Substance-tailored testing strategies in toxicology: an in silico methodology based on QSAR modeling of toxicological thresholds and Monte Carlo simulations of toxicological testing

Health risk assessment of exposure to benzene, toluene, ethyl benzene, and xylene in shoe industry-related workplaces

Benzene, toluene, ethyl benzene, and xylene (BTEX) are prevalent pollutants in shoe industry-related workplaces. The aim of this study was to assess exposure to BTEX and their carcinogenic and non-carcinogenic risks in shoe-industry-related workplaces. This study was carried out at different shoe manufactures, small shoe workshop units, shoe markets, and shoe stores in Tabriz, Iran in 2021. Personal inhalation exposure to BTEX was measured using the National Institute for Occupational Safety and Health (NIOSH) 1501 method. Carcinogenic and non-carcinogenic risks due to inhalation exposure to BTEX were estimated by United States Environmental Protection Agency (U.S. EPA) method based on Mont Carlo simulation. Results showed that the concentrations of benzene and toluene were higher than the threshold limit value (TLV) in both gluing and non-gluing units of shoe manufactures. The total carcinogenic risk (TCR) due to exposure to benzene and ethyl benzene was considerable in all shoe industry-related workplaces. Also, the hazard index (HI) as a non-carcinogenic index was higher than standard levels in all shoe industry-related workplaces. Therefore, shoe industry-related workers are at cancer and non-cancer risks due to exposure to BTEX. Prevention measures need to be implemented to reduce the concentration of BTEX in shoe industry-related workplaces.

The application of new HARD-descriptor available from the CORAL software to building up NOAEL models

Continuous QSAR models have been developed and validated for the prediction of no-observed-adverse-effect (NOAEL) in rats, using training and test sets from the Fraunhofer RepDose® database and EFSA's Chemical Hazards Database: OpenFoodTox. This paper demonstrates that the HARD index, as an integrated attribute of SMILES, improves the prediction power of NOAEL values using the continuous QSAR models and Monte Carlo simulations. The HARD-index is a line of eleven symbols, which represents the presence, or absence of eight chemical elements (nitrogen, oxygen, sulfur, phosphorus, fluorine, chlorine, bromine, and iodine) and different kinds of chemical bonds (double bond, triple bond, and stereo chemical bond). Optimal molecular descriptors calculated with the Monte Carlo technique (maximization of correlation coefficient between the descriptor and endpoint) give satisfactory predictive models for NOAEL. Optimal molecular descriptors calculated in this way with the Monte Carlo technique (maximization of correlation coefficient between the descriptor and endpoint) give amongst the best results available in the literature. The models are built up in accordance with OECD principles.

Mechanistic insights into the specificity of human cytosolic sulfotransferase 2A1 (hSULT2A1) for hydroxylated polychlorinated biphenyls through the use of fluoro-tagged probes

Determining the relationships between the structures of substrates and inhibitors and their interactions with drug-metabolizing enzymes is of prime importance in predicting the toxic potential of new and legacy xenobiotics. Traditionally, quantitative structure activity relationship (QSAR) studies are performed with many distinct compounds. Based on the chemical properties of the tested compounds, complex relationships can be established so that models can be developed to predict toxicity of novel compounds. In this study, the use of fluorinated analogues as supplemental QSAR compounds was investigated. Substituting fluorine induces changes in electronic and steric properties of the substrate without substantially changing the chemical backbone of the substrate. In vitro assays were performed using purified human cytosolic sulfotransferase hSULT2A1 as a model enzyme. A mono-hydroxylated polychlorinated biphenyl (4-OH PCB 14) and its four possible mono-fluoro analogues were used as test compounds. Remarkable similarities were found between this approach and previously published QSAR studies for hSULT2A1. Both studies implicate the importance of dipole moment and dihedral angle as being important to PCB structure in respect to being substrates for hSULT2A1. We conclude that mono-fluorinated analogues of a target substrate can be a useful tool to study the structure activity relationships for enzyme specificity.

Perspectives for integrating human and environmental risk assessment and synergies with socio-economic analysis

For more than a decade, the integration of human and environmental risk assessment (RA) has become an attractive vision. At the same time, existing European regulations of chemical substances such as REACH (EC Regulation No. 1907/2006), the Plant Protection Products Regulation (EC regulation 1107/2009) and Biocide Regulation (EC Regulation 528/2012) continue to ask for sector-specific RAs, each of which have their individual information requirements regarding exposure and hazard data, and also use different methodologies for the ultimate risk quantification. In response to this difference between the vision for integration and the current scientific and regulatory practice, the present paper outlines five medium-term opportunities for integrating human and environmental RA, followed by detailed discussions of the associated major components and their state of the art. Current hazard assessment approaches are analyzed in terms of data availability and quality, and covering non-test tools, the integrated testing strategy (ITS) approach, the adverse outcome pathway (AOP) concept, methods for assessing uncertainty, and the issue of explicitly treating mixture toxicity. With respect to exposure, opportunities for integrating exposure assessment are discussed, taking into account the uncertainty, standardization and validation of exposure modeling as well as the availability of exposure data. A further focus is on ways to complement RA by a socio-economic assessment (SEA) in order to better inform about risk management options. In this way, the present analysis, developed as part of the EU FP7 project HEROIC, may contribute to paving the way for integrating, where useful and possible, human and environmental RA in a manner suitable for its coupling with SEA.

Predictive computational toxicology to support drug safety assessment

Use of predictive technologies is an important aspect of many efforts in today's research, development, and regulatory landscapes. Computational methods as predictive tools for supporting drug safety assessments is of widespread interest as the field of in silico assessments rapidly changes with emerging technologies and the large amount of existing data available for modeling. There are challenges associated with application of in silico analyses for drug toxicity predictions and need for strategies and harmonization to enable an acceptable in silico evaluation for prediction of specific toxicity assay outcomes. This chapter will provide an overview focused on computational tools using structure-activity relationships and will highlight initiatives for use of computational assessments and realistic applications for predictive modeling in evaluating potential toxicities of drug-related molecules.

Evaluation of the OECD (Q)SAR Application Toolbox for the profiling of estrogen receptor binding affinities

The determination of binding affinities for the estrogen receptor (ER) is used extensively to assess potential hazards to human health and the environment arising from chemicals that can interfere with natural hormone homeostasis. Given the great number of chemicals to which humans and wildlife are exposed, (quantitative) structure-activity relationship (Q)SAR models for the characterization of ER disruptors represent a fast and cost-efficient alternative to experimental testing. In this toxicological context, the freely available Organisation for Economic Co-operation and Development (OECD) (Q)SAR Application Toolbox provides a profiler for the categorical profiling of chemicals according to their ER binding propensities. The aim of this study was to evaluate the predictive performances of this profiler. To achieve such a purpose, prediction results with the ER-profiler were compared with experimental binding affinities relative to two large datasets of chemicals (rat and human). The resulting Cooper statistics indicated that the binding affinities of the majority of chemicals included in the retained datasets could be correctly predicted.