A new expanded solubility parameter approach.

Area of Science:

• Physical Chemistry
• Computational Chemistry
• Materials Science

Background:

• Hansen solubility parameters (HSP) are crucial for predicting substance behavior and selecting solvents.
• Current HSP methods primarily rely on the "similarity matching" principle for solubility prediction.
• There is a need to incorporate additional solubility principles for enhanced predictive power.

Purpose of the Study:

• To enhance the predictive capacity of Hansen solubility parameters (HSP).
• To integrate the principle of "complementarity matching" into HSP evaluation.
• To improve solubility predictions for pharmaceutical systems.

Main Methods:

• Splitting the hydrogen bonding component of HSP into acidic (proton donor) and basic (proton acceptor) parts.
• Utilizing the third sigma moments from COSMO-RS theory (quantum mechanics) for splitting.
• Applying an updated group-contribution method for calculating HSP.

Main Results:

• The enhanced HSP method demonstrates significant improvement over the classical HSP approach for solubility data.
• The new method provides more accurate predictions for pharmaceutical systems.
• The integration of "complementarity matching" enhances HSP utility.

Conclusions:

• The developed method offers a more robust approach to solubility prediction by combining "similarity" and "complementarity matching."
• This enhanced HSP methodology is particularly beneficial for pharmaceutical applications.
• The study provides a practical tool for scientists without requiring complex calculations.