Comparison of two computational methods for solvent screening in countercurrent and centrifugal partition chromatography

"Sweet space" strategy for solvent system selection in countercurrent chromatography: A case study on separation of polyunsaturated fatty acids from borage oil

This study presents a comprehensive strategy for the selection and optimization of solvent systems in countercurrent chromatography (CCC) for the effective separation of compounds. With a focus on traditional organic solvent systems, the research introduces a "sweet space" strategy that merges intuitive understanding with mathematical accuracy, addressing the significant challenges in solvent system selection, a critical bottleneck in the widespread application of CCC. By employing a combination of volume ratios and graphical representations, including both regular and trirectangular tetrahedron models, the proposed approach facilitates a more inclusive and user-friendly strategy for solvent system selection. This study demonstrates the potential of the proposed strategy through the successful separation of gamma-linolenic acid, oleic acid, and linoleic acid from borage oil, highlighting the strategy's effectiveness and practical applicability in CCC separations.

Exploring the Solubility Limits of Edaravone in Neat Solvents and Binary Mixtures: Experimental and Machine Learning Study

This study explores the edaravone solubility space encompassing both neat and binary dissolution media. Efforts were made to reveal the inherent concentration limits of common pure and mixed solvents. For this purpose, the published solubility data of the title drug were scrupulously inspected and cured, which made the dataset consistent and coherent. However, the lack of some important types of solvents in the collection called for an extension of the available pool of edaravone solubility data. Hence, new measurements were performed to collect edaravone solubility values in polar non-protic and diprotic media. Such an extended set of data was used in the machine learning process for tuning the parameters of regressor models and formulating the ensemble for predicting new data. In both phases, namely the model training and ensemble formulation, close attention was paid not only to minimizing the deviation of computed values from the experimental ones but also to ensuring high predictive power and accurate solubility computations for new systems. Furthermore, the environmental friendliness characteristics determined based on the common green solvent selection criteria, were included in the analysis. Our applied protocol led to the conclusion that the solubility space defined by ordinary solvents is limited, and it is unlikely to find solvents that are better suited for edaravone dissolution than those described in this manuscript. The theoretical framework presented in this study provides a precise guideline for conducting experiments, as well as saving time and resources in the pursuit of new findings.

Modeling the Solubility of Monoterpenoids with Hybrid and Predictive Thermodynamic Tools

The Abraham and NRTL-SAC semipredictive models were employed to represent the solubility of (-)-borneol, (1R)-(+)-camphor, l-(-)-menthol, and thymol in water and organic solvents, using data measured in this work and collected from the literature. A reduced set of solubility data was used to estimate the model parameters of the solutes, and global average relative deviations (ARDs) of 27% for the Abraham model and 15% for the NRTL-SAC model were obtained. The predictive capability of these models was tested by estimating the solubilities in solvents not included in the correlation step. Global ARDs of 8% (Abraham model) and 14% (NRTL-SAC model) were obtained. Finally, the predictive COSMO-RS model was used to describe the solubility data in organic solvents, with ARD of 16%. These results show the overall better performance of NRTL-SAC in a hybrid correlation/prediction approach, while COSMO-RS can produce very satisfactory predictions even in the absence of any experimental data.

New Ferulic Acid and Amino Acid Derivatives with Increased Cosmeceutical and Pharmaceutical Potential

Ferulic acid (FA) has been widely used in the pharmaceutical and cosmetics industry due to its, inter alia, antioxidant, antiaging and anti-inflammatory effects This compound added to cosmetic preparations can protect skin because of its photoprotective activity. However, the usefulness of FA as a therapeutic agent is limited due to its low solubility and bioavailability. The paper presents the synthesis, identification, and physicochemical properties of new FA derivatives with propyl esters of three amino acids, glycine (GPr[FA]), L-leucine (LPr[FA]), and L-proline (PPr[FA]). The NMR and FTIR spectroscopy, DSC, and TG analysis were used as analytical methods. Moreover, water solubility of the new conjugates was compared with the parent acid. Both ferulic acid and its conjugates were introduced into hydrogel and emulsion, and the resulting formulations were evaluated for stability. Additionally, in vitro penetration of all studied compounds from both formulations and for comparative purposes using Franz diffusion cells was evaluated from the solution in 70% (v/v) ethanol. Finally, cytotoxicity against murine fibroblasts L929 was tested. All of the analyzed compounds permeated pig skin and accumulated in it. LPr[FA] and PPr[FA] were characterized by much better permeability compared to the parent ferulic acid. Additionally, it was shown that all the analyzed derivatives are characterized by high antioxidant activity and lack of cytotoxicity. Therefore, they can be considered as an interesting alternative to be applied in dermatologic and cosmetic preparations.