First report on predictive chemometric modeling, 3D-toxicophore mapping and in silico screening of in vitro basal cytotoxicity of diverse organic chemicals (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 Computational Chemistry, 2011
For six random splits, one-variable models of rat toxicity (minus decimal logarithm of the 50% lethal dose [pLD50], oral exposure) have been calculated with CORAL software (http://www.insilico.eu/coral/). The total number of considered compounds is 689. New additional global attributes of the simplified molecular input line entry system (SMILES) have been examined for improvement of the optimal SMILES-based descriptors. These global SMILES attributes are representing the presence of some chemical elements and different kinds of chemical bonds (double, triple, and stereochemical). The ''classic'' scheme of building up quantitative structure-property/activity relationships and the balance of correlations (BC) with the ideal slopes were compared. For all six random splits, best prediction takes place if the aforementioned BC along with the global SMILES attributes are included in the modeling process. The average statistical characteristics for the external test set are the following: n 5 119 6 6.4, R 2 5 0.7371 6 0.013, and root mean square error 5 0.360 6 0.037.
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.
Development, characterization and application of predictive-toxicology models
SAR and QSAR in Environmental Research, 1999
The adoption of SAR techniques for risk assessment purposes requires that the predictive performance of models be characterized and optimized. The development of such methods with respect to CASE/MULTICASE are described. Moreover the effects of size, informational content, ratio of actives/inactives in the model on predictivity must be determined. Characterized models can provide mechanistic insights: nature of toxicophore, reactivity, receptor binding. Comparison of toxicophores among SAR models allows a determination of mechanistic overlaps (e.g., mutagenicity, toxicity, inhibition of gap junctional intercellular communication, carcinogenicity). Methods have been developed to combine SAR submodels and thereby improve predictive performance. Now that predictive toxicology methods are gaining acceptance, the development of Good Laboratory Practices is a further priority, as is the development of graduate programs in Computational Toxicology to adequately train the needed professional.
Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association, 2017
Computational models have earned broad acceptance for assessing chemical toxicity during early stages of drug discovery or environmental safety assessment. The majority of publicly available QSAR toxicity models have been developed for datasets including mostly drugs or drug-like compounds. We have evaluated and compared chemical spaces occupied by cosmetics, drugs, and pesticides, and explored whether current computational models of toxicity endpoints can be universally applied to all these chemicals. Our analysis of the chemical space overlap and applicability domain (AD) of models built previously for twenty different toxicity endpoints showed that most of these models afforded high coverage (>90%) for all three classes of compounds analyzed herein. Only T. pyriformis models demonstrated lower coverage for drugs and pesticides (38% and 54%, respectively). These results show that, for the most part, historical QSAR models built with data available for different toxicity endpoin...
Within our everyday life we are confronted with a variety of toxic substances. A number of these compounds are already used as lead structures for the development of new drugs, but the amount of toxic substances is still a rich resource of new bioactive compounds. During the identification and development of new potential drugs, risk estimation of health hazards is an essential and topical subject in pharmaceutical industry. To face this challenge, an extensive investigation of known toxic compounds is going to be helpful to estimate the toxicity of potential drugs. "Toxicity properties" found during those investigations will also function as a guideline for the toxicological classification of other unknown substances. We have compiled a dataset of approximately 50,000 toxic compounds from literature and web sources. All compounds were classified according to their toxicity. During this study the collection of toxic compounds was investigated extensively regarding their chemical, functional, and structural properties and compaired with a dataset of drugs and natural compounds. We were able to identify differences in properties within the toxic compounds as well as in comparison to drugs and natural compounds. These properties include molecular weight, hydrogen bond donors and acceptors, and functional groups which can be regarded as "toxicity properties", i.e. attributes defining toxicity.
In silico prediction of drug toxicity
Journal of computer-aided molecular design
It is essential, in order to minimise expensive drug failures due to toxicity being found in late development or even in clinical trials, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of putative toxicity is advisable even before synthesis. Thus the use of predictive toxicology is called for. A number of in silico approaches to toxicity prediction are discussed. Quantitative structure-activity relationships (QSARs), relating mostly to specific chemical classes, have long been used for this purpose, and exist for a wide range of toxicity endpoints. However, QSARs also exist for the prediction of toxicity of very diverse libraries, although often such QSARs are of the classification type; that is, they predict simply whether or not a compound is toxic, and do not give an indication of the level of toxicity. Examples are given of all of...
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.
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.
Compound Cytotoxicity Profiling Using Quantitative High-Throughput Screening
Environmental Health Perspectives, 2007
BACKGROUND: The propensity of compounds to produce adverse health effects in humans is generally evaluated using animal-based test methods. Such methods can be relatively expensive, lowthroughput, and associated with pain suffered by the treated animals. In addition, differences in species biology may confound extrapolation to human health effects. OBJECTIVE: The National Toxicology Program and the National Institutes of Health Chemical Genomics Center are collaborating to identify a battery of cell-based screens to prioritize compounds for further toxicologic evaluation. METHODS: A collection of 1,408 compounds previously tested in one or more traditional toxicologic assays were profiled for cytotoxicity using quantitative high-throughput screening (qHTS) in 13 human and rodent cell types derived from six common targets of xenobiotic toxicity (liver, blood, kidney, nerve, lung, skin). Selected cytotoxicants were further tested to define response kinetics. RESULTS: qHTS of these compounds produced robust and reproducible results, which allowed cross-compound, cross-cell type, and cross-species comparisons. Some compounds were cytotoxic to all cell types at similar concentrations, whereas others exhibited species-or cell type-specific cytotoxicity. Closely related cell types and analogous cell types in human and rodent frequently showed different patterns of cytotoxicity. Some compounds inducing similar levels of cytotoxicity showed distinct time dependence in kinetic studies, consistent with known mechanisms of toxicity. CONCLUSIONS: The generation of high-quality cytotoxicity data on this large library of known compounds using qHTS demonstrates the potential of this methodology to profile a much broader array of assays and compounds, which, in aggregate, may be valuable for prioritizing compounds for further toxicologic evaluation, identifying compounds with particular mechanisms of action, and potentially predicting in vivo biological response. KEY WORDS: 1,536-well, cell viability, NTP 1,408 compound library, PubChem, qHTS, RT-CES.