Use of (Q)SAR genotoxicity predictions and fuzzy multicriteria decision-making for priority ranking of ethoxyquin transformation products

Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split

Structural alerts (SAs) are essential to identify chemicals for toxicity evaluation and health risk assessment. We constructed a novel SMILES split-based deep learning model (SSDL) that was trained and verified with 5850 chemicals from the ISSSTY database and 384 external test chemicals from published papers. The training accuracy was above 0.90 and the evaluation metrics (precision, recall and F1-score) all reached 0.78 or above on both internal and external test chemicals. In this model, the molecular-specific fragment importance of chemicals was first quantified independently. Then, the SA identification method based on the importance of these fragments was statistically analyzed and verified with the ISSSTY test and external test chemicals containing one of 28 typical SAs, and most of the performances were better than that of expert rules. Furthermore, a mutagenicity mechanism prediction method was developed using 237 chemicals with four known mutagenic mechanisms based on molecular similarity calibrated by the SSDL method and fragment importance, which significantly improved accuracy in three mechanisms and had comparable accuracy in the other one compared to traditional methods. Overall, the SSDL model quantifying fragment toxicity within molecules would be a novel potentially powerful tool in the determination and visualization of molecular-specific SAs and the prediction of mutagenicity mechanisms for environmental or industrial compounds and drugs.

Multi-tissue proteogenomic analysis for mechanistic toxicology studies in non-model species

New approach methodologies (NAM), including omics and in vitro approaches, are contributing to the implementation of 3R (reduction, refinement and replacement) strategies in regulatory science and risk assessment. In this study, we present an integrative transcriptomics and proteomics analysis workflow for the validation and revision of complex fish genomes and demonstrate how proteogenomics expression matrices can be used to support multi-level omics data integration in non-model species in vivo and in vitro. Using Atlantic salmon as an example, we constructed proteogenomic databases from publicly available transcriptomic data and in-house generated RNA-Seq and LC-MS/MS data. Our analysis identified ∼80,000 peptides, providing direct evidence of translation for over 40,000 RefSeq structures. The data also highlighted 183 co-located peptide groups that supported a single transcript each, and in each case, either corrected a previous annotation, supported Ensembl annotations not present in RefSeq, or identified novel previously unannotated genes. Proteogenomics data-derived expression matrices revealed distinct profiles for the different tissue types analyzed. Focusing on proteins involved in defense against xenobiotics, we detected distinct expression patterns across different salmon tissues and observed homology in the expression of chemical defense proteins between in vivo and in vitro liver systems. Our study demonstrates the potential of proteogenomic analyses in extending our understanding of complex fish genomes and provides an advanced bioinformatic toolkit to support the further development of NAMs and their application in regulatory science and (eco)toxicological studies of non-model species.

Priority control sequence of 34 typical pollutants in effluents of Chinese wastewater treatment plants

The identification of the priority control sequence of pollutants in effluents of wastewater treatment plants (WWTPs) has important implications for the management of water quality. This study chose 34 typical pollutants based on their representativeness and detection rates in municipal wastewater. The occurrence frequency and concentration of these pollutants in 168 Chinese WWTP effluents were measured at the national level. The data on in vitro toxicity (67 assays) and in vivo toxicity (216 species) for target pollutants were obtained from the public toxicity database and our experimental data. An environmental health prioritization index (EHPi) method was proposed to integrate the occurrence frequency, concentration, removal rate, and in vitro and in vivo toxicity to determine the priority control sequence of target pollutants. Ethynyl estradiol, 17β-estradiol, estrone, diclofenac, and atrazine were the top 5 pollutants identified by the EHPi score. Several pollutants with high EHPi scores showed spatial differences. Besides the EHPi method which was from the single pollutant perspective, the combined toxicity of pollutants (300 pairs of binary combinations) was also measured based on in vitro toxicity assays to evaluate the key pollutants from the pollutant-pollutant interacting perspective. The pollutants (such as ofloxacin and acetaminophen) that could have significant synergetic effects with many other pollutants are worthy of prior attention. This study shed new light on the identification of the priority control sequence of pollutants in WWTP effluents. The results provide meaningful data for the effective management and control of wastewater water quality.

Critical assessment of the Kendrick mass defect analysis as an innovative approach to process high resolution mass spectrometry data for environmental applications

The growing application of high resolution mass spectrometry (HRMS) over the last decades has dramatically improved our knowledge about the occurrence of environmental contaminants. However, most of the compounds detected remain unknown and the large volume of data generated requires specific processing approaches. Therefore, this study presents the concepts of mass defect (MD), Kendrick mass (KM) and Kendrick mass defect (KMD) to the expert and non-expert reader along with relevant examples of applications in environmental HRMS data processing. A preliminary bibliometric overview indicates that the potential benefits of KMD analysis are rather overlooked in environmental science. In practice, a simple calculation allows transforming a mass from the IUPAC system (normalized so that the mass of ¹²C is exactly 12) to its corresponding KM normalized on a specific moiety such as CH₂ (the mass of CH₂ is exactly 14). Then, plotting the KMD according to the nominal KM allows revealing groups of compounds that differ only by their number of CH₂ moieties. For instance, data processing using KM and KMD was proven particularly useful to characterize natural organic matter in a sample, to reveal the occurrence of polymers as well as poly/perfluorinated alkylated substances (PFASs), and to search for transformation products (TPs) of a given chemical.

Safety and efficacy of a feed additive consisting of ethoxyquin (6-ethoxy-1,2-dihydro-2,2,4-trimethylquinoline) for all animal species (FEFANA asbl)

Ethoxyquin is synthetised from p-phenetidine, a possible mutagen, which remains in the additive as an impurity at concentrations of < 2.5 mg/kg additive. Ethoxyquin is considered safe for all animal species at the proposed inclusion level of 50 mg/kg complete feed. However, owing the presence of p-phenetidine, no safe level of the additive in feed for long-living and reproductive animals could be identified. The FEEDAP Panel derived a health-based guidance value of 0.006 mg ethoxyquin dimer (EQDM)/kg bw per day and applied it to the sum of ethoxyquin and its transformation products. A maximum total concentration of 50 mg ethoxyquin/kg complete feed for all animal species, except dairy ruminants, would not pose a risk for the consumer. However, in the absence of data on p-phenetidine residues in tissues and products of animal origin, no conclusion on the safety for the consumer could be drawn. The conclusions on consumer safety assume that the maximum total concentration of 50 mg EQ/kg feed is expressed as the sum of EQ, EQDM, EQI and DHEQ. Exposure of the unprotected user to p-phenetidine via inhalation should be minimised. No safety concerns for groundwater are expected. It is not possible to conclude on the safety of EQ for the terrestrial compartment. A risk for the aquatic compartment cannot be excluded when ethoxyquin is used in terrestrial animals. Unacceptable risk is not expected for freshwater sediment-dwelling organisms. A risk of secondary poisoning via the terrestrial food chain is not expected, whereas a risk via the aquatic food chain cannot be excluded. No concerns for aquatic organisms are expected for ethoxyquin used in fish farmed in land-based system, a risk cannot be excluded for marine sediment dwelling organisms when ethoxyquin is used in sea-cages. Ethoxyquin is considered efficacious in the range 25-50 mg/kg complete feed.