Rudra Narayan Das

Some case studies on application of “rm2” 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 rm2 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 rm2. Further, a web application (accessible from http://aptsoftware.co.in/rmsquare/ and http://203.200.173.43:8080/rmsquare/) for calculation of the rm2 metrics has been introduced here. The present study reports that the web application can be easily used for computation of rm2 metrics provided observed and QSAR‐predicted data for a set of compounds are available. Further, scaling of response data is recommended prior to rm2 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’.

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.

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.

A Primer on QSAR/QSPR Modeling

This brief goes back to basics and describes the Quantitative structure-activity/property relationships (QSARs/QSPRs) that represent predictive models derived from the application of statistical tools correlating biological activity (including therapeutic and toxic) and properties of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure and/or properties. It explains how the sub-discipline of Cheminformatics is used for many applications such as risk assessment, toxicity prediction, property prediction and regulatory decisions apart from drug discovery and lead optimization. The authors also present, in basic terms, how QSARs and related chemometric tools are extensively involved in medicinal chemistry, environmental chemistry and agricultural chemistry for ranking of potential compounds and prioritizing experiments. At present, there is no standard or introductory publication available that introduces this important topic to students of chemistry and pharmacy. With this in mind, the authors have carefully compiled this brief in order to provide a thorough and painless introduction to the fundamental concepts of QSAR/QSPR modelling. The brief is aimed at novice readers

On some novel extended topochemical atom (ETA) parameters for effective encoding of chemical information and modelling of fundamental physicochemical properties

Extended topochemical atom (ETA) indices developed by our group have been extensively applied in our previous reports for toxicity and ecotoxicity modelling in the field of quantitative structure–activity relationships (QSARs). In the present study these indices have been further explored by defining additional novel parameters to model n-octanol–water partition coefficient (two data sets; n = 168 and 139), water solubility (n = 193), molar refractivity (n = 166), and aromatic substituent constants π, MR, σ m, and σ p (n = 99). All the models developed in the present study have undergone rigorous internal and external validation tests and the models have high statistical significance and prediction potential. In terms of Q 2 and r 2 values the models developed for the datasets of whole molecules are better than those previously reported, with topochemically arrived unique (TAU) indices on the same datasets of chemicals. An attempt has also been made to develop models using non-ETA topological and information indices. Interestingly, ETA and non-ETA models have been found to have similar predictive capacity.

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.

Development of classification and regression models for Vibrio fischeri toxicity of ionic liquids: green solvents for the future

Ionic liquids (ILs) have important industrial applications due to their unconventional and encouraging properties making them unique chemical species with extremely lowered vapour pressure, high thermal stability, and enhanced solvation characteristics. They are considered as “green solvents” chiefly due to their task-specificity and minimal release into the environment. The assessment of toxicity of ILs to living ecosystems has received considerable attention in recent years. Development of predictive quantitative structure–toxicity relationship (QSTR) models for ionic liquids can help in designing derivatives with a reduced toxicity profile, thereby making them greener and eco-friendlier. The present study attempts to develop a classification model as well as a regression model to capture specific structural information of ionic liquids responsible for their toxic manifestation to Vibrio fischeri. The models were developed using various two-dimensional chemical descriptors along with dummy variables and subjected to rigorous statistical validation employing multiple strategies, whereas other models of ILs for Vibrio fischeri toxicity reported so far have not employed such strict tools. The classification model has been characterized by acceptable Wilks λ statistics, pharmacological distribution diagram assessment, and receiver operating characteristics (ROC) analysis parameters. The regression models have been judged according to the OECD guidelines, and the best model showed encouraging external predictivity (R2pred = 0.739). The toxicity of ionic liquids to V. fischeri was found to be related to branching, molecular size, and solvation entropy of cations along with a lipophilicity contribution of the anions.

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.

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.