No abstract

In nonaqueous enzymology, control of enzyme hydration is commonly approached by fixing the thermodynamic water activity of the medium. In this work, we present a strategy for evaluating the water activity in molecular dynamics simulations of proteins in water/organic solvent mixtures. The method relies on determining the water content of the bulk phase and uses a combination of Kirkwood-Buff theory and free energy calculations to determine corresponding activity coefficients. We apply the method in a molecular dynamics study of Candida antarctica lipase B in pure water and the organic solvents methanol, tert-butyl alcohol, methyl tert-butyl ether, and hexane, each mixture at five different water activities. It is shown that similar water activity yields similar enzyme hydration in the different solvents. However, both solvent and water activity are shown to have profound effects on enzyme structure and flexibility.

ABSTRACT:A rigorous methodology is developed that addresses numerical and statistical issues when developing group contribution (GC) based property models such as regression methods, optimization algorithms, performance statistics, outlier treatment, parameter identifiability and uncertainty of the prediction. The methodology is evaluated through development of a GC method for prediction of the heat of combustion ( ) for pure components. The results showed that robust regression lead to best performance statistics for parameter estimation. Bootstrap method is found a valid alternative to calculate parameter estimation errors when underlying distribution of residuals is unknown. Many parameters (first, second, third order groups contributions) are found unidentifiable from the typically available data, with large estimation error bounds and significant correlation. Due to this poor parameter 2 identifiability issues, reporting of the 95%-confidence intervals of the predicted property values should be mandatory as opposed to reporting only single value prediction, currently the norm in literature. Moreover, inclusion of higher order groups (additional parameters) does not always lead to improved prediction accuracy for the GC-models, in some cases it may even increase the prediction error (hence worse prediction accuracy). However, additional parameters do not affect calculated 95%-confidence interval. Last but not least, the newly developed GC model of the heat of combustion ( ) shows predictions of great accuracy and quality (the most data falling within the 95% confidence intervals) and provides additional information on the uncertainty of each prediction compared to other models reported in literature.

A systematic approach is suggested for predicting the solubility of sparingly soluble solid fine chemicals and pharmaceuticals. The procedure uses group contribution methods for computing the difference in solubility at infinite dilution in the solvent of interest from an optimal reference solvent with the aim of (1) minimizing the impact of uncertainties in pure-solute properties, (2) decreasing the number of adjustable parameters to be determined by data reduction, and (3) using appropriate experimental data to fit unknown parameters. Several examples illustrate the method.

Solvent selection is one of the major concerns in the early development of many chemicals-based products from the pharmaceutical, agrochemicals, food, and specialty chemicals industries. Because of the nature of the active chemicals in the product, the most important solvent property is the solubility of complex solids. Predictive models for estimation of solid solubility in different organic solvents, especially suitable for solventselection procedures, are reviewed. Also, schemes that can be employed for solvent selection and/or solubility calculation through limited available experimental data are reviewed. For initial solvent screening and for many solvent-based calculations, the Hansen solubility parameters are useful property values to have. For this purpose, new combined group contribution-atom connectivity models for predictions of the three Hansen solubility parameters with a very wide application range are presented. These models are able to predict the Hansen solubility parameters for organic chemicals with C,

This study was aimed at evaluating different binary solvent mixtures for efficient industrial monoacylglycerol (MAG) production by enzymatic glycerolysis. Of all investigated cases, the binary mixture of tert-butanol:tert-pentanol (TB:TP) 80:20 vol % was the most suitable organic medium for continuous enzymatic glycerolysis, ensuring high MAG formation in a short time, reasonable solvent price, and easy handling during distillation/condensation processing. A minimum solvent dosage of 44-54 wt % of the reaction mixture was necessary to achieve high MAG yields of 47-56 wt %, within 20 min. The melting and boiling points of the TB:TP mixture were estimated to be 7 and 85 degrees C, respectively, using thermodynamic models. These predictions were in good agreement with experimentally determined values. In spite of the high reaction efficiency in the binary TB:TP system, the mixture of glycerol and sunflower oil (containing 97.1% triacylglycerol) yielded surprisingly a liquid/liquid phase split behavior even at high temperatures (>80 degrees C). This in contrast to thermodynamic model calculations suggested full miscibility in all proportions. These findings suggest that enhanced reaction efficiency in organic solvent also depends upon aspects other than the system homogeneity such as reduced viscosity, reduced mass transfer limitations, and the accessibility of the substrate to the active site of the enzyme.

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Process intensification in distillation systems has received much attention during the past decades, with the aim of increasing both energy and separation efficiency. Various techniques, such as internal heat-integrated distillation, membrane distillation, rotating packed bed, dividing-wall columns and reactive distillation were studied and reported in literature.All these techniques employ the conventional continuous counter-current contact of vapor and liquid phases. Cyclic distillation technology is based on an alternative operating mode using separate phase movement which leads to key practical advantages in both chemical and biochemical processes. This article provides a mini-review of cyclic distillation technology.The topics covered include the working principle, design and control methods, main benefits and limitations as well as current industrial applications. Cyclic distillation can be rather easily implemented in existing columns by simply changing the internals and the operating mode, thus bringing new life in old distillation towers by significantly increasing the column throughput, reducing the energy requirements and offering a better separation performance.