Random effects in drug dissolution.

Area of Science:

• Pharmaceutical Science
• Stochastic Modeling
• Physical Chemistry

Background:

• Deterministic dissolution models are standard in pharmaceutical research.
• Experimental data increasingly suggests dissolution processes are stochastic.
• Existing models do not fully capture the inherent randomness in dissolution.

Purpose of the Study:

• To develop stochastic modifications of deterministic dissolution models.
• To analyze the properties and probability distributions of these new stochastic models.
• To investigate the sources and impact of variability in dissolution data.

Main Methods:

• Utilized the Wiener process to create stochastic differential equations for dissolution.
• Derived probability distributions for dissolution data from the stochastic models.
• Proposed a function to describe dissolution data variability over time.
• Employed maximum-likelihood estimation for parameter identification.

Main Results:

• Developed novel stochastic models for drug dissolution.
• Quantified dissolution data variability, identifying environmental and measurement errors as key sources.
• Demonstrated the practical application of stochastic models using experimental data.
• Showcased the influence of noise sources on mean dissolution time estimates.

Conclusions:

• Stochastic models offer a more realistic representation of drug dissolution processes.
• Understanding and quantifying variability is crucial for accurate dissolution analysis.
• The proposed stochastic approach enhances the interpretation of experimental dissolution data and parameter estimation.