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An Extended Model Including Target Turnover, Ligand-Target Complex Kinetics, and Binding Properties to Describe

Lambertus A Peletier1

  • 1Mathematical Institute, Leiden University, Leiden, The Netherlands. peletier@math.leidenuniv.nl.

Methods in Molecular Biology (Clifton, N.J.)
|December 10, 2021
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Summary
This summary is machine-generated.

Target-mediated drug disposition (TMDD) models complex drug actions by integrating processes like protein binding and elimination. This overview simplifies TMDD data analysis for parameter estimation in drug development.

Keywords:
AntibodiesDrug dispositionMichaelis–MentenQuasi-equilibriumQuasi-steady stateReceptorTarget

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Area of Science:

  • Pharmacokinetics and Pharmacodynamics
  • Drug Development and Pharmacology
  • Mathematical Modeling in Biology

Background:

  • Target-mediated drug disposition (TMDD) is a key concept in modern drug development.
  • TMDD models integrate biological processes including protein binding, internalization, and elimination.
  • These models are crucial for understanding drug action and serve as a basis for complex pharmacokinetic analyses.

Purpose of the Study:

  • To provide an overview of the temporal dynamics of drug compounds under TMDD.
  • To present methods for dissecting complex datasets related to drug disposition.
  • To facilitate the estimation of key pharmacokinetic parameters within TMDD frameworks.

Main Methods:

  • Review of target-mediated drug disposition principles.
  • Analysis of temporal compound development following various drug dosing strategies.
  • Development of data dissection techniques for parameter estimation.

Main Results:

  • The chapter outlines the temporal evolution of drug compounds influenced by TMDD.
  • It offers practical approaches for analyzing experimental data.
  • Methods are presented for estimating crucial parameters like rate constants and concentrations.

Conclusions:

  • TMDD models, despite their complexity and numerous parameters, offer a robust framework for drug disposition.
  • The presented methods aid in sophisticated data analysis for parameter estimation.
  • Understanding these dynamics is vital for effective drug development and modeling.