New mathematical model from dynamic dissolution rate tests.

Area of Science:

• Pharmaceutical Sciences
• Physical Chemistry
• Mathematical Modeling

Background:

• In vitro dissolution testing is crucial for predicting drug performance, but current methods struggle with complex behaviors of low-solubility drugs.
• Drugs in subclasses 2a, 2b, and 2c of the Biopharmaceutics Classification System (BCS) exhibit pH-dependent solubility, complicating dissolution profiling.
• Replicating in vivo conditions in vitro requires advanced methods to capture dissolution and precipitation kinetics.

Purpose of the Study:

• To develop and validate a novel mathematical model for analyzing complex dissolution and precipitation phenomena.
• To assess the model's accuracy in fitting experimental data for BCS Class 2 drugs.
• To provide a practical tool for pharmaceutical researchers to understand drug behavior.

Main Methods:

• A modified double Weibull equation was employed to model dissolution and precipitation kinetics.
• The model was applied to two-stage and transfer dissolution experiments (dumping tests).
• Experimental data from BCS Class 2a and 2b drugs were used for validation, with analysis performed using Excel's Solver tool.

Main Results:

• The modified double Weibull equation demonstrated high accuracy in fitting experimental dissolution and precipitation data.
• The model successfully captured complex behaviors such as rapid dissolution, precipitation, and redissolution.
• The approach proved versatile for analyzing dissolution profiles of BCS Class 2a and 2b drugs under various conditions.

Conclusions:

• The proposed mathematical model offers a robust and accurate method for characterizing drug dissolution and precipitation kinetics.
• This approach enhances the understanding of low-solubility drug behavior in vitro.
• The model's ease of use in Excel makes it a valuable tool for pharmaceutical research, formulation development, and potentially establishing in vitro-in vivo correlations (IVIVC).