Molecular modeling as a predictive tool for the development of solid dispersions.

Area of Science:

• Computational chemistry and materials science
• Pharmaceutical sciences and drug delivery

Background:

• Pharmaceutical solid dispersions are crucial for enhancing drug solubility and bioavailability.
• Predicting drug-polymer miscibility is essential for designing stable and effective solid dispersions.
• Traditional methods for predicting miscibility have limitations in accuracy and scope.

Purpose of the Study:

• To introduce and validate quantum mechanical (QM) molecular modeling as a novel approach for developing pharmaceutical solid dispersions.
• To compare the predictive power of QM modeling with traditional methods like solubility parameters and Flory-Huggins interaction parameter.
• To investigate the intermolecular interactions and binding strengths in drug-polymer systems.

Main Methods:

• Utilized QM calculations to predict drug-polymer binding energies and interaction sites.
• Employed traditional methods (Van Krevelen/Hoftyzer, Bagley, Flory-Huggins) for comparative miscibility assessment.
• Characterized solid dispersions using thermal analysis (DSC/MTDSC), X-ray diffraction, and X-ray photoelectron spectroscopy (XPS).

Main Results:

• QM molecular modeling successfully predicted drug-polymer binding energies and preferred interaction sites.
• Experimental analyses confirmed the predicted intermolecular interactions and binding strengths.
• QM-based predictions correlated well with experimental findings on miscibility and physical state.

Conclusions:

• Quantum mechanical molecular modeling is a powerful and accurate tool for predicting drug-polymer interactions in solid dispersion development.
• This computational approach can guide the selection of optimal drug-polymer combinations for enhanced pharmaceutical formulations.
• The study validates QM modeling as a key strategy for advancing the design of pharmaceutical solid dispersions.