Elke H. J. Krekels

The role of population PK-PD modelling in paediatric clinical research

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.

Morphine glucuronidation in preterm neonates, infants and children younger than 3 years

Background and objectiveA considerable amount of drug use in children is still unlicensed or off-label. In order to derive rational dosing schemes, the influence of aging on glucuronidation capacity in newborns, including preterms, infants and children under the age of 3 years was studied using morphine and its major metabolites as a model drug.MethodsA population pharmacokinetic model was developed with the nonlinear mixed-effects modelling software NONMEM® V, on the basis of 2159 concentrations of morphine and its glucuronides from 248 infants receiving intravenous morphine ranging in bodyweight from 500 g to 18 kg (median 2.8 kg). The model was internally validated using normalized prediction distribution errors.ResultsFormation clearances of morphine to its glucuronides and elimination clearances of the glucuronides were found to be primarily influenced by bodyweight, which was parameterized using an allometric equation with an estimated exponential scaling factor of 1.44. Additionally, a postnatal age of less than 10 days was identified as a covariate for formation clearance to the glucuronides, independent of birthweight or postmenstrual age. Distribution volumes scaled linearly with bodyweight.ConclusionsModel-based simulations show that in newborns, including preterms, infants and children under the age of 3 years, a loading dose in µg/kg and a maintenance dose expressed in µg/kg1.5/h, with a 50% reduction of the maintenance dose in newborns younger than 10 days, results in a narrow range of morphine and metabolite serum concentrations throughout the studied age range. Future pharmacodynamic investigations are needed to reveal target concentrations in this population, after which final dosing recommendations can be made.

Systematic evaluation of the descriptive and predictive performance of paediatric morphine population models.

ABSTRACTPurposeA framework for the evaluation of paediatric population models is proposed and applied to two different paediatric population pharmacokinetic models for morphine. One covariate model was based on a systematic covariate analysis, the other on fixed allometric scaling principles.MethodsThe six evaluation criteria in the framework were 1) number of parameters and condition number, 2) numerical diagnostics, 3) prediction-based diagnostics, 4) η-shrinkage, 5) simulation-based diagnostics, 6) diagnostics of individual and population parameter estimates versus covariates, including measurements of bias and precision of the population values compared to the observed individual values. The framework entails both an internal and external model evaluation procedure.ResultsThe application of the framework to the two models resulted in the detection of overparameterization and misleading diagnostics based on individual predictions caused by high shrinkage. The diagnostic of individual and population parameter estimates versus covariates proved to be highly informative in assessing obtained covariate relationships. Based on the framework, the systematic covariate model proved to be superior over the fixed allometric model in terms of predictive performance.ConclusionsThe proposed framework is suitable for the evaluation of paediatric (covariate) models and should be applied to corroborate the descriptive and predictive properties of these models.

Advances in paediatric pharmacokinetics

Importance of the field: In recent years, interest in paediatric pharmacokinetics has grown. Even though pharmacokinetic and pharmacodynamic relations determine together the optimal drug dose, age-related changes in pharmacokinetics have proven of utmost importance for optimising drug dosing in children. Areas covered in this review: Constraints in paediatric studies result in data sets with specific characteristics requiring advanced analysis and validation approaches which are discussed in conjunction with directions for future research. What the reader will gain: Advances have been made in the development of descriptive paediatric pharmacokinetic models for specific drugs and age ranges, and in the identification of analysis and diagnostic tools for paediatric model building and evaluation, while sharing of data between academia and/or industry has proven crucial for limiting additional burden in children. Even though progress is being made, currently none of the scaling approaches has proven of universal value for extrapolations to other age ranges and/or other drugs. Take home message: The focus of future research should be on the development of mechanistic and validated pharmacokinetic models for specific elimination routes that have predictive and extrapolation potential, making them of use in designing algorithms to derive first-time-in-child doses and individualised dosing guidelines in paediatrics.

Predictive Performance of a Recently Developed Population Pharmacokinetic Model for Morphine and its Metabolites in New Datasets of (Preterm) Neonates, Infants and Children

Background and ObjectiveModel validation procedures are crucial when models are to be used to develop new dosing algorithms. In this study, the predictive performance of a previously published paediatric population pharmacokinetic model for morphine and its metabolites in children younger than 3 years (original model) is studied in new datasets that were not used to develop the original model.MethodsSix external datasets including neonates and infants up to 1 year were obtained from four different research centres. These datasets contained postoperative patients, ventilated patients and patients on extracorporeal membrane oxygenation (ECMO) treatment. Basic observed versus predicted plots, normalized prediction distribution error analysis, model refitting, bootstrap analysis, subpopulation analysis and a literature comparison of clearance predictions were performed with the new datasets to evaluate the predictive performance of the original morphine pharmacokinetic model.ResultsThe original model was found to be stable and the parameter estimates were found to be precise. The concentrations predicted by the original model were in good agreement with the observed concentrations in the four datasets from postoperative and ventilated patients, and the model-predicted clearances in these datasets were in agreement with literature values. In the datasets from patients on ECMO treatment with continuous venovenous haemofiltration (CVVH) the predictive performance of the model was good as well, whereas underprediction occurred, particularly for the metabolites, in patients on ECMO treatment without CVVH.ConclusionThe predictive value of the original morphine pharmacokinetic model is demonstrated in new datasets by the use of six different validation and evaluation tools. It is herewith justified to undertake a proof-of-principle approach in the development of rational dosing recommendations — namely, performing a prospective clinical trial in which the model-based dosing algorithm is clinically evaluated.

From Pediatric Covariate Model to Semiphysiological Function for Maturation: Part II—Sensitivity to Physiological and Physicochemical Properties

To develop a maturation function for drug glucuronidation in children, that can be used in population and physiologically based modeling approaches, the physiological and physicochemical basis of a semiphysiological glucuronidation function for children was untangled using Simcyp. The results show that using the currently available in vitro data, in vivo morphine and zidovudine clearances were under predicted by the physiologically based model in Simcyp. The maturation profile was similar to the clinically observed profile except for the first 2 weeks of life, and liver size and UGT2B7 ontogeny are the physiological drivers of the maturation of glucuronidation. Physicochemical drug parameters did not affect this maturation profile, although log P and pKa influenced the absolute value of clearance. The results suggest that the semiphysiological glucuronidation function for young children can be used to predict the developmental clearance profile of other UGT2B7 substrates, though scenarios with nonlinear kinetics and high‐extraction ratios require further investigation.

From Pediatric Covariate Model to Semiphysiological Function for Maturation: Part I–Extrapolation of a Covariate Model From Morphine to Zidovudine

New approaches to expedite the development of safe and effective pediatric dosing regimens and first‐in‐child doses are urgently needed. Model‐based approaches require quantitative functions on the maturation of different metabolic pathways. In this study, we directly incorporated a pediatric covariate model for the glucuronidation of morphine into a pediatric population model for zidovudine glucuronidation. This model was compared with a reference model that gave the statistically best description of the data. Both models had adequate goodness‐of‐fit plots and normalized prediction distribution errors (NPDE), similar population clearance values for each individual, and a Δobjective function value of 13 points (Δ2df). This supports our hypothesis that pediatric pharmacokinetic covariate models contain system‐specific information that can be used as semiphysiological functions in pediatric population models. Further research should explore the validity of the semiphysiological function for other UDP‐glucuronosyltransferase 2B7 substrates and patient populations and reveal how this function can be used for pediatric physiologically based pharmacokinetic models.

Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients’ (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.

Pharmacogenetics and paediatric drug development: issues and consequences to labelling and dosing recommendations.

The area of pharmacogenetics (PGt) is evolving rapidly. However, ongoing efforts in this field are not aligned with the requirements for the inclusion of clinically relevant findings into the label, especially with reference to paediatric indications. Clinical research in children poses unique issues from a practical and technical perspective, but many challenges can be overcome by applying advanced study design and data analysis methods. When investigating the role of PGt factors on treatment effect, all features that influence drug response must be taken into account. Yet, PGt often has a privileged status in research protocols, with PGt factors evaluated independently from other determinants of response, instead of being regarded as other demographic or clinical covariates (e.g., age, renal function). At present, guidelines to incorporate PGt findings into label statements are lacking in part because this is a new and incompletely understood area. This situation is no longer acceptable. To achieve the potential that PGt can offer to drug development and ultimately to drug prescription, academia, industry and regulatory agencies need to pool resources on the revision of study design and data analysis requisites, bringing in model-based methodologies to enable accurate interpretation of results and provide appropriate labelling recommendations.

Influence of Plasma Protein Binding on Pharmacodynamics: Estimation of In Vivo Receptor Affinities of β Blockers Using a New Mechanism-Based PK–PD Modelling Approach

The objective of this investigation was to examine in a systematic manner the influence of plasma protein binding on in vivo pharmacodynamics. Comparative pharmacokinetic-pharmacodynamic studies with four beta blockers were performed in conscious rats, using heart rate under isoprenaline-induced tachycardia as a pharmacodynamic endpoint. A recently proposed mechanism-based agonist-antagonist interaction model was used to obtain in vivo estimates of receptor affinities (K(B,vivo)). These values were compared with in vitro affinities (K(B,vitro)) on the basis of both total and free drug concentrations. For the total drug concentrations, the K(B,vivo) estimates were 26, 13, 6.5 and 0.89 nM for S(-)-atenolol, S(-)-propranolol, S(-)-metoprolol and timolol. The K(B,vivo) estimates on the basis of the free concentrations were 25, 2.0, 5.2 and 0.56 nM, respectively. The K(B,vivo)-K(B,vitro) correlation for total drug concentrations clearly deviated from the line of identity, especially for the most highly bound drug S(-)-propranolol (ratio K(B,vivo)/K(B,vitro) approximately 6.8). For the free drug, the correlation approximated the line of identity. Using this model, for beta-blockers the free plasma concentration appears to be the best predictor of in vivo pharmacodynamics.