The concept of physiologically based pharmacokinetic (PBPK) modeling was introduced years ago, but it has not been practiced significantly. However, interest in and implementation of this modeling technique have grown, as evidenced by the increased number of publications in this field. This paper demonstrates briefly the methodology, applications, and limitations of PBPK modeling with special attention given to discuss the use of PBPK models in pediatric drug development and some examples described in detail. Although PBPK models do have some limitations, the potential benefit from PBPK modeling technique is huge. PBPK models can be applied to investigate drug pharmacokinetics under different physiological and pathological conditions or in different age groups, to support decision‐making during drug discovery, to provide, perhaps most important, data that can save time and resources, especially in early drug development phases and in pediatric clinical trials, and potentially to help clinical trials become more “confirmatory” rather than “exploratory”.

The aim of the present study was the investigation of N-terminal pro-brain natriuretic peptide (NT-proBNP) in the pediatric population. This is essential for adequate monitoring and classification of pediatric patients with heart disease, but no consistent data are available yet. In addition, the comparability of two commercially available NT-proBNP assays and the inter-laboratory variability for the most suitable one were assessed. For this purpose, 408 subjects (1–29years) were included. NT-proBNP was determined with a non-competitive electrochemiluminescent immunoassay (Roche NT-proBNP; n=402) and a competitive enzyme-immunoassay (Biomedica NT-proBNP; n=402). Inter-laboratory variability was evaluated for the Roche assay by stepwise inclusion of four and 11 centers throughout Germany, respectively. Roche NT-proBNP ranged from 5.0 to 391.5ng/L, with higher values for younger children. The 97.5th (75th) percentile curve ranged from 319.9ng/L (231.2 ng/L, 1–3years) to 114.9ng/L (53.3 ng/L, 18years). In contrast, Biomedica NT-proBNP ranged from 253.7 to 7602.8 ng/L, with no significant age dependency. The mean difference between the assays was 1649.7ng/L (95% confidence interval 1546.3–1753.1ng/L). Inter-laboratory variability ranged from 6.5% to 3.8%, covering a range from 51.3 to 6618.1ng/L. The assay seems to influence the interpretation of resulting NT-proBNP values and therefore has to be chosen carefully. For the monitoring and classification of pediatric patients with congenital heart disease, age-based NT-proBNP values should be used.

In recent years, the increased interest in pediatric research has enforced the role of physiologically based pharmacokinetic (PBPK) models in pediatric drug development. However, an existing lack of...

What is already known about this subject • Applying in silico tools such as population pharmacokinetic analysis and simulation will help to find adequate dosing strategies and increase the probability of success for a randomized controlled trial.• Up to now, for carvedilol in paediatric patients with congestive heart failure (CHF) the dose has been linearly extrapolated from adults, but the results with this dosing strategy are ambiguous.• Further trials are necessary to establish carvedilol for paediatric patients with CHF. What this study adds • Carvedilol pharmacokinetics in paediatric patients with CHF depends on the weight and age of the patient.• Therefore, the drug exposure differs substantially between patients of different ages receiving the same dose with respect to body weight.• Simulations revealed that an age‐adjusted carvedilol dosing strategy with higher doses for younger patients with respect to body weight is preferable to a uniform one.Aims: To investigate the ontogeny of carvedilol pharmacokinetics and to develop an age‐appropriate carvedilol dosing strategy for paediatric patients.Methods: Data were derived from a prospective, nonplacebo‐controlled study of carvedilol for the treatment of paediatric patients with congestive heart failure and analysed using a nonlinear mixed‐effects modelling approach (NONMEM, Version V 1.1). The population pharmacokinetic model was further utilized for simulations of different carvedilol dosing strategies.Results: Four hundred and eighty carvedilol plasma concentrations of 41 patients (0.1–19.3 years; median 3.5) were included in the analysis. A two‐compartment model with first‐order absorption and absorption lag served as structural model. Weight and age were the most important covariates for carvedilol pharmacokinetics. The weight‐adjusted clearance was highest for the younger patients with 2.7 l h⁻¹ kg⁻¹ for a 1‐year‐old patient compared with 0.7 l h⁻¹ kg⁻¹ for a 19.3‐year‐old patient. Dose simulations revealed that the area under the plasma concentration–time curve (AUC) as a measure of drug exposure increased with age despite constant doses with respect to body weight. For infants (28 days to 23 months), children (2–11 years) and adolescents (12–15 years) daily doses of 3, 2 and 1 mg kg⁻¹, administered in two or three discrete doses, were necessary to reach an exposure comparable to adults receiving 0.7 mg kg⁻¹ day⁻¹.Conclusion: The ontogeny of carvedilol pharmacokinetics in paediatric patients depends on age and weight. Dose simulations revealed that younger patients have to be treated with higher doses with respect to body weight to reach the same exposure as adults.