Since its establishment in 2002, the European Food Safety Authority (EFSA) has been providing independent scientific advice on risks associated with the food chain. This manuscript provides a description of EFSA’s chemical hazards database OpenFoodTox (OFT), future perspectives and activities. OFT aims at mapping all the hazard identification and characterisation data that have been published in outputs from EFSA throughout the years. To date, OFT contains data for more than 5700 chemical substances in the food/feed chain. In line with the One Substance-One Assessment approach as part of the Chemicals Strategy for Sustainability, EFSA aims to further improve data quality and interoperability of OFT with IUCLID 6 and the EU Common Data Platform on Chemical Safety. To enhance its usability as a supporting tool for risk assessment activities, OFT will be migrated to IUCLID 6. More data will be collected and added to OFT, including endpoints related to in vitro assays, non-critical effects and exposure values. Furthermore, new in silico models (e.g., tools for read-across and grouping) will be developed based on the data already present in OFT for chemicals and endpoints that have been tested, with the aim of estimating the corresponding properties for the untested chemicals and endpoints.

The present document provides the summary of the activities undertaken during the fourth of the framework contract (OC/EFSA/SCER/2018/01) to maintain, update and further develop the OpenFoodTox database (“OpenFodTox 2.0”). OpenFoodTox has been developed to map hazards data published in EFSA documents (opinions, statements, and conclusions) on risk assessment of food and feed. The repository holds summary data on identification of chemicals, document descriptors, hazard identification, and hazard characterisation. Within OpenFoodTox 2.0, the collection and entry of all hazard data assessed by EFSA scientific panels was performed according to the existing data model. During the fourth year, 141 new substances and 304 new hazard assessments (collected from new 188 EFSA documents) were added to the database. The OpenFoodTox 2.0 now includes more than 10800 assessments for over 5890 chemicals (each assessment potentially including multiple (eco-) toxicity endpoints and/or hazard/risk characterisation data) extracted from the screening of about 2435 documents (opinions, statements, conclusions) published by EFSA from 2020 to August 2022. To maintain the database, its data model was further expanded to incorporate new data types, including physicochemical properties (OHT 1 to 23-5) and toxicokinetic data (OHT 58). Overall, OpenFoodTox was enriched with physicochemical properties and/or ADME/PK/TK data (including quantitative values for key toxicokinetics parameters, e.g., Cmax, AUC, T-half and T-max) for 1,263 substances from 664 EFSA outputs. In total, 16,611 database records with experimentally-derived physicochemical properties data were added to the database for 969 substances (collected from 605 EFSA outputs) as well as 7,699 ADME/PK/TK database records for 852 substances (from 577 documents) and are available for download under https://zenodo.org/records/8120114 . Using VEGA predicted values of physiochemical properties and toxicity endpoints were added for 3980 compounds. The flexible summary wad tested on a data rich scenario using the data on conazoles. Finally, new QSAR models and a new read-across tools (VERA) are presented here.

Chemoinformatics has been successfully employed in safety assessment through various regulatory programs for which information from databases, as well as predictive methodologies including computational methods, are accepted. One example is the European Union Cosmetics Products Regulations, for which Cosmetics Europe (CE) research activities in non-animal methods have been managed by the Long Range Science Strategy (LRSS)program. The vision is to use mechanistic aspects of existing non-animal methods, as well as New Approach Methodologies (NAMs), to demonstrate that safety assessment of chemicals can be performed using a combination of in silico and in vitro data. To this end, ChemTunes center dot ToxGPS (R) has been adopted as the foundation of the safety assessment system and provides a platform to integrate data and knowledge, and enable toxicity predictions and safety assessments, relevant to cosmetics industries. The ChemTunes center dot ToxGPS (R) platform provides chemical, biological, and safety data based both on experiments and predictions, and an interactive/customizable read-across platform. The safety assessment workflow enables users to compile qualified data sources, quantify their reliabilities, and combine them using a weight of evidence approach based on decision theory. The power of this platform was demonstrated through a use case to perform a safety assessment for Perilla frutescens through the workflows of threshold of toxicological concern (TTC), in silico predictions (QSAR and structural rules) and quantitative read-across (qRAX) assessment for overall safety. The system digitalizes workflows within a knowledge hub, exploiting advanced in silico tools in this age of artificial intelligence. The further design of the system for next generation risk assessment (NGRA) is scientifically guided by interactions between the work group and international regulatory entities.

Read-across is an in silico method applied in chemical risk assessment for data-poor chemicals. The read-across outcomes for repeated-dose toxicity end points include the no-observed-adverse-effect level (NOAEL) and estimated uncertainty for a particular category of effects. We have previously developed a new paradigm for estimating NOAELs based on chemoinformatics analysis and experimental study qualities from selected analogues, not relying on quantitative structure-activity relationships (QSARs) or rule-based SAR systems, which are not well-suited to end points for which the underpinning data are weakly grounded in specific chemical-biological interactions. The central hypothesis of this approach is that similar compounds have similar toxicity profiles and, hence, similar NOAEL values. Analogue quality (AQ) quantifies the suitability of an analogue candidate for reading across to the target by considering similarity from structure, physicochemical, ADME (absorption, distribution, metabolism, excretion), and biological perspectives. Biological similarity is based on experimental data; assay vectors derived from aggregations of ToxCast/Tox21 data are used to derive machine learning (ML) hybrid rules that serve as biological fingerprints to capture target-analogue similarity relevant to specific effects of interest, for example, hormone receptors (ER/AR/THR). Once one or more analogues have been qualified for read-across, a decision theory approach is used to estimate confidence bounds for the NOAEL of the target. The confidence interval is dramatically narrowed when analogues are constrained to biologically related profiles. Although this read-across process works well for a single target with several analogues, it can become unmanageable when, for example, screening multiple targets (e.g., virtual screening library) or handling a parent compound having numerous metabolites. To this end, we have established a digitalized framework to enable the assessment of a large number of substances, while still allowing for human decisions for filtering and prioritization. This workflow was developed and validated through a use case of a large set of bisphenols and their metabolites.

The purpose of this study was to update the existing Cancer Potency Database (CPDB) in order to support the development of a dataset of compounds, with associated points of departure (PoDs), to enable a review and update of currently applied values for the Threshold of Toxicological Concern (TTC) for cancer endpoints. This update of the current CPDB, last reviewed in 2012, includes the addition of new data (44 compounds and 158 studies leading to additional 359 dose-response curves). Strict inclusion criteria were established and applied to select compounds and studies with relevant cancer potency data. PoDs were calculated from dose-response modeling, including the benchmark dose (BMD) and the lower 90% confidence limits (BMDL) at a specified benchmark response (BMR) of 10%. The updated full CPDB database resulted in a total of 421 chemicals which had dose-response data that could be used to calculate PoDs. This candidate dataset for cancer TTC is provided in a transparent and adaptable format for further analysis of TTC to derive cancer potency thresholds.

The term PFAS encompasses diverse per- and polyfluorinated alkyl (and increasingly aromatic) chemicals spanning industrial processes, commercial uses, environmental occurrence, and potential concerns. With increased chemical curation, currently exceeding 14,000 structures in the PFASSTRUCTV5 inventory on EPA's CompTox Chemicals Dashboard, has come increased motivation to profile, categorize, and analyze the PFAS structure space using modern cheminformatics approaches. Making use of the publicly available ToxPrint chemotypes and ChemoTyper application, we have developed a new PFAS-specific fingerprint set consisting of 129 TxP_PFAS chemotypes coded in CSRML, a chemical-based XML-query language. These are split into two groups, the first containing 56 mostly bond-type ToxPrints modified to incorporate attachment to either a CF group or F atom to enforce proximity to the fluorinated portion of the chemical. This focus resulted in a dramatic reduction in TxP_PFAS chemotype counts relative to the corresponding ToxPrint counts (averaging 54%). The remaining TxP_PFAS chemotypes consist of various lengths and types of fluorinated chains, rings, and bonding patterns covering indications of branching, alternate halogenation, and fluorotelomers. Both groups of chemotypes are well represented across the PFASSTRUCT inventory. Using the ChemoTyper application, we show how the TxP_PFAS chemotypes can be visualized, filtered, and used to profile the PFASSTRUCT inventory, as well as to construct chemically intuitive, structure-based PFAS categories. Lastly, we used a selection of expert-based PFAS categories from the OECD Global PFAS list to evaluate a small set of analogous structure-based TxP_PFAS categories. TxP_PFAS chemotypes were able to recapitulate the expert-based PFAS category concepts based on clearly defined structure rules that can be computationally implemented and reproducibly applied to process large PFAS inventories without need to consult an expert. The TxP_PFAS chemotypes have the potential to support computational modeling, harmonize PFAS structure-based categories, facilitate communication, and allow for more efficient and chemically informed exploration of PFAS chemicals moving forward.

The Threshold of Toxicological Concern (TTC) is an exposure threshold below which there is no appreciable risk to human health. There are two main approaches: TTC values based on cancer potency data from which a one in a million excess lifetime risk is estimated and TTC values based on non-cancer effects. For the latter approach, a distribution is typically fitted to the No Observed Adverse Effect Levels (NOAELs) from repeat dose toxicity studies from which a 5th percentile value is taken and adjusted using an uncertainty factor (usually this is 100). Established TTC values are those based on oral chronic studies that were first developed by Munro et al. (1996) who subcategorised chemicals into one of three Cramer structural classes (Cramer et al., 1978). Kroes et al. (2004) presented a tiered TTC approach that established several human exposure thresholds spanning four orders of magnitude where the lowest TTC was for substances presenting structural alerts for genotoxicity (0.15 μg/d), to the next tier for organophosphates/carbamates (18 μg/d) and the remaining higher TTC values representing the same three Cramer classes originally derived by Munro et al. (1996). The World Health Organization (WHO) and the European Food Safety Authority (EFSA) (European Food Safety Authority and World Health Organization, 2016; EFSA et al., 2019) have determined that the TTC approach is a sound and fit-for-purpose risk assessment tool, with a number of caveats, in cases where chemical-specific repeat dose toxicity data are not available. More recent work has explored extending the dataset underpinning the original Cramer TTC values to demonstrate its protection and robustness (Yang et al., 2017). The work by Yang et al. (2017) was undertaken under the auspices of the EU SEURAT-1 project COSMOS whereupon a COSMOS TTC dataset was developed. This comprised 552 substances for which the toxicity data and chemical structural identifiers were carefully curated. This federated dataset was then used to derive new TTC values for two of the Cramer classes (I and III). The derived values (46 and 2.3 μg/kg bw/d respectively) have since been adopted as the current recommendations by the SCCS in relation to cosmetic substances (SCCS, 2021). In addition, a total of 476 chemicals from the RIFM (Research Institute for Fragrance Materials) dataset were added to the COSMOS/Munro TTC database for analysis, in particular bolstering Cramer class II chemicals from 40 chemicals to 111 chemicals. The derived TTC values of 49, 12.7, and 2.9 μg/kg bw/day for Cramer classes I, II or III confirm the adequacy ofMunro TTC values and further support the use of TTC for safety assessment of fragrance materials (Patel et al., 2020). Thus, these above results show that cosmetic ingredients are sufficiently represented structural classes in the current TTC dataset that had been originally developed for food. The TTC concept is scientific and convincing for it to be applied in cosmetic safety assessment. In Edited and reviewed by: Andrea Richarz, European Chemicals Agency, Finland

Threshold of toxicological concern (TTC) values are frequently used for compounds occurring at low concentrations in feed and food or as impurities in drugs. This workshop report addresses the emerging alternatives for deriving TTC values for DNA reactive carcinogens and evaluating the acceptability of the Cramer Class TTC values to be adequately protective for non-DNA reactive carcinogens.

A dental Implant is a prosthetic device made of alloplastic materials implanted into the bone to provide retention and support for a dental prosthesis. Sirtuin1 (SIRT1) molecule, a nicotinamide adenine dinucleotide (NAD)-dependent histone deacetylase, regulates a variety of physiological and pathological processes, including oxidative stress, metabolism, cell proliferation, cell differentiation, inflammatory, and apoptosis. We explored whether the expression of SIRT1 correlates in patients receiving implants with peri-implant mucositis (PIM) and peri-implantitis (PI) in comparison to patients with healthy peri-implant area (PIH).A number of 198 patients with dentition defects were enrolled in the study after their implants were functional for at least 6 months. All 198 subjects were divided into 3 groups: 1) control patients with PIH healthy implants; 2) patients with PIM mucositis; and 3) patients with PI implantitis. To distinguish these three groups, peri-implant crevicular fluid (PICF) was collected by inserting a sterile paper strip into the gap around the implant and the levels of SIRT1 and cytokines were measured by the enzyme linked immunosorbent assay (ELISA). Demographic and clinical data included age, sex, Body Mass Index (BMI), probing depth (PD), plaque index (PLI), bleeding on probing (BOP), oral health impact profile (OHIP-14), history of periodontitis and the use time of implants.The PD, PLI, OHIP-14 evaluation scores in patients with periodontitis of PIM mucositis and PI implantitis were all significantly higher than in patients with PIH healthy implants. Overall, the SIRT1 levels in PICF of the PIM and PI patients were significantly lower than of the PIH patients. In comparison with PIM patients, SIRT1 levels of the PI patients were remarkably lower than the PIH patients. Pearson's analysis showed that SIRT1 levels were negatively correlated with levels of interleukin (IL)-6, C-reactive protein (CRP) and IL-1β in patients with PIM and PI. We suggest that SIRT1 levels could serve as a potential diagnostic biomarker of PI or PIM. The PICF levels of SIRT1, CRP, IL-6, IL-1β and the history of periodontitis were the risk factors for patients with peri-implant inflammatory process.The measurement of SIRT1 expression in PICF may serve as a biomarker for the ongoing inflammatory process in patients with dental implants. The low SIRT1 levels correlated with PI implantitis and PIM mucositis as well as the elevated levels of pro-inflammatory cytokines (CRP, IL-6 and IL-1β).

This work investigates the aerosols emitted during combustion of aircraft and naval structural composite materials (epoxy resin/carbon fibers and vinyl ester/glass fibers and carbon nanotubes). Combustion tests were performed at lab-scale using a modified cone calorimeter. The aerosols emitted have been characterized using various metrological devices devoted to the analysis of aerosols. The influence of the nature of polymer matrices, the incorporation of fibers and carbon nanotubes as well as glass reinforcements on the number concentration and the size distribution of airborne particles produced, was studied in the 5 nm–10 μm range. Incorporation of carbon fibers into epoxy resin significantly reduced the total particle number concentration. In addition, the interlaced orientation of carbon fibers limited the particles production compared to the composites with unidirectional one.The carbon nanotubes loading in vinyl ester resin composites influenced the total particles production during the flaming combustion with changes during kinetics emission. Predominant populations of airborne particles generated during combustion of all tested composites were characterized by a PN50 following by PN100–500.

In silico tools and resources are now used commonly in toxicology and to support the "Next Generation Risk Assessment" (NGRA) of cosmetics ingredients or materials. This review provides an overview of the approaches that are applied to assess the exposure and hazard of a cosmetic ingredient. For both hazard and exposure, databases of existing information are used routinely. In addition, for exposure, in silico approaches include the use of rules of thumb for systemic bioavailability as well as physiologically-based kinetics (PBK) and multi-scale models for estimating internal exposure at the organ or tissue level. (Internal) Thresholds of Toxicological Concern are applicable for the safety assessment of ingredients at low concentrations. The use of structural rules, (Quantitative) Structure-Activity Relationships ((Q)SARs) and read-across are the most typically applied modelling approaches to predict hazard. Data from exposure and hazard assessment are increasingly being brought together in NGRA to provide an overall assessment of the safety of a cosmetic ingredient. All in silico approaches are reviewed in terms of their maturity and robustness for use.

The 3Rs concept, calling for replacement, reduction and refinement of animal experimentation, is receiving increasing attention around the world, and has found its way to legislation, in particular in the European Union. This is aligned by continuing high-level efforts of the European Commission to support development and implementation of 3Rs methods. In this respect, the European project called “ONTOX: ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment” was recently initiated with the goal to provide a functional and sustainable solution for advancing human risk assessment of chemicals without the use of animals in line with the principles of 21st century toxicity testing and next generation risk assessment. ONTOX will deliver a generic strategy to create new approach methodologies (NAMs) in order to predict systemic repeated dose toxicity effects that, upon combination with tailored exposure assessment, will enable human risk assessment. For proof-of-concept purposes, focus is put on NAMs addressing adversities in the liver, kidneys and developing brain induced by a variety of chemicals. The NAMs each consist of a computational system based on artificial intelligence and are fed by biological, toxicological, chemical and kinetic data. Data are consecutively integrated in physiological maps, quantitative adverse outcome pathway networks and ontology frameworks. Supported by artificial intelligence, data gaps are identified and are filled by targeted in vitro and in silico testing. ONTOX is anticipated to have a deep and long-lasting impact at many levels, in particular by consolidating Europe’s world-leading position regarding the development, exploitation, regulation and application of animal-free methods for human risk assessment of chemicals.

Since its creation in 2002, the European Food Safety Authority (EFSA) has produced risk assessments for over 5000 substances in >2000 Scientific Opinions, Statements and Conclusions through the work of its Scientific Panels, Units and Scientific Committee. OpenFoodTox is an open source toxicological database, available both for download and data visualisation which provides data for all substances evaluated by EFSA including substance characterisation, links to EFSA's outputs, applicable legislations regulations, and a summary of hazard identification and hazard characterisation data for human health, animal health and ecological assessments. The database has been structured using OECD harmonised templates for reporting chemical test summaries (OHTs) to facilitate data sharing with stakeholders with an interest in chemical risk assessment, such as sister agencies, international scientific advisory bodies, and others. This manuscript provides a description of OpenFoodTox including data model, content and tools to download and search the database. Examples of applications of OpenFoodTox in chemical risk assessment are discussed including new quantitative structure-activity relationship (QSAR) models, integration into tools (OECD QSAR Toolbox and AMBIT-2.0), assessment of environmental footprints and testing of threshold of toxicological concern (TTC) values for food related compounds. Finally, future developments for OpenFoodTox 2.0 include the integration of new properties, such as physico-chemical properties, exposure data, toxicokinetic information; and the future integration within in silico modelling platforms such as QSAR models and physiologically-based kinetic models. Such structured in vivo, in vitro and in silico hazard data provide different lines of evidence which can be assembled, weighed and integrated using harmonised Weight of Evidence approaches to support the use of New Approach Methodologies (NAMs) in chemical risk assessment and the reduction of animal testing.

California's Office of Environmental Health Hazard Assessment was tasked with conducting risk assessments for United States Food and Drug Administration-approved food dyes relative to neurobehavioral concerns. The purpose of this assessment was to evaluate the evidence for neurodevelopment effects based on three streams of evidence: 1) studies identified by OEHHA for consideration in a quantitative risk assessment; 2) studies relevant to understanding mechanisms of neurobehavioral effects; 3) an in silico assessment of the bioavailability of USFDA-approved food dyes. The results indicate a lack of adequate or consistent evidence of neurological effects, supported by a lack of bioavailability and brain penetration predicted by the in silico assessment. Further, the mechanistic evidence supports a lack of activity from in vitro neurotransmitter assays, and a lack of evidence to support molecular initiating events or key events in adverse outcome pathways associated with neurodevelopmental effects, supporting a lack of biological plausibility for neurobehavioral effects following food exposures to colors. These conclusions are consistent with other authoritative bodies, such as JECFA and EFSA, that have determined (i) other effects are more appropriate for estimating acceptable daily intakes and (ii) evidence from the neurobehavioral studies lack the strength to be relied upon for quantitative risk assessment.

The Threshold of Toxicological Concern (TTC) concept can be applied to organic compounds with the known chemical structure to derive a threshold for exposure, below which a toxic effect on human health by the compound is not expected. The TTC concept distinguishes between carcinogens that may act as genotoxic and non-genotoxic compounds. A positive prediction of a genotoxic mode of action, either by structural alerts or experimental data, leads to the application of the threshold value for genotoxic compounds. Non-genotoxic substances are assigned to the TTC value of their respective Cramer class, even though it is recognized that they could test positive in a rodent cancer bioassay. This study investigated the applicability of the Cramer classes specifically to provide adequate protection for non-genotoxic carcinogens. For this purpose, benchmark dose levels based on tumor incidence were compared with no observed effect levels (NOELs) derived from non-, pre- or neoplastic lesions. One key aspect was the categorization of compounds as non-genotoxic carcinogens. The recently finished CEFIC LRI project B18 classified the carcinogens of the Carcinogenicity Potency DataBase (CPDB) as either non-genotoxic or genotoxic compounds based on experimental or in silico data. A detailed consistency check resulted in a dataset of 137 non-genotoxic organic compounds. For these 137 compounds, NOEL values were derived from high quality animal studies with oral exposure and chronic duration using well-known repositories, such as RepDose, ToxRef, and COSMOS DB. Further, an effective tumor dose (ETD10) was calculated and compared with the lower confidence limit on benchmark dose levels (BMDL10) derived by model averaging. Comparative analysis of NOEL/EDT10/BMDL10 values showed that potentially bioaccumulative compounds in humans, as well as steroids, which both belong to the exclusion categories, occur predominantly in the region of the fifth percentiles of the distributions. Excluding these 25 compounds resulted in significantly higher but comparable fifth percentile chronic NOEL and BMDL10 values, while the fifth percentile EDT10 value was slightly higher but not statistically significant. The comparison of the obtained distributions of NOELs with the existing Cramer classes and their derived TTC values supports the application of Cramer class thresholds to all non-genotoxic compounds, such as non-genotoxic carcinogens.

In cases where chemical-specific toxicity data are absent or limited, the threshold of toxicological concern (TTC) offers an alternative to assess human exposure below which “there would be no appreciable risk to human health.” The application of TTC to non-cancer systemic endpoints has been pursued for decades using a chemical classification and Point of Departure (POD). This study presents a new POD dataset of oral subacute/subchronic toxicity studies in rats for 656 industrial chemicals retrieved from the Hazard Evaluation Support System (HESS) Integrated Platform, which contains hundreds of reliable repeated-dose toxicity test data of industrial chemicals under the Chemical Substances of Control Law in Japan. The HESS TTC dataset was found to have less duplication with substances in other reported TTC datasets. Each chemical was classified into a Cramer Class, with 68, 3, and 29% of these 656 chemicals distributed in Classes III, II, and I, respectively. For each Cramer Class, a provisional Tolerable Daily Intake (TDI) was derived from the 5th percentile of the lognormal distribution of PODs. The TDIs were 1.9 and 30 μg/kg bw/day for Classes III and I, respectively. The TDI for Cramer Class II could not be determined due to insufficient sample size. This work complements previous studies of the TTC approach and increases the confidence of the thresholds for non-cancer endpoints by including unique chemical structures. This new TTC dataset is publicly available and can be merged with existing databases to improve the TTC approach.