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

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.

Quantitative structure-toxicity relationship model for acute toxicity of organophosphates via multiple administration routes in rats and mice

Organophosphates (OPs) are highly toxic compounds, with widespread application in agricultural and chemical industries, whose introduction into the environment poses serious hazards to humans and ecological systems. To assess and ultimately mitigate these hazards, this study predicted the acute toxicity of OPs according to their chemical structure and administration route. The acute toxicity data of 161 OPs in two species via six different administration routes were manually collected and used to develop a series of quantitative structure-toxicity relationship (QSTR) models with robust and practical predictive abilities. The random forest algorithm was used to develop the models, employing both quantum chemical and two-dimensional descriptors according to OECD guidelines. Correlation results and feature similarities indicated that whereas acute toxicity data from rats and mice via the same administration route were combinable for modeling, data from different routes were not. Six QSTR models for each route in a single species and two QSTR models for a single route in the two species were constructed, achieving practical predictive performance. Despite significant variances in their datasets, the prediction models could predict the acute toxicity of novel or unknown OPs, realize rapid assessment, and provide guidance for regulatory decisions to reduce the hazards of OPs.

Species sensitivity distributions for use in environmental protection, assessment, and management of aquatic ecosystems for 12 386 chemicals

The present study considers the collection and use of ecotoxicity data for risk assessment with species sensitivity distributions (SSDs) of chemical pollution in surface water, which are used to quantify the likelihood that critical effect levels are exceeded. This fits the European Water Framework Directive, which suggests using models to assess the likelihood that chemicals affect water quality for management prioritization. We derived SSDs based on chronic and acute ecotoxicity test data for 12 386 compounds. The log-normal SSDs are characterized by the median and the standard deviation of log-transformed ecotoxicity data and by a quality score. A case study illustrates the utility of SSDs for water quality assessment and management prioritization. We quantified the chronic and acute mixture toxic pressure of mixture exposures for >22 000 water bodies in Europe for 1760 chemicals for which we had both exposure and hazard data. The results show the likelihood of mixture exposures exceeding a negligible effect level and increasing species loss. The SSDs in the present study represent a versatile and comprehensive approach to prevent, assess, and manage chemical pollution problems. Environ Toxicol Chem 2019;38:905-917. © 2019 SETAC.

Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach

An extensive use of pharmaceuticals and the widespread practices of their erroneous disposal measures have made these products contaminants of emerging concern (CEC). Especially, active pharmaceutical ingredients (APIs) are ubiquitously detected in surface water and soil, mainly in the aquatic compartment, where they do affect the living systems. Unfortunately, there is a huge gap in the availability of ecotoxicological data on pharmaceuticals' environmental behavior and ecotoxicity which force EMEA (European Medicines Agency) to release guidelines for their risk assessment. In silico modeling approaches are vital tools to exploit the existing information to rapidly emphasize the potentially most hazardous and toxic pharmaceuticals and prioritize the most environmentally hazardous ones for focusing further on their experimental studies. The quantitative structure-activity relationship (QSAR) models are capable of predicting missing properties for toxic end-points required to prioritize existing, or newly synthesized chemicals for their potential hazard. This chapter reviews the information regarding occurrence and impact of pharmaceuticals and their metabolites in the environment along with their persistence, environmental fate, risk assessment, and risk management. A bird's eye view about the necessity of in silico methods for fate prediction of pharmaceuticals in the environment as well as existing successful models regarding ecotoxicity of pharmaceuticals are discussed. Available toxicity endpoints, ecotoxicity databases, and expert systems frequently used for ecotoxicity predictions of pharmaceuticals are also reported. The overall discussion justifies the requirement to build up additional in silico models for quick prediction of ecotoxicity of pharmaceuticals economically, without or involving only limited animal testing.

The “ETA” Indices in QSAR/QSPR/QSTR Research

Descriptors are one of the most essential components of predictive Quantitative Structure-Activity/Property/Toxicity Relationship (QSAR/QSPR/QSTR) modeling analysis, as they encode chemical information of molecules in the form of quantitative numbers, which are used to develop mathematical correlation models. The quality of a predictive model not only depends on good modeling statistics, but also on the extraction of chemical features. A significant amount of research since the beginning of QSAR analysis paradigm has led to the introduction of a large number of predictor variables or descriptors. The Extended Topochemical Atom (ETA) indices, developed by the authors' group, successfully address the aspects of molecular topology, electronic information, and different types of bonded interactions, and have been extensively employed for the modeling of different types of activity/property and toxicity endpoints. This chapter provides explicit information regarding the basis, algorithm, and applicability of the ETA indices for a predictive modeling paradigm.

In Silico Models for Ecotoxicity of Pharmaceuticals

Pharmaceuticals and their active metabolites are one of the significantly emerging environmental toxicants. The major routes of entry of pharmaceuticals into the environment are industries, hospitals, or direct disposal of unwanted or expired drugs made by the patient. The most important and distinct features of pharmaceuticals are that they are deliberately designed to have an explicit mode of action and designed to exert an effect on humans and other living systems. This distinctive feature makes pharmaceuticals and their metabolites different from other chemicals, and this necessitates the evaluation of the direct effects of pharmaceuticals in various environmental compartments as well as to living systems. In this background, the alarming situation of ecotoxicity of diverse pharmaceuticals have forced government and nongovernment regulatory authorities to recommend the application of in silico methods to provide quick information about the risk assessment and fate properties of pharmaceuticals as well as their ecological and indirect human health effects. This chapter aims to offer information regarding occurrence of pharmaceuticals in the environment, their persistence, environmental fate, and toxicity as well as application of in silico methods to provide information about the basic risk management and fate prediction of pharmaceuticals in the environment. Brief ideas about toxicity endpoints, available ecotoxicity databases, and expert systems employed for rapid toxicity predictions of ecotoxicity of pharmaceuticals are also discussed.

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.

QSTR modeling for predicting aquatic toxicity of pharmacological active compounds in multiple test species for regulatory purpose

High concentrations of pharmacological active compounds (PACs) detected in global drinking water resources and their toxicological implications in aquatic life has become a matter of concern compelling for the development of reliable QSTRs (qualitative/quantitative structure-toxicity relationships) for their risk assessment. Robust QSTRs, such as decision treeboost (DTB) and decision tree forest (DTF) models implementing stochastic gradient boosting and bagging algorithms were established by experimental toxicity data of structurally diverse PACs in daphnia using molecular descriptors for predicting toxicity of new untested compounds in multiple test species. Developed models were rigorously validated using OECD recommended internal and external validation procedures and predictive power tested with external data of different trophic level test species (algae and fish). Classification QSTRs (DTB, DTF) rendered accuracy of 98.73% and 97.47%, respectively in daphnia and 84.38%, 85.94% (algae), 78.46% and 79.23% (fish). On the other hand, the regression QSTRs (DTB, DTF) yielded squared correlation coefficient values of 0.831, 0.852 (daphnia), 0.534, 0.556 (algae) and 0.620, 0.637 (fish). QSTRs developed in this study passed the OECD validation criteria and performed better than reported earlier for predicting toxicity of PACs, and can be used for screening the new untested compounds for regulatory purpose.

The “ETA” Indices in QSAR/QSPR/QSTR Research

Descriptors are one of the most essential components of predictive Quantitative Structure-Activity/Property/Toxicity Relationship (QSAR/QSPR/QSTR) modeling analysis, as they encode chemical information of molecules in the form of quantitative numbers, which are used to develop mathematical correlation models. The quality of a predictive model not only depends on good modeling statistics, but also on the extraction of chemical features. A significant amount of research since the beginning of QSAR analysis paradigm has led to the introduction of a large number of predictor variables or descriptors. The Extended Topochemical Atom (ETA) indices, developed by the authors' group, successfully address the aspects of molecular topology, electronic information, and different types of bonded interactions, and have been extensively employed for the modeling of different types of activity/property and toxicity endpoints. This chapter provides explicit information regarding the basis, algorithm, and applicability of the ETA indices for a predictive modeling paradigm.

Multispecies QSAR modeling for predicting the aquatic toxicity of diverse organic chemicals for regulatory toxicology

The research aims to develop multispecies quantitative structure-activity relationships (QSARs) modeling tools capable of predicting the acute toxicity of diverse chemicals in various Organization for Economic Co-operation and Development (OECD) recommended test species of different trophic levels for regulatory toxicology. Accordingly, the ensemble learning (EL) approach based classification and regression QSAR models, such as decision treeboost (DTB) and decision tree forest (DTF) implementing stochastic gradient boosting and bagging algorithms were developed using the algae (P. subcapitata) experimental toxicity data for chemicals. The EL-QSAR models were successfully applied to predict toxicities of wide groups of chemicals in other test species including algae (S. obliguue), daphnia, fish, and bacteria. Structural diversity of the selected chemicals and those of the end-point toxicity data of five different test species were tested using the Tanimoto similarity index and Kruskal-Wallis (K-W) statistics. Predictive and generalization abilities of the constructed QSAR models were compared using statistical parameters. The developed QSAR models (DTB and DTF) yielded a considerably high classification accuracy in complete data of model building (algae) species (97.82%, 99.01%) and ranged between 92.50%-94.26% and 92.14%-94.12% in four test species, respectively, whereas regression QSAR models (DTB and DTF) rendered high correlation (R(2)) between the measured and model predicted toxicity end-point values and low mean-squared error in model building (algae) species (0.918, 0.15; 0.905, 0.21) and ranged between 0.575 and 0.672, 0.18-0.51 and 0.605-0.689 and 0.20-0.45 in four different test species. The developed QSAR models exhibited good predictive and generalization abilities in different test species of varied trophic levels and can be used for predicting the toxicities of new chemicals for screening and prioritization of chemicals for regulation.