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Physiologically-based Modeling of Monoclonal Antibody Pharmacokinetics in Drug Discovery and Development

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Publisher Elsevier
Date 2018 Dec 8
PMID 30522890
Citations 28
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Abstract

Over the past few decades, monoclonal antibodies (mAbs) have become one of the most important and fastest growing classes of therapeutic molecules, with applications in a wide variety of disease areas. As such, understanding of the determinants of mAb pharmacokinetic (PK) processes (absorption, distribution, metabolism, and elimination) is crucial in developing safe and efficacious therapeutics. In the present review, we discuss the use of physiologically-based pharmacokinetic (PBPK) models as an approach to characterize the in vivo behavior of mAbs, in the context of the key PK processes that should be considered in these models. Additionally, we discuss current and potential future applications of PBPK in the drug discovery and development timeline for mAbs, spanning from identification of potential target molecules to prediction of potential drug-drug interactions. Finally, we conclude with a discussion of currently available PBPK models for mAbs that could be implemented in the drug development process.

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References
1.
Glassman P, Balthasar J . Application of a catenary PBPK model to predict the disposition of "catch and release" anti-PCSK9 antibodies. Int J Pharm. 2016; 505(1-2):69-78. DOI: 10.1016/j.ijpharm.2016.03.066. View

2.
Li L, Gardner I, Rose R, Jamei M . Incorporating Target Shedding Into a Minimal PBPK-TMDD Model for Monoclonal Antibodies. CPT Pharmacometrics Syst Pharmacol. 2014; 3:e96. PMC: 3910015. DOI: 10.1038/psp.2013.73. View

3.
Jain R . Physiological barriers to delivery of monoclonal antibodies and other macromolecules in tumors. Cancer Res. 1990; 50(3 Suppl):814s-819s. View

4.
Yuan D, Rode F, Cao Y . A Minimal Physiologically Based Pharmacokinetic Model with a Nested Endosome Compartment for Novel Engineered Antibodies. AAPS J. 2018; 20(3):48. PMC: 6486833. DOI: 10.1208/s12248-017-0183-4. View

5.
Baxter L, Zhu H, Mackensen D, BUTLER W, Jain R . Biodistribution of monoclonal antibodies: scale-up from mouse to human using a physiologically based pharmacokinetic model. Cancer Res. 1995; 55(20):4611-22. View