Physiologically Based Pharmacokinetic Modeling of Monoclonal Antibodies in Pediatric Populations Using PK-Sim - Basu, Sumit; Lien, Yi Ting (Kayla); Vozmediano, Valvanera; Schlender, Jan-Frederik; Eissing, Thomas; Schmidt, Stephan; Niederalt, Christoph (Frontiers in Pharmacology, 2020)
Physiologically‐based pharmacokinetic (PBPK) models are increasingly used to support pediatric dose selection for small molecule drugs. In contrast, only few pediatric PBPK models for therapeutic antibodies have been published recently and the knowledge on the maturation of the processes relevant for antibody pharmacokinetics (PK) is limited in comparison to the knowledge for small molecules. The aim of this study was thus to evaluate predictions from antibody PBPK models for children which were scaled from PBPK models for adults in order to identify respective knowledge gaps. For this we used the generic PBPK model implemented in PK-Sim without further modifications. Focusing on general clearance and distribution mechanisms, we selected palivizumab and bevacizumab as examples for this evaluation since they show simple, linear PK which is not governed by drug-specific target mediated disposition at usual therapeutic dosages and their PK has been studied in pediatric populations after intravenous application. The evaluation showed that the PK of palivizumab was overall reasonably well predicted, while the clearance for bevacizumab seems to be underestimated. Without implementing additional ontogeny for antibody PK specific processes into the PBPK model, bodyweight normalized clearance increases only moderately in young children compared to adults. If growth during aging at the time of the simulation was considered, the apparent clearance is higher by around 20% compared to simulations for which growth was not considered for newborns due to the long half-life of antibodies. To fully understand the differences and similarities in the PK of antibodies between adults and children, further research is needed. By integrating available information and data, PBPK modelling can contribute to reveal the relevance of involved processes as well as to generate and test hypothesis.