Przybyłek, M.; Jeliński, T.; Mianowana, M.; Misiak, K.; Cysewski, P. Exploring the Solubility Limits of Edaravone in Neat Solvents and Binary Mixtures: Experimental and Machine Learning Study. Molecules2023, 28, 6877.
Przybyłek, M.; Jeliński, T.; Mianowana, M.; Misiak, K.; Cysewski, P. Exploring the Solubility Limits of Edaravone in Neat Solvents and Binary Mixtures: Experimental and Machine Learning Study. Molecules 2023, 28, 6877.
Przybyłek, M.; Jeliński, T.; Mianowana, M.; Misiak, K.; Cysewski, P. Exploring the Solubility Limits of Edaravone in Neat Solvents and Binary Mixtures: Experimental and Machine Learning Study. Molecules2023, 28, 6877.
Przybyłek, M.; Jeliński, T.; Mianowana, M.; Misiak, K.; Cysewski, P. Exploring the Solubility Limits of Edaravone in Neat Solvents and Binary Mixtures: Experimental and Machine Learning Study. Molecules 2023, 28, 6877.
Abstract
This study explores the Edaravone solubility space encompassing both neat and binary dissolution media. The efforts were made toward revealing the concentration limits inherently associated with common pure and mixed solvents. For this purpose, the published solubility data of the title drug were scrupulously inspected and cured. This enabled to make 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 for collecting Edaravone solubility in polar non-protic and polar di-protic media. Such an extended set of data was used for tuning the parameters of regressors 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 the high predictive power and computing solubility for new systems. Furthermore, the environmental friendliness characteristics determined based on the common green solvent selection criteria was 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 more suited for Edaravone dissolution than those depicted in this manuscript. The theoretical framework presented in this study provides a precise guide for conducting experiments, saving time and resources in the pursuit of new findings.
Keywords
edaravone; solubility; green solvents; deep learning; COSMO-RS; learning curve analysis; hyperparameters tuning
Subject
Chemistry and Materials Science, Physical Chemistry
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
