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IONTOX

A Project Funded by the European Commission (August 2013 – August 2015)

Host Institute: The University of Manchester, United Kingdom

Ionic liquids (ILs) have recently attracted increasing scientific interest due to the fact that they can provide unique combinations of properties enabling industrial processes with enhanced efficiency and/or products of better quality.  However, they can pose an acute health issue, which must be understood and controlled. ILs might not uncritically be regarded as intrinsically “green”.  Although, ILs are attractive to industry, their toxicological effect on the environment has not been profiled to a sufficient extent. In principle, there are approximately 1018 anion–cation combinations which can be synthesized. Measuring the toxicity information of various ILs under a wide range of conditions through experimental techniques is impractical. Therefore, it is necessary to develop and employ new models for the estimation of the toxicity of ILs (Mol Divers 17, 2013, 151-196, http://dx.doi.org/10.1007/s11030-012-9413-y). The purpose of the present project has been to develop and validate predictive models for toxicity of ionic liquids against different endpoints with relevance to the ecotoxicity and human toxicity.

Quantitative structure-activity relationships (QSARs) represent predictive models derived from application of statistical tools correlating biological activity (including therapeutic and toxic) or other properties of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure and/or property. QSARs are applied in many disciplines like risk assessment, toxicity prediction, property prediction, and regulatory decisions apart from drug discovery and lead optimization (https://books.google.co.uk/books/reader?id=bkFOBQAAQBAJ&printsec=frontcover&output=reader&source=gbs_atb&hl=en&pg=GBS.PP1). In this project, the principles of QSAR have been applied to develop predictive models in relation to the toxicity of ionic liquids against seven different endpoints.

 

1.      Quantitative structure-activity relationship for toxicity of ionic liquids to Daphnia magna: aromaticity vs. Lipophilicity (Chemosphere, 112, 2014, 120-127, http://dx.doi.org/10.1016/j.chemosphere.2014.04.002)

Water solubility of ionic liquids (ILs) allows their dispersion into aquatic systems and raises concerns on their pollutant potential. Again, lipophilicity can contribute to the toxicity of ILs due to increased ability of the compounds to cross lipoidal bio-membranes. In the present work, we have performed statistical model development for toxicity of a set of ionic liquids to Daphnia magna, a widely accepted model organism for toxicity testing, using computed lipophilicity, atom-type fragment, quantum topological molecular similarity (QTMS) and extended topochemical atom (ETA) descriptors. The models have been developed and validated in accordance with the Organization for Economic Co-operation and Development (OECD) guidelines for quantitative structure-activity relationships (QSARs). The best partial least squares (PLS) model outperforms the previously reported multiple linear regression (MLR) model in statistical quality and predictive ability (R2=0.955, Q2=0.917, R2pred=0.848). In this work, the ETA descriptors show importance of branching and aromaticity while the QTMS descriptor ellipticity efficiently shows which compounds are influential in the data set, with reference to the model. While obvious importance of lipophilicity is evident from the models, the best model clearly shows the importance of aromaticity suggesting that more lipophilic ILs with less toxicity may be designed by avoiding aromaticity, nitrogen atoms and increasing branching in the cationic structure. The developed quantitative models are in consonance with the recent hypothesis of importance of aromaticity for toxicity of ILs.

 

2.      Predictive QSAR modeling of algal toxicity of ionic liquids and its interspecies correlation with Daphnia toxicity (Environ Sci Pollut Res,  22, 2015, 6634-6641, http://dx.doi.org/10.1007/s11356-014-3845-0)

Predictive toxicology using chemometric tools can be very useful in order to fill the data gaps for ionic liquids (ILs) with limited available experimental toxicity information, in view of their growing industrial uses. Though originally promoted as green chemicals, ILs have now been shown to possess considerable toxicity against different ecological endpoints. Against this background, quantitative structureactivity relationship (QSAR) models have been developed here for the toxicity of ILs against the green algae Scenedesmus vacuolatus using computed descriptors with definite physicochemical meaning. The final models emerged from E-state indices, extended topochemical atom (ETA) indices and quantum topological molecular similarity (QTMS) indices. The developed partial least squares models support the established mechanism of toxicity of ionic liquids in terms of a surfactant action of cations and chaotropic action of anions. The models have been developed within the guidelines of the Organization of Economic Co-operation and Development (OECD) for regulatory QSAR models, and they have been validated both internally and externally using multiple strategies and also tested for applicability domain. A preliminary attempt has also been made, for the first time, to develop interspecies quantitative toxicity-toxicity relationship (QTTR) models for the algal toxicity of ILs with Daphnia toxicity, which should be interesting while predicting toxicity of ILs for an endpoint when the data for the other are available.

 

3.      Chemometric modeling of the chromatographic lipophilicity parameter logk0 of ionic liquid cations with ETA and QTMS descriptors (J Mol Liq, 200B, 2014, 223-228, http://dx.doi.org/10.1016/j.molliq.2014.10.018)

Ionic liquids, though promoted as green solvents,  exhibit significant toxicity against various organisms in the ecosystem. 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 parameter logk0 of ionic liquid cations using Extended Topochemical Atom (ETA) descriptors and Quantum Topological 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 size of the cations, and decreases with their electron-richness and hydrogen bonding propensity. The electronic character of the bond joining the quaternary atom with sp3 hybridized carbon is also important.

 

4.   Cytotoxicity towards CCO cells of imidazolium ionic liquids with functionalized side chains. Preliminary QSAR modeling using regression and classification based approaches (Ecotox Environ Saf, 112, 2015, 22-28,  http://dx.doi.org/10.1016/j.ecoenv.2014.10.029)

 

Within this work we evaluated the cytotoxicity towards the Channel Catfish Ovary (CCO) cell line of some imidazolium-based ionic liquids containing different functionalized and unsaturated side chains. The toxic effects were measured by the reduction of the WST-1 dye after 72 h exposure resulting in dose- and structure-dependent toxicities.  The obtained data on cytotoxic effects of 14 different imidazolium ionic liquids in CCO cells, expressed as EC50 values, were used in a preliminary Quantitative Structure-Activity Relationship (QSAR) study employing regression- and classification-based approaches. The toxicity of ILs towards CCO was chiefly related to the shape and hydrophobicity parameters of cations. A significant influence of the quantum topological molecular similarity descriptor ellipticity (ε) of the imine bond was also observed.

 

5.   Exploring simple, transparent, interpretable and predictive QSAR models for classification and quantitative prediction of rat toxicity of ionic liquids using OECD recommended guidelines (Chemosphere, 139, 2015, 163-173,  http://dx.doi.org/10.1016/j.chemosphere.2015.06.022)

 

The present study explores the chemical attributes of diverse ionic liquids responsible for their cytotoxicity in a rat leukemia cell line (IPC-81) by developing predictive classification as well as regression-based mathematical models. Simple and interpretable descriptors derived from a two-dimensional representation of the chemical structures along with quantum topological molecular similarity indices have been used for model development, employing unambiguous modeling strategies that strictly obey the guidelines of the organization for economic development and cooperation (OECD) for quantitative structure-activity relationship (QSAR) analysis. The structure-toxicity relationships that emerged from both classification and regression-based models were in accordance with the findings of some previous studies. The models suggested that the cytotoxicity of ionic liquids is dependent on the cationic surfactant action, long alkyl side chains, cationic lipophilicity as well as aromaticity, the presence of a dialkylamino substituent at the 4-position of the pyridinium nucleus and a bulky anionic moiety. The models have been transparently presented in the form of equations, thus allowing their easy transferability in accordance with the OECD guidelines. The models have also been subjected to rigorous validation tests proving their predictive potential and can hence be used for designing novel and “greener” ionic liquids. The major strength of the present study lies in the use of a diverse and large dataset, use of simple reproducible descriptors and compliance with the OECD norms.

 

6.   Interspecies quantitative structure-toxicity-toxicity (QSTTR) relationship modeling of ionic liquids. Toxicity of ionic liquids to V. fischeri, D. magna and S. vacuolatus

(Ecotox Environ Saf, 112, 2015, 22- 28, http://dx.doi.org/10.1016/j.ecoenv.2014.10.029).

 

Using the rationale of taxonomic relatedness, development of predictive quantitative structure-toxicity-toxicity relationship (QSTTR) models can allow predicting the toxicity of ILs to a particular species using available experimental toxicity data towards a different species. Such studies may employ, along with the available experimental toxicity data to a species, molecular structure features and physicochemical properties of chemicals as independent variables for prediction of the toxicity profile against another closely related species. Although a few such interpecies toxicity correlation models have been reported in the literature for diverse chemicals in general, but this approach has been rarely applied to the class of ionic liquids. The present study involves the use of IL toxicity data towards the bacteria Vibrio fischeri along with molecular structure derived information or computational descriptors like extended topochemical atom (ETA) indices, quantum topological molecular similarity (QTMS) descriptors and computed lipophilicity measure (logk0) for the interspecies exploration of the toxicity data towards green algae Scenedesmus vacuolatus and crustacea Daphnia magna, separately. This modeling study has been performed in accordance with the OECD guidelines. Finally predictions for a true external set have been performed to fill the data gap of toxicity towards daphnids and algae using the Vibrio toxicity data and molecular structure attributes.

 

7.   Development of chemometric models for V. fischeri toxicity of ionic liquids followed by true external and experimental validation tests (Manuscript in preparation)

 

This study aims in the development of predictive QSTR models for the ecotoxicity of ionic liquids using a marine bacterium (Vibrio fischeri) as the indicator response species. We have used a large set of 310 diverse functional ionic liquid compounds with quantitative toxicity data. We have here developed multiple models in an attempt to capture completely the structural features of the ionic liquid compounds responsible for the toxicity. The derived models have been validated internally (based on the leave-one-out technique), externally (using the test set compounds) and also true externally using a data set which became available in the literature very recently. Based on the information of crucial chemical attributes derived from the models, new analogues have been designed and predicted to have low toxicity. Some of the compounds have been synthesized as well as tested for their toxicity against the same indicator organism. The predictive QSTR models reported here can be used for toxicity prediction of new ionic liquids against V. fischeri and for data-gap filling, while the synthesized low-toxic ionic liquids can be considered for evaluation as well as application in appropriate chemical processes serving the purpose of industry/ academia.

 

 

OUTREACH ACTIVITY: The above models can be used for prediction of toxicity against different endpoints of new or untested ionic liquids thus bridging data gaps. Structures of such query ionic liquids (.mol file or SMILES notation) may be sent to kunalroy_in@yahoo.com for possible prediction of the endpoints using the developed models, provided that such query compounds are within the applicability domain of the models. The models may also be utilized for the design of new and safer ionic liquids. We may be contacted (kunalroy_in@yahoo.com) without hesitation for discussion on any of these issues.

 

 

Contact Persons: Dr. Kunal Roy (kunalroy_in@yahoo.com); Prof. P Popelier (paul.popelier@manchester.ac.uk)

 

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