Determination of thermodynamic parameters of complexation using a nonlinear regression method

Area of Science:

• Pharmaceutical Science
• Analytical Chemistry
• Physical Chemistry

Background:

• Drug complexation with cyclodextrins is crucial for drug delivery.
• Analyzing drug-excipient interactions requires robust thermodynamic data.
• Current methods may limit the simultaneous determination of multiple interaction parameters.

Purpose of the Study:

• Introduce a novel model and method for determining multiple unknowns from single experimental datasets.
• Enable meaningful comparison of thermodynamic parameters in drug-cyclodextrin systems.
• Enhance the analysis of solubility data to understand drug-complexing agent interactions.

Main Methods:

• Development of a new mathematical model for data analysis.
• Application of the model to experimental data from phenytoin-cyclodextrin complexation.
• Utilizing solubility measurements as the primary experimental technique.

Main Results:

• Successfully determined multiple unknowns from a single set of raw experimental data.
• Demonstrated the method's applicability to drug-cyclodextrin interactions.
• Facilitated the comparison of thermodynamic parameters across different drug-cyclodextrin combinations.

Conclusions:

• The new method offers a versatile approach for analyzing complexation phenomena.
• This methodology can be extended to various drug interaction systems beyond cyclodextrins.
• Enhances the understanding of drug-excipient interactions and guides formulation development.