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A Physiologically‐Based Pharmacokinetic Model for the Prediction of “Half‐Life Extension” and “Catch and Release” Monoclonal Antibody Pharmacokinetics

Authors: Hannah M. Jones; John Tolsma; Zhiwei Zhang; Paul Jasper; Haobin Luo; Gregory L. Weber; Katherine Wright; +6 Authors

A Physiologically‐Based Pharmacokinetic Model for the Prediction of “Half‐Life Extension” and “Catch and Release” Monoclonal Antibody Pharmacokinetics

Abstract

Monoclonal antibodies (mAbs) can be engineered to have “extended half‐life” and “catch and release” properties to improve target coverage. We have developed a mAb physiologically‐based pharmacokinetic model that describes intracellular trafficking, neonatal Fc receptor (FcRn) recycling, and nonspecific clearance of mAbs. We extended this model to capture target binding as a function of target affinity, expression, and turnover. For mAbs engineered to have an extended half‐life, the model was able to accurately predict the terminal half‐life (82% within 2‐fold error of the observed value) in the human FcRn transgenic (Tg32) homozygous mouse and human. The model also accurately captures the trend in pharmacokinetic and target coverage data for a set of mAbs with differing catch and release properties in the Tg32 mouse. The mechanistic nature of this model allows us to explore different engineering techniques early in drug discovery, potentially expanding the number of “druggable” targets.

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Keywords

Mice, Transgenic, RM1-950, Receptors, Fc, HIV Antibodies, Antibodies, Monoclonal, Humanized, Protein Engineering, Mice, Drug Development, Drug Discovery, Animals, Humans, Computer Simulation, Research, Histocompatibility Antigens Class I, Homozygote, Models, Immunological, Antibodies, Monoclonal, Mutation, Therapeutics. Pharmacology, Broadly Neutralizing Antibodies, Half-Life, Protein Binding

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    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Top 10%
Average
Top 10%
Green
gold