Predictive Modeling of Micellar Solubilization by Single and Mixed Nonionic Surfactants

Area of Science:

• Physical Chemistry
• Pharmaceutical Sciences
• Drug Delivery

Background:

• Micellar solubilization is crucial for delivering poorly water-soluble drugs.
• Current solubility studies for formulation development are time-consuming and resource-intensive.
• Efficient drug formulation relies on accurate surfactant selection and quantity.

Purpose of the Study:

• To develop predictive solubility models independent of specific drug molecules.
• To establish if the solubilization capacity ratio is constant across different nonionic surfactants.
• To reduce the experimental burden in early-stage drug development.

Main Methods:

• Performed comprehensive solubility studies using 17 insoluble compounds and 8 nonionic surfactants.
• Defined and evaluated the solubilization capacity ratio.
• Developed predictive models for drug solubility in single and mixed nonionic surfactant systems.

Main Results:

• A constant solubilization capacity ratio was observed across the tested nonionic surfactants.
• Developed predictive models accurately estimated solubility values, with most within a 2-fold difference.
• Ionic surfactants, like sodium dodecyl sulfate, did not follow the established trend.

Conclusions:

• Established predictive models for nonionic micellar solutions based on a single experimental measurement.
• The models significantly reduce the resources required for solubility prediction.
• Enhanced efficiency in drug product development through streamlined formulation studies.