CORAL: Quantitative structure-activity relationship models for estimating toxicity of organic compounds in rats (original) (raw)
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Methods, 1998
tative correlation of chemical structure with biological Quantitative modeling methods, relating aspects of chemical activity and spurred many developments in the field of structure to biological activity, have long been applied to the quantitative structure-activity relationships (QSARs) prediction and characterization of chemical toxicity. The early (1, 2). In addition to modeling of chemical toxicity, these linear free-energy approaches of Hansch and Free Wilson promethods have been extensively applied to modeling of vided a fundamental scientific framework for the quantitative medicinal properties of chemicals. However, there are correlation of chemical structure with biological activity and important differences in the nature and objectives of spurred many developments in the field of quantitative structhese two applications which have led to the evolution ture-activity relationships (QSARs). In addition to modeling of of different modeling approaches. chemical toxicity, these methods have been extensively applied QSAR modeling studies of medicinal endpoints are to modeling of medicinal properties of chemicals. However, generally concerned with the optimization of a desired there are important differences in the nature and objectives of pharmacological activity, and with modeling differthese two applications, which have led to the evolution of differences in quantitative potency among active congeners. ent modeling approaches (namely, the need for treating sets of In addition, while detailed structural and functional noncongeneric toxic compounds). In this paper are discussed information pertaining to a biomolecular receptor site those approaches to chemical toxicity that have taken a more may or may not be available, generally, such a receptor ''personalized'' configuration and have undergone implementainteraction is implicitly assumed and provides a clear tion into software programs able to perform the various steps focus and constraint for modeling of the pharmacologiof the assessment of the hazard posed by the chemicals. These cal activity. Particularly in the area of de novo drug models focus both on a variety of toxicological endpoints and on key elements of toxicity mechanisms, such as metabolism. design, a number of chemical congeners, with relatively ᭧ 1998 Academic Press minor structural variations from a ''lead'' chemical, might be synthesized expressly for the purpose of exploring the nature of the receptor interaction and testing SAR hypotheses.
Journal of Hazardous Materials, 2016
Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q 2 ext and the Root Mean Square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.
Regulatory Toxicology and Pharmacology, 2011
Over forty years have elapsed since Hansch and Fujita published their pioneering work of quantitative structure-activity relationships (QSAR). Following the introduction of Comparative Molecular Field Analysis (CoMFA) by Cramer in 1998, other three-dimensional QSAR methods have been developed. Currently, combination of classical QSAR and other computational techniques at three-dimensional level is of greatest interest and generally used in the process of modern drug discovery and design. During the last several decades, a number of different mythologies incorporating a range of molecular descriptors and different statistical regression ways have been proposed and successfully applied in developing of new drugs, thus QSAR method has been proven to be indispensable in not only the reliable prediction of specific properties of new compounds, but also the help to elucidate the possible molecular mechanism of the receptorligand interactions. Here, we review the recent developments in QSAR and their applications in rational drug design, focusing on the reasonable selection of novel molecular descriptors and the construction of predictive QSAR models by the help of advanced computational techniques.
2008
Within the EU, the management of the risks of chemicals currently falls under a new legislation called Registration, Evaluation, and Authorization of Chemicals (REACH). Within the next 10 years, existing (eco)toxicological data gaps for the more than 100 000 chemicals on the European Inventory of Existing Commercial Substances (EINECS) should be filled. The challenge is to provide this toxicity information in a fast, cost effective manner, avoiding the use of experimental animals as much as possible. In this regard, REACH has provisions to allow for the use of in vitro and/or in silico methods, e.g. those based on (Quantitative) Structure Activity Relationships [(Q)SARs], to provide toxicity information or identify hazards of chemicals. This information can subsequently be used to identify priority chemicals for further risk evaluation. A QSAR is based on the assumption that the biological activity of a new or untested chemical can be inferred from the molecular structure, or properties of similar compounds whose activities have already been assessed. Therefore, using the chemical structure of chemical compounds as the sole input, one can build a toxicity prediction model based on parameters that define the physico-chemical properties and relative reactivity of the compounds. The objective of this thesis was to apply OECD guidelines in the development of validated QSAR models that describe acute toxicity of selected groups of EINECS chemicals to various organisms. In addition, an estimate was made of the total number of EINECS chemicals that could be possibly evaluated using (Q)SAR approaches. Based on experimental toxicity data from literature and in silico calculated log Kow (a measure of hydrophobicity) values, a QSAR advisory tool was developed that directs users to the appropriate QSAR model to apply for predicting toxicity of substituted mononitrobenzenes to five types of organisms within specified log Kow ranges. In a next study, QSAR models were developed to predict in vivo acute toxicity of chlorinated alkanes to fish based on data from in vitro experiments, and even based on in silico log Kow data only. Furthermore, using toxicity data from acute immobilization experiments with daphnids, an interspecies QSAR model was developed to predict toxicity of organothiophosphate pesticides to fish based on these data for daphnids and in silico log Kow values. The QSAR models for the mononitrobenzenes, chlorinated alkanes, and organothiophosphates covered in total 0.7 % of the 100 196 EINECS chemicals. In a final step, using chemical classification software, 54 % of the EINECS chemicals were grouped into specific classes that can in theory be subject to QSAR modeling. The safety assessment of one group of compounds that could not be classified e.g. botanical extracts might be done by further development of a method recently reported for the safety assessment of natural flavour complexes used as ingredients in food. This would result in an additional 3 % of the EINECS chemicals that could potentially be covered by SAR approaches, bringing the total percentage of EINECS compounds that can be covered by (Q)SAR approaches to 57. In conclusion, the results of this thesis reveal that, (i) in vitro experiments and even in silico calculations can help to reduce or replace animals used for experimental toxicity testing and (ii) despite the fact that individual QSARs may often each cover only limited, i.e. less than 1%, of the EINECS compounds, (Q)SAR approaches have the potential to cover about 57 % of the EINECS compounds.
Environmental Health Perspectives, 1996
Fuzzy adaptive least-squares (FALS), a pattern recognition method recently developed in our laboratory for correlating structure with activity rating, was used to generate quantitative structure-activity relationship (QSAR) models on the carcinogenicity of organic compounds of several chemical classes. Using the predictive models obtained from the chemical class-based FALS QSAR approach, the rodent carcinogenicity or noncarcinogenicity of a group of organic chemicals currently being tested by the U.S. National Toxicology Program was estimated from their chemical structures.
Current Topics in Medicinal Chemistry, 2014
Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity.
Testing computational toxicology models with phytochemicals
Molecular Nutrition & Food Research, 2010
Computational toxicology employing quantitative structure-activity relationship (QSAR) modeling is an evidence-based predictive method being evaluated by regulatory agencies for risk assessment and scientific decision support for toxicological endpoints of interest such as rodent carcinogenicity. Computational toxicology is being tested for its usefulness to support the safety assessment of drug-related substances (e.g. active pharmaceutical ingredients, metabolites, impurities), indirect food additives, and other applied uses of value for protecting public health including safety assessment of environmental chemicals. The specific use of QSAR as a chemoinformatic tool for estimating the rodent carcinogenic potential of phytochemicals present in botanicals, herbs, and natural dietary sources is investigated here by an external validation study, which is the most stringent scientific method of measuring predictive performance. The external validation statistics for predicting rodent carcinogenicity of 43 phytochemicals, using two computational software programs evaluated at the FDA, are discussed. One software program showed very good performance for predicting noncarcinogens (high specificity), but both exhibited poor performance in predicting carcinogens (sensitivity), which is consistent with the design of the models. When predictions were considered in combination with each other rather than based on any one software, the performance for sensitivity was enhanced, However, Chi-square values indicated that the overall predictive performance decreases when using the two computational programs with this particular data set. This study suggests that complementary multiple computational toxicology software need to be carefully selected to improve global QSAR predictions for this complex toxicological endpoint.
A Simple and Readily Integratable Approach to Toxicity Prediction
Journal of Chemical Information and Modeling, 2003
A simple, highly extensible computational strategy to assess compound toxicity has been developed with the premise that a compound's toxicity can be gauged from the toxicities of structurally similar compounds. Using a reference set of 13645 compounds with reported acute toxicity endpoint dose data (oral, rat-LD 50 data normalized in mg/kg), a generic utility which assigns a compound the average toxicity of structurally similar compounds is shown to correlate well with reported values. In a leave-one-out simulation using the requirement that at least one structurally similar member in a "voting consortium" is present within a reference set, the strategy demonstrates a predictive correlation (q∧2) of 0.82 with 57.3% of the compounds being predicted. Similar leave-one-out simulations on a set of 1781 drugs demonstrate a q∧2 of 0.74 with 51.8% of the compounds being predicted. Simulations to optimize similarity cutoff definitions, consortium member size, and reference set size illustrate that a significant improvement in the quality and quantity of predictions can be obtained by increasing the reference set size. Similar application of the strategy to subchronic and chronic toxicity data should be possible given reasonably sized reference sets.
Chemosphere, 2012
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.