Anticancer potential and through study of the cytotoxicity mechanism of ionic liquids that are based on the trifluoromethanesulfonate and bis(trifluoromethylsulfonyl)imide anions

Mechanisms of Biological Effects of Ionic Liquids: From Single Cells to Multicellular Organisms

The review presents a detailed discussion of the evolving field studying interactions between ionic liquids (ILs) and biological systems. Originating from molten salt electrolytes to present multiapplication substances, ILs have found usage across various fields due to their exceptional physicochemical properties, including excellent tunability. However, their interactions with biological systems and potential influence on living organisms remain largely unexplored. This review examines the cytotoxic effects of ILs on cell cultures, biomolecules, and vertebrate and invertebrate organisms. Our understanding of IL toxicity, while growing in recent years, is yet nascent. The established findings include correlations between harmful effects of ILs and their ability to disturb cellular membranes, their potential to trigger oxidative stress in cells, and their ability to cause cell death via apoptosis. Future research directions proposed in the review include studying the distribution of various ILs within cellular compartments and organelles, investigating metabolic transformations of ILs in cells and organisms, detailed analysis of IL effects on proteins involved in oxidative stress and apoptosis, correlation studies between IL doses, exposure times and resulting adverse effects, and examination of effects of subtoxic concentrations of ILs on various biological objects. This review aims to serve as a critical analysis of the current body of knowledge on IL-related toxicity mechanisms. Furthermore, it can guide researchers toward the design of less toxic ILs and the informed use of ILs in drug development and medicine.

Ionic liquids revolutionizing biomedicine: recent advances and emerging opportunities

Ionic liquids (ILs), due to their inherent structural tunability, outstanding miscibility behavior, and excellent electrochemical properties, have attracted significant research attention in the biomedical field. As the application of ILs in biomedicine is a rapidly emerging field, there is still a need for systematic analyses and summaries to further advance their development. This review presents a comprehensive survey on the utilization of ILs in the biomedical field. It specifically emphasizes the diverse structures and properties of ILs with their relevance in various biomedical applications. Subsequently, we summarize the mechanisms of ILs as potential drug candidates, exploring their effects on various organisms ranging from cell membranes to organelles, proteins, and nucleic acids. Furthermore, the application of ILs as extractants and catalysts in pharmaceutical engineering is introduced. In addition, we thoroughly review and analyze the applications of ILs in disease diagnosis and delivery systems. By offering an extensive analysis of recent research, our objective is to inspire new ideas and pathways for the design of innovative biomedical technologies based on ILs.

Cancer-specific cytotoxicity of pyridinium-based ionic liquids by regulating hypoxia-inducible factor-1α-centric cancer metabolism

Owing to their unique properties and biological activities, ionic liquids (ILs) have attracted research interest in pharmaceutics and medicine. Hypoxia-inducible factor (HIF)- 1α is an attractive cancer drug target involved in cancer malignancy in the hypoxic tumor microenvironment. Herein, we report the inhibitory activity of ILs on the HIF-1α pathway and their mechanism of action. Substitution of a dimethylamino group on pyridinium reduced hypoxia-induced HIF-1α activation. It selectively inhibited the viability of the human colon cancer cell line HCT116, compared to that of the normal fibroblast cell line WI-38. These activities were enhanced by increasing the alkyl chain length in the pyridinium. Under hypoxic conditions, dimethylaminopyridinium reduced the accumulation of HIF-1α and its target genes without affecting the HIF1A mRNA level in cancer cells. It suppressed the oxygen consumption rate and ATP production by directly inhibiting electron transfer chain complex I, which led to enhanced intracellular oxygen content and oxygen-dependent degradation of HIF-1α under hypoxia. These results indicate that dimethylaminopyridinium suppresses the mitochondria and HIF-1α-dependent glucose metabolic pathway in hypoxic cancer cells. This study provides insights into the anticancer activity of pyridinium-based ILs through the regulation of cancer metabolism, making them promising candidates for cancer treatment.

Machine Learning Quantitative Structure–Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons

Ionic liquids (ILs) are known for their unique characteristics as solvents and electrolytes. Therefore, new ILs are being developed and adapted as innovative chemical environments for different applications in which their properties need to be understood on a molecular level. Computational data-driven methods provide means for understanding of properties at molecular level, and quantitative structure–property relationships (QSPRs) provide the framework for this. This framework is commonly used to study the properties of molecules in ILs as an environment. The opposite situation where the property is considered as a function of the ionic liquid does not exist. The aim of the present study was to supplement this perspective with new knowledge and to develop QSPRs that would allow the understanding of molecular interactions in ionic liquids based on the structure of the cationic moiety. A wide range of applications in electrochemistry, separation and extraction chemistry depends on the partitioning of solutes between the ionic liquid and the surrounding environment that is characterized by the gas-ionic liquid partition coefficient. To model this property as a function of the structure of a cationic counterpart, a series of ionic liquids was selected with a common bis-(trifluoromethylsulfonyl)-imide anion, [Tf2N]⁻, for benzene, hexane and cyclohexane. MLR, SVR and GPR machine learning approaches were used to derive data-driven models and their performance was compared. The cross-validation coefficients of determination in the range 0.71–0.93 along with other performance statistics indicated a strong accuracy of models for all data series and machine learning methods. The analysis and interpretation of descriptors revealed that generally higher lipophilicity and dispersion interaction capability, and lower polarity in the cations induces a higher partition coefficient for benzene, hexane, cyclohexane and hydrocarbons in general. The applicability domain analysis of models concluded that there were no highly influential outliers and the models are applicable to a wide selection of cation families with variable size, polarity and aliphatic or aromatic nature.