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

In silico aquatic toxicity prediction of chemicals toward Daphnia magna and fathead minnow using Monte Carlo approaches

The fast-increasing use of chemicals led to large numbers of chemical compounds entering the aquatic environment, raising concerns about their potential effects on ecosystems. Therefore, assessment of the ecotoxicological features of organic compounds on aquatic organisms is very important. Daphnia magna and Fathead minnow are two aquatic species that are commonly tested as standard test organisms for aquatic risk assessment and are typically chosen as the biological model for the ecotoxicology investigations of chemical pollutants. Herein, global quantitative structure-toxicity relationship (QSTR) models have been developed to predict the toxicity (pEC(LC)50) of a large dataset comprising 2106 chemicals toward Daphnia magna and Fathead minnow. The optimal descriptor of correlation weights (DCWs) is calculated using the notation of simplified molecular input line entry system (SMILES) and is used to construct QSTR models. Three target functions, TF1, TF2, and TF3 are utilized to generate 12 QSTR models from four splits, and their statistical characteristics are also compared. The designed QSTR models are validated using both internal and external validation criteria and are found to be reliable, robust, and excellently predictive. Among the models, those generated using the TF3 demonstrate the best statistical quality with R2 values ranging from 0.9467 to 0.9607, Q2 values ranging from 0.9462 to 0.9603 and RMSE values ranging from 0.3764 to 0.4413 for the validation set. The applicability domain and the mechanistic interpretations of generated models were also discussed.

Are we ready to combat the ecotoxicity of COVID-19 pharmaceuticals? An in silico aquatic risk assessment

To fight COVID-19 with uncountable medications and bioproducts throughout the world has taken us to another challenge of ecotoxicity. The indiscriminate usage followed by improper disposal of unused antibacterials, antivirals, antimalarials, immunomodulators, angiotensin II receptor blockers, corticosteroids, anthelmintics, anticoagulants etc. can lead us to an unimaginable ecotoxicity in the long run. A series of studies already identified active pharmaceutical ingredients (APIs) of the mentioned therapeutic classes and their metabolites in aquatic bodies as well as in wastewater treatment plants. Therefore, an initial ecotoxicity assessment of the majorly used pharmaceuticals is utmost requirement of the present time. The present in silico risk assessment study is focused on the aquatic toxicity prediction of 81 pharmaceuticals where 77 are most-used pharmaceuticals for COVID-19 throughout the world based on the literature along with one drug nirmatrelvir [PF-07321332] approved for emergency use by US-FDA and three other molecules under clinical trial. The ecotoxicity of the studied compounds were predicted based on the three aquatic species fish, algae and crustaceans employing the highest quality QSAR models available from the literature as well as using ECOSAR and QSAR Toolbox. To compare the toxicity thresholds, we have also used 4 control pharmaceuticals based on the worldwide occurrence from river, lake, STP, WWTPs, influent and effluent followed by high reported aquatic toxicity over the years as per the literature. Based on the statistical comparison, we have proposed top 3 pharmaceuticals used for the COVID-19 most toxic to the aquatic environment. The study will provide confident predictions of aquatic ecotoxicity data related to abundant use of COVID-19 drugs. The major aim of the study is to fill up the aquatic ecotoxicity data gap of major medications used for COVID-19.

A QSAR-ICE-SSD model prediction of the PNECs for alkylphenol substances and application in ecological risk assessment for rivers of a megacity

Alkylphenols (APs) are ubiquitous and generally present in higher residue levels in the environment. The present work focuses on the development of a set of in silico models to predict the aquatic toxicity of APs with incomplete/unknown toxicity data in aquatic environments. To achieve this, a QSAR-ICE-SSD model was constructed for aquatic organisms by combining quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD) models in order to obtain the hazardous concentrations (HCs) of selected APs. The research indicated that the keywords "alkylphenol" and "nonylphenol" were most commonly studied. The selected ICE models were robust (R2: 0.70-0.99; p-value < 0.01). All models had a high reliability cross- validation success rates (>75%), and the HC5 predicted with the QSAR-ICE-SSD model was 2-fold than that derived with measured experimental data. The HC5 values demonstrated nearly linear decreasing trend from 2-MP to 4-HTP, while the decreasing trend from 4-HTP to 4-DP became shallower, indicates that the toxicity of APs to aquatic organisms increases with the addition of alkyl carbon chain lengths. The ecological risks assessment (ERA) of APs revealed that aquatic organisms were at risk from exposure to 4-NP at most river stations (the highest risk quotient (RQ) = 1.51), with the highest relative risk associated with 2.9% of 4-NP detected in 82.9% of the sampling sites. The targeted APs posed potential ecological risks in the Yongding and Beiyun River according to the mixture ERA. The potential application of QSAR-ICE-SSD models could satisfy the immediate needs for HC5 derivations without the need for additional in vivo testing.

Experimental investigation of density, viscosity, and surface tension of aqueous tetrabutylammonium-based ionic liquids

The physical properties such as density, dynamic viscosity, and surface tension of aqueous tetrabutylammonium-based ionic liquids were measured experimentally by varying temperature (283.4 to 333.4 K) and concentration of ILs (10-50 wt%) at an interval of 10 K and 10 wt% respectively. In this study, the aqueous tetrabutylammonium-based ionic liquids namely tetrabutylammonium acetate [TBA][OAC], tetrabutylammonium bromide [TBA][Br], and tetrabutylammonium hydroxide [TBA][OH] was used to investigate the influence of temperature and concentration of ILs on the physical properties data was examined. It is observed that both density and surface tension increase with increasing concentration of [TBA][Br], whereas the opposite trend is observed for [TBA][OAC] and [TBA][OH] respectively. This is due to stronger molecular interaction between [TBA][Br] and water compared to other ILs. The dynamic viscosity of all aqueous ILs increases with increasing IL concentration. The measured physical properties of ILs decrease as temperature increases. Furthermore, the experimental data is correlated and compared with that of the calculated model; the agreement was satisfactory. Graphical abstract.

Prediction of chemical reproductive toxicity to aquatic species using a machine learning model: An application in an ecological risk assessment of the Yangtze River, China

The endocrine disrupting chemicals (EDCs) have been at the forefront of environmental issues for over 20 years and are a principle factor considered in every ecological risk assessment, but this kind of risk assessment faces difficulties. The expense, time cost of in vivo tests, and lack of toxicity data are key limiting factors for the ability to conduct ecological risk assessments of EDCs to aquatic species. In this study, a machine learning model named the support vector machine (SVM) was used to predict the reproductive toxicity of EDCs, and the performance of the models was evaluated. The results showed that the SVM model provided more accurate toxicity prediction data compared with the interspecies correlation estimation (ICE) model developed by previous study to predict the reproductive toxicity. The application of the predicted toxicity data was an important supplement to the observed data for the ecological risk assessment of EDCs in the Yangtze River, where estrogens and phenolic compounds have been found at some sampling sites in the middle and lower reaches. The results showed that the ecological risk of estrone, 17β-estradiol, and ethinyl estradiol were significant. This study revealed the application potential of machine learning models for the prediction of reproductive toxicity effects of EDCs. This can provide reliable alternative toxicity data for the ecological risk assessments of EDCs.

Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.

Evaluating the hazardous impact of ionic liquids - Challenges and opportunities

Ionic liquids (ILs), being related to the design of new environmentally friendly solvents, are widely considered for applications within the "green chemistry" concept. Due to their unique properties and wide diversity, ILs allow tailoring new separation procedures and producing new materials for advanced applications. However, despite the promising technical performance, environmental concerns highlighted in recent studies focused on the toxicity and biodegradability of ILs and their metabolites have revealed that ILs safety labels are not as benign as previously claimed. This review refers to the fundamentals about the properties and applications of ILs also in the context of their potential environmental effect. Toxicological issues and harmful effects related to the use of ILs are discussed, including the evaluation of their biodegradability and ecological impact on diverse organisms and ecosystems, also with respect to bacteria, fungi, and cell cultures. In addition, this review covers the tools used to assess the toxicity of ILs, including the predictive computational models and the results of studies involving cell membrane models and molecular simulations. Summing up the knowledge available so far, there are still no reliable criteria for unequivocal attribution of toxicity and environmental impact credentials for ILs, which is a challenging research task.

Predictive classification-based QSTR models for toxicity study of diverse pesticides on multiple avian species

Protection and restoration of different endangered bird species from pesticide exposure is crucial from the point of safety assessment of ecosystem. Toxicity predictions or risk assessment of pesticides by chemometric tools is one of the challenging fields in recent era. In the present study, classification-based quantitative structure toxicity relationship (QSTR) models were developed for a large dataset (516) of diverse pesticides on multiple avian species mallard duck, bobwhite quail, and zebra finch according to the Organization for Economic Co-operation and Development guidelines. The QSTR models were developed by linear discriminant analysis method with genetic algorithm for feature selection from 2D descriptors using QSAR-Co software. Different statistical metrics assured the reliability and robustness of the developed models. External compound prediction highlighted predictive nature of the models. The mechanistic interpretation suggested that presence of phosphate, halogens (Cl, Br), ether linkage, and NCOO influence the avian toxicity. Furthermore, model reliability was checked by the application of the standardization approach of the applicability domain (AD). Finally, the developed models provided a priori toxic and non-toxic classification for unknown pesticides (inside AD), with particular emphasis on organophosphate pesticides. The interspecies toxicity correlation and predictions encouraged for their further applicability for the fulfilment of data gaps in vital missing species.

QSAR study for predicting the ecotoxicity of NADES towards Aliivibrio fischeri. Exploring the use of mixing rules

(Eco)toxicological information of natural deep eutectic solvents (NADES) is scarce, and thus, quantitative structure activity relationship (QSAR) models are an important tool for achieving the prediction of toxicity in this case. For that reason, in this manuscript, a new QSAR model for predicting the ecotoxicity of NADES towards the Aliivibrio fischeri biomodel, using mixing rules, is proposed. The main advantage of the method is that the individual components of the mixtures are molecularly modelled, and then, a mixing rule is used, which simplifies the process. For developing the model, a total of 11 descriptors for each component is used: the dissociation constant, partition coefficient, Van der Waals volume, Van der Waals surface area, topological polar surface area, solvent accessible surface area, minimum projection area, maximum projection area, minimum projection radius, maximum projection radius and molecular weight. The final obtained model includes the topological polar surface area and the dissociation constant, mechanistically interpreted as the ability of a NADES to transport through biological membranes and the severe negative effect of pH on the toxicity and biological response of Aliivibrio fischeri bacteria. The OECD Guidance Document on the Validation of (Quantitative) Structure-Activity Relationships is followed to develop the mathematical model.

Alteration between inhibition and stimulation in individual and mixture effects of [amim]Br and [apyr]Br on Aliivibrio fischeri: Time and side-chain dependence

The exposure reality of chemicals is usually in mixtures, the effects of which are usually extrapolated from individual results. Yet, such extrapolation is challenged by the alteration between monotonic concentration-response curves (CRCs) and non-monotonic hormetic CRCs in individual and mixture effects. In the present study, we aimed to investigate the occurrence of such alterations using 1-alkylimidazolium bromide ([amim]Br) and 1-alkylpyridinium bromide ([apyr]Br) ionic liquids (ILs) as model chemicals. Effects of four [amim]Br, four [apyr]Br, and their quaternary mixtures designed by uniform design were measured on Aliivibrio fischeri in a time-dependent fashion. Results showed that the individual [amim]Br showed monotonic CRCs. Their inhibition increased over the length of the side-chain and decreased over the exposure time. The [amim]Br mixtures showed non-monotonic hormetic CRCs, where the stimulations increased over exposure time. The individual [apyr]Br had non-monotonic hormetic CRCs, and their stimulation increased over the length of the side-chain. Meanwhile, the [apyr]Br mixtures had monotonic CRCs without any stimulation. Notably, the positive contributors to the mixture effects were [emim]Br or [epyr]Br which had the shortest side-chain among the components. The findings can facilitate accurate prediction on the environmental effects of ILs with specific considerations on hormetic and mixture effects.

Comparison of modes of action among different trophic levels of aquatic organisms for pesticides and medications based on interspecies correlations and excess toxicity: Theoretical consideration

Pesticides and medications have adverse effects in non-target organisms that can lead to different modes of action (MOAs). However, no study has been performed to compare the MOAs between different levels of aquatic species. In this study, theoretical equations of interspecies relationship and excess toxicity have been developed and used to investigate the MOAs among fish, Daphnia magna, Tetrahymena pyriformis and Vibrio fischeri for pesticides and medications. The analysis on the interspecies correlation and excess toxicity suggested that fungicides, herbicides and medications share the similar MOAs among the four species. On the other hand, insecticides share different MOAs among the four species. Exclusion of insecticides from the interspecies correlation can significantly improve regression coefficient. Interspecies relationship is dependent not only on the difference in interaction of chemicals with the target receptor(s), but also on the difference in bio-uptake between two species. The difference in physiological structures will result in the difference in bioconcentration potential between two different trophic levels of organisms. Increasing of molecular size or hydrophobicity will increase the toxicity to higher level of aquatic organisms; on the other hand, chemical ionization will decrease the toxicity to higher level organisms. Hydrophilic compounds can more easily pass through cell membrane than skin or gill, leading to greater excess toxicity to Vibrio fischeri, but not to fish and Daphnia magna.

Effect of differently methyl-substituted ionic liquids on Scenedesmus obliquus growth, photosynthesis, respiration, and ultrastructure

Concerns have been raised regarding the ecotoxicity of ionic liquids (ILs) owing to their wide usage in numerous fields. Three imidazolium chloride ILs with different numbers of methyl substituents, 1-decyl-imidazolium chloride ([C₁₀IM]Cl), 1-decyl-3-methylimidazolium chloride ([C₁₀MIM]Cl), and 1-decyl-2,3-dimethylimidazolium chloride ([C₁₀DMIM]Cl), were examined to assess their effects on growth, photosynthesis pigments content, chlorophyll fluorescence, photosynthetic and respiration rate, and cellular ultrastructure of Scenedesmus obliquus. The results showed that algal growth was significantly inhibited by ILs treatments. The observed IC_50,48h doses were 0.10 mg/L [C₁₀IM]Cl, 0.01 mg/L [C₁₀MIM]Cl, and 0.02 mg/L [C₁₀DMIM]Cl. The chlorophyll a, chlorophyll b, and total chlorophyll content declined, and the chlorophyll fluorescence parameters, minimal fluorescence yield (F₀), maximal fluorescence yield (F_m), maximum quantum yield of PSII photochemistry (F_v/F_m), effective quantum yield of PSII [Y(II)], non-photochemical quenching (NPQ) and non-photosynthetic losses yield [Y(NO)] were notably affected by ILs in a dose-dependent manner. ILs affected the primary photosynthetic reaction, impaired heat dissipation capability, and diminished photosynthetic efficiency, indicating negative effects on photosystem II. The photosynthetic and respiration rates of algal cells were also reduced due to the ILs treatments. The adverse effects of ILs on plasmolysis and chloroplast deformation were examined using ultrastructural analyses; chloroplast swelling and lamellar structure almost disappeared after the [C₁₀MIM]Cl treatment, and an increased number of starch grains and vacuoles was observed after all ILs treatments. The results indicated that one-methyl-substituted ILs were more toxic than non-methyl-substituted ILs, which were also more toxic than di-methyl-substituted ILs. The toxicity of the examined ILs showed the following order: [C₁₀IM]Cl < [C₁₀DMIM]Cl ≤ [C₁₀MIM]Cl.

QSAR model for predicting the toxicity of organic compounds to fathead minnow

In this work, a new norm descriptor is proposed based on atomic properties. A quantitative structure-activity relationship (QSAR) model for predicting the toxicity of organic compounds to fathead minnow is further developed by norm descriptors. Results indicate that this new model based on the norm descriptors has satisfactory predictive results with the squared correlation coefficient (R²) and squared relation coefficient of the cross validation (Q²) of 0.8174 and 0.7923, respectively. Combining with Y-randomization test, applicability domain test, and comparison with other references, calculation results indicate that the QSAR model performs well both in the stability and the accuracy with wide application domain, which might be further used effectively for the safe and risk assessment of various organics.

Environmental Impact of Ionic Liquids: Recent Advances in (Eco)toxicology and (Bio)degradability

This Review aims to integrate the most recent and pertinent data available on the (bio)degradability and toxicity of ionic liquids for global and critical analysis and on the conscious use of these compounds on a large scale thereafter. The integrated data will enable focus on the recognition of toxicophores and on the way the community has been dealing with them, with the aim to obtain greener and safer ionic liquids. Also, an update of the most recent biotic and abiotic methods developed to overcome some of these challenging issues will be presented. The review structure aims to present a potential sequence of events that can occur upon discharging ionic liquids into the environment and the potential long-term consequences.

Comprehensive approach for predicting toxicological effects of ionic liquids on several biological systems using unified descriptors

The challenge and opportunity for design of environmentally-benign ionic liquids (ILs) would start from prediction of their toxicological effects on several endpoints solely based on the structural formulas. Especially, a comprehensive yet simple equation able to predict several biological responses to IL toxicity is of much advantage. Therefore, based on 50 toxicity testing systems on ILs a comprehensively approachable prediction method was developed. For the modelling, approximately 1600 toxicity values measured by several biological systems and an amended linear free energy relationship (LFER) model were used. Since the toxicological activities of an IL could be differently described according to sensitivity of toxicity testing systems, the sensitivity of each of toxicity testing systems was also estimated in the modelling. By statistical analysis with the calculated descriptors, a LFER model was built. Also the sensitivity value of each system on the basis of the comprehensively approachable model was numerically estimated. In results, it was observed that the combination of single model and sensitivity terms was able to predict each of 50 toxicological effects of ILs with R(2) of 0.593~0.978, and SE of 0.098~0.699 log unit, and the total data set with R(2) of 0.901 and SE of 0.426 log unit.

Interpretation of toxicological activity of ionic liquids to acetylcholinesterase inhibition via in silico modelling

For designing environmentally friendly ionic liquids (ILs), their structural effects on the toxicity should be interpreted via modelling based on the quantitative-structure-activity-relationship (QSAR) concept. For the purpose, QSAR models for predicting IL toxicity in acetylcholinesterase activity were developed by using linear free-energy relationship (LFER) descriptors, whose chemical meanings are well defined. These are excess molar refraction (Ec or a), dipolarity/polarizability (Sc or a), H-bonding acidity (Ac or a), H-bonding basicity (Bc or a), McGowan volume (Vc or a), and ionic interactions of cation (J(+)) and anion (J(-)). Since the experimentally determined LFER descriptors are not available, we calculated them based on density functional theory, conductor-like screening model and the open-source software, obprop. The toxicity values of imidazolium- and pyridinium-based ILs could be predicted by a combination of four descriptors (Ac, Bc, Vc and Sa) with an R(2) of 0.828, and (Ec, Ac, Ea and Sa) with an R(2) of 0.879, respectively. In prediction study using the overall dataset containing various IL structures, the six calculated terms (Ec, Sc, Ac, J(+), Ea, and Sa) were selected and correlated with the observed toxicity values in R(2) of 0.748 for the training set, R(2) of 0.711 for the test set and R(2) of 0.655 for external validation set. And this study explains how the selected terms are contributing to the prediction models, and their chemical meanings were understood.

Correlating toxicological effects of ionic liquids on Daphnia magna with in silico calculated linear free energy relationship descriptors

In silico prediction model for toxicological effects of ionic liquids (ILs) is useful to understand ILs' toxicological interactions and to design environmentally benign IL structures. Actually, it is essential since the types of ILs are extremely numerous. Accordingly, prediction models were developed in this study. For the modelling, well-defined linear free energy relationship (LFER) descriptors - i.e. excess molar refraction (E), dipolarity/polarizability (S), H-bonding acidity (A), H-bonding basicity (B), McGowan volume (V), cation interaction (J(+)) and anion interaction (J(-)) - were in silico calculated using density functional theory and conductor-like screening model. These descriptors were then correlated with the toxicological values of ILs to Daphnia magna. First, a model established by Hoover et al. (2007) using measured LFER descriptors of 97 neutral compounds was applied to the prediction of ILs' toxicity. As expected, the model by Hoover et al. (2007) needs to be amended for ILs. To that end, the difference in toxicological interactions between neutral compounds and ILs was addressed by additional single J(+) or five LFER descriptors of cation i.e. Ec, Sc, Bc, Vc, and J(+). Secondly, a prediction model for only ILs was developed by using the three LFER descriptors Ec, Bc, and J(+). The model had a reasonable predictability and robustness of R(2) = 0.880 for the training set, 0.848 for the test set, and 0.867 for the overall set. The established models can be used to design environmentally benign IL structures and to reduce labour, danger, time, and materials compared to the experiment-based study.

Molecular Mechanism of Ionic-Liquid-Induced Membrane Disruption: Morphological Changes to Bilayers, Multilayers, and Vesicles

The application of ionic liquids (ILs) in many industrially relevant processes provides an urgent need to better understand their molecular interactions with biological systems. A detailed understanding of the cytotoxicity mechanism of ILs can be helpful in facilitating the molecular design of nontoxic ILs. Using coarse-grained molecular dynamics (MD) simulations, we investigate the effects of imidazolium-based ILs on several lipid bilayer morphologies. Our results demonstrate that the asymmetric insertion of IL cations into one side of a lipid bilayer leaflet enhances the leaflet strain, which upon reaching a critical value triggers a morphological disruption in the bilayer. Consistently, the bending modulus of the bilayer is reduced by 1 to 2 orders of magnitude relative to that of an IL-free planar bilayer prior to the disruption event. Our results suggest that ILs that can easily insert into the lipid bilayer without diffusing across or inducing lipid flip-flop can be more disruptive to a lipid biomembrane.

Modeling the toxicity of chemical pesticides in multiple test species using local and global QSTR approaches

The safety assessment processes require the toxicity data of chemicals in multiple test species and thus, emphasize the need for computational methods capable of toxicity prediction in multiple test species. Pesticides are designed toxic substances and find extensive applications worldwide. In this study, we have established local and global QSTR (quantitative structure-toxicity relationship) and ISC QSAAR (interspecies correlation quantitative structure activity-activity relationship) models for predicting the toxicities of pesticides in multiple aquatic test species using the toxicity data in crustacean (Daphnia magna, Americamysis bahia, Gammarus fasciatus, and Penaeus duorarum) and fish (Oncorhynchus mykiss and Lepomis macrochirus) species in accordance with the OECD guidelines. The ensemble learning based QSTR models (decision tree forest, DTF and decision tree boost, DTB) were constructed and validated using several statistical coefficients derived on the test data. In all the QSTR and QSAAR models, Log P was an important predictor. The constructed local, global and interspecies QSAAR models yielded high correlations (R2) of >0.941; >0.943 and >0.826, respectively between the measured and model predicted endpoint toxicity values in the test data. The performances of the local and global QSTR models were comparable. Furthermore, the chemical applicability domains of these QSTR/QSAAR models were determined using the leverage and standardization approaches. The results suggest for the appropriateness of the developed QSTR/QSAAR models to reliably predict the aquatic toxicity of structurally diverse pesticides in multiple test species and can be used for the screening and prioritization of new pesticides.

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.

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.