Towards Rational Biosurfactant Design-Predicting Solubilization in Rhamnolipid Solutions

Abstract

The efficiency of micellar solubilization is dictated inter alia by the properties of the solubilizate, the type of surfactant, and environmental conditions of the process. We, therefore, hypothesized that using the descriptors of the aforementioned features we can predict the solubilization efficiency, expressed as molar solubilization ratio (MSR). In other words, we aimed at creating a model to find the optimal surfactant and environmental conditions in order to solubilize the substance of interest (oil, drug, etc.). We focused specifically on the solubilization in biosurfactant solutions. We collected data from literature covering the last 38 years and supplemented them with our experimental data for different biosurfactant preparations. Evolutionary algorithm (EA) and kernel support vector machines (KSVM) were used to create predictive relationships. The descriptors of biosurfactant (logP_BS, measure of purity), solubilizate (logP_sol, molecular volume), and descriptors of conditions of the measurement (T and pH) were used for modelling. We have shown that the MSR can be successfully predicted using EAs, with a mean R² _val of 0.773 ± 0.052. The parameters influencing the solubilization efficiency were ranked upon their significance. This represents the first attempt in literature to predict the MSR with the MSR calculator delivered as a result of our research.

Keywords: MSR; QSAR; biosurfactant; efficiency; micellar solubilization; prediction; rhamnolipid.

Figure 1

Efficiency of dodecane solubilization in biosurfactant solutions: (A) The influence of pH on the dodecane concentration in rhamnolipid biocomplex solutions (RBC) and (B) corresponding molar solubilization ratios (MSRs) values; (C) dodecane concentration dependence on the concentration of different biosurfactant preparations at pH 7, and (D) corresponding MSRs. JBR represents pure RLs (JBR425 of Jeneil), mixtures of JBR and alginate represent model RBC. With increasing pH rhamnolipid aggregates change from vesicles to micelles and the sharp decrease of solubilization efficiency is observed for RBC solutions.

Figure 2

The estimated predictive power of the model used for logMSR calculation. The opaque region is 95% confidence interval (CI) of the regression. As can be seen the evolutionary algorithm (EA) approach performs better (A) than the kernel support vector machines (KSVM) (B). Variable x in regression line equations denotes the log(MSR) measurement. The R² values displayed in the figures are given with respect to outcomes of all ten testing sets.

Figure 3

Sensitivity analysis for modelling of logMSR in biosurfactant solutions: (A) Average sensitivity towards each of descriptors weighted according to number of appearances, obtained with evolutionary algorithm (EA); (B) average variable importance in projection (VIP) analysis performed with PLS regression method. Error bars represent 95% CI. The Eureqa evolutionary algorithm (EA) provides random model equations. The error in the sensitivity of the CMC is due to the large range from 0.78 to 158 as shown in Table S5.

Figure 4

The results of the PCA analysis are shown. (A) shows the scree plot of the eigen-values, indicating that the data can be reduced to about six variables. In (B), correlation plot of descriptors is presented.

Figure 5

Determination of the modeling endpoint by the analysis of mean R² and mean MSE with SD. The dashed line indicates the endpoint at 115,000 generations. The endpoint was chosen due to the plateau region and lower SD in MSE and R².

Figure 6

Schematic representation of the procedure used to create the models. EA — evolutionary algorithm; KSVM—kernel support vector machine; MICE—multiple implementation by chain equations; PCA—principal component analysis; PLS—partial least squares.