Global Assessment of Emerging Contaminant Removal in Wastewater Treatment Plants: Hazard Screening and Risk Evaluation.

Pharmaceuticals and personal care products (PPCPs) are emerging contaminants (ECs), whose presence in the environment is of increasing concern due to their widespread use and possible detrimental effects on wildlife and humans. These chemicals may present multiple hazardous properties such as environmental persistence, toxicity, high mobility, and the potential for bioaccumulation. In this study, extended bibliographic research was conducted to characterize the removal efficiency (RE) of PPCPs in wastewater treatment plants (WWTPs) considering different technologies.

In silico models for the screening of human transthyretin disruptors.

The use of New Approach Methodologies (NAMs), such as Quantitative Structure-Activity Relationship (QSAR) models, is highly recommended by international regulations to speed up hazard and risk assessment of Endocrine Disruptors, which are known to be linked to a wide spectrum of severe diseases on humans and wildlife. A very sensitive target for these chemicals is the thyroid hormone system, which plays a key role in regulating metabolic and cognitive functions. Several chemicals have been demonstrated to compete with the thyroid hormone thyroxine (T4) for binding to human thyroid hormone distributor protein transthyretin (hTTR).

approaches for the prediction of the breakthrough of organic contaminants in wastewater treatment plants.

The removal efficiency (RE) of organic contaminants in wastewater treatment plants (WWTPs) is a major determinant of the environmental impact of chemicals which are discharged to wastewater. In a recent study, non-target screening analysis was applied to quantify the percentage removal efficiency (RE%) of more than 300 polar contaminants, by analyzing influent and effluent samples from a Swedish WWTP with direct injection UHPLC-Orbitrap-MS/MS. Based on subsets extracted from these data, we developed quantitative structure-property relationships (QSPRs) for the prediction of WWTP breakthrough (BT) to the effluent water.

QSAR Models for the Prediction of Dietary Biomagnification Factor in Fish.

Xenobiotics released in the environment can be taken up by aquatic and terrestrial organisms and can accumulate at higher concentrations through the trophic chain. Bioaccumulation is therefore one of the PBT properties that authorities require to assess for the evaluation of the risks that chemicals may pose to humans and the environment. The use of an integrated testing strategy (ITS) and the use of multiple sources of information are strongly encouraged by authorities in order to maximize the information available and reduce testing costs.

Predicting the Bioconcentration Factor in Fish from Molecular Structures.

The bioconcentration factor (BCF) is one of the metrics used to evaluate the potential of a substance to bioaccumulate into aquatic organisms. In this work, linear and non-linear regression QSARs were developed for the prediction of log BCF using different computational approaches, and starting from a large and structurally heterogeneous dataset. The new MLR-OLS and ANN regression models have good fitting with R2 values of 0.

Aquatic toxicity prediction of diverse pesticides on two algal species using QSTR modeling approach.

With the aim of identification of toxic nature of the diverse pesticides on the aquatic compartment, a large dataset of pesticides (n = 325) with experimental toxicity data on two algal test species (Pseudokirchneriella subcapitata (PS) (synonym: Raphidocelis subcapitata, Selenastrum capricornutum) and Scenedemus subspicatus (SS)) was gathered and subjected to quantitative structure toxicity relationship (QSTR) analysis to predict aquatic toxicity of pesticides. The QSTR models were developed by multiple linear regressions (MLRs), and the genetic algorithm (GA) was used for the variable selection. The developed GA-MLR models were statistically robust enough internally (Q = 0.

New BODIPYs for photodynamic therapy (PDT): Synthesis and activity on human cancer cell lines.

A new class of compounds based on the 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene core, known as BODIPYs, has attracted significant attention as photosensitizers suitable for application in photodynamic therapy (PDT), which is a minimally invasive procedure to treat cancer. In PDT the combination of a photosensitizer (PS), light, and oxygen leads to a series of photochemical reactions generating reactive oxygen species (ROS) exerting cytotoxic action on tumor cells. Here we present the synthesis and the study of the in vitro photodynamic effects of two BODIPYs which differ in the structure of the substituent placed on the meso (or 8) position of the dipyrrolylmethenic nucleus.

Are In Silico Approaches Applicable As a First Step for the Prediction of e-Liquid Toxicity in e-Cigarettes?

Recent studies have raised concerns about e-cigarette liquid inhalation toxicity by reporting the presence of chemicals with European Union CLP toxicity classification. In this scenario, the regulatory context is still developing and is not yet up to date with vaping current reality. Due to the paucity of toxicological studies, robust data regarding which components in e-liquids exhibit potential toxicities, are still inconsistent.

Application of chemometric methods and QSAR models to support pesticide risk assessment starting from ecotoxicological datasets.

The EFSA 'Guidance on tiered risk assessment for edge-of-field surface waters' underscores the importance of in silico models to support the pesticide risk assessment. The aim of this work was to use in silico models starting from an available, structured and harmonized pesticide dataset that was developed for different purposes, in order to stimulate the use of QSAR models for risk assessment. The present work focuses on the development of a set of in silico models, developed to predict the aquatic toxicity of heterogeneous pesticides with incomplete/unknown toxic behavior in the water compartment.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

Background: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) approaches and computational modeling.

High-throughput evaluation of organic contaminant removal efficiency in a wastewater treatment plant using direct injection UHPLC-Orbitrap-MS/MS.

The removal efficiency (RE) of organic contaminants in wastewater treatment plants (WWTPs) is a major determinant of the environmental impact of these contaminants. However, RE data are available for only a few chemicals due to the time and cost required for conventional target analysis. In the present study, we applied non-target screening analysis to evaluate the RE of polar contaminants, by analyzing influent and effluent samples from a Swedish WWTP with direct injection UHPLC-Orbitrap-MS/MS.

QSAR modeling of cumulative environmental end-points for the prioritization of hazardous chemicals.

The hazard of chemicals in the environment is inherently related to the molecular structure and derives simultaneously from various chemical properties/activities/reactivities. Models based on Quantitative Structure Activity Relationships (QSARs) are useful to screen, rank and prioritize chemicals that may have an adverse impact on humans and the environment. This paper reviews a selection of QSAR models (based on theoretical molecular descriptors) developed for cumulative multivariate endpoints, which were derived by mathematical combination of multiple effects and properties.

Development of human biotransformation QSARs and application for PBT assessment refinement.

Toxicokinetics heavily influence chemical toxicity as the result of Absorption, Distribution, Metabolism (Biotransformation) and Elimination (ADME) processes. Biotransformation (metabolism) reactions can lead to detoxification or, in some cases, bioactivation of parent compounds to more toxic chemicals. Moreover, biotransformation has been recognized as a key process determining chemical half-life in an organism and is thus a key determinant for bioaccumulation assessment for many chemicals.

Modeling ready biodegradability of fragrance materials.

In the present study, quantitative structure activity relationships were developed for predicting ready biodegradability of approximately 200 heterogeneous fragrance materials. Two classification methods, classification and regression tree (CART) and k-nearest neighbors (kNN), were applied to perform the modeling. The models were validated with multiple external prediction sets, and the structural applicability domain was verified by the leverage approach.

The QSPR-THESAURUS: the online platform of the CADASTER project.

The aim of the CADASTER project (CAse Studies on the Development and Application of in Silico Techniques for Environmental Hazard and Risk Assessment) was to exemplify REACH-related hazard assessments for four classes of chemical compound, namely, polybrominated diphenylethers, per and polyfluorinated compounds, (benzo)triazoles, and musks and fragrances. The QSPR-THESAURUS website (http: / /qspr-thesaurus.eu) was established as the project's online platform to upload, store, apply, and also create, models within the project.

Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis.

Bioaccumulation in fish is a function of competing rates of chemical uptake and elimination. For hydrophobic organic chemicals bioconcentration, bioaccumulation and biomagnification potential are high and the biotransformation rate constant is a key parameter. Few measured biotransformation rate constant data are available compared to the number of chemicals that are being evaluated for bioaccumulation hazard and for exposure and risk assessment.

Assessing predictive uncertainty in comparative toxicity potentials of triazoles.

Comparative toxicity potentials (CTPs) quantify the potential ecotoxicological impacts of chemicals per unit of emission. They are the product of a substance's environmental fate, exposure, and hazardous concentration. When empirical data are lacking, substance properties can be predicted.

Daphnia and fish toxicity of (benzo)triazoles: validated QSAR models, and interspecies quantitative activity-activity modelling.

Due to their chemical properties synthetic triazoles and benzo-triazoles ((B)TAZs) are mainly distributed to the water compartments in the environment, and because of their wide use the potential effects on aquatic organisms are cause of concern. Non testing approaches like those based on quantitative structure-activity relationships (QSARs) are valuable tools to maximize the information contained in existing experimental data and predict missing information while minimizing animal testing. In the present study, externally validated QSAR models for the prediction of acute (B)TAZs toxicity in Daphnia magna and Oncorhynchus mykiss have been developed according to the principles for the validation of QSARs and their acceptability for regulatory purposes, proposed by the Organization for Economic Co-operation and Development (OECD).

Experimental assessment of the environmental fate and effects of triazoles and benzotriazole.

The environmental fate and effects of triazoles and benzotriazoles are of concern within the context of chemical regulation. As part of an intelligent testing strategy, experimental tests were performed on endpoints that are relevant for risk assessment. The experimental tests included the assessment of ecotoxicity to an alga, a daphnid and zebrafish embryos, and the assessment of ready biodegradability.

Evaluation of CADASTER QSAR models for the aquatic toxicity of (benzo)triazoles and prioritisation by consensus prediction.

QSAR regression models of the toxicity of triazoles and benzotriazoles ([B]TAZs) to an alga (Pseudokirchneriella subcapitata), Daphnia magna and a fish (Onchorhynchus mykiss), were developed by five partners in the FP7-EU Project, CADASTER. The models were developed by different methods - Ordinary Least Squares (OLS), Partial Least Squares (PLS), Bayesian regularised regression and Associative Neural Network (ASNN) - by using various molecular descriptors (DRAGON, PaDEL-Descriptor and QSPR-THESAURUS web). In addition, different procedures were used for variable selection, validation and applicability domain inspection.