Utility of PK-PD Modeling and Simulation to Improve Decision Making for Antibody-Drug Conjugate Development
Comprehension of the pharmacokinetics (PK) and pharmacodynamics (PD) of Antibody-drug Conjugates (ADCs) can be challenging as it requires integration of the information stemming from various moieties (i.e. the antibody, the drug, and the conjugate). Computational modeling provides an excellent tool to overcome these challenges by providing an opportunity to integrate all the available information within a mathematical framework. With an ever-increasing pipeline of more than 60 ADC molecules currently in the clinic, plenty of resources and time are invested towards discerning some key questions associated with PK, efficacy, and toxicity of the most promising candidates. In order to streamline the process of finding the answers to these questions and to expedite the development of ADCs, mathematical modeling and simulation (M&S) can be employed at different stages of ADC development. Successful application of this tool can not only enhance the scientific understanding of the processes underlying PK-PD of ADCs but can also provide comprehensive model-derived outcomes that can help accelerate the decision-making process. Within this book chapter, we have discussed an array of different PK-PD models and modeling strategies that could be employed at discovery, preclinical, or clinical stages, to make rational decisions for the development of ADCs. In addition, suitable examples from the literature are discussed where M&S has been utilized to make key go/no-go decisions.
KeywordsPK-PD Modeling Antibody-Drug Conjugate Model-Based Drug Development Preclinical-to-Clinical Translation Decision Making Population PK-PD Analysis
This work was supported by NIH grant GM114179 to D.K.S., and the Centre for Protein Therapeutics at the State University of New York at Buffalo. Authors would also like to thank Dr. Amrita V. Kamath (Genentech®, Inc) for her helpful discussion while conception of this book chapter.
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