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Abstract Background Bioconcentration factor (BCF) describes the behaviour of a chemical in terms of its likelihood of concentrating in organisms in the environment. It is a fundamental property in recent regulations, such as the European Community Regulation on chemicals and their safe use or the Globally Harmonized System for classification, labelling and packaging. These new regulations consider the possibility of reducing or waiving animal tests using alternative methods, such as in silico methods. This study assessed and validated the CAESAR predictive model for BCF in fish. Results To validate the model, new experimental data were collected and used to create an external set, as a second validation set (a first validation exercise had been done just after model development). The performance of the model was compared with BCFBAF v3.00. For continuous values and for classification purposes the CAESAR BCF model gave better results than BCFBAF v3.00 for the chemicals in the applicability domain of the model. R2 and Q2 were good and accuracy in classification higher than 90%. Applying an offset of 0.5 to the compounds predicted with BCF close to the thresholds, the number of false negatives (the most dangerous errors) dropped considerably (less than 0.6% of chemicals). Conclusions The CAESAR model for BCF is useful for regulatory purposes because it is robust, reliable and predictive. It is also fully transparent and documented and has a well-defined applicability domain, as required by REACH. The model is freely available on the CAESAR web site and easy to use. The reliability of the model reporting the six most similar compounds found in the CAESAR dataset, and their experimental and predicted values, can be evaluated.

Abstract Background One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration. Results Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B. Conclusion Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.

Abstract Background The new European Regulation on chemical safety, REACH, (Registration, Evaluation, Authorisation and Restriction of CHemical substances), is in the process of being implemented. Many chemicals used in industry require additional testing to comply with the REACH regulations. At the same time EU member states are attempting to reduce the number of animals used in experiments under the 3 Rs policy, (refining, reducing, and replacing the use of animals in laboratory procedures). Computational techniques such as QSAR have the potential to offer an alternative for generating REACH data. The FP6 project CAESAR was aimed at developing QSAR models for 5 key toxicological endpoints of which skin sensitisation was one. Results This paper reports the development of two global QSAR models using two different computational approaches, which contribute to the hybrid model freely available online. Conclusions The QSAR models for assessing skin sensitisation have been developed and tested under stringent quality criteria to fulfil the principles laid down by the OECD. The final models, accessible from CAESAR website, offer a robust and reliable method of assessing skin sensitisation for regulatory use.

The development of models of the physicochemical and biochemical behavior of nanomaterials is useful for improving the evaluation and management of this material. Quasi-SMILES technology makes it possible to quite successfully cope with this kind of modeling task, accounting for various experimental conditions, where the use of other approaches is difficult or even impossible. Here, we describe the results of using quasi-SMILES to model the toxicity of mixtures of titanium nano oxide with various inorganic substances towards Daphnia magna. The approach is based on the stochastic process of the optimization of correlation weights for different codes used in quasi-SMILES. The optimization process was carried out using special statistical criteria for predictive potential. It is shown that models built using quasi-SMILES have the best predictive potential. [ABSTRACT FROM AUTHOR]

The present report describes the work performed in the EFSA‐project 'Data collection, update and further development of biologically‐based models for humans and animal species to support transparency in food and feed safety'. Here, Focus is given to case studies for food and feed chemicals to predict kinetic parameters and profiles using generic and substance‐specific physiologically‐based kinetic (PBK) models for humans, including human subgroups, laboratory animal species, farm animals and a kinetic‐dynamic model in salmon. For humans, five case studies were conducted to compare kinetic predictions using the human generic PBK 6‐compartment COSMOS/TKPlatewith i) in vivo data from human clinical or biomonitoring studies, ii) substance‐specific model predictions using molecules relevant to food safety. Another five case studies assessed the impact of physiological variability (including pregnancy, renal excretion, metabolism variability, or ontogeny) and their impact on biomarkers of exposure. Case studies on laboratory and farm animals focused on theophylline, caffeine, cannabinoids, alkaloids and mycotoxins using the generic 11/12 PBK compartment models integrated in EFSA's TKPlate to assess predicted and experimental parameters i.e. plasma concentrations, excretion via milk or eggs. Overall, predictions from the human generic and substance‐specific PBK models for parameters of chronic exposure were similar and robust compared to the available experimental data. For test species and farm animals, model predictions from the generic TKPlate PBK models also performed well and were mostly within 2‐fold compared to available experimental in vivo data. In addition, 3D molecular modelling case studies were also conducted to investigate transport of chemicals (ochratoxin A, perfluoroalkyls) and cytochrome P450 metabolism (ochratoxin A, safrole and other alkenylbenzenes) as a useful tool to generate metabolism information at the molecular level. Conclusions and recommendations for future work are formulated to further develop generic PBK models for parent compounds and metabolites and further guidance to use and parameterise these models in next generation risk assessment. [ABSTRACT FROM AUTHOR]

Models of toxicity to tadpoles have been developed as single parameters based on special descriptors which are sums of correlation weights, molecular features, and experimental conditions. This information is presented by quasi-SMILES. Fragments of local symmetry (FLS) are involved in the development of the model and the use of FLS correlation weights improves their predictive potential. In addition, the index of ideality correlation (IIC) and correlation intensity index (CII) are compared. These two potential predictive criteria were tested in models built through Monte Carlo optimization. The CII was more effective than IIC for the models considered here. [ABSTRACT FROM AUTHOR]

Endocrine-disrupting chemicals (EDCs) are chemicals that can interfere with homeostatic processes. They are a major concern for public health, and they can cause adverse long-term effects such as cancer, intellectual impairment, obesity, diabetes, and male infertility. The endocrine system is a complex machinery, with the estrogen (E), androgen (A), and thyroid hormone (T) modes of action being of major importance. In this context, the availability of in silico models for the rapid detection of hazardous chemicals is an effective contribution to toxicological assessments. We developed Qualitative Gene expression Activity Relationship (QGexAR) models to predict the propensities of chemically induced disruption of EAT modalities. We gathered gene expression profiles from the LINCS database tested on two cell lines, i.e., MCF7 (breast cancer) and A549 (adenocarcinomic human alveolar basal epithelial). We optimized our prediction protocol by testing different feature selection methods and classification algorithms, including CATBoost, XGBoost, Random Forest, SVM, Logistic regression, AutoKeras, TPOT, and deep learning models. For each EAT endpoint, the final prediction was made according to a consensus prediction as a function of the best model obtained for each cell line. With the available data, we were able to develop a predictive model for estrogen receptor and androgen receptor binding and thyroid hormone receptor antagonistic effects with a consensus balanced accuracy on a validation set ranging from 0.725 to 0.840. The importance of each predictive feature was further assessed to identify known genes and suggest new genes potentially involved in the mechanisms of action of EAT perturbation. [ABSTRACT FROM AUTHOR]

Typical in silico models for ecotoxicology focus on a few endpoints, but there is a need to increase the diversity of these models. This study proposes models using the NOEC for the harlequin fly (Chironomus riparius) and EC50 for swollen duckweed (Lemna gibba) for the first time. The data were derived from the EFSA OpenFoodTox database. The models were based on the correlation weights of molecular features used to calculate the 2D descriptor in CORAL software. The Monte Carlo method was used to calculate the correlation weights of the algorithms. The determination coefficients of the best models for the external validation set were 0.74 (NOAEC) and 0.85 (EC50). [ABSTRACT FROM AUTHOR]