Fractal Kinetic Implementation in Population Pharmacokinetic Modeling

Area of Science:

• Pharmacokinetics
• Pharmacometrics
• Systems Pharmacology

Background:

• Conventional compartment modeling assumes homogeneity, which is physiologically unrealistic.
• This limitation questions its applicability for novel drugs with complex physiological or physical characteristics.
• Fractal kinetics offer an alternative but lack comprehensive evaluation in this context.

Purpose of the Study:

• To investigate the application and benefits of fractal kinetics in conventional pharmacokinetic modeling.
• To evaluate the impact of fractal rates on model performance and parameter estimation.
• To assess the physiological relevance and robustness of fractal kinetic models.

Main Methods:

• Simulations were conducted to explore fractal kinetics applications in population pharmacokinetic models.
• Model performance was assessed using objective function value (OFV), Akaike's information criterion (AIC), and corrected Akaike's information criterion (AICc).
• Goodness of fit (GOF), visual predictive check (VPC), and normalized prediction distribution error (NPDE) were used for visual diagnostics.

Main Results:

• Fractal rates generally enhanced model performance across multiple population models.
• Model parameters, including the fractal rate, were successfully estimated within the physiological range (0 to 1).
• Fractal kinetic models demonstrated good fit, robustness, and improved prediction-observation agreement, particularly in early time points.

Conclusions:

• Incorporating fractal rates improves pharmacokinetic model performance and reflects true physiological behavior.
• Fractal kinetics provide explainable parameters and can offer valuable insights for pharmacological decision-making.
• This approach enhances the physiological relevance of pharmacokinetic models, addressing limitations of conventional methods.