Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset

Toward Metabolic Similarity in Read-Across: A Case Study Using Graph Convolutional Networks to Predict Genotoxicity Outcomes from Simulated Metabolic Networks

Metabolic similarity is a key consideration in evaluating candidate source analogues for read-across (RAx), but approaches to systematically characterize metabolism for read-across prediction are still evolving. Metabolic similarity is multifaceted, considering the similarity of the metabolic tree, the metabolites simulated, and the transformation pathways. The structure of metabolic trees lends itself naturally to graph representations, for which several methods, including graph convolutional networks (GCNs), can be applied to quantify the pairwise similarity between the target and source analogue(s) within an analogue or category approach. In this study, we compared metabolic graph representations of metabolites with structural similarities in predicting genotoxicity outcomes using a data set comprising 5403 chemicals. Xenobiotic metabolism pathways were predicted using the rat liver models within the commercial expert system, TIssue MEtabolism Simulator (TIMES), and the phase I and II xenobiotic metabolism modules within the freely available system BioTransformer. Metabolic pathways were converted to graphs and used to train GCNs, generating embeddings for each chemical. The classification performance of generalized read-across (GenRA), random forest (RF), logistic regression (LR), and multilayer perceptron (MLP) was compared using GCN-derived embeddings versus both Morgan and MACCS chemical fingerprints to identify genotoxic chemicals. GCN embeddings with LR, based on in vivo TIMES metabolism predictions using MACCS fingerprints as node features, achieved the highest area under the curve of the receiver operating characteristic of 0.807, outperforming GenRA and LR with MACCS fingerprints by 14.47% and 5.49%, respectively. Our findings suggest that GCN embeddings of predicted metabolism pathways perform substantially better than structural features of the parent chemicals in predicting genotoxicity outcomes. Such GCN embeddings offer new avenues of systematically encoding end point metabolic information to facilitate analogue identification for read-across.

Advancing the developmental and reproductive assessment in medical device preclinical safety evaluations

Currently, a standardized developmental and reproductive toxicity (DART) test of chemicals present in and released from medical devices does not exist. The objective of this study is to investigate if selected non-commercial and commercial in silico models, individually or in combination, can be used as a first step in addressing DART of medical device chemicals. A total of 478 polymeric material chemicals (268 DART and 210 non-DART chemicals) commonly used in medical devices were obtained from the European Chemicals Agency Database. Parameters, including sensitivity, specificity, and accuracy, were calculated and compared. Median sensitivity, specificity, and accuracy of the individual non-commercial or commercial in silico models were 0.529, 0.601, and 0.535, respectively. Median sensitivity, specificity, and accuracy of all possible combinations of two or three in silico models were 0.659, 0.467, and 0.573, respectively. Combining Leadscope and Percepta models resulted in the sensitivity of 0.524, specificity of 0.845, and best performing accuracy of 0.766. Integration of different in silico models may act as a useful first step for screening medical device-related chemicals with potential DART toxicity in addition to the effort in replacing, reducing, and refining in vivo DART testing in medical device preclinical safety assessments.

Applicability of In Silico New Approach Methods for the Risk Assessment of Tattoo Ink Ingredients

Tattoo inks contain several substances, including organic and inorganic pigments, additives, and solvents, which may pose a health risk to not only the tattooed skin but also to other parts of the human body due to intradermal exposure. Substances in tattoo inks are regulated by entry 75 in Annex XVII of REACH Regulation (EC) No. 1907/2006. However, despite these legal requirements, a well-defined criterion for the safety assessment of tattoo inks remains lacking. In this context, 2021 BfR opinion titled "Tattoo inks: minimum requirements and test methods" proposed a comprehensive risk assessment of pigments using in vitro/in-chemico data in accordance with OECD Guidelines and CLP Regulations. In the absence of experimental data, new approach methodologies (NAMs) may be used for data-gap filling. Therefore, this work evaluates the applicability of in silico NAMs for data-gap filling for a list of tattoo ink ingredients identified by the Joint Research Centre (JRC) and BfR for genotoxicity assessment. Experimental in vitro genotoxicity data were acquired from the International Uniform Chemical Information Database (IUCLID) which makes non-confidential REACH Study Results publicly accessible. The specific aims of this analysis were the evaluation of in silico genotoxicity predictions from publicly available QSAR tools and structural alerts, the development and validation of new QSAR models specific to tattoo ink ingredients, and the application of in silico models for categorization and prioritization of data-poor ingredients for further screening. Based on the workflow developed in this study, 4 high priority, 18 medium priority, and 2 low priority substances were identified for further assessment.

EFSA Panel on Food Contact Materials (FCM), Lambré C, Crebelli R, da Silva M, Grob K, Lampi E, Milana MR, Pronk M, Ščetar M, Theodoridis G, Van Hoeck E, Waegeneers N, Bolognesi C, Cariou R, Castle L, Di Consiglio E, Franz R, Wölfle D, Al Harraq Z, Barthélémy E, Comandella D, Vela JF, Halamoda B, Rivière G. Update of the safety assessment of N,N-bis(2-hydroxyethyl)alkyl(C8-C18)amines (FCM No 19) and N,N-bis(2-hydroxyethyl)alkyl(C8-C18)amine hydrochlorides (FCM No 20) for their use in plastic materials and articles intended to come into contact with food

The European Commission asked EFSA to review whether the authorisation of N,N-bis(2-hydroxyethyl)alkyl(C8-C18)amine (FCM No 19) and N,N-bis(2-hydroxyethyl)alkyl(C8-C18)amine hydrochlorides (FCM No 20) is still in accordance with Regulation (EC) No 1935/2004, as provided for in Article 12(3). The FCM Panel concluded that some uses of the substance N,N-bis(2-hydroxyethyl)alkyl(C8-C18)amine (FCM No 19) are not in accordance with this Regulation, since the migration is likely to exceed the current SML(T) of 1.2 mg/kg food under certain conditions of use. Based on the provided data, the FCM Panel concluded that the FCM substance No 19, N,N-bis(2-hydroxyethyl)alkyl(C8-C18)amine, is not of safety concern for the consumer if (i) the substance is used at up to 0.1% w/w as polymer production aid and as processing aid to manufacture polyolefin materials and articles of thickness up to 1 mm that are intended for contact with all types of food except infant foods. This exception for infant foods and the restriction for maximum thickness do not apply to caps of bottles; (ii) the migration does not exceed 5 mg/kg food; (iii) the source of the alkyl group is either from hydrogenated vegetable oil or synthetic from ethylene oligomers with a high degree of linear structure and (iv) the impurities do not exceed 5% w/w. As they bear unsaturation, PFAEO-coco, PFAEO-oleyl, PFAEO-HT, PFAEO-T and PFAO-C18 do not fall within the scope of the FCM substance No 19. The information related to these substances was only considered supportive for FCM substance No 19. If they were intended to be used to manufacture FCMs, a proper application following the EFSA Guidance documents should be submitted. No uses of the FCM substance No 20, N,N-bis(2-hydroxyethyl)alkyl(C8-C18)amine hydrochlorides, were claimed and no information was provided to support that the current authorisation is in accordance with the Regulation (EC) No 1935/2004.

EFSA Panel on Food Additives and Flavourings (FAF), Castle L, Andreassen M, Aquilina G, Bastos ML, Boon P, Fallico B, FitzGerald R, Frutos Fernandez MJ, Grasl‐Kraupp B, Gundert‐Remy U, Gürtler R, Houdeau E, Kurek M, Louro H, Morales P, Passamonti S, Batke M, Bruzell E, Chipman J, Cheyns K, Crebelli R, Fortes C, Fürst P, Halldorsson T, LeBlanc J, Mirat M, Lindtner O, Mortensen A, Ntzani E, Shah R, Wallace H, Wright M, Barmaz S, Civitella C, Georgelova P, Lodi F, Mazzoli E, Rasinger J, Maria Rincon A, Tard A, Zakidou P, Younes M. Re-evaluation of saccharin and its sodium, potassium and calcium salts (E 954) as food additives

This opinion deals with the re-evaluation of saccharin and its sodium, potassium and calcium salts (E 954) as food additives. Saccharin is the chemically manufactured compound 1,2-benzisothiazol-3(2H)-one-1,1-dioxide. Along with its sodium (Na), potassium (K) and calcium (Ca) salts, they are authorised as sweeteners (E 954). E 954 can be produced by two manufacturing methods i.e. Remsen-Fahlberg and Maumee. No analytical data on potential impurities were provided for products manufactured with the Maumee process; therefore, the Panel could only evaluate saccharins (E 954) manufactured with the Remsen-Fahlberg process. The Panel concluded that the newly available studies do not raise a concern for genotoxicity of E 954 and the saccharins impurities associated with the Remsen-Fahlberg manufacturing process. For the potential impurities associated with the Maumee process, a concern for genotoxicity was identified. The data set evaluated consisted of animals and human studies. The Panel considered appropriate to set a numerical acceptable daily intake (ADI) and considered the decrease in body weight in animal studies as the relevant endpoint for the derivation of a reference point. An ADI of 9 mg/kg body weight (bw) per day, expressed as free imide, was derived for saccharins (E 954). This ADI replaces the ADI of 5 mg /kg bw per day (expressed as sodium saccharin, corresponding to 3.8 mg /kg bw per day saccharin as free imide) established by the Scientific Committee on Food. The Panel considered the refined brand-loyal exposure assessment scenario the most appropriate exposure scenario for the risk assessment. The Panel noted that the P95 exposure estimates for chronic exposure to saccharins (E 954) were below the ADI. The Panel recommended the European Commission to consider the revision of the EU specifications of saccharin and its sodium, potassium and calcium salts (E 954).

Computational modeling of air pollutants for aquatic risk: Prediction of ecological toxicity and exploring structural characteristics

Assessing the aquatic toxicity originating from air pollutants is essential in sustaining water resources and maintaining the ecosystem's safety. Quantitative structure-activity relationship (QSAR) models provide a computational tool for predicting pollutant toxicity, facilitating the identification/evaluation of the contaminants and identifying responsible structural fragments. One-vs-all (OvA) QSAR is a tailored approach to address multi-class QSAR problems. The study aims to determine five distinct levels of aquatic hazard categories for airborne pollutants using OvA-QSAR modeling containing 254 air contaminants. This QSAR analysis reveals the critical descriptors of air pollutants to target for molecular modification. Various factors, including the selection of relevant mechanistic descriptors, data quality, and outliers, determine the reliability of QSAR models. By employing feature selection and outlier identification approaches, the robustness and accuracy of our QSAR models were significantly increased, leading to more reliable predictions in chemical hazard assessment. The results revealed that models using the Random Forest algorithm performed the best based on the selected descriptors, with internal and external validation accuracy ranging from 71.90% to 97.53% and 76.47%-98.03%, respectively. This study indicated that the aquatic risk of air contaminants might be attributed predominantly to their sp3/sp2 carbon ratio, hydrogen-bond acceptor capability, hydrophilicity/lipophilicity, and van der Waals volumes. These structures can be critical in developing innovative strategies to mitigate or avoid the chemicals' harmful effects. Supporting air quality improvement, this study contributes to the rapid implementation of measures to protect aquatic ecosystems affected by air pollution.

Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning

Background

Chemicals may lead to acute liver injuries, posing a serious threat to human health. Achieving the precise safety profile of a compound is challenging due to the complex and expensive testing procedures. In silico approaches will aid in identifying the potential risk of drug candidates in the initial stage of drug development and thus mitigating the developmental cost.

Methods

In current studies, QSAR models were developed for hepatotoxicity predictions using the ensemble strategy to integrate machine learning (ML) and deep learning (DL) algorithms using various molecular features. A large dataset of 2588 chemicals and drugs was randomly divided into training (80%) and test (20%) sets, followed by the training of individual base models using diverse machine learning or deep learning based on three different kinds of descriptors and fingerprints. Feature selection approaches were employed to proceed with model optimizations based on the model performance. Hybrid ensemble approaches were further utilized to determine the method with the best performance.

Results

The voting ensemble classifier emerged as the optimal model, achieving an excellent prediction accuracy of 80.26%, AUC of 82.84%, and recall of over 93% followed by bagging and stacking ensemble classifiers method. The model was further verified by an external test set, internal 10-fold cross-validation, and rigorous benchmark training, exhibiting much better reliability than the published models.

Conclusion

The proposed ensemble model offers a dependable assessment with a good performance for the prediction regarding the risk of chemicals and drugs to induce liver damage.

EFSA Panel on Food Additives and Flavourings (FAF), Younes M, Aquilina G, Castle L, Degen G, Engel K, Fowler P, Frutos Fernandez MJ, Fürst P, Gürtler R, Gundert‐Remy U, Husøy T, Manco M, Mennes W, Moldeus P, Passamonti S, Shah R, Waalkens‐Berendsen I, Wright M, Boon P, Crebelli R, Di Domenico A, Filipic M, Mortensen A, Woutersen R, Henk Van Loveren H, Gagliardi G, Mazzoli E, Lodi F, Rasinger JD, Rincon AM, Tard A, Frutos Fernandez MJ. Re-evaluation of shellac (E 904) as a food additive and a new application on the extension of use of shellac (E 904) in dietary foods for special medical purposes

The present opinion deals with the re-evaluation of shellac (E 904) when used as a food additive and with the new application on the extension of use of shellac (E 904) in dietary foods for special medical purposes. The Panel derived an acceptable daily intake (ADI) of 4 mg/kg body weight (bw) per day for wax-free shellac (E 904) produced by physical decolouring, based on a NOAEL of 400 mg/kg bw per day and applying an uncertainty factor of 100. The Panel concluded that the ADI of 4 mg/kg bw per day should be considered temporary for wax-free shellac (E 904) produced by chemical bleaching, while new data are generated on the identity and levels of the organochlorine impurities in E 904. This ADI is not applicable for wax-containing shellac as a food additive. For several age groups, the ADI was exceeded at the 95th percentile in the non-brand-loyal exposure assessment scenario and maximum level exposure assessment scenario. Considering the low exceedance and the fact that both the exposure estimation and the toxicological evaluation of shellac were conservative, the panel concluded that the calculated exceedance of the ADI does not indicate a safety concern. The Panel recommended to the European Commission separating specifications for E 904 depending on the manufacturing process, chemical bleaching and physical decolouring, because they result in different impurities; revising the definition of the food additive to include a description of each manufacturing process; deleting information on wax-containing shellac from the EU specifications; revising the acid value for wax-free shellac produced by chemical bleaching; lowering the maximum limit for lead; to consider introducing limits for other toxic elements potentially present in shellac; including a maximum limit for chloroform and total inorganic chloride in the EU specification for shellac produced by chemical bleaching.

Next generation risk assessment for occupational chemical safety - A real world example with sodium-2-hydroxyethane sulfonate

Next Generation Risk Assessment (NGRA) is an exposure-led approach to safety assessment that uses New Approach Methodologies (NAMs). Application of NGRA has been largely restricted to assessments of consumer use of cosmetics and is not currently implemented in occupational safety assessments, e.g. under EU REACH. By contrast, a large proportion of regulatory worker safety assessments are underpinned by toxicological studies using experimental animals. Consequently, occupational safety assessment represents an area that would benefit from increasing application of NGRA to safety decision making. Here, a workflow for conducting NGRA under an occupational safety context was developed, which is illustrated with a case study chemical; sodium 2-hydroxyethane sulphonate (sodium isethionate or SI). Exposures were estimated using a standard occupational exposure model following a comprehensive life cycle assessment of SI and considering factory-specific data. Outputs of this model were then used to estimate internal exposures using a Physiologically Based Kinetic (PBK) model, which was constructed with SI specific Absorption, Distribution, Metabolism and Excretion (ADME) data. PBK modelling indicated a worst-case plasma maximum concentration (Cmax) of 0.8 μM across the SI life cycle. SI bioactivity was assessed in a battery of NAMs relevant to systemic, reproductive, and developmental toxicity; a cell stress panel, high throughput transcriptomics in three cell lines (HepG2, HepaRG and MCF-7 cells), pharmacological profiling and specific assays relating to developmental toxicity (Reprotracker and devTOX quickPredict). Points of Departure (PoDs) for SI ranged from 104 to 5044 µM. Cmax values obtained from PBK modelling of occupational exposures to SI were compared with PoDs from the bioactivity assays to derive Bioactivity Exposure Ratios (BERs) which demonstrated the safety for workers exposed to SI under current levels of factory specific risk management. In summary, the tiered and iterative workflow developed here represents an opportunity for integrating non animal approaches for a large subset of substances for which systemic worker safety assessment is required. Such an approach could be followed to ensure that animal testing is only conducted as a "last resort" e.g. under EU REACH.

Advances in Activity/Property Prediction from Chemical Structures

Recent technological advancement in AI modeling of molecular property databases has significantly expanded the opportunities for drug design and development. Quantitative structure-activity relationships (QSARs) are shown to provide more accurate predictions with regards to biological activity as well as toxicological assessment. By using a combination of in-silico models or by combining disparate structure-activity databases, researchers have been able to improve accuracy for a variety of drug discovery and analysis methods, generating viable compounds, which in certain cases, can be synthesized and further studied in vitro to find candidates for potential development. Additionally, the development of compounds of determined toxicology can be discontinued earlier, allowing alternative routes to be evaluated, preventing wasted time and resources. Although the progress that has been made is tremendous, expert review is still necessary for most in-silico generated predictions. Regardless, the scientific community continues to move ever closer to completely automated drug discovery and evaluation.

Development and application of consensus in silico models for advancing high-throughput toxicological predictions

Computational toxicology models have been successfully implemented to prioritize and screen chemicals. There are numerous in silico (quantitative) structure-activity relationship ([Q]SAR) models for the prediction of a range of human-relevant toxicological endpoints, but for a given endpoint and chemical, not all predictions are identical due to differences in their training sets, algorithms, and methodology. This poses an issue for high-throughput screening of a large chemical inventory as it necessitates several models to cover diverse chemistries but will then generate data conflicts. To address this challenge, we developed a consensus modeling strategy to combine predictions obtained from different existing in silico (Q)SAR models into a single predictive value while also expanding chemical space coverage. This study developed consensus models for nine toxicological endpoints relating to estrogen receptor (ER) and androgen receptor (AR) interactions (i.e., binding, agonism, and antagonism) and genotoxicity (i.e., bacterial mutation, in vitro chromosomal aberration, and in vivo micronucleus). Consensus models were created by combining different (Q)SAR models using various weighting schemes. As a multi-objective optimization problem, there is no single best consensus model, and therefore, Pareto fronts were determined for each endpoint to identify the consensus models that optimize the multiple-criterion decisions simultaneously. Accordingly, this work presents sets of solutions for each endpoint that contain the optimal combination, regardless of the trade-off, with the results demonstrating that the consensus models improved both the predictive power and chemical space coverage. These solutions were further analyzed to find trends between the best consensus models and their components. Here, we demonstrate the development of a flexible and adaptable approach for in silico consensus modeling and its application across nine toxicological endpoints related to ER activity, AR activity, and genotoxicity. These consensus models are developed to be integrated into a larger multi-tier NAM-based framework to prioritize chemicals for further investigation and support the transition to a non-animal approach to risk assessment in Canada.

Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

Quantification of pharmaceuticals in hospital effluent: Weighted ranking of environmental risk using a fuzzy hybrid multicriteria method

An analytical method for quantification of seventeen pharmaceuticals and one metabolite was validated and applied in the analysis of hospital effluent samples. Two different sampling strategies were used: seasonal sampling, with 7 samples collected bimonthly; and hourly sampling, with 12 samples collected during 12 h. Thus, the variability was both seasonal and within the same day. High variability was observed in the measured concentrations of the pharmaceuticals and the metabolite. The quantification method, performed using weighted linear regression model, demonstrated results of average concentrations in seasonal samples ranged between 0.19 μgL-1 (carbamazepine) and higher than 61.56 μgL-1 (acetaminophen), while the hourly samples showed average concentrations between 0.07 μgL-1 (diazepam) and higher than 54.91 μgL-1 (acetaminophen). It is described as higher because the maximum concentration of the calibration curve took into account the dilution factor provided by DLLME. The diurnal results showed a trend towards higher concentrations in the first and last hours of sampling. The risk quotient (RQ) was calculated using organisms from three different trophic levels, for all the analytes quantified in the samples. Additionally, in order to understand the level of importance of each RQ, an expert panel was established, with contributions from 23 specialists in the area. The results were analyzed using a hybrid decision-making approach based on a Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, in order to rank the compounds by environmental risk priority. The compounds of greatest concern were losartan, acetaminophen, 4-aminoantipyrine, sulfamethoxazole, and metoclopramide. Comparison of the environmental risk priority ranking with the potential human health risk was performed by applying the same multicriteria approach, with the prediction of endpoints using in silico (Q)SAR models. The results obtained suggested that sulfamethoxazole and acetaminophen were the most important analytes to be considered for monitoring.

Machine Learning-Based Prediction of Drug-Induced Hepatotoxicity: An OvA-QSTR Approach

Drug-induced hepatotoxicity, also known as drug-induced liver injury (DILI), is among the possible adverse effects of pharmacotherapy. This clinical condition is accepted as one of the factors leading to patient mortality and morbidity. The LiverTox database was built by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) to predict potential liver damage from medications and take appropriate precautions. The database has classified medicines into seven risk categories (A, B, C, D, E, E*, and X) to avoid medicine-induced liver toxicity. The hepatic damage risk decreases from group A to group E. This study did not include the E* and X classes because they contained unverified and unknown data groups. Our study aims to predict potential liver damage of new drug molecules without using experimental animals. We predict which of the LiverTox risk category drugs with unknown liver toxicity potential will fall into using our one-vs-all quantitative structure-toxicity relationship (OvA-QSTR) model. Our dataset, consisting of 678 organic drug molecules from different pharmacological classes, was collected from LiverTox. The OvA-QSTR models implemented by Bayesian Network (BayesNet) performed well based on the selected descriptors, with the precision-recall curve (PRC) areas ranging from 0.718 to 0.869. Our OvA-QSTR models provide a reliable premarketing risk evaluation of pharmaceutical-induced liver damage potential and offer predictions for different risk levels in DILI.

Per- and polyfluoroalkyl substances (PFASs) registered under REACH-What can we learn from the submitted data and how important will mobility be in PFASs hazard assessment?

The EU is planning to restrict the manufacture, placing on the market and use of per- and polyfluoroalkyl substances (PFASs) as a class. For such a broad regulatory approach, a lot of different data are required, including data on the hazardous properties of PFASs. Here, we analyze substances that fulfill the OECD definition of PFASs and that are registered under the regulation on Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) in the EU to obtain a better data basis for PFASs and to elucidate the range of PFASs on the market in the EU. As of September 2021, at least 531 PFASs had been registered under REACH. Our hazard assessment of the PFASs registered under REACH shows that the currently available data are not sufficient to identify those PFASs that are persistent, bioaccumulative and toxic (PBT) or very persistent and very bioaccumulative (vPvB). Using some basic assumptions - which are 1) PFASs or their metabolites do not mineralize, 2) neutral hydrophobic substances bioaccumulate unless they are metabolized and 3) all chemicals exhibit baseline toxicity, and effect concentrations cannot be above effect concentrations for baseline toxicity - shows that at least 17 of the 177 PFASs with full registration are PBT substances, 14 more than currently identified. Moreover, if mobility is considered as a hazard criterion, at least 19 additional substances will need to be considered hazardous. The regulation of persistent, mobile and toxic (PMT) and very persistent and very mobile (vPvM) substances would therefore also affect PFASs. However, many of the substances that have not been identified as PBT, vPvB, PMT or vPvM are either persistent and toxic, persistent and bioaccumulative or persistent and mobile. The planned PFASs restriction will therefore be important for a more effective regulation of these substances.

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.

MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS

To achieve water quality objectives of the zero pollution action plan in Europe, rapid methods are needed to identify the presence of toxic substances in complex water samples. However, only a small fraction of chemicals detected with nontarget high-resolution mass spectrometry can be identified, and fewer have ecotoxicological data available. We hypothesized that ecotoxicological data could be predicted for unknown molecular features in data-rich high-resolution mass spectrometry (HRMS) spectra, thereby circumventing time-consuming steps of molecular identification and rapidly flagging molecules of potentially high toxicity in complex samples. Here, we present MS2Tox, a machine learning method, to predict the toxicity of unidentified chemicals based on high-resolution accurate mass tandem mass spectra (MS²). The MS2Tox model for fish toxicity was trained and tested on 647 lethal concentration (LC₅₀) values from the CompTox database and validated for 219 chemicals and 420 MS² spectra from MassBank. The root mean square error (RMSE) of MS2Tox predictions was below 0.89 log-mM, while the experimental repeatability of LC₅₀ values in CompTox was 0.44 log-mM. MS2Tox allowed accurate prediction of fish LC₅₀ values for 22 chemicals detected in water samples, and empirical evidence suggested the right directionality for another 68 chemicals. Moreover, by incorporating structural information, e.g., the presence of carbonyl-benzene, amide moieties, or hydroxyl groups, MS2Tox outperforms baseline models that use only the exact mass or log K_OW.

Migration of styrene oligomers from food contact materials: in silico prediction of possible genotoxicity

Styrene oligomers (SO) are well-known side products formed during styrene polymerization. They consist mainly of dimers (SD) and trimers (ST) that have been shown to be still residual in polystyrene (PS) materials. In this study migration of SO from PS into sunflower oil at temperatures between 5 and 70 °C and contact times between 0.5 h and 10 days was investigated. In addition, the contents of SD and ST in the fatty foodstuffs créme fraiche and coffee cream, which are typically enwrapped in PS, were measured and the amounts detected (of up to 0.123 mg/kg food) were compared to literature data. From this comparison, it became evident, that the levels of SO migrating from PS packaging into real food call for a comprehensive risk assessment. As a first step towards this direction, possible genotoxicity has to be addressed. Due to technical and experimental limitations, however, the few existing in vitro tests available are unsuited to provide a clear picture. In order to reduce uncertainty of these in vitro tests, four different knowledge and statistics-based in silico tools were applied to such SO that are known to migrate into food. Except for SD4 all evaluated SD and ST showed no alert for genotoxicity. For SD4, either the predictions were inconclusive or the substance was assigned as being out of the chemical space (out of domain) of the respective in silico tool. Therefore, the absence of genotoxicity of SD4 requires additional experimental proof. Apart from SD4, in silico studies supported the limited in vitro data that indicated the absence of genotoxicity of SO. In conclusion, the overall migration of all SO together into food of up to 50 µg/kg does not raise any health concerns, given the currently available in silico and in vitro data.

Implementing in vitro bioactivity data to modernize priority setting of chemical inventories

Internationally, there are thousands of existing and newly introduced chemicals in commerce, highlighting the ongoing importance of innovative approaches to identify emerging chemicals of concern. For many chemicals, there is a paucity of hazard and exposure data. Thus, there is a crucial need for efficient and robust approaches to address data gaps and support risk-based prioritization. Several studies have demonstrated the utility of in vitro bioactivity data from the ToxCast program in deriving points of departure (PODs). ToxCast contains data for nearly 1,400 endpoints per chemical, and the bioactivity concentrations, indicative of potential adverse outcomes, can be converted to human-equivalent PODs using high-throughput toxicokinetics (HTTK) modeling. However, data gaps need to be addressed for broader application: the limited chemical space of HTTK and quantitative high-throughput screening data. Here we explore the applicability of in silico models to address these data needs. Specifically, we used ADMET predictor for HTTK predictions and a generalized read-across approach to predict ToxCast bioactivity potency. We applied these models to profile 5,801 chemicals on Canada’s Domestic Substances List (DSL). To evaluate the approach’s performance, bioactivity PODs were compared with in vivo results from the EPA Toxicity Values database for 1,042 DSL chemicals. Comparisons demonstrated that the bioac­tivity PODs, based on ToxCast data or read-across, were conservative for 95% of the chemicals. Comparing bioactivity PODs to human exposure estimates supports the identification of chemicals of potential interest for further work. The bioac­tivity workflow shows promise as a powerful screening tool to support effective triaging of chemical inventories.

Integrating publicly available information to screen potential candidates for chemical prioritization under the Toxic Substances Control Act: A proof of concept case study using genotoxicity and carcinogenicity

The Toxic Substances Control Act (TSCA) became law in the U.S. in 1976 and was amended in 2016. The amended law requires the U.S. EPA to perform risk-based evaluations of existing chemicals. Here, we developed a tiered approach to screen potential candidates based on their genotoxicity and carcinogenicity information to inform the selection of candidate chemicals for prioritization under TSCA. The approach was underpinned by a large database of carcinogenicity and genotoxicity information that had been compiled from various public sources. Carcinogenicity data included weight-of-evidence human carcinogenicity evaluations and animal cancer data. Genotoxicity data included bacterial gene mutation data from the Salmonella (Ames) and Escherichia coli WP2 assays and chromosomal mutation (clastogenicity) data. Additionally, Ames and clastogenicity outcomes were predicted using the alert schemes within the OECD QSAR Toolbox and the Toxicity Estimation Software Tool (TEST). The evaluation workflows for carcinogenicity and genotoxicity were developed along with associated scoring schemes to make an overall outcome determination. For this case study, two sets of chemicals, the TSCA Active Inventory non-confidential portion list available on the EPA CompTox Chemicals Dashboard (33,364 chemicals, 'TSCA Active List') and a representative proof-of-concept (POC) set of 238 chemicals were profiled through the two workflows to make determinations of carcinogenicity and genotoxicity potential. Of the 33,364 substances on the 'TSCA Active List', overall calls could be made for 20,371 substances. Here 46.67%% (9507) of substances were non-genotoxic, 0.5% (103) were scored as inconclusive, 43.93% (8949) were predicted genotoxic and 8.9% (1812) were genotoxic. Overall calls for genotoxicity could be made for 225 of the 238 POC chemicals. Of these, 40.44% (91) were non-genotoxic, 2.67% (6) were inconclusive, 6.22% (14) were predicted genotoxic, and 50.67% (114) genotoxic. The approach shows promise as a means to identify potential candidates for prioritization from a genotoxicity and carcinogenicity perspective.