[Model building in pharmacokinetics/Part I: General strategy (author's transl)]

Area of Science:

• Pharmacokinetics and Pharmacological Modeling
• Systems Biology and Computational Science

Context:

• Pharmacokinetic (PK) model construction and evaluation typically follow a repetitive cycle of hypothesis generation, experimentation, and data analysis.
• The complexity of biological systems necessitates robust methodologies for accurate PK modeling.

Purpose:

• To present a general strategy for the rigorous construction and evaluation of pharmacokinetic models.
• To ensure the requirements of correct modeling, proper experimental design, careful execution of experiments, valid parameter estimation, and reliable model identification are met.

Summary:

• The proposed strategy integrates hypothesis-driven research with systematic experimental design and analysis.
• It emphasizes the iterative refinement of PK models through a cycle of hypothesis, experiment, and analysis.
• Key aspects include careful experimental execution, valid parameter estimation, and robust model identification.

Impact:

• Facilitates the development of more accurate and reliable pharmacokinetic models.
• Improves the understanding of drug disposition and behavior in biological systems.
• Supports better-informed decision-making in drug development and clinical pharmacology through enhanced predictive modeling.