48 VOLUME 92 NUMBER 1 | JULY 2012 | www.nature.com/cpt
scientic reporting and the ecient regulatory review of PBPK
analyses and for their potential future application in clinical
practice.
A nal but major impediment to the successful application of
PBPK M&S, in both pediatric and other settings, is that there are
far fewer scientists who are appropriately knowledgeable to per-
form these analyses than there are scientists qualied to conduct
traditional PK and PK–PD analyses. For example, the knowledge
of acid/base chemistry, membrane transport, drug metabolism,
and human physiology is perhaps a new requirement for many
members of the M&S and the wider pharmacometric commu-
nity. Unfortunately, the ease with which the newer generations of
PBPK soware can be used may even promote a certain amount
of “misuse.” Hence, training and some level of accreditation for
the appropriate knowledge base and skills required to gener-
ate reliable PBPK results will be necessary. e potential of this
technology is great, but so are the risks, particularly where chil-
dren are concerned.
CONFLICT OF INTEREST
The authors declared no conflict of interest.
© 2012 American Society for Clinical Pharmacology and Therapeutics
1. Klassen, T.P., Hartling, L., Hamm, M., van der Lee, J.H., Ursum, J. & Oringa, M.
StaR Child Health: an initiative for RCTs in children. Lancet 374, 1310–1312
(2009).
2. Johnson, T.N. The problems in scaling adult drug doses to children. Arch. Dis.
Child. 93, 207–211 (2008).
3. European Medicines Agency. ICH Topic E11: Note for Guidance on Clinical
Investigation of Medicinal Products in the Paediatric Population (CPMP/
ICH/2711/99) <http://www.ema.europa.eu/docs/en_GB/document_library/
Scientic_guideline/2009/09/WC500002926.pdf>. (Accessed 12 December
2011).
4. Lack, J.A. & Stuart-Taylor, M.E. Calculation of drug dosage and body surface
area of children. Br. J. Anaesth. 78, 601–605 (1997).
5. Kearns, G.L., Abdel-Rahman, S.M., Alander, S.W., Blowey, D.L., Leeder, J.S. &
Kauman, R.E. Developmental pharmacology–drug disposition, action, and
therapy in infants and children. N. Engl. J. Med. 349, 1157–1167 (2003).
6. Läer, S., Barrett, J.S. & Meibohm, B. The in silico child: using simulation to guide
pediatric drug development and manage pediatric pharmacotherapy. J. Clin.
Pharmacol. 49, 889–904 (2009).
7. Bellanti, F. & Della Pasqua, O. Modelling and simulation as research tools in
paediatric drug development. Eur. J. Clin. Pharmacol. 67 (suppl. 1), 75–86
(2011).
8. Manolis, E. & Pons, G. Proposals for model-based paediatric medicinal
development within the current European Union regulatory framework.
Br. J. Clin. Pharmacol. 68, 493–501 (2009).
9. Jadhav, P.R. & Kern, S.E. The need for modeling and simulation to design
clinical investigations in children. J. Clin. Pharmacol. 50, 121S–129S (2010).
10. Manolis, E. et al. Role of modeling and simulation in pediatric investigation
plans. Paediatr. Anaesth. 21, 214–221 (2011).
11. Björkman, S. Prediction of drug disposition in infants and children by means
of physiologically based pharmacokinetic (PBPK) modelling: theophylline
and midazolam as model drugs. Br. J. Clin. Pharmacol. 59, 691–704 (2005).
12. Mouksassi, M.S., Marier, J.F., Cyran, J. & Vinks, A.A. Clinical trial simulations
in pediatric patients using realistic covariates: application to teduglutide, a
glucagon-like peptide-2 analog in neonates and infants with short-bowel
syndrome. Clin. Pharmacol. Ther. 86, 667–671 (2009).
13. Meibohm, B., Läer, S., Panetta, J.C. & Barrett, J.S. Population pharmacokinetic
studies in pediatrics: issues in design and analysis. AAPS J. 7, E475–E487
(2005).
14. Läer, S. et al. Development of a safe and eective pediatric dosing regimen for
sotalol based on population pharmacokinetics and pharmacodynamics in
children with supraventricular tachycardia. J. Am. Coll. Cardiol. 46, 1322–1330
(2005).
15. Poulin, P. et al. PHRMA CPCDC initiative on predictive models of human
pharmacokinetics, part 5: prediction of plasma concentration-time proles
in human by using the physiologically-based pharmacokinetic modeling
approach. J. Pharm. Sci. 100, 4127–4157 (2011).
16. Rowland, M., Peck, C. & Tucker, G. Physiologically-based pharmacokinetics
in drug development and regulatory science. Annu. Rev. Pharmacol. Toxicol.
51, 45–73 (2011).
17. Edginton, A.N., Schmitt, W., Voith, B. & Willmann, S. A mechanistic approach for
the scaling of clearance in children. Clin. Pharmacokinet. 45, 683–704 (2006).
18. Bartelink, I.H., Rademaker, C.M., Schobben, A.F. & van den Anker,
J.N. Guidelines on paediatric dosing on the basis of developmental
physiology and pharmacokinetic considerations. Clin. Pharmacokinet.
45, 1077–1097 (2006).
19. Cella, M., Gorter de Vries, F., Burger, D., Danhof, M. & Della Pasqua, O. A
model-based approach to dose selection in early pediatric development.
Clin. Pharmacol. Ther. 87, 294–302 (2010).
20. Edginton, A.N., Schmitt, W. & Willmann, S. Development and evaluation
of a generic physiologically based pharmacokinetic model for children.
Clin. Pharmacokinet. 45, 1013–1034 (2006).
21. Aarons, L. Physiologically based pharmacokinetic modelling: a sound
mechanistic basis is needed. Br. J. Clin. Pharmacol. 60, 581–583 (2005).
22. Lowe, P.J., Hijazi, Y., Luttringer, O., Yin, H., Sarangapani, R. & Howard, D. On
the anticipation of the human dose in rst-in-man trials from preclinical
and prior clinical information in early drug development. Xenobiotica.
37, 1331–1354 (2007).
23. Jamei, M., Dickinson, G.L. & Rostami-Hodjegan, A. A framework for assessing
inter-individual variability in pharmacokinetics using virtual human
populations and integrating general knowledge of physical chemistry,
biology, anatomy, physiology and genetics: a tale of ‘bottom-up’ vs ‘top-down’
recognition of covariates. Drug Metab. Pharmacokinet. 24, 53–75 (2009).
24. Alcorn, J. & McNamara, P.J. Ontogeny of hepatic and renal systemic clearance
pathways in infants: part I. Clin. Pharmacokinet. 41, 959–998 (2002).
25. Johnson, T.N., Rostami-Hodjegan, A. & Tucker, G.T. Prediction of the clearance
of eleven drugs and associated variability in neonates, infants and children.
Clin. Pharmacokinet. 45, 931–956 (2006).
26. Johnson, T.N., Tucker, G.T., Tanner, M.S. & Rostami-Hodjegan, A. Changes
in liver volume from birth to adulthood: a meta-analysis. Liver Transpl.
11, 1481–1493 (2005).
27. Ginsberg, G., Hattis, D., Russ, A. & Sonawane, B. Physiologically based
pharmacokinetic (PBPK) modeling of caeine and theophylline in neonates
and adults: implications for assessing children’s risks from environmental
agents. J. Toxicol. Environ. Health Part A 67, 297–329 (2004).
28. Edginton, A.N. & Willmann, S. Physiology-based simulations of a pathological
condition: prediction of pharmacokinetics in patients with liver cirrhosis.
Clin. Pharmacokinet. 47, 743–752 (2008).
29. Hurtt, M.E. et al. Juvenile animal studies: testing strategies and design.
Birth Defects Res. B Dev. Reprod. Toxicol. 71, 281–288 (2004).
30. US Food and Drug Administration. Center for Drug Evaluation and
Research. Guidance for Industry: Nonclinical Safety Evaluation of Pediatric
Drug Products, February 2006 <http://www.fda.gov/downloads/Drugs/
GuidanceComplianceRegulatoryInformation/Guidances/ucm079247.pdf>.
Accessed 16 December 2011.
31. European Medicines Agency. Committee for Medicinal Products for Human
Use. Guideline on the need for non-clinical testing in juvenile animals of
pharmaceuticals for paediatric indications (CHMP/SWP/169215/05), August
2008 <http://www.ema.europa.eu/docs/en_GB/document_library/Scientic_
guideline/2009/09/WC500003305.pdf>. Accessed 12 December 2011.
32. Parrott, N. et al. Development of a physiologically based model for
oseltamivir and simulation of pharmacokinetics in neonates and infants.
Clin. Pharmacokinet. 50, 613–623 (2011).
33. Baldrick, P. Juvenile animal testing in drug development–is it useful? Regul.
Toxicol. Pharmacol. 57, 291–299 (2010).
34. Zhang, F. et al. Whole-body physiologically based pharmacokinetic model
for nutlin-3a in mice after intravenous and oral administration. Drug Metab.
Dispos. 39, 15–21 (2011).
35. Bradshaw-Pierce, E.L., Eckhardt, S.G. & Gustafson, D.L. A physiologically
based pharmacokinetic model of docetaxel disposition: from mouse to man.
Clin. Cancer Res. 13, 2768–2776 (2007).
36. Horrobin, D.F. Modern biomedical research: an internally self-consistent
universe with little contact with medical reality? Nat. Rev. Drug Discov.
2, 151–154 (2003).
37. Kamb, A. What’s wrong with our cancer models? Nat. Rev. Drug Discov.
4, 161–165 (2005).
38. Danhof, M., de Lange, E.C., Della Pasqua, O.E., Ploeger, B.A. & Voskuyl,
R.A. Mechanism-based pharmacokinetic-pharmacodynamic (PK-PD)
modeling in translational drug research. Trends Pharmacol. Sci. 29, 186–191
(2008).
state artstate art
CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 92 NUMBER 1 | JULY 2012 49
39. Timchalk, C., Kousba, A.A. & Poet, T.S. An age-dependent physiologically
based pharmacokinetic/pharmacodynamic model for the organophosphorus
insecticide chlorpyrifos in the preweanling rat. Toxicol. Sci. 98, 348–365 (2007).
40. Burghaus, R. et al. Evaluation of the ecacy and safety of rivaroxaban using a
computer model for blood coagulation. PLoS ONE 6, e17626 (2011).
41. Edginton, A.N., Schmitt, W. & Willmann, S. Application of physiology-based
pharmacokinetic and pharmacodynamic modeling to individualized target-
controlled propofol infusions. Adv. Ther. 23, 143–158 (2006).
42. Johnson, M. et al. Mechanism-based pharmacokinetic-pharmacodynamic
modeling of the dopamine D2 receptor occupancy of olanzapine in rats.
Pharm. Res. 28, 2490–2504 (2011).
43. Blesch, K.S., Gieschke, R., Tsukamoto, Y., Reigner, B.G., Burger, H.U. & Steimer,
J.L. Clinical pharmacokinetic/pharmacodynamic and physiologically based
pharmacokinetic modeling in new drug development: the capecitabine
experience. Invest. New Drugs 21, 195–223 (2003).
44. Yim, D.S., Zhou, H., Buckwalter, M., Nestorov, I., Peck, C.C. & Lee, H. Population
pharmacokinetic analysis and simulation of the time-concentration prole
of etanercept in pediatric patients with juvenile rheumatoid arthritis. J. Clin.
Pharmacol. 45, 246–256 (2005).
45. Gentry, P.R., Covington, T.R. & Clewell, H.J. 3
rd
. Evaluation of the potential
impact of pharmacokinetic dierences on tissue dosimetry in ospring
during pregnancy and lactation. Regul. Toxicol. Pharmacol. 38, 1–16 (2003).
46. Pelekis, M., Gephart, L.A. & Lerman, S.E. Physiological-model-based derivation
of the adult and child pharmacokinetic intraspecies uncertainty factors for
volatile organic compounds. Regul. Toxicol. Pharmacol. 33, 12–20 (2001).
47. Price, K., Haddad, S. & Krishnan, K. Physiological modeling of age-specic
changes in the pharmacokinetics of organic chemicals in children. J. Toxicol.
Environ. Health Part A 66, 417–433 (2003).
48. DeWoskin, R.S. & Thompson, C.M. Renal clearance parameters for PBPK model
analysis of early lifestage dierences in the disposition of environmental
toxicants. Regul. Toxicol. Pharmacol. 51, 66–86 (2008).
49. Edginton, A.N. Knowledge-driven approaches for the guidance of rst-in-
children dosing. Paediatr. Anaesth. 21, 206–213 (2011).
50. Johnson, T.N. & Rostami-Hodjegan, A. Resurgence in the use of physiologically
based pharmacokinetic models in pediatric clinical pharmacology: parallel
shift in incorporating the knowledge of biological elements and increased
applicability to drug development and clinical practice. Paediatr. Anaesth.
21, 291–301 (2011).
51. Baber, N.S. Tripartite meeting. Paediatric regulatory guidelines: do they help
in optimizing dose selection for children? Br. J. Clin. Pharmacol. 59, 660–662
(2005).
52. Reigner, B.G. & Blesch, K.S. Estimating the starting dose for entry into humans:
principles and practice. Eur. J. Clin. Pharmacol. 57, 835–845 (2002).
53. Muller, P.Y., Milton, M., Lloyd, P., Sims, J. & Brennan, F.R. The minimum
anticipated biological eect level (MABEL) for selection of rst human dose in
clinical trials with monoclonal antibodies. Curr. Opin. Biotechnol. 20, 722–729
(2009).
54. Rocchetti, M., Simeoni, M., Pesenti, E., De Nicolao, G. & Poggesi, I. Predicting
the active doses in humans from animal studies: a novel approach in
oncology. Eur. J. Cancer 43, 1862–1868 (2007).
55. Tod, M., Jullien, V. & Pons, G. Facilitation of drug evaluation in children by
population methods and modelling. Clin. Pharmacokinet. 47, 231–243 (2008).
56. Berry, L.M., Roberts, J., Be, X., Zhao, Z. & Lin, M.H. Prediction of V(ss) from in
vitro tissue-binding studies. Drug Metab. Dispos. 38, 115–121 (2010).
57. Holford, N.H., Kimko, H.C., Monteleone, J.P. & Peck, C.C. Simulation of clinical
trials. Annu. Rev. Pharmacol. Toxicol. 40, 209–234 (2000).
58. Barrett, JS. Modeling and simulation in pediatric research and development.
In Clinical Trial Simulations: Applications & Trends (eds. Kimko, H. & Peck, C.),
401–433 (Springer Science + Business Media, New York, 2011).
59. Derendorf, H. & Meibohm, B. Modeling of pharmacokinetic/
pharmacodynamic (PK/PD) relationships: concepts and perspectives.
Pharm. Res. 16, 176–185 (1999).
60. Lowe, P.J., Tannenbaum, S., Wu, K., Lloyd, P. & Sims, J. On setting the rst
dose in man: quantitating biotherapeutic drug-target binding through
pharmacokinetic and pharmacodynamic models. Basic Clin. Pharmacol.
Toxicol. 106, 195–209 (2010).
61. Stephenson, T. How children’s responses to drugs dier from adults.
Br. J. Clin. Pharmacol. 59, 670–673 (2005).
62. Anderson, B.J. & Larsson, P. A maturation model for midazolam clearance.
Paediatr. Anaesth. 21, 302–308 (2011).
63. Price, P.S. et al. Modeling interindividual variation in physiological factors used
in PBPK models of humans. Crit. Rev. Toxicol. 33, 469–503 (2003).
64. Willmann, S. et al. Development of a physiology-based whole-body
population model for assessing the inuence of individual variability on the
pharmacokinetics of drugs. J. Pharmacokinet. Pharmacodyn. 34, 401–431
(2007).
65. Barrett, J. et al. Model-based Drug Development for Oncology Agents. Exp.
Opin. Drug Discov. 2, 185–209 (2007).
66. Steimer, J.L. et al. Modelling the genesis and treatment of cancer: the potential
role of physiologically based pharmacodynamics. Eur. J. Cancer 46, 21–32
(2010).
67. Bruckner, J.V. Dierences in sensitivity of children and adults to
chemical toxicity: the NAS panel report. Regul. Toxicol. Pharmacol. 31,
280–285 (2000).
68. de Zwart, L.L., Haenen, H.E., Versantvoort, C.H., Wolterink, G., van Engelen, J.G.
& Sips, A.J. Role of biokinetics in risk assessment of drugs and chemicals in
children. Regul. Toxicol. Pharmacol. 39, 282–309 (2004).
69. Stone, K.D. Atopic diseases of childhood. Curr. Opin. Pediatr. 15, 495–511
(2003).
70. Breitner, J.C. et al. Risk of dementia and AD with prior exposure to NSAIDs in an
elderly community-based cohort. Neurology 72, 1899–1905 (2009).
71. Johnson, T.N. & Thomson, M. Intestinal metabolism and transport of drugs in
children: the eects of age and disease. J. Pediatr. Gastroenterol. Nutr. 47, 3–10
(2008).
72. Hoppu, K. Can we get the necessary clinical trials in children and avoid the
unnecessary ones? Eur. J. Clin. Pharmacol. 65, 747–748 (2009).
73. Khalil, F. & Läer, S. Physiologically based pharmacokinetic modeling:
methodology, applications, and limitations with a focus on its role in pediatric
drug development. J. Biomed. Biotechnol. 90, 74–61 (2011).
74. Zhao, P. et al. Applications of physiologically based pharmacokinetic (PBPK)
modeling and simulation during regulatory review. Clin. Pharmacol. Ther. 89,
259–267 (2011).
75. Huang, S.M. & Rowland, M. The role of physiologically based pharmacokinetic
modeling in regulatory review. Clin. Pharmacol. Ther. 91, 542–549 (2012).
