# The role of population PK–PD modelling in paediatric clinical research

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## Abstract

Children differ from adults in their response to drugs. While this may be the result of changes in dose exposure (pharmacokinetics [PK]) and/or exposure response (pharmacodynamics [PD]) relationships, the magnitude of these changes may not be solely reflected by differences in body weight. As a consequence, dosing recommendations empirically derived from adults dosing regimens using linear extrapolations based on body weight, can result in therapeutic failure, occurrence of adverse effect or even fatalities. In order to define rational, patient-tailored dosing schemes, population PK–PD studies in children are needed. For the analysis of the data, population modelling using non-linear mixed effect modelling is the preferred tool since this approach allows for the analysis of sparse and unbalanced datasets. Additionally, it permits the exploration of the influence of different covariates such as body weight and age to explain the variability in drug response. Finally, using this approach, these PK–PD studies can be designed in the most efficient manner in order to obtain the maximum information on the PK–PD parameters with the highest precision. Once a population PK–PD model is developed, internal and external validations should be performed. If the model performs well in these validation procedures, model simulations can be used to define a dosing regimen, which in turn needs to be tested and challenged in a prospective clinical trial. This methodology will improve the efficacy/safety balance of dosing guidelines, which will be of benefit to the individual child.

## Keywords

Pharmacokinetics Pharmacodynamics Population modelling Paediatric population Validation## Introduction

Children differ from adults in their response to drugs. These differences may be caused by changes in the pharmacokinetics (PK) and/or pharmacodynamics (PD) between children and adults and may also vary among children of different ages. The PK of a drug includes the processes of absorption, distribution, metabolism and elimination of a drug, whereas the PD comprises the physiological and biological response to the administered drug and therefore may represent both efficacy and safety measures. While a child grows, enzyme pathways (involved in the PK), function and expression of receptors and proteins (involved in the PD) mature, which can be referred to as “developmental changes” in childhood. The maturation rates of these developmental changes vary, however, between the pathways and receptors and often do not correlate solely with the increase in body weight of the child. The question is therefore how to obtain data in children that allow for the study of these developmental changes, ultimately resulting in evidence-based dosing regimens for drugs in children.

To date, only a small number of drugs used in children are licensed for use in this specific group. Up to 70% of the drugs in paediatric intensive care, and 90% of the drugs in neonatal intensive care, are prescribed in an off-label or unlicensed manner [1, 2, 3, 4]. Paediatric dosing regimens are usually empirically derived from adult regimens using linear extrapolations based on body weight. Since these developmental changes are non-linear dynamic processes, this dosing paradigm may result in under- or over-dosing, particularly in specific age groups. This may cause therapeutic failure, occurrence of severe adverse effects or even fatalities, such as fatalities occurring after long-term sedation with high doses of propofol [5, 6] and occurrence of the grey baby syndrome in neonates after treatment with chloramphenicol [7, 8]. As a result, dose adjustments in the younger age groups are often proposed. For vancomycin, for example, lower doses are administered in neonates younger than 1 week (20 mg/kg/day) compared with 1- to 4-week-old neonates (30 mg/kg/day) and children between 1 month and 18 years (40 mg/kg/day) [9].

Instead of the a priori use of body weight for dosing guidelines in children, detailed information on PK and potentially also the PD needs to be considered in order to define effective and safe dosing regimens throughout the paediatric age range. The lack of PK and PD information on drugs in children has led to the European Regulation, which came into force in 2007. This law imposes on pharmaceutical companies to perform research over the whole paediatric age range for all drugs that are developed for the European market, by requiring the submission of a paediatric investigational plan (PIP) in the early stages of the development of a new drug. In this PIP, a full description has to be given of the studies and of drug formulation in the paediatric population. If little information is available about the efficacy and safety of a drug, studies in children are only performed after more information has been obtained in the adult population to increase the safety of the paediatric study [10, 11, 12]. The main aims of introducing the paediatric regulation were to facilitate development and availability of medicines in children aged between 0 and 17 years, to improve the availability of information about medicines used in children, to ensure that the medicines are of high quality, can be administered in a safe and effective way and that paediatric studies are performed in an ethically correct way [10]. The reward for this effort is a 6-month supplementary production certificate for the pharmaceutical company.

Both for industry and for academic researchers, performing (PK–PD) studies in children in order to develop rational dosing schemes is very challenging because of ethical and practical issues. Unlike studies in healthy adults, research in healthy children is considered to be unethical, so all paediatric studies are performed in the vulnerable group of children suffering from a disease. In all clinical trials, informed consent has to be signed by the patient before he or she can be enrolled into a trial. In paediatric trials, this informed consent cannot be obtained by the patient that participates in the trial, and is therefore replaced by the consent of the parents or guardians. In older age groups, in addition to this consent, an assent is used in which the aim of the study is explained in age-appropriate language so that children can understand. [1, 4, 13].

Apart from ethical issues, practical challenges also occur when performing studies in children. There are limitations to the number and volume of samples that can be obtained, resulting in infrequent sampling possibilities and the need for advanced drug assay techniques with improved sensitivity. Another complicating factor is the limited available number of subjects that suffer from the same disease. Finally, pharmacodynamic endpoints that measure the efficacy of the drug and that are validated for children may be lacking. All these factors call for highly advanced study designs and analysis techniques so that the burden for each child can be kept to a minimum while still addressing all the study objectives.

This article aims to inform clinical pharmacologists, paediatricians and pharmacists about population PK–PD modelling in paediatric drug research. Advanced statistical tools are discussed that can be used to develop rational dosing schemes based on the PK and PD of a drug in children, despite practical and ethical restrictions. Using these tools, covariates can be identified in order to define appropriate doses and dosing intervals based on the individual characteristics of each child with minimal burden to each patient. The article also describes how to evaluate the predictive performance of the models by different validation methods including a prospective clinical trial. Ultimately, the efforts result in an individualised dosing regimen based on the PK–PD relation throughout the paediatric age range.

## PK–PD in children

Developmental changes in childhood can affect all PK processes from absorption until elimination as well as the pharmacodynamic effects. For example, in neonates intra-gastric pH is elevated (>4), which may increase the bioavailability of acid-labile compounds (penicillin G) and decrease the bioavailability of weak acids (phenobarbital) when given orally [14]. Additionally, gastric emptying in neonates is delayed, which means that also the absorption of drugs, e.g. paracetamol is slower in neonates [15, 16]. Other examples are changes in the metabolising enzyme capacity in children. Although most uridine 5′-diphosphate (UDP)-glucuronosyltransferases (UGTs) and P-450 cytochromes (CYPs) are expressed during the first week of life, the activity at birth in comparison with adults is often low, e.g. UGT2B7 activity at birth is around 10% of the adult level and different enzyme systems are known to mature at different rates [14, 17, 18, 19, 20].

In addition, renal function and liver flow are influenced by physiological changes depending on age, e.g. the glomerular filtration rate (GFR) in mL/min/70 kg in full-term neonates is 35% of the adult value, while mL/min/70 kg adult values are reached at approximately 1 year old [21]). When using units of mL/min/70 kg, however, it should be realised that actual values of GFR in children are still very low compared with adult values because of the correction for differences in total body weight between adults and infants.

Furthermore, the body composition of children changes continuously, resulting in an age-dependent proportion of body water and fat, which influences the distribution of drugs. For example, the total amount of body water (80–90% of the body weight) is higher in neonates than in adults (55–60%). Hydrophilic drugs like aminoglycosides have a larger volume of distribution in neonates, which can be explained by the larger amount of extra-cellular fluid (45% of the body weight) compared with adults (20%) [14, 22].

In order to characterise the specific influence of developmental changes in childhood on the PK of a drug, concentration–time profiles are necessary, which require measurements of drug concentrations. For ethical reasons, in paediatric studies, discomfort, like pain and anxiety associated with venipuncture, must be restricted and practical issues limit the volume and amount of blood samples that can be obtained. Therefore, sensitive analysis techniques requiring only small blood samples should be used. While HPLC methods have been reported to require only 50 μL of blood [23], more recently LC-MS methods can measure up to ten different drugs in volumes as low as 50–100 μL [24]. Additionally, also alternative matrices such as saliva should be explored as a non-invasive, more child-friendly alternative to measure a drug concentration. An example in this respect is an LC-MS/MS method that was developed and validated for the measurement of busulphan in saliva [24]. Also, the use of a dried blood spot method, e.g. for tacrolimus, can facilitate the measurement of drugs in children [25]. Another method is capillary electrophoresis, which requires only a low sample volume for the quantification of drugs in biological fluids [26].

Changes between children and adults may also result from differences in the pharmacodynamics of a drug in children, e.g. by changes in the relative number and function of receptors. These age-related PD differences are until present rarely reported in the literature, but one of the few examples is the increased sensitivity to d-tubocurarine, an antagonist of nicotinic neuromuscular acetylcholine receptors, in neonates and infants compared with children and adults [27]. Other examples are the observed lower minimum alveolar concentration (MAC) of isoflurane in pre-term neonates compared with full-term neonates and older children [28, 29] and the different sensitivity to bronchodilators because of the lack of smooth muscles in the airways in neonates [30] .

To study the PD of a drug in children, the use of a PD endpoint that has been validated for use in children is a prerequisite. An illustrative example is the measurement of pain in young children. Since they are not able to report their pain using a visual analogue scale, an observational scale has been developed. This comfort behavioural (COMFORT-B) scale was developed and validated for use in children under the age of 3 years [31]. The scale assesses six behavioural items: alertness, calmness, muscle tone, body movement, facial tension and crying (non-ventilated children) or respiratory response (ventilated children). All items range from 1 (no distress) to 5 (severe distress), resulting in a total score varying from 6 to 30. This validated scale can then be used as a PD endpoint for the development of PD models for pain and/or sedation in children of different ages [32, 33, 34].

## Methods of analysing data: standard two-stage or population approach

Since usually only a limited number of observations can be obtained in paediatric subjects, the population approach using non-linear mixed effect modelling to obtain PK and PD parameters is the preferred approach [37]. The population approach differs from the standard two-stage approach in the fact that the analysis is based on simultaneous analysis of all data of the entire population, while still taking into account that different observations come from different patients (Fig. 3b). Additionally, the population approach allows not only for the analysis of dense data, but also for sparse (limited number of observations per individual) and unbalanced data (unequal distribution of observations in various parts of the concentration–time profile in the individuals) or a combination of both. Finally, both the inter-individual and intra-individual variability are separately estimated in the dataset using this approach.

As a result of this methodology, when designing a paediatric study of which the data will be analysed using the population approach, it is advisable to collect samples at different times (or time windows) or to set alternating sampling schemes in subgroups of patients. This also means that (part of the) samples can be collected during routine clinical sampling. Consequently, the burden for the child who participates in the trial is reduced and the statistical power to develop a model describing the concentration–time or concentration–effect profile is not affected or improved.

_{max}model for PD). The random effects quantify the variability that is not explained by the fixed effects. These random effects include inter-subject and intra-subject random variability (Fig. 4), which are both simultaneously and separately estimated. It is often assumed that the variability between subjects follows a normal distribution with a mean of zero and variance ω

^{2}. Equation 1 is used to describe the relationship between individual and population parameter estimates.

*θ*

_{ i }represents the parameter of the

*i*th subject,

*θ*

_{ mean }the population mean, and ηi the variability between subjects. The residual error is generally described using a proportional error (error is dependent on the concentration, which means a higher absolute error at higher concentrations (Eq. 2) or an additive error (constant for all observations (Eq. 3) or a combination of both. This means for the

*j*th observed concentration of the

*I*th individual the relation (Y

_{ij}):

_{pred}is predicted concentration and ε

_{ij}is a random variable with a mean of zero and a variance of σ

^{2}.

In general, model building requires three different steps. First, a structural model (fixed effects) has to be designed, then a statistical sub-model (random effects) has to be developed and in the final step a covariate sub-model is identified.

The structural model describes the overall trend in the data. The choice of structural model (e.g. one-, two- or three-compartment model for PK and an E_{max} model for PD) is to be based upon the best a priori information about the drug to be studied [40]. The structural model uses fixed effects parameters such as clearance and volume of distribution for PK or E_{max} and EC_{50} for PD. The population values for these parameters are called typical values (TV).

After selecting the structural model, the statistical sub-model, which accounts for the inter-individual as well as the residual variability, is chosen and tested. Information on the inter- and intra-individual or residual variability is of clinical value, because it describes differences in clinical response between and within patients and may therefore provide guidance for rational dose adjustments. With the population approach, both these random effects are obtained, apart from estimates of both the population values (TV) and the individual values of PK and PD parameters (so-called post hoc parameter estimates).

In the final step the covariate sub-model is determined, which expresses relationships between covariates and parameters of the structural model (e.g. influence of body weight on volume of distribution or clearance). Covariates can be individual-specific (age, body weight, genetic profile, etc.) or time-varying (renal function, haemodynamic parameters, body temperature, etc.). The covariate analysis will be explained in more detail in the following section.

As these three models are inter-related, the choice of the structural (and statistical) model may affect the choice of the covariate model and vice versa. The process of finding a model that adequately describes the data is thus an elaborate task, where model checking/refining is performed in several steps. To assess model fit in relation to the observed concentrations or effect measures, scatter plots or the so-called goodness-of-fit plots are created (see Validation of the PK–PD models). Free software packages (Xpose, PSN, etc.) are available to generate these plots.

The most commonly used software package for model building, which is also supported by the European Medicines Agency (EMEA) is the non-linear mixed-effect modelling program NONMEM (GloboMax/ ICON, Ellicott City, MD, USA) [4, 41, 42, 43]. NONMEM estimates parameters (e.g. clearance, volume of distribution or EC_{50}) via a maximum likelihood approach. This means that with the given data, the estimations of the parameters are those that occur with the highest probability. Alternative software packages that can be used are, for example, Monolix, WinNonMix, USC*PAC, which uses non-parametric maximum likelihood methods [44], or ADAPT, using maximum a posteriori (MAP) methods [45].

## Covariate analysis

To determine the optimal dose based on the individual characteristics of a patient, a covariate analysis has to be performed [40, 46, 47]. The aim of the covariate analysis is to identify specific predictors (covariates) of PK and PD variability and can typically be studied in population models. Covariate analysis involves the modelling of the distribution of the individual parameter estimates as a function of covariates, which can be of demographic (e.g. age, body weight, gender), patho-physiological (e.g. renal or hepatic function) and genetic/environmental origin, and/or be the result of the concomitant use of other drugs, which may influence the PK and/or PD. The identification of predictive covariates for variability provides the scientific basis for rational and individualised, patient-tailored dosing schemes.

The influence of developmental changes in childhood can be explored primarily by using size and/or age as covariates. Size (body weight) can be incorporated into the model using two different approaches. The first approach, the “allometric size approach”, includes size a priori by using a body weight-based exponential equation with a fixed exponent of 0.75 for clearance and 1 for volume of distribution [48, 49, 50, 51, 52]. Once size is incorporated in the model using this fixed method, the influence of age is investigated, being the difference between the actual value of the PK parameter and the 0.75 allometric equation. When incorporating age as a covariate, different age descriptors may be used like postmenstrual age (PMA), gestational age (GA) or postnatal age (PNA) [53]. The choice of any of these age descriptors is based on the results of the systematic covariate analysis as described below [50, 54]. In the second approach, the “systematic covariate analysis”, body weight is regarded as a covariate as any other, which means that the descriptive properties on the PK parameters are evaluated in a systematic covariate analysis as described below [55, 56, 57].

In a systematic analysis, when studying the influence of covariates, scatter plots and summary plots of individual parameter estimates and/or weighted residuals versus covariates are used to screen for appropriate covariates to include in the covariate sub-model. Additionally, these plots are used to explore the nature of the influence of the covariate (linear, exponential, allometric, subpopulations, etc.). Likely candidate covariates are then added to the model (forward inclusion). The influence of each covariate on the parameters is examined separately and compared with the simple model (no covariates). To assess whether the model with the covariate statistically improved the fit to the data, the difference between their objective function value, referred to as the log–likelihood ratio, is calculated. This ratio is assumed to be Chi-squared distributed, which means that a reduction in objective function of 3.84 is considered to be significant (*P* < 0.05) [43, 58]. In addition, the reduction in objective function, goodness-of-fit plots of the simple model and covariate model are explored for diagnostic purposes. Furthermore, the confidence interval of the parameter estimates, the correlation matrix (indicates the relationship between two structural parameters) and visual improvement of the individual plots are used to evaluate the model. Finally, a superior model is expected to reduce the inter-subject variance and/or the residual error terms. This procedure of covariate modelling implies that each covariate is only implemented if it can be fully justified by the data and the results of the statistic evaluations.

When two or more covariates are found to significantly improve the model, the covariate that reduced the objective function most is included in the model after which the other covariates are tested again for their significance. After all covariates that significantly improved the objective function are added to the simple model, a backward deletion is performed, which means that each covariate is removed from the full model, one at a time (the one that causes the smallest increase in objective function first). Retaining or removing the covariate is statistically tested by the use of the objective function (Chi-squared test) until each covariate has been tested.

In datasets containing sparse data, there may not be enough information to accurately estimate inter- and intra-individual variability. This causes the values of these parameters to shrink to 0, resulting in individual parameter estimates that are closer to the population parameter estimates than they really are. This phenomenon is called shrinkage [59]. Shrinkage may cause individual predictions, individual parameter estimates and diagnostics based on them to be less reliable. It can also hide, falsely introduce or distort the shape of covariate relationships.

Shrinkage is the result of the properties of the data and is therefore difficult to avoid. One can only be aware of the presence of shrinkage, realise the influence it may have on the covariate analysis and use diagnostics other than those based on individual predictions or individual weighted residuals in the model building and model evaluation procedures.

## Validation of PK–PD models

The objective of a PK or PK–PD modelling exercise is usually not just to describe the dataset of the sample of individuals that were studied. Generally, models are used to simulate which concentrations and/or effects and their variability can be expected when different doses are given to future patients. These simulations may therefore lead to optimised dosing recommendations or to optimisation of new studies for the entire population to which the sample of individuals belongs. It is often said that “all models are wrong, but some are useful” [60]. In order to define whether a model is useful and valid for clinical and trial simulations, thorough evaluation and validation of the model is necessary. Although validations of PK models are only performed in 17% of the published paediatric studies [4] and in 28% of the adult studies [61], proper model validations are an essential step in model building. For this purpose, different evaluation and validation methods are available. As described before [62], a proper validation and evaluation procedure includes an internal model evaluation followed by an external evaluation and a prospective clinical study.

The first evaluation method is the basic internal model validation used to assess whether the model is able to describe the learning dataset (the dataset used to develop the model) accurately and without bias. This evaluation should actually be considered the final stage of the model building procedure. Subsequently, in the external evaluation, it is assessed whether the model is able to describe one or more external datasets (the datasets other than the one used to develop the model) adequately. Alternatively, if a dataset is sufficiently large the original dataset may be split in two so that the model is developed using one part (about two thirds) of the dataset and evaluated externally using the other part (one third) of the dataset. In paediatric studies, it is then especially important to stratify the data correctly and ascertain that all age groups are represented in equal proportions in both datasets.

Various techniques are available for the validation and evaluation of population PK and PK–PD models (both for internal and external validation procedures).

### Basic goodness-of-fit plots

- 1.
Individual predicted vs observed concentrations

- 2.
Population predicted vs observed

- 3.
(Conditional) weighted residual vs time

- 4.
(Conditional) weighted residuals vs dependent variable

These plots are used in model building, but can also be used to ascertain that there is no trend or bias in the model predictions of the final model. Furthermore, these plots can be used for both the internal and external evaluation of the model.

### Bootstrap analysis

In a bootstrap analysis new datasets are generated by resampling from the original dataset and it is therefore an internal validation of the model. The new datasets are subsequently refitted to the original model, yielding mean values and standard errors for every model parameter.

A bootstrap analysis provides information on the stability of the model and its dependence on specific individuals in the learning dataset. With the freely available PSN or Wings for NONMEM software packages an automated bootstrap analysis can be performed.

### Visual predictive check

The VPC is a simulation-based diagnostic that can be used when the PK or PD profiles for all individuals in the dataset are similar and it allows for easy interpretation of the result. For this diagnostic tool, there are no statistical tests and all evaluations are based on visual assessment. When the individual profiles are expected to deviate largely from one another because there is for instance a large variability in the time and amount of dose administrated, or when there are many covariates, the use of this diagnostic becomes more difficult.

### Normalised prediction distribution error

If the model performed well in both evaluation procedures, the dosing algorithm that results from the PK–PD model needs to be tested and challenged in a prospective (clinical) trial. If the predictive performance of the model is corroborated by the trial it can be used with confidence in clinical practice.

## Optimal design of paediatric studies

When new population PK–PD studies are performed, it is important to design these studies in the most efficient manner possible to obtain maximum information about the PK and PD parameters so that they can be determined with the highest precision [51, 67]. When designing PK–PD studies in paediatrics certain factors need to be taken into account, e.g. the age range of the paediatric group, the therapeutic index, the possibility of collecting blood samples, the availability of validated PD endpoints for children and the availability of sensitive analytical methods.

*n*= 6) compared with the study by Peeters et al. (

*n*= 24) can be explained because there often exists a lower variability in PK than in PD, which results in a lower number of patients required (Table 1).

Factors influencing the required number of patients and/or samples per patient

Factor | Number of patients/samples |
---|---|

Study of PK only | Relatively small number of patients/samples |

Study of PK–PD relationship | Relatively high number of patients/samples |

Even distribution of covariates (age, body weight) | ↓ Number of patients/samples |

↑ Number of changes in dose | ↑/- Number of patients/samples (depending on other aspects of the study design) |

↑ Number of samples/child | ↓ Number of patients |

Use of optimal sampling strategies | ↓ Number of patients/samples |

Different sampling windows (e.g. two or three sampling schemes) | ↓ Number of patients/samples |

## Conclusions and perspectives

In view of the European Regulation, which came into force in 2007, it now seems time to use the progress that has been made in the field of integrated PK–PD modelling [72] to develop rational and individualised dosing schemes for children. Because of the possibility of analysing sparse and unbalanced datasets, thereby minimising the burden for each child, population PK–PD modelling and simulation using non-linear mixed effect modelling has become the preferred tool to develop effective and safe dosing regimens for children. Specifically in paediatrics, where developmental changes have to be taken into account, which may influence the PK and/or the PD of the drugs, this advanced statistical tool is of critical value.

Before dosing regimens can be tested in clinical practice, proper validations of the models should be performed, for which adequate tools have recently been developed. Besides internal and external validation, prospective clinical trials, which allow for the evaluation of the model-based dosing regimens, are needed, not only to adjust the proposed dosing regimen, but also to convince paediatricians to use the information that has been generated using these modelling exercises.

Furthermore, one of the future goals may be to explore the possibilities of cross-validation of the models, in which the reported influences of developmental changes on a certain PK or PD parameter of one drug are evaluated for use in another drug that goes through the same metabolic route or shares the same mechanism of action. In this respect, physiologically based pharmacokinetic (PBPK) models are needed. PBPK models consider the physiological and biochemical processes by using in vitro data to describe the PK of drugs [73, 74]. The combination of these two approaches may use the information that is already available in an optimal way in defining effective and safe dosing regimens for each individual patient.

In conclusion, analyses of paediatric data using population PK–PD modelling and covariate analysis will result in individualised dosing regimens for children of different ages, body weights and genetic backgrounds. Thus, population PK–PD modelling constitutes an innovative approach to the study of drug effects in this very special patient population, which is otherwise difficult to study.

## Notes

### Acknowledgements

The clinical research of K. Allegaert is supported by the Fund for Scientific Research, Flanders (Belgium) (FWO Vlaanderen) by a Fundamental Clinical Investigatorship (1800209 N). The clinical research of C.A.J. Knibbe is supported by the Innovational Research Incentives Scheme (Veni grant, July 2006) of the Dutch Organisation for Scientific Research (NWO). The research of E.H.J. Krekels is supported by the Top Institute Pharma project number D2-104. The authors would like to thank Professor Nick Holford and Professor Mats Karlsson for providing figure 5, Dr. Rifka Peeters for providing figure 2 and Pieter De Cock for critically reading the manuscript.

### Open Access

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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