2019 - Stockholm - Sweden

PAGE 2019: Drug/Disease modelling - Paediatrics
Erik Sjögren

A PBPK Framework to Predict Drug Exposure in Malnourished Children

Erik Sjögren (1) and E. Niclas Jonsson (1)

(1) Pharmetheus, Sweden

Introduction: Protein energy malnutrition in children is a global health problem, particularly in developing countries. The effects of nutritional status on body composition and physiological functions may have implications for drug disposition and ultimately affect the clinical outcome in this already vulnerable population. Physiologically based pharmacokinetic (PBPK) modeling can be used to predict the effect of protein starvation as it links physiological changes to pharmacokinetic (PK) consequences. Still, the absence of detailed information on body composition and the scarce availability of controlled clinical trials in malnourished children, complicates the establishment and evaluation of a generic PBPK model in this population. However, by combining information on a) the differences in body composition between healthy and malnourished adults and b) the differences in physiology between healthy adults and children, a physiologically based bridge to a malnourished pediatric population can be made.

Objectives: To develop and evaluate a physiologically based translational framework for prediction of drug disposition and PK characteristics in children with severe protein energy malnutrition.

Methods: Changes to body composition and plasma protein concentrations due to protein energy malnutrition were derived for adults from the literature and implemented in PK-Sim® (v7.4.0) [1][2][3]. To accommodate the differences between a healthy and a malnourished adult population in PK-Sim, compiled physiological data were converted to a set of physiological scaling parameters. Based on the assumption that the physiological changes occurring in malnourished adults are similar to those occurring in children, the physiological scaling parameters were used to create a malnourished pediatric population from a healthy pediatric population. The healthy pediatric population had been generated using the population algorithm in PK-Sim® including maturation of biological systems, e.g., metabolic enzymes and plasma proteins.

Observed and simulated plasma concentration versus time profiles and PK parameters for three model drugs were compared to evaluate the performance of the suggested modelling approach and thereby to verify the appropriateness of the virtual malnourished pediatric population generated. PBPK models in PK-Sim® for the model drugs ciprofloxacin, caffeine and cefoxitin were either developed, based on drug specific information and a middle out modelling approach towards clinical data in healthy adults collected from the literature, or adopted from previous publications [4] [5] [6] [7] [8] [9]. All PBPK modelling and analyses were performed in PK-Sim®. Prediction error (PE) (predicted/observed) within the range of 0.5-2.0 were considered as adequate predictive performance.

Results: The drug models for ciprofloxacin, caffeine and cefoxitin were verified against clinical data from healthy adults. Plasma concentration-time profiles, as well as the inter individual variability, in malnourished children were well captured by the model predictions applying the suggested PBPK framework. Adequate predictions of model drug exposure were achieved for all investigated cases. The PE of AUC for ciprofloxacin in malnourished pediatric populations with an average age (years) of 0.5, 1, 2, 5 and 10 was 1.24, 0.95, 1.14, 1.25 and 0.71, respectively. For caffein and cefoxitin the PE of AUC was 0.57 and 1.17, respectively, in malnourished pediatric populations of an average age of 2.6 and 2.3 years, respectively. The appropriateness of the derived physiological scaling parameters and the proposed physiologically based translational modeling strategy were supported by the results.

Conclusions: The results demonstrate that the proposed modelling strategy is appropriate for predictions of drug disposition and PK in malnourished children. 

[1] Barac-Nieto M et al. Am J Clin Nutr. (1978) Jan;31(1):23-40.
[2] Bosy-Westphal A et al. Int J Obes Relat Metab Disord. (2004) Jan;28(1):72-9.
[3] https://github.com/Open-Systems-Pharmacology
[4] Thuo N et al. J Antimicrob Chemother. (2011) Oct;66(10):2336-45.
[5] http://atper.eu/wp-content/uploads/2015/12/ATPER2013_Wanicha.pdf
[6] Schlender JF et al. Clin Pharmacokinet. (2018) Dec;57(12):1613-1634
[7] Akinyinka OO et al. Eur J Clin Pharmacol. (2000) May;56(2):153-8.
[8] Buchanan et al.Br. J. Clin. Pharmac. (1980) Jun;9(6):623-7
[9] Kampf et al. Antimicrob Agents Chemother. (1981) Dec;20(6):741-6.

Reference: PAGE 28 (2019) Abstr 8968 [www.page-meeting.org/?abstract=8968]
Poster: Drug/Disease modelling - Paediatrics