A Spectral-SAR Model for the Anionic-Cationic Interaction in Ionic Liquids: Application to Vibrio fischeri Ecotoxicity

Abstract

Within the recently launched the spectral-structure activity relationship (S-SAR)analysis, the vectorial anionic-cationic model of a generic ionic liquid is proposed, alongwith the associated algebraic correlation factor in terms of the measured and predictedactivity norms. The reliability of the present scheme is tested by assessing the Hanschfactors, i.e. lipophylicity, polarizability and total energy, to predict the ecotoxicityendpoints of wide types of ionic liquids with ammonium, pyridinium, phosphonium,choline and imidazolium cations on the aquatic bacteria Vibrio fischeri. The results, whileconfirming the cationic dominant influence when only lipophylicity is considered,demonstrate that the anionic effect dominates all other more specific interactions. It wasalso proved that the S-SAR vectorial model predicts considerably higher activity for theionic liquids than for its anionic and cationic subsystems separately, in all consideredcases. Moreover, through applying the least norm-correlation path principle, the completetoxicological hierarchies are presented, unfolding the ecological rules of combined cationicand anionic influences in ionic liquid toxicity

Spectral-Structure Activity Relationship (Spectral-SAR) Assessment of Ionic Liquids’ in Silico Ecotoxicity

“…The first model is based on the vectorial summation of the produced anionic and cationic biological effects. In other words, this so-called |1+> model is constructed from the superposition of the anionic (subscripted with A) and cationic (subscripted with C) activities, and can be formally represented as [27,30]:…”

“…The next step in the ecotoxicological evaluation of the synthetized mesoporous silica is to use the logistic enzyme kinetic and Spectral-SAR methods [26][27][28][29][30] with acetylcholine esterase as a working enzyme. Acetylcholine is a notably good marker for monitoring the evolution in ionic liquids, due to the similar chemical structures of many ionic liquids cations and acetylcholine and especially because this enzyme does not follow the standard Michaelis-Menten kinetics mechanism.…”

“…The logistic enzyme kinetics [31][32][33][34][35] is an analytical substitute for the Michaelis-Menten mechanism, and it is based on some probabilistic considerations that better accommodate the QSSA (quasi-steady state approximation) conditions, while providing the analytical transformation of the W-Lambert function into a logarithmic function; this so-called logistic transformation was successfully evaluated for different cases of enzyme kinetics [31][32][33][34][35][36][37][38][39]. The analytical information contained in the logistic enzymatic temporal curves could be further combined with a QSAR algorithm, such as the Spectral-SAR, for designing molecular mechanisms of the ecotoxicity; this procedure has already been applied for bare ILs [27,28,38,40]. Eventually, this procedure is intended to be used for some toxicity tests [41,42] or even for obtaining the efficiency of the material as a drug carrier in biological media, if the proper conditions are satisfied.…”

Application of Rough Set Theory to Prediction of Antimicrobial Activity of Bis-Quaternary Imidazolium Chlorides

“…The quaternary imidazolium compounds have good antielectrostatic properties and they are used in cosmetic, textile and pharmaceutical industries [8]. The broad spectrum of antimicrobial activity, low toxicity, good stability, lack of odour of usable solutions cause those compounds to be widely used as disinfectant and cleaning agents in food and pharmaceutical industries and in hospitals [15,16]. Numerous tests indicated that antimicrobial properties of bisquaternary imidazolium chlorides depend on their structure (e.g.…”

Three Dimensional Quantitative Structure-Activity Relationship of 5H-Pyrido[4,3-b]indol-4-carboxamide JAK2 Inhibitors

“…QSAR, which quantitatively correlates the variations in biological activity with the properties or molecular structures, is one of the most effective approaches for designing new chemical identities and understanding the action mechanisms of drugs [14,15]. In order to establish the QSAR model, suitable statistical methods are applied to establish correlations between chemical structures and their biological activities, such as Multiple Linear Regression (MLR), Principal Component Analysis (PCA), Partial Least Squares (PLS), artificial neural networks (ANN), Spectral-SAR Algorithm [16,17], and so on. When a model is established, the findings can be used to predict the properties of new compounds and see which structural factors influence those properties.…”