Drugs solubility parameter prediction using modified Cubic Plus Chain equation of state

Area of Science:

• Physical Chemistry
• Computational Chemistry
• Pharmaceutical Sciences

Background:

• Accurate drug solubility parameter prediction is crucial for drug formulation and understanding drug-solvent interactions.
• Experimental methods for solubility determination are often hindered by low solubility and complexity.
• Developing reliable predictive models is essential for efficient pharmaceutical research and development.

Purpose of the Study:

• To estimate drug solubility parameters using a modified Cubic-Plus-Chain (CPC) equation of state (EoS).
• To couple the CPC EoS with the Two-State Theory (TST) for enhanced modeling of complex molecular interactions in drug systems.
• To demonstrate the practicality and accuracy of the CPC-TST model for pharmaceutical applications.

Main Methods:

• Modification of the Cubic-Plus-Chain (CPC) equation of state.
• Coupling CPC EoS with the Two-State Theory (TST) to model association without explicit site definition.
• Determination of model parameters using experimental solubility data for drug-solvent systems.
• Estimation of drug solubility in pure and mixed solvents using the developed CPC-TST model.

Main Results:

• The CPC-TST model accurately predicted drug solubility parameters, achieving an average RMSD of 0.034 and AAD% of 0.168 with temperature-dependent binary interaction parameters.
• The model showed good accuracy compared to regression-based, group-contribution, and PC-SAFT EoS methods, especially for strongly associating systems.
• The CPC-TST model maintains low computational complexity, making it suitable for commercial simulation software.

Conclusions:

• The proposed CPC-TST model is a robust and efficient tool for predicting solubility parameters in pharmaceutical systems.
• This approach simplifies the modeling of associating components in complex drug molecules.
• The CPC-TST model offers a practical alternative for guiding drug formulation design and understanding drug-solvent interactions.