The quest for balance between capturing data and model complexity: A quantitative clinical pharmacology approach applied to monoclonal antibodies

Author:

Pressly Michelle A.1ORCID,Peletier Lambertus A.2ORCID,Zheng Songmao34,Sharma Vishnu D.1,Lien Yi Ting (Kayla)15,Wang Weirong3,Zhou Honghui36,Schmidt Stephan1ORCID

Affiliation:

1. Department of Pharmaceutics, College of Pharmacy, Center for Pharmacometrics and System Pharmacology at Lake Nona (Orlando) University of Florida Gainesville Florida USA

2. Mathematical Institute Leiden University Leiden The Netherlands

3. Biologics Clinical Pharmacology Janssen R&D Spring House Pennsylvania USA

4. Adagene, Inc. San Diego California USA

5. Clinical Pharmacology Genentech Research and Early Development South San Francisco California USA

6. Kira Pharmaceuticals Cambridge Maryland USA

Abstract

AbstractThe main objective of this tutorial is to provide the readers with a roadmap of how to establish increasingly complex target‐mediated drug disposition (TMDD) models for monoclonal antibodies. To this end, we built mathematical models, each with a detailed visualization, starting from the basic TMDD model by Mager and Jusko to the well‐established, physiologically based model by Li et al. in a step‐wise fashion to highlight the relative importance of key physiological processes that impact mAb kinetics and system dynamics. As the models become more complex, the question of structural and parameter identifiability arises. To address this question, we work through a trastuzumab case example to guide the modeler's choice for model and parameter optimization in light of the context of use. We leave the readers of this tutorial with a brief summary of the advantages and limitations of each model expansion, as well as the model source codes for further self‐guided exploration and hands‐on analysis.

Publisher

Wiley

Subject

Pharmacology (medical),Modeling and Simulation

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