Comparative PBT screening using (Q)SAR tools within REACH legislation

Functional modification of HHCB: Strategy for obtaining environmentally friendly derivatives

Galaxolide (HHCB), one of the most widely used synthetic musks in personal care products (PCPs), has been recognized as an emerging contaminant with potential human health concerns. To overcome such adverse effects, a systematic molecular design, screening and performance evaluation approach was developed to generate functionally improved and environmentally friendly HHCB derivatives. Among the 90 designed HHCB derivatives, 15 were screened with improved functional properties (i.e., odor stability and intensity) and less environmental impacts (i.e., lower bio-toxicity, bio-accumulation ability, and mobility) using 3D-QSAR models and density functional theory methods. Their human health risks were then assessed by toxicokinetic analysis, which narrowed the candidates to four. Derivative 7, the designed molecule with the least dermal adsorption potential, was evaluated for its interaction with other PCPs additives (i.e., anti-photosensitivity materials and moisturizer) and such impacts on human health risks using molecular docking and molecular dynamic simulation. The environmental fate of Derivative 7 after transformation (i.e., photodegradation, biotransformation, and chlorination) was also discussed. Biotransformation and chlorination were recognized as optimum options for Derivative 7 mitigation. This study provided the theoretical basis for the design of functionally improved and environmentally friendly HHCB alternatives and advanced the understanding of their environmental behaviors and health risks.

Assessment on biodegradability prediction of tannery wastewater using EPI Suite BIOWIN model

Biodegradation of organic compounds would reveal important information on the final fate of a chemical in the environment. However, establishing biodegradability and fate of a chemical is cumbersome. In this scenario, the use of multimedia models help in predicting the fate and half-life of any compound to establish biodegradability. The study commenced with collection of wastewater samples, after primary and secondary treatment, from a Common Effluent Treatment Plant (CETP) treating tannery wastewater. The samples were subjected to gas chromatography-mass spectrometry (GC-MS) analysis. The GC-MS analysis identified that polyphenolic compounds were detected after biological treatment. The identified compounds emanated from tanning, dyeing, and fatliquoring process of leather making. Estimation Program Interface (EPI) Suite BIOWIN 3 and BIOWIN 4 model prediction revealed that while the primary biodegradation time-frame ranged from days to weeks, the ultimate biodegradation took weeks in the case of all the detected compounds. This study established that BIOWIN model could be used as a screening tool to determine biodegradability of complex chemicals used in tanneries and help to design better treatment facility with enhanced efficiency for removal of polyphenolic compounds. This methodology can also be applied to other industrial wastewaters containing recalcitrant chemicals, and with the help of BIOWIN model, information on biodegradability of chemicals present in the wastewater can be obtained.

Combined QSAR/QSPR and molecular docking study on fluoroquinolones to reduce biological enrichment

With the aim of reducing the adverse effects of fluoroquinolones in the environment, a complete design and screening system for the low biological enrichment and high photodegradabilities of 29 fluoroquinolones was established through a three-dimensional quantitative structure-activity relationship (3D-QSAR) model and molecular docking. The interaction mechanisms of the fluoroquinolones with Gram-negative bacteria (DNA gyrase in Escherichia coli) and Gram-positive bacteria (Topoisomerase IV in Staphylococcus aureus) were also evaluated. Consequently, the 3D-QSAR model showed that the 3- and 18-positions of the fluoroquinolones strongly affected their biological enrichment, and that the introduction of electropositive or hydrophobic groups at these positions reduced the logarithm of the octanol-water partition coefficient. Using nadifloxacin as a template, 23 derivatives with lower biological enrichment than nadifloxacin (decreased by 30.12%-94.18%) were designed. Meanwhile, the photodegradabilities of 15 derivatives were increased compared with nadifloxacin. Finally, the further screening by molecular docking of nadifloxacin and the above 15 derivatives with DNA gyrase and Topoisomerase IV showed that 13 of the derivatives had lower biological enrichment (decreased by 0.30%-16.76%) than nadifloxacin in the bacteria.

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. The cumulative end-points provide an integrated holistic point of view to address environmentally relevant properties of chemicals.

External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics

Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure-biodegradation relationship (SBR) and quantitative structure-biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite™ includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.

The exposure data landscape for manufactured chemicals

The U.S. Environmental Protection Agency is developing chemical screening and prioritization programs to evaluate environmental chemicals for potential risk to human health in a rapid and efficient manner. As part of these efforts, it is important to catalog available information on chemical toxicity and exposure from widely dispersed sources. The main objective of this analysis is to define important aspects of the exposure space and to catalog the available exposure information for chemicals being considered for analysis as part of the U.S. EPA ToxCast™ screening and prioritization program. Publicly available exposure data have been extracted into ACToR (Aggregated Computational Toxicology Resource), which combines information for hundreds of thousands of chemicals from >600 public sources. We use data from ACToR to assess the exposure data landscape for environmental chemicals. Of the roughly 100,000 chemicals that have at least limited toxicity information available, less than one-fifth also have exposure information - and for most of these the information is of limited utility (e.g., production volume). Readily accessible data on concentrations in exposure-related media are only available for a much smaller fraction. Among these, the largest number of chemicals is measured in water with over 1150 unique compounds, followed by 788 substances measured in soil, and 670 in air. These small numbers clearly reflect a focus of resources on those substances previously identified as possibly posing a hazard to human health. Exposure to a much broader number of chemicals will need to be measured in order to fully realize the envisioned goal of using exposure information to guide toxicity testing.

Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 1. Aromatic amines

The Ames Salmonella typhimurium mutagenicity assay is a short-term bacterial reverse mutation test that was designed to detect mutagens. For several decades, it has been used in research laboratories and by regulatory agencies throughout the world for the detection and characterization of potential mutagens among natural products and man-made chemicals. Faced with the ever-growing number of chemicals available on the market, congeneric and non-congeneric (Q)SAR models have been designed from Ames test results obtained on specific S. typhimurium strains such as TA 100 or TA 98. Such models have great potential for a quick and cheap identification and classification of large numbers of potential chemical mutagens. The OECD QSAR Application Toolbox and Toxtree, which were developed for facilitating the practical use of (Q)SAR approaches in regulatory contexts, include two mechanistic SAR models for predicting the mutagenicity of aromatic amines and α-β unsaturated aliphatic aldehydes. The aim of this study was to estimate the interest and limitations of the former model. The model was first re-computed to check its transparency and to verify its statistical validity. Then, it was tested on about 150 chemicals not previously used for the design of the model but belonging to its domain of application. A critical analysis of the results was performed and proposals were made for increasing the model performances.