ToxAlerts: A Web Server of Structural Alerts for Toxic Chemicals and Compounds with Potential Adverse Reactions

The article presents a Web-based platform for collecting and storing toxicological structural alerts from literature and for virtual screening of chemical libraries to flag potentially toxic chemicals and compounds that can cause adverse side effects. An alert is uniquely identified by a SMARTS template, a toxicological endpoint, and a publication where the alert was described. Additionally, the system allows storing complementary information such as name, comments, and mechanism of action, as well as other data. Most importantly, the platform can be easily used for fast virtual screening of large chemical datasets, focused libraries, or newly designed compounds against the toxicological alerts, providing a detailed profile of the chemicals grouped by structural alerts and endpoints. Such a facility can be used for decision making regarding whether a compound should be tested experimentally, validated with available QSAR models, or eliminated from consideration altogether. The alert-based screening can also be helpful for an easier interpretation of more complex QSAR models. The system is publicly accessible and tightly integrated with the Online Chemical Modeling Environment (OCHEM, http://ochem.eu ). The system is open and expandable: any registered OCHEM user can introduce new alerts, browse, edit alerts introduced by other users, and virtually screen his/her data sets against all or selected alerts. The user sets being passed through the structural alerts can be used at OCHEM for other typical tasks: exporting in a wide variety of formats, development of QSAR models, additional filtering by other criteria, etc. The database already contains almost 600 structural alerts for such endpoints as mutagenicity, carcinogenicity, skin sensitization, compounds that undergo metabolic activation, and compounds that form reactive metabolites and, thus, can cause adverse reactions. The ToxAlerts platform is accessible on the Web at http://ochem.eu/alerts , and it is constantly growing.     

Quantum-chemical foundations of the topological substructural molecular design

The topological substructural molecular design (TOPS-MODE) approach is formulated as a tight-binding quantum-chemical method. The approach is based on certain postulates that permit to express any molecular property as a function of the spectral moments of certain types of molecular and environment-dependent energies. We use several empirical potentials to account for these intrinsic and external molecular energies. We prove that any molecular property expressed in terms of a quantitative structure-property and structure-activity relationships (QSPR/QSAR) model developed by using the TOPS-MODE method can be expressed as a bond additivity function. In addition, such a property can also be expressed as a substructural cluster expansion function. The conditions for such bond contributions being transferable are also analyzed here. Several new statistical-mechanical electronic functions are introduced as well as a bond-bond thermal Green's function or a propagator accounting for the electronic hopping between pairs of bonds. All these new concepts are applied to the development and application of a new QSAR model for describing the toxicity of polyhalogenated-dibenzo-1,4-dioxins. The QSAR model obtained displays a significant robustness and predictability. It permits an easy structural interpretation of the structure-activity relationship in terms of bond additivity functions, which display some resemblances with other theoretical parameters obtained from first principle quantum-chemical methods.     

Comparison of criteria used to access carcinogenicity in CPANN QSAR models versus the knowledge-based expert system Toxtree

The primary goal of this study was to describe and compare the criteria used to assess carcinogenic activity. The statistically-based predictive quantitative structure–activity relationship (QSAR) models based on the counter propagation artificial neural network (CPANN) algorithm, and knowledge-based expert systems based on a decision tree structural alert (SA) approach (Toxtree application), were considered. The integration of the QSAR (CPANN models) and SAR (Toxtree SA application) approach contributed to the mechanistic understanding of the QSAR model considered. The mapping technique inherent to CPANN Kohonen enables us to relate the similarities or dissimilarities within a congeneric set of chemicals with particular SAs for carcinogenicity. The focus of our investigations was the similarities and dissimilarities of the features used in the QSAR and SAR methods. Due to the complexity of the carcinogenic endpoint, the integration of different approaches allows the models to be improved and provides a valuable technique for evaluating the safety of chemicals.     

Integrated decision support for assessing chemical liabilities

Chemical liabilities, such as adverse effects and toxicity, have a major impact on today's drug discovery process. In silico prediction of chemical liabilities is an important approach which can reduce costs and animal testing by complementing or replacing in vitro and in vivo liability models. There is a lack of integrated, extensible decision support systems for chemical liability assessment which run quickly and have easily interpretable results. Here we present a method which integrates similarity searches, structural alerts, and QSAR models which all are available from the Bioclipse workbench. Emphasis has been placed on interpretation of results, and substructures which are important for predictions are highlighted in the original chemical structures. This allows for interactively changing chemical structures with instant visual feedback and can be used for hypothesis testing of single chemical structures as well as compound collections. The system has a clear separation between methods and data, and the extensible architecture enables straightforward extension via addition of more plugins (such as new data sets and computational models). We demonstrate our method on three important safety end points: mutagenicity, carcinogenicity, and aryl hydrocarbon receptor (AhR) activation. Bioclipse and the decision support implementation are free, open source, and available from http://www.bioclipse.net/decision-support .     

From knowledge generation to knowledge archive. A general strategy using TOPS-MODE with DEREK to formulate new alerts for skin sensitization

A general strategy for knowledge flow concerning skin sensitization based on the combined use of TOPS-MODE and DEREK expert system is proposed. TOPS-MODE is used as a knowledge generator, while DEREK represents the knowledge archive. A TOPS-MODE classification model allows the identification of structural fragments and groups responsible for strong/moderate skin sensitization. These structural contributions are sorted, analyzed, and graphically displayed in an appropriate way allowing the identification of several structural alerts for skin sensitization. Nine structural alerts already implemented in DEREK are identified using this strategy. They comprise, among others, alkyl halides, aldehydes, alpha,beta-unsaturated compounds, aromatic amines, phenols, hydroquinone, isothiazolinone, and alkyl sulfonates. Four new hypotheses are generated using TOPS-MODE structural contributions to skin sensitization, which are not recognized as structural alerts by DEREK. They include the reduction of aromatic nitro groups and epoxidation reaction of double bonds as metabolic activation steps that can lead to reactive haptens which can trigger the skin sensitization mechanism. Another new alert is based on 1,2,5-thiadiazole-1,1-dioxide for which we have identified a possible mechanism explaining its strong skin sensitization profile. It is based on the existence of a tautomeric equilibrium and further reaction with nucleophiles, which are both supported by experimental evidence. Finally, we have identified a possible new mechanism for the skin sensitization of nonreactive compounds, which involves the formation of noncovalent complexes with proteins in a processing- and metabolism-independent way.     

Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of <Emphasis Type="Italic">myasthenia gravis</Emphasis>

The Monte Carlo method was used for QSAR modeling of dimeric pyridinium compounds as acetylcholine esterase inhibitors. QSAR model was developed for a series of 39 dimeric pyridinium compounds. QSAR models were calculated with the representation of the molecular structure by the simplified molecular-input line-entry system. One split into the training and test set have been examined. The statistical quality of the developed model is very good. The calculated model for dimeric pyridinium derivatives had following statistical parameters: r ² = 0.9477 for the training set and r ² = 0.9332 the test set. Structural indicators considered as molecular fragments responsible for the increase and decrease in the inhibition activity have been defined. The computer-aided design of new dimeric pyridinium compounds potential acetylcholine esterase inhibitors with the application of defined structural alerts has been presented.     

Quantitative Structure–Toxicity Relationships Using Tops-Mode. 1. Nitrobenzene Toxicity to Tetrahymena Pyriformis

Abstract

The TOPological Sub-Structural MOlecular DEsign (TOPS-MODE) approach (Estrada, E. SAR QSAR Environ. Res. 2000, 11, 55–73) has been introduced to the study of toxicological properties. The toxicity of 42 nitrobenzenes was studied with this approach obtaining a good quantitative structure–toxicity model. For the first time we compare the use of eight different weights in the diagonal entries of the bond matrix for selecting the best TOPS-MODE model. TOPS-MODE was used to derive the contribution of different fragments to the toxicity of studied compounds. These contributions were applied to calculate toxicity substituent constants for the groups present in the nitrobenzenes studied.     

Estimation of carcinogenicity using molecular fragments tree

Carcinogenicity is an important toxicological endpoint that poses high concern to drug discovery. In this study, we developed a method to extract structural alerts (SAs) and modulating factors of carcinogens on the basis of statistical analyses. First, the Gaston algorithm, a frequent subgraph mining method, was used to detect substructures that occurred at least six times. Then, a molecular fragments tree was built and pruned to select high-quality SAs. The p-value of the parent node in the tree and that of its children nodes were compared, and the nodes that had a higher statistical significance in binomial tests were retained. Finally, modulating factors that suppressed the toxic effects of SAs were extracted by three self-defining rules. The accuracy of the 77 SAs plus four SA/modulating factor pairs model for the training set, and the test set was 0.70 and 0.65, respectively. Our model has higher predictive ability than Benigni's model, especially in the test set. The results highlight that this method is preferable in terms of prediction accuracy, and the selected SAs are useful for prediction as well as interpretation. Moreover, our method is convenient to users in that it can extract SAs from a database using an automated and unbiased manner that does not rely on a priori knowledge of mechanism of action.     

A Plausible Mechanism for the Mutagenic Activity (Salmonella typhimurium TA100) of MX Compounds: A Formation of CG-CG+-CG Radical Cation by One-electron Reduction

Abstract

Combining our previous QSAR work with recent high-level quantum mechanical calculations, a plausible mechanism for the mutagenic activity of halogenated furanones (so called MX compounds) in Salmonella typhimurium TA100 tester strain is proposed. The mechanism involves one-electron reduction as a key step and it seems reasonable to suggest that the mutagenicity of these direct-acting compounds may be a purely thermodynamic phenomenon, rather than the result of site-specific binding or adduct formation. Overall, the proposed model is consistent with the most experimental findings.     

The definition of the molecular structure for potential anti-malaria agents by the Monte Carlo method

A series of 53 endochin analogs (4(1-H)-quinolone derivatives) with anti-malarial activity against the clinically relevant multidrug resistant malarial strain TM-90-C2B has been studied. The CORAL (http://www.insilico.eu/coral) software has been used as a tool to build up the quantitative structure–activity relationships (QSAR) for the anti-malaria activity. The QSAR models were calculated with the representation of the molecular structure by simplified molecular input-line entry system and by the molecular graph of atomic orbitals. The method for splitting data into the sub-training set, the calibration set, the test set, and the validation set is suggested. Three various splits were examined. Statistical quality of models for the validation sets (which are not involved in the building up models) is good. Structural indicators (alerts) for increase and decrease of the anti-malaria activity are defined.