Towards Rational Biosurfactant Design—Predicting Solubilization in Rhamnolipid Solutions

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 (logPBS, measure of purity), solubilizate (logPsol, 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 R2val 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

Towards Rational Biosurfactant Design—Predicting Solubilization in Rhamnolipid Solutions

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 (logPBS, measure of purity), solubilizate (logPsol, 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 R2val 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

INFOGEST inter-laboratory recommendations for assaying gastric and pancreatic lipases activities prior to in vitro digestion studies

International audienceIn vitro digestion studies often use animal digestive enzyme extracts as substitutes of human gastric and pancreatic secretions. Pancreatin from porcine origin is thus commonly used to provide relevant pancreatic enzymes such as proteases, amylase and lipase. Rabbit gastric extracts (RGE) have been recently introduced to provide gastric lipase in addition to pepsin. Before preparing simulated gastric and pancreatic extracts with targeted enzyme activities as described in in vitro digestion protocols, it is important to determine the activities of enzyme preparations using validated methods. The purpose of this inter-laboratory study within the INFOGEST network was to test the repeatability and reproducibility of lipase assays using the pH-stat technique for measuring the activities of gastric and pancreatic lipases from various sources. Twenty-one laboratories having different pH-stat devices received the same protocol with identical batches of RGE and two pancreatin sources. Lipase assays were performed using tributyrin as a substrate and three different amounts (50, 100 and 200 µg) of each enzyme preparation. The repeatability results within individual laboratories were satisfactory with coefficients of variation (CVs) ranging from 4 to 8% regardless of the enzyme amount tested. However, the inter-laboratory variability was high (CV > 15%) compared to existing standards for bioanalytical assays. We identified and weighted the contributions to inter-laboratory variability of several parameters associated with the various pH-stat equipment used in this study (e.g. reaction vessel volume and shape, stirring mode and rate, burette volume for the automated delivery of sodium hydroxide). Based on this, we established recommendations for improving the reproducibility of lipase assays using the pH-stat technique. Defining accurate and complete recommendations on how to correctly quantify activity levels of enzyme preparations is a gateway to promising comparison of in vitro data obtained from different laboratories following the same in vitro digestion protocol