Prediction of Small-molecule Pharmaceuticals Solubility Parameters Using a Thermodynamic SAFT-based Equation of State

Area of Science:

• Physical Chemistry
• Pharmaceutical Sciences
• Computational Chemistry

Background:

• Accurate drug solubility parameter prediction is essential for pharmaceutical formulation optimization.
• Existing group contribution (GC) methods have limitations in capturing steric hindrance, intramolecular hydrogen bonding, and novel chemical groups found in pharmaceuticals.
• Experimental data for drug solubility parameters are often scarce, necessitating reliable predictive models.

Purpose of the Study:

• To estimate drug solubility parameters using the Perturbed Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state (EoS).
• To address the limitations of current group contribution methods for predicting solubility of complex pharmaceutical compounds.
• To investigate the role of intermolecular interactions, particularly hydrogen bonding, in solubility parameter prediction.

Main Methods:

• Utilized the Perturbed Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state (EoS) for solubility parameter estimation.
• Calculated PC-SAFT EoS parameters from binary experimental solubility data.
• Explicitly considered drug-drug and drug-solvent association interactions to enhance model performance.
• Employed an unconstrained regression approach (URA) with experimental data to correlate Hansen solubility terms as a complementary method.

Main Results:

• The PC-SAFT approach demonstrated satisfactory accuracy in predicting drug solubility parameters, outperforming GC methods.
• Hydrogen-bonding interactions were identified as a critical factor for accurate solubility parameter prediction.
• The PC-SAFT model successfully predicted solubility parameters for pharmaceuticals, including those with rare or novel functional groups.

Conclusions:

• The PC-SAFT equation of state offers a robust and accurate method for predicting drug solubility parameters, overcoming limitations of GC approaches.
• This predictive tool can aid in the pre-design of new drug candidates by optimizing solvent selection in pharmaceutical development and chemical processes.
• The explicit consideration of association interactions significantly improves the reliability of solubility parameter predictions.