Towards Rational Biosurfactant Design-Predicting Solubilization in Rhamnolipid Solutions

Area of Science:

• Colloid and Surface Chemistry
• Computational Chemistry
• Formulation Science

Background:

• Micellar solubilization efficiency depends on solubilizate properties, surfactant type, and environmental conditions.
• Predicting and optimizing solubilization is crucial for applications involving oils, drugs, and other substances.
• Biosurfactants offer a sustainable alternative to synthetic surfactants.

Purpose of the Study:

• To develop a predictive model for molar solubilization ratio (MSR) in biosurfactant solutions.
• To identify optimal surfactant and environmental conditions for enhanced solubilization.
• To create a tool for predicting solubilization efficiency based on key descriptors.

Main Methods:

• Literature data compilation (38 years) and experimental data collection for biosurfactant preparations.
• Utilized Evolutionary Algorithm (EA) and Kernel Support Vector Machines (KSVM) for predictive modeling.
• Incorporated descriptors: biosurfactant properties (logP_BS, purity), solubilizate characteristics (logP_sol, molecular volume), and environmental factors (T, pH).

Main Results:

• Successfully predicted MSR with a mean cross-validated R² of 0.773 ± 0.052 using EAs.
• Ranked the significance of parameters influencing solubilization efficiency.
• Developed a novel MSR calculator for predicting solubilization outcomes.

Conclusions:

• The developed model accurately predicts micellar solubilization efficiency in biosurfactant systems.
• This research provides the first computational approach to predict MSR, offering a valuable tool for formulation development.
• The findings facilitate the optimization of biosurfactant-based formulations for diverse applications.