title: Pharmacokinetics of Anti-VEGF Agent Aflibercept in Cancer Predicted by Data-Driven, Molecular-Detailed Model.
abstract: Mathematical models can support the drug development process by predicting the pharmacokinetic (PK) properties of the drug and optimal dosing regimens. We have developed a pharmacokinetic model that includes a biochemical molecular interaction network linked to a whole-body compartment model. We applied the model to study the PK of the anti-vascular endothelial growth factor (VEGF) cancer therapeutic agent, aflibercept. Clinical data is used to infer model parameters using a Bayesian approach, enabling a quantitative estimation of the contributions of specific transport processes and molecular interactions of the drug that cannot be examined in other PK modeling, and insight into the mechanisms of aflibercept's antiangiogenic action. Additionally, we predict the plasma and tissue concentrations of unbound and VEGF-bound aflibercept. Thus, we present a computational framework that can serve as a valuable tool for drug development efforts.
authors.affiliation: Department of Biomedical Engineering University of Southern California Los Angeles California USA.
authors.forename: S D
authors.initials: SD
authors.lastname: Finley
authors.affiliation: Computational Science and Engineering Laboratory, Department of Mechanical and Process Engineering, ETH Zurich Switzerland.
authors.forename: P
authors.initials: P
authors.lastname: Angelikopoulos
authors.affiliation: Computational Science and Engineering Laboratory, Department of Mechanical and Process Engineering, ETH Zurich Switzerland.
authors.forename: P
authors.initials: P
authors.lastname: Koumoutsakos
authors.affiliation: Department of Biomedical Engineering Johns Hopkins University School of Medicine Baltimore Maryland USA.
authors.forename: A S
authors.initials: AS
authors.lastname: Popel