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

Considering the increasing uses of ionic liquids (ILs) in various industrial processes and chemical engineering operations, a complete assessment of their hazardous profile is essential. In the absence of adequate experimental data, in silico modeling might be helpful in filling data gaps for the toxicity of ILs towards various ecological indicator organisms. Using the rationale of taxonomic relatedness, the development of predictive quantitative structure-toxicity-toxicity relationship (QSTTR) models allows 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. A few such interspecies 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 S. 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.

Cytotoxicity towards CCO cells of imidazolium ionic liquids with functionalized side chains: preliminary QSTR modeling using regression and classification based approaches.

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-toxicity relationship (QSTR) 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.

Some case studies on application of "r(m)2" metrics for judging quality of quantitative structure-activity relationship predictions: emphasis on scaling of response data.

Quantitative structure-activity relationship (QSAR) techniques have found wide application in the fields of drug design, property modeling, and toxicity prediction of untested chemicals. A rigorous validation of the developed models plays the key role for their successful application in prediction for new compounds. The r(m)(2) metrics introduced by Roy et al. have been extensively used by different research groups for validation of regression-based QSAR models. This concept has been further advanced here with introduction of scaling of response data prior to computation of r(m)(2). Further, a web application (accessible from http://aptsoftware.co.in/rmsquare/ and http://203.200.173.43:8080/rmsquare/) for calculation of the r(m)(2) metrics has been introduced here. The present study reports that the web application can be easily used for computation of r(m)(2) metrics provided observed and QSAR-predicted data for a set of compounds are available. Further, scaling of response data is recommended prior to r(m)(2) calculation.

Advances in QSPR/QSTR models of ionic liquids for the design of greener solvents of the future.

In order to protect the life of all creatures living in the environment, the toxicity arising from various hazardous chemicals must be controlled. This imposes a serious responsibility on different chemical, pharmaceutical, and other biological industries to produce less harmful chemicals. Among various international initiatives on harmful aspects of chemicals, the 'Green Chemistry' ideology appears to be one of the most highlighted concepts that focus on the use of eco-friendly chemicals. Ionic liquids are a comparatively new addition to the huge garrison of chemical compounds released from the industry. Extensive research on ionic liquids in the past decade has shown them to be highly useful chemicals with a good degree of thermal and chemical stability, appreciable task specificity and minimal environmental release resulting in a notion of 'green chemical'. However, studies have also shown that ionic liquids are not intrinsically non-toxic agents and can pose severe degree of toxicity as well as the risk of bioaccumulation depending upon their structural components. Moreover, ionic liquids possess issues of waste generation during synthesis as well as separation problems. Predictive quantitative structure-activity relationship (QSAR) models constitute a rational opportunity to explore the structural attributes of ionic liquids towards various physicochemical and toxicological endpoints and thereby leading to the design of environmentally more benevolent analogues with higher process selectivity. Such studies on ionic liquids have been less extensive compared to other industrial chemicals. The present review attempts to summarize different QSAR studies performed on these chemicals and also highlights the safety, health and environmental issues along with the application specificity on the dogma of 'green chemistry'.

Preliminary studies on model development for rodent toxicity and its interspecies correlation with aquatic toxicities of pharmaceuticals.

Environmental toxicity due to pharmaceuticals has been an issue of serious concern for long time. Development of chemometric models with reliable predictive power has been considered as an effective tool for the design of new drug agents with reduced or without ecotoxic potential. Considering a higher degree of similarity in genetic homology towards drug receptor with mammals, we have used a dataset of 194 compounds with reported rodent, fish, daphnia and algae toxicity data for extrapolation of their toxicity towards humans. Allowing for rodents as the most surrogate to human physiology, attempts have also been made to develop interspecies correlation models keeping rodent toxicity as dependent variable so that any drug without reported rodent toxicity can be predicted using fish, daphnia or algae toxicity data which can be consequently extrapolated to human toxicity. The developed models have been subjected to multiple validation strategies. Acceptable results have been obtained in both cases of direct and interspecies extrapolation quantitative structure-activity relationship models.

Comparative studies on some metrics for external validation of QSPR models.

Quantitative structure-property relationship (QSPR) models used for prediction of property of untested chemicals can be utilized for prioritization plan of synthesis and experimental testing of new compounds. Validation of QSPR models plays a crucial role for judgment of the reliability of predictions of such models. In the QSPR literature, serious attention is now given to external validation for checking reliability of QSPR models, and predictive quality is in the most cases judged based on the quality of predictions of property of a single test set as reflected in one or more external validation metrics. Here, we have shown that a single QSPR model may show a variable degree of prediction quality as reflected in some variants of external validation metrics like Q²(F1), Q²(F2), Q²(F3), CCC, and r²(m) (all of which are differently modified forms of predicted variance, which theoretically may attain a maximum value of 1), depending on the test set composition and test set size. Thus, this report questions the appropriateness of the common practice of the "classic" approach of external validation based on a single test set and thereby derives a conclusion about predictive quality of a model on the basis of a particular validation metric. The present work further demonstrates that among the considered external validation metrics, r²(m) shows statistically significantly different numerical values from others among which CCC is the most optimistic or less stringent. Furthermore, at a given level of threshold value of acceptance for external validation metrics, r²(m) provides the most stringent criterion (especially with Δr²(m) at highest tolerated value of 0.2) of external validation, which may be adopted in the case of regulatory decision support processes.

Development of predictive QSAR models for Vibrio fischeri toxicity of ionic liquids and their true external and experimental validation tests.

Despite possessing an interesting chemical nature and tuneable physicochemical properties, ionic liquids (ILs) must have their ecotoxicity tested in order to be commercialized. The water solubility of ILs allows their easy access to the aquatic compartment of the ecosystem creating a potential hazard to aquatic organisms. Hence, it is relevant to design ionic liquids with lower toxicity while keeping the desired properties of interest. Considering the possibility of an enormous number of combinations of different cations and anions, a rational guidance for the structural design of ionic liquids is essential in order to prioritize the synthesis as well as testing of selected molecules only. Predictive in silico models, such as quantitative structure-activity relationship (QSAR) models, can play a pivotal role in exploring the important chemical attributes contributing to the response activity. These models may then lead to the design of novel ionic liquids. The present study aims at developing predictive QSAR models for the ecotoxicity of ionic liquids using the bacteria Vibrio fischeri as an indicator response species. Instead of a single model, here we have used multiple models to capture more complete structural information of ionic liquids for toxicity towards Vibrio fischeri. The derived chemical attributes have been implemented in designing new analogues, some of which have been synthesized and had their ecotoxicity tested for the same model organism. The predictive QSAR models reported here can be used for ecotoxicity prediction of new IL chemicals and for data-gap filling. Moreover, the synthesized low-toxic ILs could be considered for evaluation as well as for application in suitable processes serving the purpose of industry and academia.

Predictive QSAR modelling of algal toxicity of ionic liquids and its interspecies correlation with Daphnia toxicity.

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 structure-activity 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.

Exploring simple, transparent, interpretable and predictive QSAR models for classification and quantitative prediction of rat toxicity of ionic liquids using OECD recommended guidelines.

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 Co-operation and Development (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.

A review on principles, theory and practices of 2D-QSAR.

The central axiom of science purports the explanation of every natural phenomenon using all possible logics coming from pure as well as mixed scientific background. The quantitative structure-activity relationship (QSAR) analysis is a study correlating the behavioral manifestation of compounds with their structures employing the interdisciplinary knowledge of chemistry, mathematics, biology as well as physics. Several studies have attempted to mathematically correlate the chemistry and property (physicochemical/ biological/toxicological) of molecules using various computationally or experimentally derived quantitative parameters termed as descriptors. The dimensionality of the descriptors depends on the type of algorithm employed and defines the nature of QSAR analysis. The most interesting feature of predictive QSAR models is that the behavior of any new or even hypothesized molecule can be predicted by the use of the mathematical equations. The phrase "2D-QSAR" signifies development of QSAR models using 2D-descriptors. Such predictor variables are the most widely practised ones because of their simple and direct mathematical algorithmic nature involving no time consuming energy computations and having reproducible operability. 2D-descriptors have a deluge of contributions in extracting chemical attributes and they are also capable of representing the 3D molecular features to some extent; although in no case they should be considered as the ultimate one, since they often suffer from the problems of intercorrelation, insufficient chemical information as well as lack of interpretation. However, by following rational approaches, novel 2D-descriptors may be developed to obviate various existing problems giving potential 2D-QSAR equations, thereby solving the innumerable chemical mysteries still unexplored.

Predictive modeling studies for the ecotoxicity of ionic liquids towards the green algae Scenedesmus vacuolatus.

Hazardous potential of ionic liquids is becoming an issue of high concern with increasing application of these compounds in various industrial processes. Predictive toxicological modeling on ionic liquids provides a rational assessment strategy and aids in developing suitable guidance for designing novel analogues. The present study attempts to explore the chemical features of ionic liquids responsible for their ecotoxicity towards the green algae Scenedesmus vacuolatus by developing mathematical models using extended topochemical atom (ETA) indices along with other categories of chemical descriptors. The entire study has been conducted with reference to the OECD guidelines for QSAR model development using predictive classification and regression modeling strategies. The best models from both the analyses showed that ecotoxicity of ionic liquids can be decreased by reducing chain length of cationic substituents and increasing hydrogen bond donor feature in cations, and replacing bulky unsaturated anions with simple saturated moiety having less lipophilic heteroatoms.

Quantitative structure-activity relationship for toxicity of ionic liquids to Daphnia magna: aromaticity vs. lipophilicity.

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 (R(2)=0.955, Q(2)=0.917, Rpred(2)=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.

QSTR with extended topochemical atom (ETA) indices. 16. Development of predictive classification and regression models for toxicity of ionic liquids towards Daphnia magna.

Ionic liquids have been judged much with respect to their wide applicability than their considerable harmful effects towards the living ecosystem which has been observed in many instances. Hence, toxicological introspection of these chemicals by the development of predictive mathematical models can be of good help. This study presents an attempt to develop predictive classification and regression models correlating the structurally derived chemical information of a group of 62 diverse ionic liquids with their toxicity towards Daphnia magna and their interpretation. We have principally used the extended topochemical atom (ETA) indices along with various topological non-ETA and thermodynamic parameters as independent variables. The developed quantitative models have been subjected to extensive statistical tests employing multiple validation strategies from which acceptable results have been reported. The best models obtained from classification and regression studies captured necessary structural information on lipophilicity, branching pattern, electronegativity and chain length of the cationic substituents for explaining ecotoxicity of ionic liquids towards D. magna. The derived information can be successfully used to design better ionic liquid analogues acquiring the qualities of a true eco-friendly green chemical.

QSTR with extended topochemical atom (ETA) indices. 14. QSAR modeling of toxicity of aromatic aldehydes to Tetrahymena pyriformis.

Aldehydes are a toxic class of chemicals causing severe health hazards. In this background, quantitative structure-toxicity relationship (QSTR) models have been developed in the present study using Extended Topochemical Atom (ETA) indices for a large group of 77 aromatic aldehydes for their acute toxicity against the protozoan ciliate Tetrahymena pyriformis. The ETA models have been compared with those developed using various non-ETA topological indices. Attempt was also made to include the n-octanol/water partition coefficient (logK(o/w)) as an additional descriptor considering the importance of hydrophobicity in toxicity prediction. Thirty different models were developed using different chemometric tools. All the models have been validated using internal validation and external validation techniques. The statistical quality of the ETA models was found to be comparable to that of the non-ETA models. The ETA models have shown the important effects of steric bulk, lipophilicity, presence of electronegative atom containing substituents and functionality of the aldehydic oxygen to the toxicity of the aldehydes. The best ETA model (without using logK(o/w)) shows encouraging statistical quality (Q(int)(2)=0.709,Q(ext)(2)=0.744). It is interesting to note that some of the topological models reported here are better in statistical quality than previously reported models using quantum chemical descriptors.