Quasi-SMILES: Self-consistent models for toxicity of organic chemicals to tadpoles.

Simplified molecular input-line entry systems (SMILES) are the representation of the molecular structure that can be used to establish quantitative structure-property/activity relationships (QSPRs/QSARs) for various endpoints expressed as mathematical functions of the molecular architecture. Quasi-SMILES is extending the traditional SMILES by means of additional symbols that reflect experimental conditions. Using the quasi-SMILES models of toxicity to tadpoles gives the possibility to build up models by taking into account the time of exposure. Toxic effects of experimental situations expressed via 188 quasi-SMILES (the negative logarithm of molar concentrations which lead to lethal 50% tadpoles effected during 12 h, 24 h, 48 h, 72 h, and 96 h) were modelled with good results (the average determination coefficient for the validation sets is about 0.97). In this way, we developed new models for this amphibian endpoint, which is poorly studied.

Monte Carlo technique to study the adsorption affinity of azo dyes by applying new statistical criteria of the predictive potential.

Azo dyes are broadly used in different industries through their chemical stability and ease of synthesis. However, these dyes are usually identified as critical environmental pollutants. Hence, a mathematical model for the adsorption affinity of azo dyes can be applied for solving tasks of medicine and ecology. Quantitative structure-property relationships for the adsorption affinity of azo dyes to a substrate (DAF, kJ/mol) were established using the Monte Carlo method by generating optimal SMILES-based descriptors. The index of ideality of correlation (IIC) and the correlation intensity index (CII) improved the model's predictive potential, especially when they were used simultaneously. The statistical quality of the best model on the validation set was characterized by n = 18, r2 = 0.9468, and RMSE = 1.26 kJ/mol.

Carcinogenicity prediction using the index of ideality of correlation.

Carcinogenicity testing is necessary to protect human health and comply with regulations, but testing it with the traditionally used two-year rodent studies is time-consuming and expensive. In certain cases, such as for impurities, alternative methods may be convenient. Thus there is an urgent need for alternative approaches for reliable and robust assessments of carcinogenicity. The Monte Carlo technique with CORAL software is a tool to tackle this task for unknown compounds using available experimental data for a representative set of compounds. The models can be constructed with the simplified molecular input line entry system without additional physicochemical descriptors. We describe here a model based on a data set of 1167 substances. Matthew's correlation coefficient values for calibration and validation sets are 0.747 and 0.577, respectively. Double bonds between carbon atoms and double bonds of oxygen atoms are the molecular features that indicate the carcinogenic potential of a compound.

The Monte Carlo method to build up models of the hydrolysis half-lives of organic compounds.

The hydrolysis of organic chemicals such as pesticides, pollutants, or drugs can affect the fate and behaviour of environmental contaminants, so it is of interest to evaluate the stability of substances in water for various purposes. For the registration of organic compounds in Europe, information on hydrolysis must be presented. However, the experimental measurements of all chemicals would require enormous resources, and computational models may become attractive. Applying the CORAL software (http://www.insilico.eu/coral) quantitative structure-property relationships (QSPRs) were built up to model hydrolysis. The 2D-optimal descriptor is calculated with so-called correlation weights for attributes of simplified molecular input-line entry systems (SMILES). The correlation weights are obtained as results of the special Monte Carlo optimization. The nature of (five- and six-member) rings is an important component of this approach. Another important component is the atom pair proportions for nitrogen, oxygen, and sulphur. The statistical quality of the best model is: n = 44, r2 = 0.74 (training set); n = 14, r2 = 0.75 (calibration set); and n = 12, r2 = 0.80 (validation set).

EFSA's OpenFoodTox: An open source toxicological database on chemicals in food and feed and its future developments.

Since its creation in 2002, the European Food Safety Authority (EFSA) has produced risk assessments for over 5000 substances in >2000 Scientific Opinions, Statements and Conclusions through the work of its Scientific Panels, Units and Scientific Committee. OpenFoodTox is an open source toxicological database, available both for download and data visualisation which provides data for all substances evaluated by EFSA including substance characterisation, links to EFSA's outputs, applicable legislations regulations, and a summary of hazard identification and hazard characterisation data for human health, animal health and ecological assessments. The database has been structured using OECD harmonised templates for reporting chemical test summaries (OHTs) to facilitate data sharing with stakeholders with an interest in chemical risk assessment, such as sister agencies, international scientific advisory bodies, and others. This manuscript provides a description of OpenFoodTox including data model, content and tools to download and search the database. Examples of applications of OpenFoodTox in chemical risk assessment are discussed including new quantitative structure-activity relationship (QSAR) models, integration into tools (OECD QSAR Toolbox and AMBIT-2.0), assessment of environmental footprints and testing of threshold of toxicological concern (TTC) values for food related compounds. Finally, future developments for OpenFoodTox 2.0 include the integration of new properties, such as physico-chemical properties, exposure data, toxicokinetic information; and the future integration within in silico modelling platforms such as QSAR models and physiologically-based kinetic models. Such structured in vivo, in vitro and in silico hazard data provide different lines of evidence which can be assembled, weighed and integrated using harmonised Weight of Evidence approaches to support the use of New Approach Methodologies (NAMs) in chemical risk assessment and the reduction of animal testing.

Evaluation of non-commercial models for genotoxicity and carcinogenicity in the assessment of EFSA's databases.

Over the past years, the European Food Safety Authority (EFSA) released to the public domain several databases, with the main objectives of collecting and storing hazard data on the substances considered in EFSA's risk assessment and secondly to serve as a basis for further development of in silico tools such as quantitative structure-activity relationship (QSAR) models. In this work, we evaluated the ability of freely available QSAR models to estimate genotoxicity and carcinogenicity properties and their possible use for screening purposes on three different EFSA's databases. With an accuracy close to 90%, the results showed good capabilities of QSAR models to predict genotoxicity in terms of bacterial reverse mutation test, while statistics for in vivo micronucleus test are not satisfactory (accuracy in the predictions close to 50%). Interestingly, results on the carcinogenicity assessment showed an accuracy in prediction close to 70% for the best models. In addition, an example of the potential application of in silico models is presented in order to provide a preliminary screening of genotoxicity properties of botanicals intended for use as food supplements.

A large comparison of integrated SAR/QSAR models of the Ames test for mutagenicity$.

Results from the Ames test are the first outcome considered to assess the possible mutagenicity of substances. Many QSAR models and structural alerts are available to predict this endpoint. From a regulatory point of view, the recommendation from international authorities is to consider the predictions of more than one model and to combine results in order to develop conclusions about the mutagenicity risk posed by chemicals. However, the results of those models are often conflicting, and the existing inconsistency in the predictions requires intelligent strategies to integrate them. In our study, we evaluated different strategies for combining results of models for Ames mutagenicity, starting from a set of 10 diverse individual models, each built on a dataset of around 6000 compounds. The novelty of our study is that we collected a much larger set of about 18,000 compounds and used the new data to build a family of integrated models. These integrations used probabilistic approaches, decision theory, machine learning, and voting strategies in the integration scheme. Results are discussed considering balanced or conservative perspectives, regarding the possible uses for different purposes, including screening of large collection of substances for prioritization.

Integrating QSAR and read-across for environmental assessment.

Read-across and QSAR have different traditions and drawbacks. We address here two main questions: (1) How do we solve the issue of the subjectivity in the evaluation of data and results, which may be particularly critical for read-across, but may have a role also for the QSAR assessment? (2) How do we take advantage of the results of both approaches to support each other? The QSAR model starts from the training set. The presence of similar chemicals with property values close to that predicted can support the result. The approach in read-across is the opposite. The assessment is focused on the few substances similar to the target. The data quality of the similar chemicals is fundamental. A risk is poor standardization in the definition of 'similarity', because different approaches may be applied. Inspired by the principles of high transparency and reproducibility, a new program for read-across, called ToxRead, has been developed and made freely available ( www.toxgate.eu ). The output of ToxRead can be compared and integrated with the output of QSAR, within a weight-of-evidence strategy. We discuss the evaluation and integration of ToxRead and QSAR with examples of the assessment of bioconcentration factors of chemicals.

Comparison of in silico tools for evaluating rat oral acute toxicity.

Different in silico models have been developed and implemented for the evaluation of mammalian acute toxicity, exploring acute oral toxicity data expressed as median lethal dose (LD(50)). We compared five software programs (TOPKAT, ACD/ToxSuite, TerraQSAR, ADMET Predictor and T.E.S.T.) using a dataset of 7417 chemicals. We tested the models' performance using the quantitative results and, in classification, the toxicity threshold defined within the Classifying, Labelling and Packaging (CLP) regulation. ACD gave the best results with r(2) of 0.79 and 0.66 accuracy. However, its performance dropped when considering the molecules not present in its training set, and the other models behaved similarly. We also considered the information on the applicability domain (AD), which improved the models' performance, but not enough for the molecules external to the models' training set. We also considered the chemical classes and found that all models gave high performance for certain classes (e.g. hydrazones and sulphides) while other classes were always badly predicted (e.g. aromatic secondary amides).

CORAL: QSAR models for acute toxicity in fathead minnow (Pimephales promelas).

CORrelation And Logic (CORAL) is a software that generates quantitative structure activity relationships (QSAR) for different endpoints. This study is dedicated to the QSAR analysis of acute toxicity in Fathead minnow (Pimephales promelas). Statistical quality for the external test set is a complex function of the split (into training and test subsets), the number of epochs of the Monte Carlo optimization, and the threshold that is a criterion for dividing the correlation weights into two classes rare (blocked) and not rare (active). Computational experiments with three random splits (data on 568 compounds) indicated that this approach can satisfactorily predict the desired endpoint (the negative decimal logarithm of the 50% lethal concentration, in mmol/L, pLC50). The average correlation coefficients (r2) are 0.675 ± 0.0053, 0.824 ± 0.0242, 0.787 ± 0.0101 for subtraining, calibration, and test set, respectively. The average standard errors of estimation (s) are 0.837 ± 0.021, 0.555 ± 0.047, 0.606 ± 0.049 for subtraining, calibration, and test set, respectively. The CORAL software together with three random splits into subtraining, calibration, and test sets can be downloaded on the Internet (http://www.insilico.eu/coral/).

CORAL: building up the model for bioconcentration factor and defining it's applicability domain.

CORAL (CORrelation And Logic) software can be used to build up the quantitative structure--property/activity relationships (QSPR/QSAR) with optimal descriptors calculated with the simplified molecular input line entry system (SMILES). We used CORAL to evaluate the applicability domain of the QSAR models, taking a model of bioconcentration factor (logBCF) as example. This model's based on a large training set of more than 1000 chemicals. To improve the model is predictivity and reliability on new compounds, we introduced a new function, which uses the Delta(obs) = logBCF(expr)--logBCF(calc) of the predictions on the chemicals in the training set. With this approach, outliers are eliminated from the phase of training. This proved useful and increased the model's predictivity.

QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects.

The in silico modelling of bio-concentration factor (BCF) is of considerable interest in environmental sciences, because it is an accepted indicator for the accumulation potential of chemicals in organisms. Numerous QSAR models have been developed for the BCF, and the majority utilize the octanol/water partition coefficient (log P) to account for the penetration characteristics of the chemicals. The present work used descriptors from a variety of software packages for the development of a multi-linear regression model to estimate BCF. The modelled data set of 473 diverse compounds covers a wide range of log BCF values. In the proposed QSAR model, most of the variation is described by the calculated solubility in water. Other contributing descriptors describe, for instance, hydrophobic surface area, hydrogen bonding and other electronic effects. The model was validated internally by using a variety of statistical approaches. Two external validations were also performed. For the former validation, a subset from the same data source was used. The 2nd external validation was based on an independent data set collected from different resources. All validations showed the consistency of the model. The applicability domain of the model was discussed and described and a thorough outlier analysis was performed.

A new bioconcentration factor model based on SMILES and indices of presence of atoms.

Indices of the presence of atoms (IPA) encode the presence or absence of atoms, such as nitrogen, oxygen, sulphur, phosphorus, fluorine, chlorine, and bromine in a molecule. They are calculated with the simplified molecular input line entry system (SMILES). Using the Monte Carlo method for correlation weights of these indices, one can improve the predictive ability of optimal SMILES-based descriptors in quantitative structure-activity relationships (QSAR) for bioconcentration factor. The model without IPA gave the following results: n=503, r(2)=0.6803, q(2)=0.6781, s=0.759, F=1066 (subtraining set); n=322, r(2)=0.8181, r(pred)(2)=0.8159, s=0.565, F=1439 (calibration set); n=105, r(2)=0.6703, r(pred)(2)=0.6577, R(m)(2)=0.6628, s=0.728, F=209 (test set); n=106, r(2)=0.6624, r(pred)(2)=0.6502, R(m)(2)=0.6212, s=0.757, F=204 (validation set) The model with IPA gave: n=503, r(2)=0.7082, q(2)=0.7062, s=0.725, F=1216 (subtraining set); n=322, r(2)=0.8401, r(pred)(2)=0.8383, s=0.528, F=1682 (calibration set); n=105, r(2)=0.7489, r(pred)(2)=0.7402, R(m)(2)=0.7252, s=0.637, F=307 (test set); n=106, r(2)=0.7306, r(pred)(2)=0.7217, R(m)(2)=0.7010, s=0.680, F=282 (validation set).

Binary classification models for endocrine disrupter effects mediated through the estrogen receptor.

Endocrine disrupters (EDs) form an interesting field of application attracting great attention in the recent years. They represent a number of exogenous substances interfering with the function of the endocrine system, including the interfering with developmental processes. In particular EDs are mentioned as substances requiring a more detailed control and specific authorization within REACH, the new European legislation on chemicals, together with other groups of chemicals of particular concern. QSAR represents a challenging method to approach data gap which is foreseen by REACH. The aim of this study was to provide an insight into the use of QSAR models to address ED effects mediated through the estrogen receptor (ER). New predictive models were derived to assess estrogenicity for a very large and heterogeneous dataset of chemical compounds. QSAR binary classifiers were developed based on different data mining techniques such as classification trees, decision forest, fuzzy logic, neural networks and support vector machines. The focus was given to multiple endpoints to better characterize the effects of EDs evaluating both binding (RBA) and transcriptional activity (RA). A possible combination of the models was also explored. A very good accuracy was reached for both RA and RBA models (higher than 80%).

Ecotoxicity prediction by adaptive fuzzy partitioning: comparing descriptors computed on 2D and 3D structures.

Classification models were established on four endpoints, i.e. trout, daphnia, quail and bee, including from 100 to 300 pesticides subdivided into 3 toxicity classes. For each species, five separate sets of molecular descriptors, computed by several software, were compared, including parameters related to 2D or 3D structures. The most relevant descriptors were selected with help of a procedure based on genetic algorithms. Then, structure-activity relationships were built by Adaptive Fuzzy Partition (AFP), a recursive partitioning method derived from Fuzzy Logic concepts.Globally, satisfactory results were obtained for each animal species. The best cross-validation and test set scores reached values of about 70-75%. More important, the relationships derived from the descriptors calculated from 2D structures were superior or similar to those computed from 3D structures. These results underline that the long computational time employed to compute 3D descriptors is often useless to improve the prediction ability of the ecotoxicity models. Finally, the differences in the prediction ability between the different software used were quite reduced and show the possibility to use different descriptor packages for obtaining similar satisfactory models.

Predicting logP of pesticides using different software.

We compared experimental and calculated logP values using a data set of 235 pesticides and experimental values from four different sources: The Pesticide Manual, Hansch Manual, ANPA and KowWin databases. LogP were calculated with four softwares: HyperChem, Pallas, KowWin and TOPKAT. Crossed comparison of the experimental and calculated values proved useful, especially for pesticides. These are harder to study than simpler organic compounds. Structurally they are complex, heterogeneous and similar to drugs from a chemical point of view. They offer an interesting way to verify the goodness of the different methods. Other studies compared several logP predictors using a single set of experimental values taken as a reference. Here we discuss the utility of the different logP predictors, with reference to experimental data found in different databases. This offers three advantages: (1) it avoids bias due to the assumption that one single data set is correct; (2) a given predictor can be developed on the same data set used for evaluation; (3) it takes account of experimental variability and can compare it with the predictor's variability. In our study Pallas and KowWin gave the best results for prediction, followed by TOPKAT.

Factors influencing predictive models for toxicology.

Comparisons of different models to predict toxicity and evaluation of the predictive power of a model are affected by the variability of the data. We assessed this problem by considering experimental toxicity data and chemical descriptors. We evaluated several toxicological end-points (Oncorhynchus mykiss, Daphnia magna, Acceptable Daily Intake, Anas Platyrhynchos, Colinus virginianus and Muridae) in the case of pesticides and also considered the availability of toxicological data. We calculated hundreds of molecular descriptors (divided into constitutional, electrostatic, geometrical, quantum-chemical and topological ones) for the selected compounds using CODESSA, HyperChem and Pallas. Molecular descriptors may vary depending on the conformation of the molecules and on the software used. We evaluated the extent of this variability, and compared it with the variability of the experimental toxicological values.