Estimating the asymptotic characteristic time scales for diffusion-controlled drug release systems using partially...

Area of Science:

• Pharmacokinetics and Drug Delivery
• Computational Modeling
• Materials Science

Background:

• Drug release studies often yield partial data within a finite time.
• Accurate estimation of asymptotic time scales is crucial for understanding drug release kinetics.
• Existing methods may require complete data or specific physical models.

Purpose of the Study:

• To develop fitting-based procedures for estimating asymptotic time scales (global relaxation time τ* and terminal relaxation time τ0) of drug release from partially sampled data.
• To evaluate the proposed procedures using synthetic and experimental data, including the Weibull function.
• To demonstrate the ability to determine the diffusion coefficient directly from the characteristic time τ0.

Main Methods:

• Development of fitting-based procedures to estimate τ* and τ0 from partial drug release data.
• Application and testing of these procedures on both synthetic and experimental datasets.
• Utilizing the Weibull function as a case study to analyze fitting parameter sensitivity.

Main Results:

• The proposed fitting procedures effectively estimate asymptotic time scales from limited drug release data.
• The Weibull function requires careful application due to parameter sensitivity to the data range (tf/τ0 ratio).
• The procedures are independent of the initial drug loading profile and do not necessitate a physically based fitting function.

Conclusions:

• Fitting-based methods offer a practical approach to determine drug release kinetics from incomplete datasets.
• The derived characteristic time τ0 enables direct calculation of the diffusion coefficient.
• These findings enhance the understanding and prediction of drug release behavior in diffusion-controlled systems.