Chemometric modeling of the chromatographic lipophilicity parameter logk 0 of ionic liquid cations with ETA and QTMS descriptors Kunal Roy a,b, ⁎, Paul L.A. Popelier a, ⁎⁎ a Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, Great Britain b Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India abstract article info Article history: Received 30 August 2014 Received in revised form 1 October 2014 Accepted 16 October 2014 Available online 18 October 2014 Keywords: Computation Computational chemistry Mathematical modeling Simulation QSPR Ionic liquids Ionic liquids, though promoted as green solvents, exhibit significant toxicity against various organisms in the eco- system. The lipophilicity of cations plays a determining role in this toxicity. The experimental lipophilicity values being available for a limited number of cations, we modeled the chromatographically derived lipophilicity pa- rameter logk 0 of ionic liquid cations using Extended Topochemical Atom (ETA) descriptors and Quantum Topo- logical Molecular Similarity (QTMS) descriptors. Both types of descriptor were previously found to be important in modeling selected toxicity endpoints of ionic liquids. We have performed both internal and external validation tests and randomization experiments while developing the models. The present study suggests that the ETA and QTMS descriptors are efficient in developing robust and reliable lipophilicity models for cations of ionic liquids. The developed models show that lipophilicity increases with the size of cations, and decreases with their electron-richness and hydrogen bonding propensity. The electronic character of the bond joining the quaternary atom with sp 3 hybridized carbon is also important. The computed lipophilicity obtained from the developed model was also applied to the computation of rat toxicity data of a large number of compounds, and gave highly satisfactory results. © 2014 Elsevier B.V. All rights reserved. 1. Introduction In the past decade, ionic liquids have emerged as a class of highly use- ful chemicals with good thermal and chemical stability, appreciable task specificity and minimal environmental release, overall resulting in a no- tion of ‘green chemicals’ [25]. The applicability of ionic liquids encom- passes five major fields: (a) synthetic chemistry, (b) electrochemistry, (c) analytical chemistry, (d) separation and extraction, and (e) other en- gineering and biological applications [6]. However, recent studies have also shown that ionic liquids are potentially toxic agents and that they can pose a severe degree of toxicity as well as the risk of bioaccumulation depending upon their structural components. It is, however, possible to tune the physicochemical properties of ionic liquids via the choice of cer- tain anionic and cationic components when designing a specific ionic liq- uid ideally suited for a specific process [34]. Such task-specific or functionalized ionic liquids are created through incorporation of appro- priate functional groups into the alkyl chains [24]. Since the pioneering work of Meyer and Overton on the interaction between anesthetics and lipids, the correlation between lipophilicity and toxicity of chemical substances has been described and reviewed many times. For ionic liquids, it has been found that the cation species is the main effector for the observed toxicity, especially if substituted with a longer alkyl side chain [24]. The anion can also contribute to the toxicity, but in most cases anion effects are less important compared to the side chain effect [34]. Thus, it is worth modeling cationic lipophi- licity with computed descriptors so that lipophilicity, and hence the tox- icity, can be predicted for new and untested cations, which may help to design safer ionic liquids. Ranke et al. have found a correlation between a HPLC derived lipophilicity parameter (logk 0 ) and observed cytotoxic- ity in a rat leukemia cell line [24]. The side chain was identified to be the main effector to alter both cytotoxicity and lipophilicity. The logk 0 values were determined [24] from the retention behavior of the com- pounds in reverse phase HPLC experiments using the C18 column as the stationary phase and 0.25% acetic acid mixed with acetonitrile as the eluent. Quantitative structure–property relationships (QSPR) have emerged as an important computational tool with application in property predic- tion and predictive toxicology [9]. It is possible to predict a property or toxicity endpoint for a new or untested chemical (which may be virtual, thus offering no possibility to instantly obtain the experimental data) from a robust and validated QSPR model. Different regulatory agencies have already recognized the use of such models for property and risk assessment of chemical compounds. The Organization of Economic Co-operation and Development (OECD) has recommended a set of Journal of Molecular Liquids 200 (2014) 223–228 ⁎ Correspondence to: K. Roy, Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India. ⁎⁎ Corresponding author. E-mail addresses: kunal.roy@manchester.ac.uk (K. Roy), paul.popelier@manchester.ac.uk (P.L.A. Popelier). http://dx.doi.org/10.1016/j.molliq.2014.10.018 0167-7322/© 2014 Elsevier B.V. All rights reserved. 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