QSAR Modeling of Large Heterogeneous Sets of Molecules

Abstract

In aquatic toxicology, QSAR models are generally designed for chemicals presenting the same mode of toxic action. Their proper use provides good simulation results. Problems arise when the mechanism of toxicity of a chemical is not clearly identified. Indeed, in that case, the inappropriate application of a specific QSAR model can lead to a dramatic error in the toxicity estimation. With the advent of powerful computers and easy access to them, and the introduction of soft modeling and artificial intelligence in SAR and QSAR, radically different models, designed from large non-congeneric sets of chemicals have been proposed. Some of these new QSAR models are reviewed and their originality, advantages, and limitations are stressed.     

Combining unsupervised and supervised artificial neural networks to predictaquatic toxicity

Most quantitative structure-activity relationship (QSAR) models are linear relationships and significant for only a limited domain of compounds. Here we propose a data-driven approach with a flexible combination of unsupervised and supervised neural networks able to predict the toxicity of a large set of different chemicals while still respecting the QSAR postulates. Since QSAR is applicable only to similar compounds, which have similar biological and physicochemical properties, large numbers of compounds are clustered before building local models, and local models are ensembled to obtain the final result. The approach has been used to develop models to predict the fish toxicity of Pimephales promelas and Tetrahymena pyriformis, a protozoan.     

Topological structural alerts modulations of mammalian cell mutagenicity for halogenated derivatives

Genotoxicity is a key toxicity endpoint for current regulatory requirements regarding new and existing chemicals. However, genotoxicity testing is time-consuming and costly, and involves the use of laboratory animals. This has motivated the development of computational approaches, designed to predict genotoxicity without the need to conduct laboratory tests. Currently, many existing computational methods, like quantitative structure–activity relationship (QSAR) models, provide limited information about the possible mechanisms involved in mutagenicity or predictions based on structural alerts (SAs) do not take statistical models into account. This paper describes an attempt to address this problem by using the TOPological Substructural MOlecular Design (TOPS-MODE) approach to develop and validate improved QSAR models for predicting the mutagenicity of a range of halogenated derivatives. Our most predictive model has an accuracy of 94.12%, exhibits excellent cross-validation and external set statistics. A reasonable interpretation of the model in term of SAs was achieved by means of bond contributions to activity. The results obtained led to the following conclusions: primary halogenated derivatives are more mutagenic than secondary ones; and substitution of chlorine by bromine increases mutagenicity while polyhalogenation decreases activity. The paper demonstrates the potential of the TOPS-MODE approach in developing QSAR models for identifying structural alerts for mutagenicity, combining high predictivity with relevant mechanistic interpretation.     

Identification of Phototoxic Polycyclic Aromatic Hydrocarbons in Sediments Through Sample Fractionation and QSAR Analysis

Abstract

The toxicity of certain polycyclic aromatic hydrocarbons (PAHs) can be greatly increased by simultaneous exposure of test organisms to ultraviolet (UV) wavelengths present in sunlight. This phenomenon, commonly termed photoinduced toxicity, had been evaluated extensively in laboratory settings where only one chemical of concern was present. However, more recent studies have demonstrated that complex mixtures of PAHs present, for example in sediments, also can cause phototoxicity to a variety of aquatic species when the samples are tested in simulated sunlight. Unfortunately, because these types of samples can contain thousands of substituted and unsubstituted PAHs it is difficult, if not impossible, to use conventional analytical techniques to identify those responsible for photoinduced toxicity. The objective of the present study was to link two powerful ecotoxicology tools, toxicity-based fractionation techniques and QSAR models, to identify phototoxic chemicals in a sediment contaminated with PAHs emanating from an oil refinery. Extensive chromatographic fractionation of pore water from the sediment, in conjunction with toxicity testing, yielded a simplified set of sample fractions containing 12 PAHs that were identified via mass spectroscopy. Evaluation of these compounds using a recently developed QSAR model revealed that, based upon their HOMO-LUMO gap energies, about half were capable of producing photoinduced toxicity. We further evaluated the phototoxic potential of the reduced set of PAHs by determining their propensity to bioaccumulate in test organisms, through calculation of octanol-water partition coefficients for the chemicals. These studies represent a novel linkage of sample fractionation methods with QSAR models for conducting an ecological risk assessment.     

Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals

Bioconcentration factors (BCFs) have traditionally been used to describe the tendency of chemicals to concentrate in aquatic organisms. A reexamination of the log-log QSAR between the BCF and Kow for non-congener narcotic chemicals is presented on the basis of recommended data for fish. The model is extended to give a simple correlation between BCF and the toxicity of highly, moderately and weakly hydrophilic chemicals. For the first time, in this study an equation for calculating BCF was applied in a QSAR model for predicting the acute toxicity of chemicals to aquatic organisms.     

Development of an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system,March 2009 version

The KAshinhou Tool for Ecotoxicity (KATE) system, including ecotoxicity quantitative structure–activity relationship (QSAR) models, was developed by the Japanese National Institute for Environmental Studies (NIES) using the database of aquatic toxicity results gathered by the Japanese Ministry of the Environment and the US EPA fathead minnow database. In this system chemicals can be entered according to their one-dimensional structures and classified by substructure. The QSAR equations for predicting the toxicity of a chemical compound assume a linear correlation between its log P value and its aquatic toxicity. KATE uses a structural domain called C-judgement, defined by the substructures of specified functional groups in the QSAR models. Internal validation by the leave-one-out method confirms that the QSAR equations, with r ² > 0.7, RMSE ≤ 0.5, and n > 5, give acceptable q ² values. Such external validation indicates that a group of chemicals with an in-domain of KATE C-judgements exhibits a lower root mean square error (RMSE). These findings demonstrate that the KATE system has the potential to enable chemicals to be categorised as potential hazards.     

Qsar Models for Predicting the Acute Toxicity of Selected Organic Chemicals with Diverse Structures to Aquatic Non-Vertebrates and Humans

Abstract

The linear and non-linear relationships of acute toxicity (as determined on five aquatic non-vertebrates and humans) to molecular structure have been investigated on 38 structurally-diverse chemicals. The compounds selected are the organic chemicals from the 50 priority chemicals prescribed by the Multicentre Evaluation of In Vitro Cytotoxicity (MEIC) programme. The models used for the evaluations are the best combination of physico-chemical properties that could be obtained so far for each organism, using the partial least squares projection to latent structures (PLS) regression method and backpropagated neural networks (BPN). Non-linear models, whether derived from PLS regression or backpropagated neural networks, appear to be better than linear models for describing the relationship between acute toxicity and molecular structure. BPN models, in turn, outperform non-linear models obtained from PLS regression. The predictive power of BPN models for the crustacean test species are better than the model for humans (based on human lethal concentration). The physico-chemical properties found to be important to predict both human acute toxicity and the toxicity to aquatic non-vertebrates are the n?octanol water partition coefficient (Pow) and heat of formation (HF). Aside from the two former properties, the contribution of parameters that reflect size and electronic properties of the molecule to the model is also high, but the type of physico-chemical properties differs from one model to another. In all of the best BPN models, some of the principal component analysis (PCA) scores of the ¹³C-NMR spectrum, with electron withdrawing/accepting capacity (LUMO, HOMO and IP) are molecular size/volume (VDW or MS1) parameters are relevant. The chemical deviating from the QSAR models include non-pesticides as well as some of the pesticides tested. The latter type of chemical fits in a number of the QSAR models. Outliers for one species may be different from those of other test organisms.     

Predictions for existing chemicals-a multilateral QSAR project

Abstract Following a previous collaborative EU/EPA project focussed on QSAR predictions for a selection of new chemicals which had been notified in the EU, a similar exercise was started in 1993 on existing chemicals. In a first phase, the project addresses the High Production Volume (HPV) chemicals which are produced or imported at levels above a 1000t/year in the EU and 454t/year in the US. The relevant EU (Annex 1 of Existing Chemicals Regulation No. 793/93) and US-EPA lists contain 1036 and 2881 organic substances respectively of which HPV 749 chemicals are in common. The joint project aims at an estimation through validated QSAR models of the physical-chemical, ecotoxicity and toxicity endpoints which are included in the regulation and where experimental data will become available in IUCLID (International Unified Chemicals Information Database). Next to EC-JRC (ECB) and US-EPA, various laboratories in the EU are contributing to the project and recently, two institutes in Japan have joined in this project.     

A Noncongeneric Model for Predicting Toxicity of Organic Molecules to Vibrio Fischeri

Abstract

A general Quantitative Structure-Activity Relationship (QSAR) model on Vibrio fischeri (Microtox^? test) was derived using the autocorrelation method for describing the molecules and a neural network as statistical tool. From a training set of 1068 organic chemicals described by means of four different autocorrelation vectors, it was possible to obtain valuable models but presenting some large outliers. Addition of the time of exposure as variable allowed us to derive a more powerful model from 2795 toxicity results. The predictive power of this 36/26/1 neural network model was tested on an external testing set of 385 toxicity data and compared with the performances of linear models designed for polar narcotic amines and for weak acid respiratory uncouplers.     

A conceptual framework for predicting the toxicity of reactive chemicals: modeling soft electrophilicity

Although the literature is replete with QSAR models developed for many toxic effects caused by reversible chemical interactions, the development of QSARs for the toxic effects of reactive chemicals lacks a consistent approach. While limitations exit, an appropriate starting-point for modeling reactive toxicity is the applicability of the general rules of organic chemical reactions and the association of these reactions to cellular targets of importance in toxicology. The identification of plausible "molecular initiating events" based on covalent reactions with nucleophiles in proteins and DNA provides the unifying concept for a framework for reactive toxicity. This paper outlines the proposed framework for reactive toxicity. Empirical measures of the chemical reactivity of xenobiotics with a model nucleophile (thiol) are used to simulate the relative rates at which a reactive chemical is likely to bind irreversibly to cellular targets. These measures of intrinsic reactivity serve as correlates to a variety of toxic effects; what's more they appear to be more appropriate endpoints for QSAR modeling than the toxicity endpoints themselves.     

QSARs for Photoinduced Toxicity of Aromatic Compounds

Abstract

This paper reviews the results of a series of efforts to develop QSAR models for aromatic chemicals whose toxicity is enhanced by the ultraviolet radiation present in sunlight. Photoinduced toxicity of polycyclic aromatic hydrocarbons (PAHs) was found to be a result of competing factors: structural (such as molecular stability and light absorbance) and external (irradiation energy and intensity). These two factors interact, producing a complex, multilinear relationship between toxicity and electronic structure. The HOMO-LUMO gap provided a useful ground-state index to explain the persistence, light absorption, and eventually, the photoinduced toxicity of PAHs. The derived QSAR clearly distinguished phototoxic differences between pairs of structurally similar PAHs, such as phenanthrene and anthracene, benzo [a] anthracene and tetracene, et cetera. Those PAHs exhibiting photoinduced toxicity were consistently within a specific range of the electronic parameter. Further modeling revealed a significant correlation between molecular electronic structure of excited-state PAHs and toxicity. The effect of substituents on photoinduced acute toxicity of PAHs also was investigated. Some substituents such as alkyl and hydroxy moieties do not significantly reduce the HOMO-LUMO gap of parent PAHs. Nitro- and chloro- moieties cause more significant variations of the HOMO-LUMO gap. It is concluded that photoinduced toxicity of PAHs is mainly dictated by the electronic structure of the parent chemicals. Evaluation of the phototoxicity of flexible aromatic molecules (α-terthienyls), generally supported the PAH models.     

Computational Chemistry in Environmental Toxicology QSAR

Abstract

Computational chemistry provides a means for the calculation or estimation of three-dimensional chemical structure, organization and analysis of chemical data, classification of industrial chemicals by structure and properties, prediction of toxicity, and identification of chemical structure. The development of the EPA National Environmental Supercomputer Center (NESC) in Bay City, Michigan, makes available to scientists in EPA Headquarters, the ability to perform advanced QSAR modeling. This provides the means to develop and apply QSAR models for chemicals acting by a variety of molecular mechanisms. The work makes possible improved programmatic support to the Office of Pollution Prevention and Toxics under the Toxic Substances Control Act and the Pollution Prevention Act.