Influence of covariate distribution on the predictive performance of pharmacokinetic models in paediatric research

Abstract

AIMSThe accuracy of model-based predictions often reported in paediatric research has not been thoroughly characterized. The aim of this exercise is therefore to evaluate the role of covariate distributions when a pharmacokinetic model is used for simulation purposes. METHODSPlasma concentrations of a hypothetical drug were simulated in a paediatric population using a pharmacokinetic model in which body weight was correlated with clearance and volume of distribution. Two subgroups of children were then selected from the overall population according to a typical study design, in which pre-specified body weight ranges (10-15 kg and 30-40 kg) were used as inclusion criteria. The simulated data sets were then analyzed using non-linear mixed effects modelling. Model performance was assessed by comparing the accuracy of AUC predictions obtained for each subgroup, based on the model derived from the overall population and by extrapolation of the model parameters across subgroups. RESULTSOur findings show that systemic exposure as well as pharmacokinetic parameters cannot be accurately predicted from the pharmacokinetic model obtained from a population with a different covariate range from the one explored during model building. Predictions were accurate only when a model was used for prediction in a subgroup of the initial population. CONCLUSIONSIn contrast to current practice, the use of pharmacokinetic modelling in children should be limited to interpolations within the range of values observed during model building. Furthermore, the covariate point estimate must be kept in the model even when predictions refer to a subset different from the original population. WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Modelling and simulation (M&S) is often applied as a design tool for pharmacokinetic and pharmacokinetic-pharmacodynamic bridging in paediatric research.• When models are used for the selection of dose and prediction of drug exposure and/or effects in a new population the identification of influential covariates can play a major role in the accuracy of parameter estimates and subsequent predictions.• The identification of the correct covariates in children is often complex due to the presence of correlations and co-linearity between covariates. WHAT THIS STUDY ADDS• Unless a mechanism-based model can be warranted, the use of a stepwise approach for covariate analysis is not recommended when small datasets are available.• Extrapolation of the covariate effects beyond the parameter distributions explored during model building cannot be performed without bias and, consequently, lead to erroneous dosing recommendations.• The covariate point estimate must be retained in the model when predictions refer to a population in which median or mean values differ from the population used during model building.

How to optimise drug study design: pharmacokinetics and pharmacodynamics studies introduced to paediatricians

“…Variability, in this context, is typically split into between-patient variability, between-occasion variability (within the same patient on different occasions during the course of treatment) and residual variability in the measurements. [22] The implementation of this type of analysis can be performed using different techniques and software programs. The most commonly used software for population PK and PKPD modelling is NONMEM (Icon Development Plc, South County Business Park, Leopardstown Dublin 18, Ireland).…”

“…This ‘data‐rich’ approach has severe limitations in paediatric practice for both ethical and practical reasons: the fixed sampling strategy potentially interferes with patient care; and the requirement for multiple blood samples (perhaps 12–15) raises concerns about venous access and blood loss. Population PK (using sparse sampling schemes in which less blood samples are taken per individual without the need for a rigid sampling time as compared to classical PK studies) and PKPD modelling (using statistical models to characterise the exposure–response relationship of a drug) are now well established . This approach prevents children being exposed to the practice of large numbers and volumes of blood sampling seen in adult PK and PKPD studies.…”

“…Identifiable sources of variability are converted into covariate factors during the analysis, whereas non‐identifiable sources are expressed as statistical distributions. Variability, in this context, is typically split into between‐patient variability, between‐occasion variability (within the same patient on different occasions during the course of treatment) and residual variability in the measurements . The implementation of this type of analysis can be performed using different techniques and software programs.…”

“…Population PK (using sparse sampling schemes in which less blood samples are taken per individual without the need for a rigid sampling time as compared to classical PK studies) and PKPD modelling (using statistical models to characterise the exposure-response relationship of a drug) are now well established. [18][19][20][21][22][23] This approach prevents children being exposed to the practice of large numbers and volumes of blood sampling seen in adult PK and PKPD studies.…”

Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques

“…However to be effective as a design tool, it is essential to ensure that the patient populations used in the simulation process reflect real patients and that covariate correlations are well described. Particularly important is the covariate distribution defining the correlation between patient demographic and disease characteristics, given that they are often related to the study outcome ( 12 , 27 ). Such correlations have so far been overlooked in previous investigations in which covariate effects are treated as structural parameters in pharmacokinetic or pharmacokinetic-pharmacodynamic models.…”