Indirect pharmacodynamic models for responses with multicompartmental distribution or polyexponential disposition

Area of Science:

• Pharmacology
• Pharmacodynamics
• Mathematical Modeling

Background:

• Basic indirect response models assume one-compartment distribution and first-order loss.
• These models are crucial for understanding drug effects on physiological responses.

Purpose of the Study:

• To extend indirect response models to incorporate multicompartment distribution and polyexponential loss.
• To develop theoretical equations and data analysis methods for these complex models.
• To evaluate the impact of model complexity on pharmacodynamic parameter estimation.

Main Methods:

• Utilized convolution theory to extend basic indirect response models.
• Developed theoretical equations and data analysis techniques.
• Applied extended models to data on prednisolone inhibition of cortisol secretion.
• Performed simulations to illustrate biexponential response dissipation.

Main Results:

• Extended models account for two-compartment distribution and/or polyexponential loss of response variables.
• Model misselection analysis showed basic models do not severely perturb parameters for complex scenarios.
• The area under the effect curve (AUCE) remains consistent across basic and extended models.

Conclusions:

• Multicompartment distribution and polyexponential loss complicate indirect response modeling.
• Careful experimental and data analysis are needed for accurate pharmacodynamic evaluation.
• Extended models provide improved insights into complex indirect pharmacodynamic responses.