Model-based comparison of mAb clearance in pediatric populations*
Saskia Fuhrmann (1,2), Wilhelm Huisinga (3) and Hans Peter Grimm (4)
(1) Institute of Biochemistry, Universitaet Potsdam, Germany; (2) Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling, Freie Universitaet Berlin and Universitaet Potsdam, Germany (3) Institute of Mathematics, Universitaet Potsdam, Germany; (4) Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center, Basel *This work was conducted during a PharMetrX-Internship (I2) at Roche.
Objectives: Health authorities demand the early use of predictive models to support pediatric drug development. So far, few models to predict monoclonal antibody (mAb) pharmacokinetics (PK) in children have been used. However, no comparison of the model-based predictions across age exists. The objective of this study was to quantitatively compare age-dependency of clearance (mean and spread) using the available models.
Methods: We reviewed literature on POP-PK models involving pediatric populations (including individuals < 12 years of age). Linear clearance (CL) was compared using these models by linking the reported covariates (typically body weight) to age, e.g., using CDC/WHO growth charts [1]. In addition, we included simulations of the age-dependency of CL using commercial PBPK software. Mean and variability of predicted CL vs. age profiles were compared.
Results: Current POP-PK models mainly include purely body weight-based scaling methods. Only for Palivizumab, an explicit age-based ‘maturation’ function was used [2]. Pronounced differences in the trend of CL across age are visible between the investigated mAbs, in particular, a difference in predicted CL between ‘maturation’ and body weight-based methods for infants. The Simcyp® PBPK model incorporates age-related changes in system parameters and ontogeny of endogenous IgG. However, maturation processes, e.g., FcRn ontogeny, is lacking (Simcyp®Simulator v16). As a consequence, Simcyp® predicts a different trend of CL across age especially during the first years of age. Ontogeny of FcRn expression and of endogenous IgG concentration is not included in the currently available version of GASTROPLUS® 9.0.
Conclusions: Investigating the impact of empirically-based and PBPK models on predictions helps to gain confidence in predicting the PK of mAbs in children. It remains to be elucidated within further research whether differences in age dependency of CL between mAbs are related to a bias in assessment due to usually sparse data below 6 years of age (except Palivizumab) or due to the effects of disease and study population.
References:
[1] WHO/CDC growth charts. https://www.cdc.gov/growthcharts. Accessed 28 September 2016
[2] Robbie et al. Antimicrob Agents Chemother, 56: 4927-4936, 2012.
[3] Johnson et al. Clin Pharmacokinet, 45: 931-956, 2006.
[4] Sumpter et al. Paediatric Anaesthesia, 21: 309-315, 2011.
[5] Edlund et al. Clin Pharmacokinet, 54: 35-80, 2014.
