Supratik Kar

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.

QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors.

One of the major economic alternatives to experimental toxicity testing is the use of quantitative structure-activity relationships (QSARs) which are used in formulating regulatory decisions of environmental protection agencies. In this background, we have modeled a large diverse group of 297 chemicals for their toxicity to Daphnia magna using mechanistically interpretable descriptors. Three-dimensional (3D) (electronic and spatial) and two-dimensional (2D) (topological and information content indices) descriptors along with physicochemical parameter logK(o/w) (n-octanol/water partition coefficient) and structural descriptors were used as predictor variables. The QSAR models were developed by stepwise multiple linear regression (MLR), partial least squares (PLS), genetic function approximation (GFA), and genetic PLS (G/PLS). All the models were validated internally and externally. Among several models developed using different chemometric tools, the best model based on both internal and external validation characteristics was a PLS equation with 7 descriptors and three latent variables explaining 67.8% leave-one-out predicted variance and 74.1% external predicted variance. The PLS model suggests that higher lipophilicity and electrophilicity, less negative charge surface area and presence of ether linkage, hydrogen bond donor groups and acetylenic carbons are responsible for greater toxicity of chemicals. The developed model may be used for prediction of toxicity, safety and risk assessment of chemicals to achieve better ecotoxicological management and prevent adverse health consequences.

Nano-quantitative structure–activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells

As experimental evaluation of the safety of nanoparticles (NPs) is expensive and time-consuming, computational approaches have been found to be an efficient alternative for predicting the potential toxicity of new NPs before mass production. In this background, we have developed here a regression-based nano quantitative structure-activity relationship (nano-QSAR) model to establish statistically significant relationships between the measured cellular uptakes of 109 magnetofluorescent NPs in pancreatic cancer cells with their physical, chemical, and structural properties encoded within easily computable, interpretable and reproducible descriptors. The developed model was rigorously validated internally as well as externally with the application of the principles of Organization for Economic Cooperation and Development (OECD). The test for domain of applicability was also carried out for checking reliability of the predictions. Important fragments contributing to higher/lower cellular uptake of NPs were identified through critical analysis and interpretation of the developed model. Considering all these identified structural attributes, one can choose or design safe, economical and suitable surface modifiers for NPs. The presented approach provides rich information in the context of virtual screening of relevant NP libraries.

First report on interspecies quantitative correlation of ecotoxicity of pharmaceuticals

Pharmaceuticals being extensively and progressively used in human and veterinary medicine are emerging as significant environmental contaminants. Pharmaceuticals are designed to have a specific mode of action and many of them are persistent in the body. These features among others make pharmaceuticals to be evaluated for potential effects on aquatic flora and fauna. Low levels of pharmaceuticals have been detected in many countries in sewage treatment plant effluents, surface waters, groundwater and drinking waters. In contrast, there is a general scarcity of publicly available ecotoxicological data concerning pharmaceuticals. Interspecies toxicity correlations provide a tool for estimating contaminant sensitivity with known levels of uncertainty for a diversity of wildlife species. In this context, we have developed interspecies toxicity correlation between Daphnia magna (zooplankton) and fish (species according to OECD guidelines) assessing the ecotoxicological hazard potential of diverse 77 pharmaceuticals. The developed models are validated and consensus models are presented to predict toxicity of the individual compounds for any one species when the data for the other species are available. Informative illustrations of the contributing structural fragments which are responsible for the greater toxicity of the diverse pharmaceuticals are identified by the developed models. Developed models are also used to predict fish toxicities of 59 pharmaceuticals (for which Daphnia toxicities are present) and Daphnia toxicities of 30 pharmaceuticals (for which fish toxicities are present). This study will allow a better and comprehensive risk assessment of pharmaceuticals for which toxicity data is missing for a particular endpoint.

Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: A mechanistic QSTR approach

Nanotechnology has evolved as a frontrunner in the development of modern science. Current studies have established toxicity of some nanoparticles to human and environment. Lack of sufficient data and low adequacy of experimental protocols hinder comprehensive risk assessment of nanoparticles (NPs). In the present work, metal electronegativity (χ), the charge of the metal cation corresponding to a given oxide (χox), atomic number and valence electron number of the metal have been used as simple molecular descriptors to build up quantitative structure-toxicity relationship (QSTR) models for prediction of cytotoxicity of metal oxide NPs to bacteria Escherichia coli. These descriptors can be easily obtained from molecular formula and information acquired from periodic table in no time. It has been shown that a simple molecular descriptor χox can efficiently encode cytotoxicity of metal oxides leading to models with high statistical quality as well as interpretability. Based on this model and previously published experimental results, we have hypothesized the most probable mechanism of the cytotoxicity of metal oxide nanoparticles to E. coli. Moreover, the required information for descriptor calculation is independent of size range of NPs, nullifying a significant problem that various physical properties of NPs change for different size ranges.

How far can virtual screening take us in drug discovery

Introduction: Virtual screening (VS) has emerged as an important tool in identifying bioactive compounds through computational means, by employing knowledge about the protein target or known bioactive ligands. VS has appeared as an adaptive response to the massive throughput synthesis and screening paradigm as necessity has forced the computational chemistry community to develop tools that screen against any given target and/or property millions or perhaps billions of molecules in short period of time. Areas covered: This editorial review attempts to catalog most commonly exercised VS methods, available databases for screening, advantages of VS methods along with pitfalls and technical traps with the aim to make VS as one of the most effective tools in drug discovery process. Finally, several case studies are cited where the VS technology has been applied successfully. Expert opinion: In recent times, many successful examples have been demonstrated in the field of computer-aided VS with the objective of increasing the probability of finding novel hit and lead compounds in terms of cost-effectiveness and commitment in time and material. Despite the inherent limitations, VS is still the best option now available to explore a large chemical space.

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

Pharmacophore mapping-based virtual screening followed by molecular docking studies in search of potential acetylcholinesterase inhibitors as anti-Alzheimer's agents.

Alzheimers disease (AD) is turning out to be one of the lethal diseases in older people. Acetylcholinesterase (AChE) is a crucial target in designing of drugs against AD. The present in silico study was carried out to explore natural compounds as potential AChE inhibitors. Virtual screening, via drug-like ADMET filter, best pharmacophore model and molecular docking analyses, has been utilized to identify putative novel AChE inhibitors. The InterBioScreens Natural Compound (NC) database was first filtered by applying drug-like ADMET properties and then with the pharmacophore-based virtual screening followed by molecular docking analyses. Based on docking score, interaction patterns and calculated activity, the final hits were selected and these consist of coumarin and non-coumarin classes of compounds. Few hits were found to have been already reported for their AChE inhibitory activity in different literatures confirming reliability of our pharmacophore model. The remaining hits are suggested to be potential AChE inhibitors for AD.

First report on development of quantitative interspecies structure–carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines

Different regulatory agencies in food and drug administration and environmental protection worldwide are employing quantitative structure-activity relationship (QSAR) models to fill the data gaps related with properties of chemicals affecting the environment and human health. Carcinogenicity is a toxicity endpoint of major concern in recent times. Interspecies toxicity correlations may provide a tool for estimating sensitivity towards toxic chemical exposure with known levels of uncertainty for a diversity of wildlife species. In this background, we have developed quantitative interspecies structure-carcinogenicity correlation models for rat and mouse [rodent species according to the Organization for Economic Cooperation and Development (OECD) guidelines] based on the carcinogenic potential of 166 organic chemicals with wide diversity of molecular structures, spanning a large number of chemical classes and biological mechanisms. All the developed models have been assessed according to the OECD principles for the validation of QSAR models. Consensus predictions for carcinogenicity of the individual compounds are presented here for any one species when the data for the other species are available. Informative illustrations of the contributing structural fragments of chemicals which are responsible for specific carcinogenicity endpoints are identified by the developed models. The models have also been used to predict mouse carcinogenicities of 247 organic chemicals (for which rat carcinogenicities are present) and rat carcinogenicities of 150 chemicals (for which mouse carcinogenicities are present). Discriminatory features for rat and mouse carcinogenicity values have also been explored.

QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes to Tetrahymena pyriformis

Extensive production and utilization of aromatic aldehydes and their derivatives without proper certification is alarming with regard to environmental safety. This concern motivated our construction of predictive quantitative structure–activity relationship (QSAR) models for the toxicity of aldehydes to the ecologically important species Tetrahymena pyriformis. Quantum topological molecular similarity (QTMS) descriptors, along with the lipid-water partition coefficient (log K o/w), were used as predictor variables. The QTMS descriptors were calculated at different levels of theory including AM1, HF/3-21G(d), HF/6-31G(d), B3LYP/6-31 + G(d,p), B3LYP/6-311 + G(2d,p) and MP2/6-311+G(2d,p). The data set of 77 aromatic aldehydes was divided into a training set (n = 58) and a test (n = 19) set, and 58 models were developed using partial least squares (PLS) and genetic partial least squares (G/PLS). We evaluated the overall predictive capacity of the models based on leave-one-out predictions for the training set compounds and model derived predictions for the test set compounds. For both PLS and G/PLS, the models built at the HF/6-31G(d) level show better predictivity (based on overall prediction) than the models developed at any of the other five levels. Further validation was also performed utilizing (process and model) randomization tests. We show that improved predictive QSAR models for aldehydic toxicity to Tetrahymena pyriformis can be generated using QTMS descriptors along with log K o/w.