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

Area of Science:

• Pharmaceutical Science
• Physical Chemistry
• Computational Chemistry

Background:

• Accurate drug solubility parameter prediction is crucial for drug formulation.
• Experimental methods are limited by solubility and complexity.
• Predictive models are needed for efficient drug development.

Purpose of the Study:

• To estimate drug solubility parameters using a modified Cubic-Plus-Chain (CPC) equation of state (EoS).
• To enhance the CPC EoS with Two-State Theory (TST) for complex molecular interactions in drug systems.
• To provide a practical and accurate predictive tool for pharmaceutical applications.

Main Methods:

• Modified Cubic-Plus-Chain (CPC) equation of state coupled with Two-State Theory (TST).
• Model parameter determination using experimental solubility data for drug-solvent systems.
• Comparison with regression-based, group-contribution, and PC-SAFT EoS methods.

Main Results:

• The CPC-TST model accurately predicted drug solubility parameters, with an average RMSD of 0.034 and AAD% of 0.168.
• The model showed good accuracy, especially for strongly associating systems.
• Incorporating a temperature-dependent binary interaction parameter improved model performance.

Conclusions:

• The CPC-TST model offers a robust and efficient alternative for modeling solubility in pharmaceutical systems.
• The model simplifies the prediction of complex associating systems without explicit site definition.
• This approach is practical for implementation in commercial simulation software.