Pharmacometrics modeling and simulation to assist phenobarbital dose optimization in dogs

Area of Science:

• Veterinary Pharmacology
• Pharmacokinetics
• Canine Medicine

Background:

• Phenobarbital (PB) is a first-line treatment for canine epilepsy.
• Its narrow therapeutic index and significant inter-individual variability require careful dosing and monitoring.
• Individualized dosing strategies are needed to optimize efficacy and minimize toxicity.

Purpose of the Study:

• To develop and validate a population pharmacokinetic (popPK) model for PB in dogs.
• To support model-informed precision dosing (MIPD) in clinical practice.
• To enable individualized PB dosing based on patient-specific factors.

Main Methods:

• A popPK model was developed using nonlinear mixed-effects (NLME) techniques with 121 serum samples from 100 dogs.
• External validation used 53 samples from 50 dogs.
• Covariate analysis included age and body weight (WT); model performance was assessed using goodness-of-fit and visual predictive checks.

Main Results:

• A one-compartment model with autoinductive clearance best described PB pharmacokinetics.
• Body weight and age were significant covariates influencing apparent clearance.
• The model accurately predicted PB concentrations in an external dataset.

Conclusions:

• The validated popPK model facilitates individualized PB dosing in dogs by considering age and weight variability.
• This approach supports MIPD implementation in veterinary practice.
• Optimized dosing regimens can improve therapeutic outcomes and reduce toxicity in dogs with epilepsy.