Non-targeted analysis and toxicity prediction for evaluation of photocatalytic membrane distillation removing organic contaminants from hypersaline oil and gas field-produced water.

Membrane distillation (MD) has received ample recognition for treating complex wastewater, including hypersaline oil and gas (O&G) produced water (PW). Rigorous water quality assessment is critical in evaluating PW treatment because PW consists of numerous contaminants beyond the targets listed in general discharge and reuse standards. This study evaluated a novel photocatalytic membrane distillation (PMD) process, with and without a UV light source, against a standard vacuum membrane distillation (VMD) process for treating PW, utilizing targeted analyses and a non-targeted chemical identification workflow coupled with toxicity predictions. PMD with UV light resulted in better removals of dissolved organic carbon, ammoniacal nitrogen, and conductivity. Targeted organic analyses identified only trace amounts of acetone and 2-butanone in distillates. According to non-targeted analysis, the number of suspects reduced from 65 in feed to 25-30 across all distillate samples. Certain physicochemical properties of compounds influenced contaminant rejection in different MD configurations. According to preliminary toxicity predictions, VMD, PMD with and without UV distillate samples, respectively contained 21, 22, and 23 suspects associated with critical toxicity concerns. Overall, non-targeted analysis together with toxicity prediction provides a competent supportive tool to assess treatment efficiency and potential impacts on public health and the environment during PW reuse.

Application of systematic evidence mapping to identify available data on the potential human health hazards of selected market-relevant azo dyes.

BACKGROUND: Azo dyes are used in textiles and leather clothing. Human exposure can occur from wearing textiles containing azo dyes. Since the body's enzymes and microbiome can cleave azo dyes, potentially resulting in mutagenic or carcinogenic metabolites, there is also an indirect health concern on the parent compounds. While several hazardous azo dyes are banned, many more are still in use that have not been evaluated systematically for potential health concerns. This systematic evidence map (SEM) aims to compile and categorize the available toxicological evidence on the potential human health risks of a set of 30 market-relevant azo dyes. METHODS: Peer-reviewed and gray literature was searched and over 20,000 studies were identified. These were filtered using Sciome Workbench for Interactive computer-Facilitated Text-mining (SWIFT) Review software with evidence stream tags (human, animal, in vitro) yielding 12,800 unique records. SWIFT Active (a machine-learning software) further facilitated title/abstract screening. DistillerSR software was used for additional title/abstract, full-text screening, and data extraction. RESULTS: 187 studies were identified that met populations, exposures, comparators, and outcomes (PECO) criteria. From this pool, 54 human, 78 animal, and 61 genotoxicity studies were extracted into a literature inventory. Toxicological evidence was abundant for three azo dyes (also used as food additives) and sparse for five of the remaining 27 compounds. Complementary search in ECHA's REACH database for summaries of unpublished study reports revealed evidence for all 30 dyes. The question arose of how this information can be fed into an SEM process. Proper identification of prioritized dyes from various databases (including U.S. EPA's CompTox Chemicals Dashboard) turned out to be a challenge. Evidence compiled by this SEM project can be evaluated for subsequent use in problem formulation efforts to inform potential regulatory needs and prepare for a more efficient and targeted evaluation in the future for human health assessments.

Predicting molecular initiating events using chemical target annotations and gene expression.

BACKGROUND: The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical space and contain reference chemicals offer utility for the prediction of molecular initiating events associated with chemical exposure. Here, we integrate data from a large compendium of transcriptomic responses to chemical exposure with a comprehensive database of chemical-protein associations to train binary classifiers that predict mechanism(s) of action from transcriptomic responses. First, we linked reference chemicals present in the LINCS L1000 gene expression data collection to chemical identifiers in RefChemDB, a database of chemical-protein interactions. Next, we trained binary classifiers on MCF7 human breast cancer cell line derived gene expression profiles and chemical-protein labels using six classification algorithms to identify optimal analysis parameters. To validate classifier accuracy, we used holdout data sets, training-excluded reference chemicals, and empirical significance testing of null models derived from permuted chemical-protein associations. To identify classifiers that have variable predicting performance across training data derived from different cellular contexts, we trained a separate set of binary classifiers on the PC3 human prostate cancer cell line. RESULTS: We trained classifiers using expression data associated with chemical treatments linked to 51 molecular initiating events. This analysis identified and validated 9 high-performing classifiers with empirical p-values lower than 0.05 and internal accuracies ranging from 0.73 to 0.94 and holdout accuracies of 0.68 to 0.92. High-ranking predictions for training-excluded reference chemicals demonstrating that predictive accuracy extends beyond the set of chemicals used in classifier training. To explore differences in classifier performance as a function of training data cellular context, MCF7-trained classifier accuracies were compared to classifiers trained on the PC3 gene expression data for the same molecular initiating events. CONCLUSIONS: This methodology can offer insight in prioritizing candidate perturbagens of interest for targeted screens. This approach can also help guide the selection of relevant cellular contexts for screening classes of candidate perturbagens using cell line specific model performance.

Sourcing data on chemical properties and hazard data from the US-EPA CompTox Chemicals Dashboard: A practical guide for human risk assessment.

For the past six decades, human health risk assessment of chemicals has relied on in vivo data from human epidemiological and experimental animal toxicological studies to inform the derivation of non-cancer toxicity values. The ongoing evolution of this risk assessment paradigm in an environmental landscape of data-poor chemicals has highlighted the need to develop and implement non-testing methods, so-called New Approach Methodologies (NAMs). NAMs include a growing number of in silico and in vitro data streams designed to inform hazard properties of chemicals, including kinetics and dynamics at different levels of biological organization, environmental fate and transport, and exposure. NAMs provide a fit-for-purpose science-basis for human hazard and risk characterization of chemicals ranging from data-gap filling applications to broad evidence-based decision-making. Systematic assembly and delivery of empirical and predicted data for chemicals are paramount to advancing chemical evaluation, and software tools serve an essential role in delivering these data to the scientific community. The CompTox Chemicals Dashboard (from here on referred to as the "Dashboard") is one such tool and is a publicly available web-based application developed by the US Environmental Protection Agency to provide access to chemistry, toxicity and exposure information for ~900,000 chemicals. The Dashboard is increasingly becoming a valuable resource for assessors tasked with the evaluation of potential human health risks associated with chemical exposures. In this context, the significant amount of information present in the Dashboard facilitates: 1) assembly of information on physicochemical properties and environmental fate and transport and exposure parameters and metrics; 2) identification of cancer and non-cancer health effects from extant human and experimental animal studies in the public domain and/or information not available in the public domain (i.e., "grey literature"); 3) systematic literature searching and review for developing cancer and non-cancer hazard evidence bases; and 4) access to mechanistic information that can aid or augment the analysis of traditional toxicology evidence bases, or potentially, serve as the primary basis for informing hazard identification and dose-response when traditional bioassay data are lacking. Finally, in silico predictive tools developed to conduct structure-activity or read-across analyses are also available within the Dashboard. This practical tutorial is intended to address key questions from the human health risk assessment community dealing with chemicals in both food and in the environment. Perspectives for future development or refinement of the Dashboard highlight foreseen activities to further support the research and risk assessment community in cancer and non-cancer chemical evaluations.

Bioactivity profiling of per- and polyfluoroalkyl substances (PFAS) identifies potential toxicity pathways related to molecular structure.

Per- and polyfluoroalkyl substances (PFAS) are a broad class of hundreds of fluorinated chemicals with environmental health concerns due to their widespread presence and persistence in the environment. Several of these chemicals have been comprehensively studied for experimental toxicity, environmental fate and exposure, and human epidemiology; however, most chemicals have limited or no data available. To inform methods for prioritizing these data-poor chemicals for detailed toxicity studies, we evaluated 142 PFAS using an in vitro screening platform consisting of two multiplexed transactivation assays encompassing 81 diverse transcription factor activities and tested in concentration-response format ranging from 137 nM to 300 µM. Results showed activity for various nuclear receptors, including three known PFAS targets--specifically estrogen receptor alpha and peroxisome proliferator receptors alpha and gamma. We also report activity against the retinoid X receptor beta, the key heterodimeric partner of type II, non-steroidal nuclear receptors. Additional activities were found against the pregnane X receptor, nuclear receptor related-1 protein, and nuclear factor erythroid 2-related factor 2, a sensor of oxidative stress. Using orthogonal assay approaches, we confirmed activity of representative PFAS against several of these targets. Finally, we identified key PFAS structural features associated with nuclear receptor activity that can inform future predictive models for use in prioritizing chemicals for risk assessment and in the design of new structures devoid of biological activity.

Turning Spiroketals Inside Out: A Rearrangement Triggered by an Enol Ether Epoxidation.

Invited for this month's cover picture is the group of Professor Mark Peczuh at the University of Connecticut. The cover picture compares the rearrangement of a small molecule to the process of turning a stuffed animal inside out. The recycled, inside-out stuffed animals are both artistic and philosophically provocative. They capture the essence of the rearrangement reaction because the compounds themselves turn inside out over the course of the reaction, extending the diversity of products that can arise from simple starting materials. Small molecules often have functional groups with latent reactivity; under the appropriate conditions, those groups can react with other compounds (e.g., reagents) and also with other groups in the same molecule in an intramolecular reaction. The research team found that the epoxidation of some highly functionalized spiroketal compounds promoted rearrangements of their structures that turned them inside out. Some of the features of the products led them to use X-ray crystallography or a combination of computer-assisted structure elucidation, computation, and a new version of the 1,1-ADEQUATE NMR experiment to determine their structures. For more details, see the Communication on p. 577 ff.

Turning Spiroketals Inside Out: A Rearrangement Triggered by an Enol Ether Epoxidation.

Spiroketals organize small molecule structures into well-defined, three-dimensional configurations that make them good ligands of proteins. We recently discovered a tandem cycloisomerization-dimerization reaction of alkynyl hemiketals that delivered polycyclic, enol-ether-containing spiroketals. Here we describe rearrangements of those compounds, triggered by epoxidation of their enol ethers that completely remodel their structures, essentially turning them "inside out". Due to the high level of substitution on the carbon skeletons of the substrates and products, characterization resorted to X-ray crystallography and advanced computation and NMR techniques to solve the structures of representative compounds. In particular, a new proton-detected ADEQUATE NMR experiment (1,1-HD-ADEQUATE) enabled the unequivocal assignment of the carbon skeleton of one of the new compounds. Solution of the structures of the representative compounds allowed for the assignment of product structures for the other compounds in two separate series. Both the rearrangement and the methods used for structural determination of the products are valuable tools for the preparation of characterization of new small molecule compounds.