Chemical Safety Assessment Research Articles

Using the Super-learner to Predict the Chemical Acute Toxicity on Rats

With the rapid increase in the number of commercial chemicals, testing methods regarding on median lethal dose (LD50) relying animal experiments face challenges such as high costs and ethical concerns. Classical quantitative structure-activity relationship models relying on single algorithm always lack interpretability and precision, given the complexity of the mechanisms underlying acute toxicity. To address these issues, this study has developed a predictive framework using an ensemble learning model based on Super-learner. Particularly, we first obtained LD50 data for 9,843 compounds and constructed 16 meta models using 4 molecular descriptors and machine learning algorithms. The Super-learner model performed well, achieving R² values of 0.61 and 0.64 in five-fold cross-validation and test sets, respectively, with corresponding root mean square errors of 0.55 and 0.64, significantly outperforming the results of individual model. Additionally, we incorporated data filtering and applicability domain methods, which demonstrated that the Super-learner can mitigate the impact of dataset noise to some extent. The model achieved an R² of 0.76 within an applicability domain, ensuring prediction accuracy within the chemical space. Compared to previous studies, the model developed here using Super-learner generally achieved better performance across a larger applicability domain. Finally, we has launched an online tool (http://sltox.hhra.net), allowing users to quickly predict LD50 of compounds, greatly simplifying the chemical safety assessment process. This study not only provides an effective and cost-efficient method for predicting chemical toxicity but also offers technical support and data for risk assessments of chemicals.

A Novel Machine Learning Model and a Web Portal for Predicting the Human Skin Sensitization Effects of Chemical Agents

Skin sensitization is a significant concern for chemical safety assessments. Traditional animal assays often fail to predict human responses accurately, and ethical constraints limit the collection of human data, necessitating a need for reliable in silico models of skin sensitization prediction. This study introduces HuSSPred, an in silico tool based on the Human Predictive Patch Test (HPPT). HuSSPred aims to enhance the reliability of predicting human skin sensitization effects for chemical agents to support their regulatory assessment. We have curated an extensive HPPT database and performed chemical space analysis and grouping. Binary and multiclass QSAR models were developed with Bayesian hyperparameter optimization. Model performance was evaluated via five-fold cross-validation. We performed model validation with reference data from the Defined Approaches for Skin Sensitization (DASS) app. HuSSPred models demonstrated strong predictive performance with CCR ranging from 55 to 88%, sensitivity between 48 and 89%, and specificity between 37 and 92%. The positive predictive value (PPV) ranged from 84 to 97%, versus negative predictive value (NPV) from 22 to 65%, and coverage was between 75 and 93%. Our models exhibited comparable or improved performance compared to existing tools, and the external validation showed the high accuracy and sensitivity of the developed models. HuSSPred provides a reliable, open-access, and ethical alternative to traditional testing for skin sensitization. Its high accuracy and reasonable coverage make it a valuable resource for regulatory assessments, aligning with the 3Rs principles. The publicly accessible HuSSPred web tool offers a user-friendly interface for predicting skin sensitization based on chemical structure.

An automated computational data pipeline to rapidly acquire, score, and rank toxicological data for ecological hazard assessment.

Biological Evaluations support Endangered Species Act (ESA) consultation with the US Fish and Wildlife Service and National Marine Fisheries Service by federal action agencies, such as the USEPA, regarding impacts of federal activities on threatened or endangered species. However, they are often time-consuming and challenging to conduct. The identification of pollutant benchmarks or guidance to protect taxa for states and tribes when USEPA has not yet developed criteria recommendations is also of importance to ensure a streamlined approach to Clean Water Act program implementation. Due to substantial workloads, tight regulatory timelines, and the often-protracted length of ESA consultations, there is a need to streamline the development of biological evaluation toxicity assessments for determining the impact of chemical pollutants on ESA-listed species. Moreover, there is limited availability of species-specific toxicity data for many contaminants, further complicating the consultation process. New approach methodologies are being increasingly used in toxicology and chemical safety assessment to rapidly and cost-effectively provide data that can fill gaps in hazard and/or exposure characterization. Here, we present the development of an automated computational pipeline-RASRTox (Rapidly Acquire, Score, and Rank Toxicological data)-to rapidly extract and categorize ecological toxicity benchmark values from curated data sources (ECOTOX, ToxCast) and well-established quantitative structure-activity relationships (TEST, ECOSAR). As a proof of concept, points-of-departure (PODs) generated in RASRTox for 13 chemicals were compared against benchmark values derived using traditional methods-toxicity reference values (TRVs) and water quality criteria (WQC). The RASRTox PODs were generally within an order of magnitude of corresponding TRVs, though less concordant compared with WQC. The greatest utility of RASRTox, however, lies in its ability to quickly and systematically identify critical studies that may serve as a basis for screening value derivation by toxicologists as part of an ecological hazard assessment. As such, the strategy described in this case study can potentially be adapted for other risk assessment contexts and stakeholder needs. Integr Environ Assess Manag 2024;20:2203-2217. © 2024 Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

ToxProfiler: A novel human-based reporter assay for in vitro chemical safety assessment

In vitro chemical safety assessment often relies on simple and general cytotoxicity endpoint measurements and fails to adequately predict human toxicity. To improve the in vitro chemical safety assessment, it is important to understand the underlying mechanisms of toxicity. Here we introduce ToxProfiler, a novel human-based reporter assay that quantifies the chemical-induced stress responses at a single-cell level and reveals the toxicological mode-of-action (MoA) of novel drugs and chemicals. The assay accurately measures the activation of seven major cellular stress response pathways (oxidative stress, cell cycle stress, endoplasmic reticulum stress, ion stress, protein stress, autophagy and inflammation) that play a role in the adaptive responses prior to cellular toxicity. To assess the applicability of the assay in predicting the toxicity MoA of chemicals, we tested a set of 100 chemicals with well-known in vitro and in vivo toxicological profiles. Concentration response modeling and point-of-departure estimation for each reporter protein allowed for chemical potency ranking and revealed the primary toxicological MoA of chemicals. Furthermore, the assay could effectively group chemicals based on their shared toxicity signatures and link them to specific toxicological targets, e.g. mitochondrial toxicity and genotoxicity, and different human pathologies, including liver toxicity and cardiotoxicity. Overall, ToxProfiler is a quantitative in vitro reporter assay that can accurately provide insight into the toxicological MoA of compounds, thereby assisting in the future mechanism-based safety assessment of chemicals.

Chemical Safety Assessment of Pitch-Based Activated Carbon Pellets via Highly Toxic Gas Adsorption Properties

This study focuses on the preparation of activated carbon using a petroleum-residue-based pitch, as well as the HCl gas adsorption properties of the resulting activated carbon pellets relative to their specific surface area and pore structure. Activated carbon was prepared under various oxidation and chemical activation conditions using pitch with a softening point of 220 °C. The activated carbon was mixed with distilled water, an acrylic binder, and carboxymethyl cellulose in a specific ratio to form pellets. These pellets were then dried in an oven at 80 °C. Scanning electron microscopy, energy-dispersive X-ray spectroscopy, and Brunauer–Emmett–Teller analyses were performed to evaluate the surface structure and specific surface area of the finalized pellets. HCl gas was adsorbed at a concentration of 50 ppm while examining the adsorption characteristics relative to the pore structure and specific surface area.

Animal-free safety assessment of chemicals: an innovation system perspective.

This perspective paper, which is the result of a collaborative effort between toxicologists and scholars in innovation and transition studies, presents a heuristic framework based on innovation system literature for understanding and appraising mission achievement to animal-free chemical safety assessment using New Approach Methodologies (NAMs). While scientific and technical challenges in this area are relatively well known, the recent establishment of missions and roadmaps to accelerate the acceptance and effective use of NAMs for chemical safety assessment raises new questions about how we can grasp the systemic nature of all changes needed in this transition. This includes recognising broader societal, institutional, and regulatory shifts necessary for NAM acceptance and uptake. Our paper discusses how the innovation system approach offers insights into key processes and associated activities that include as well as transcend the technical and scientific realm, and can help to accelerate acceptance and uptake of NAMs. Based on these insights, we present a comprehensive framework that, next to scientific and technological developments, recognises the need for coordinated efforts in areas like education, training, funding, policy-making, and public engagement to promote the acceptance and uptake of NAMs. Our framework can be used to perform structural and functional analyses of the innovation system of NAMs and animal-free safety assessment and as such provides handholds to track progress and organise collective efforts of actors to make sure we are moving in the right direction.

A Twin Transition in Regulatory Toxicology-moving towards Chemicals 2.0 and phasing out animal testing.

The European regulatory framework on chemicals is at a crossroads. There are calls for the framework to be more effective, by better protecting people and the environment. There is also room for it to be more efficient and cost-effective, by harmonising assessment practices across sectors and avoiding the need for unnecessary testing. At the same time, there is a political commitment to phase out animal testing in chemical safety assessments. In this commentary, we argue that these needs are not at odds with other. On the contrary, the roadmap to phase out animal testing could also be the transition pathway to a more efficient, effective and sustainable regulatory ecosystem. Central to our proposal is a framework based on biological reasoning in which biological questions can be answered by a choice of methods, with non-animal methods progressively becoming the only choice. Within this framework, a tiered approach to testing and assessment allows for greater efficiency and effectiveness, while also introducing considerations of proportionality and cost-effectiveness. Testing strategies, and their component methods, should be developed in tandem and judged in terms of their outcomes, the protection levels they inform, rather than their ability to predict the outputs of animal tests.

Pioneering bioinformatics with agent-based modelling: an innovative protocol to accurately forecast skin or respiratory allergic reactions to chemical sensitizers.

The assessment of theallergenic potential of chemicals, crucial for ensuring public health safety, faces challenges in accuracy and raises ethical concerns due to reliance on animal testing. This paper presents a novel bioinformatic protocol designed to address the critical challenge of predicting immune responses to chemical sensitizers without the use of animal testing. The core innovation lies in the integration of advanced bioinformatics tools, including the Universal Immune System Simulator (UISS), which models detailed immune system dynamics. By leveraging data from structural predictions and docking simulations, our approach provides a more accurate and ethical method for chemical safety evaluations, especially in distinguishing between skin and respiratory sensitizers. Our approach integrates a comprehensive eight-step process, beginning with the meticulous collection of chemical and protein data from databases like PubChem and the Protein Data Bank. Following data acquisition, structural predictions are performed using cutting-edge tools such as AlphaFold to model proteins whose structures have not been previously elucidated. This structural information is then utilized in subsequent docking simulations, leveraging both ligand-protein and protein-protein interactions to predict how chemical compounds may trigger immune responses. The core novelty of our method lies in the application of UISS-an advanced agent-based modelling system that simulates detailed immune system dynamics. By inputting the results from earlier stages, including docking scores and potential epitope identifications, UISS meticulously forecasts the type and severity of immune responses, distinguishing between Th1-mediated skin and Th2-mediated respiratory allergic reactions. This ability to predict distinct immune pathways is a crucial advance over current methods, which often cannot differentiate between the sensitization mechanisms. To validate the accuracy and robustness of our approach, we applied the protocol to well-known sensitizers: 2,4-dinitrochlorobenzene for skin allergies and trimellitic anhydride for respiratory allergies. The results clearly demonstrate the protocol's ability to differentiate between these distinct immune responses, underscoring its potential for replacing traditional animal-based testing methods. The results not only support the potential of our method to replace animal testing in chemical safety assessments but also highlight its role in enhancing the understanding of chemical-induced immune reactions. Through this innovative integration of computational biology and immunological modelling, our protocol offers a transformative approach to toxicological evaluations, increasing the reliability of safety assessments.

FGTN: Fragment-based graph transformer network for predicting reproductive toxicity.

Reproductive toxicity is one of the important issues in chemical safety. Traditional laboratory testing methods are costly and time-consuming with raised ethical issues. Only a few in silico models have been reported to predict human reproductive toxicity, but none of them make full use of the topological information of compounds. In addition, most existing atom-based graph neural network methods focus on attributing model predictions to individual nodes or edges rather than chemically meaningful fragments or substructures. In current studies, we develop a novel fragment-based graph transformer network (FGTN) approach to generate the QSAR model of human reproductive toxicity by considering internal topological structure information of compounds. In the FGTN model, the compound is represented by a graph architecture using fragments to be nodes and bonds linking two fragments to be edges. A super molecule-level node is further proposed to connect all fragment nodes by undirected edges, obtaining global molecular features from fragment embeddings. The FGTN model achieved an accuracy (ACC) of 0.861 and an area under the receiver operating characteristic curve (AUC) value of 0.914 on nonredundant blind tests, outperforming traditional fingerprint-based machine learning models and atom-based GCN model. The FGTN model can attribute toxic predictions to fragments, generating specific structural alerts for the positive compound. Moreover, FGTN may also have the capability to distinguish various chemical isomers. We believe that FGTN can be used as a reliable and effective tool for human reproductive toxicity prediction in contribution to the advancement of chemical safety assessment.

Food for thought — Paving the way for a UK roadmap towards optimum consumer safety: Development, Endorsement and Regulatory acceptance of New Approach Methodologies (NAMs) in Chemical Risk Assessment and Beyond

Advances in biosciences, chemistry, technology, and computer sciences have resulted in the unparalleled development of candidate New Approach Methodologies over the last few years. Many of these are potentially invaluable in the safety assessment of chemicals, but very few have been adopted for regulatory decision making. There is an immediate opportunity to use NAMs in safety assessment where the vision is to be able to predict risk more rapidly, accurately, and efficiently to further assure consumer safety.In order to achieve this, the UK Food Standards Agency (FSA) and the Committee on Toxicity of Chemicals in Food, Consumer Products and the Environment (COT) have developed a roadmap towards acceptance and integration of these new approach methodologies into safety and risk assessments for regulatory decision making. The roadmap provides a UK blueprint for the transition of NAMs from the research laboratory to their use in regulatory decision making. This will require close collaboration across disciplines (chemists, toxicologists, informaticians, risk assessors and others), and across chemical sectors, to develop, verify and utilise appropriate models. Linking up internationally, and harmonization will be fundamental.

S18-03 Regulatory perspective for the opportunities afforded by quantitative systems toxicology in chemical safety assessment

S18-03 Regulatory perspective for the opportunities afforded by quantitative systems toxicology in chemical safety assessment

S27-01 Toxicokinetic and toxicodynamic uncertainties in chemical safety assessment

S27-01 Toxicokinetic and toxicodynamic uncertainties in chemical safety assessment

OS01-07 European Commission Roadmap towards phasing out animal testing for chemical safety assessments

OS01-07 European Commission Roadmap towards phasing out animal testing for chemical safety assessments

S18-02 Application of quantitative systems toxicology to support chemical safety assessment in the cosmetics industry

S18-02 Application of quantitative systems toxicology to support chemical safety assessment in the cosmetics industry

Use of the threshold of toxicological concern (TTC) approach as an alternative tool for regulatory purposes: A case study with an inert ingredient used in pesticide products

This study investigates the potential of the Threshold of Toxicological Concern (TTC) as an alternative to traditional animal testing in pesticide regulatory risk assessments. The TTC is a principle that establishes exposure threshold values for chemicals with certain structural features, below which there is no appreciable risk to human health.A case study was conducted with α-terpineol, an inert ingredient proposed to be used at low concentrations in pesticide products, to compare a conventional risk assessment using animal data with one using the TTC method. For the conventional risk assessment, animal data showed that there was no toxicity endpoint of concern, which resulted in a qualitative assessment and no risks of concern identified. For the risk assessment using the TTC method, a 5th percentile no-observed-effect level (NOEL) selected based on α-terpineol's Cramer classification was used as a point of departure (POD) for a quantitative risk assessment that resulted in no risks of concern identified. Therefore, the same conclusion was reached with both approaches and α-terpineol is considered safe for use in pesticide products at low concentrations. A comparative analysis was also performed to determine the applicability of the TTC method in calculating potential dietary risk from common pesticide use patterns for chemicals that fall within different Cramer classes. Results showed that use of the TTC method may be feasible for inert ingredient risk assessments when chemicals are used in a pesticide product at concentrations below 1%.This research underscores the TTC as a valuable and robust tool for assessing the potential hazards from inert ingredient use in pesticide formulations, considering factors such as chemical properties and the concentrations at which a chemical may be used in pesticide products. These findings contribute to the ongoing efforts by the United States Environmental Protection Agency (US EPA) to reduce animal testing in chemical safety assessments. The TTC method presents a viable alternative for risk evaluations of chemicals used at low concentrations, with anticipated low exposure, and with a predicted low toxicity potential.

Assessment of performance of the profilers provided in the OECD QSAR toolbox for category formation of chemicals

Growing restrictions and bans on animal testing for chemical safety assessment under different regulations have led to an increasing use of alternative methods. Read-across is one of the major approaches used for this purpose, which relies on the identification of toxicological hazards of a data-poor or untested (target) chemical from data on other already-tested (source) similar chemicals. This requires the target substance to be first assigned to a group or category of ‘similar’ chemicals. The ‘similarity’ may be in terms of structural features alone, or in combination with certain rules that are based on mechanistic and/or toxicological aspects. In this regard, the OECD QSAR toolbox—a major free-access in silico platform—is widely used to derive toxicity predictions for a range of (eco) toxicological endpoints. The Toolbox allows the user to identify a set of similar chemicals (analogues) by computational ‘profilers’ that incorporate different structural alerts, or a combination of structural alerts and physicochemical and/or toxicokinetic rules relevant to a specific toxicological endpoint. The overall aim of this study was to assess the performance of the in silico profilers provided in the OECD QSAR Toolbox for reliability for identifying chemical analogues for category formation in a number of high-quality databases on mutagenicity, carcinogenicity, and skin sensitisation. The study also aimed to identify the reasons for any limitations in the performance of the profilers, and propose ways to improve their overall accuracy. The results showed that whilst some structural alerts are fit-for-purpose as such within the acceptable limits, others need refinement or a consideration for their possible exclusion from the profiler. Such refinements are imperative for a reliable use of the profilers in read-across and grouping/categorisation for classification, labelling and risk assessment of chemicals.

가습기살균제 피해사건의 국가배상책임 인정 판결에 대한 고찰

The “Humidifier Disinfectant Incident,” where numerous citizens suffered serious health damages from humidifier disinfectants sold in Korea from 1994 to 2011, can be attributed to the irresponsible actions of companies that failed to ensure product safety and the negligence of the government and related organizations in properly managing and supervising these products. Victims and their families claimed damages against manufacturers and the state, but only the manufacturers' liability for damages was primarily recognized, with state compensation liability not being acknowledged. Additionally, the court decided on the liability of manufacturers and sales companies based on whether the primary ingredient of the humidifier disinfectant was of the P series (PHMG/PGH) or C series (CMIT/MIT). Around January 2012, the Seoul Central District Court, through the 2012Gahab4515 damages case, dismissed the claims of the victims and their families on January 19, 2015, not recognizing the state's fault. However, recently, on February 6, 2024, the Seoul High Court issued a significant ruling through case 2015Na2014486, marking the first time state compensation liability was recognized in this matter. This judgment emphasizes the responsibility of the state and relevant agencies in the safety assessment and supervision process of chemicals, reaffirming the importance of state measures to protect the health and safety of its citizens. Notably, this ruling provides critical insights on how the state should respond to ensure citizens' rights to a healthy and comfortable environment against harmful chemicals. Therefore, this study examines the significant legal grounds and principles through this landmark decision, affirming the state's legal responsibilities and duties in public health management and crisis response through chemical management, thereby contributing to reestablishing the role of the state in protecting the lives and safety of its citizens.

Next-generation risk assessment read-across case study: application of a 10-step framework to derive a safe concentration of daidzein in a body lotion.

Introduction: We performed an exposure-based Next Generation Risk Assessment case read-across study using New Approach Methodologies (NAMs) to determine the highest safe concentration of daidzein in a body lotion, based on its similarities with its structural analogue, genistein. Two assumptions were: (1) daidzein is a new chemical and its dietary intake omitted; (2) only in vitro data were used for daidzein, while in vitro and legacy in vivo data for genistein were considered. Methods: The 10-step tiered approach evaluating systemic toxicity included toxicokinetics NAMs: PBPK models and in vitro biokinetics measurements in cells used for toxicogenomics and toxicodynamic NAMs: pharmacology profiling (i.e., interaction with molecular targets), toxicogenomics and EATS assays (endocrine disruption endpoints). Whole body rat and human PBPK models were used to convert external doses of genistein to plasma concentrations and in vitro Points of Departure (PoD) to external doses. The PBPK human dermal module was refined using in vitro human skin metabolism and penetration data. Results: The most relevant endpoint for daidzein was from the ERα assay (Lowest Observed Effective Concentration was 100 ± 0.0nM), which was converted to an in vitro PoD of 33nM. After application of a safety factor of 3.3 for intra-individual variability, the safe concentration of daidzein was estimated to be 10nM. This was extrapolated to an external dose of 0.5μg/cm2 for a body lotion and face cream, equating to a concentration of 0.1%. Discussion: When in vitro PoD of 33nM for daidzein was converted to an external oral dose in rats, the value correlated with the in vivo NOAEL. This increased confidence that the rat oral PBPK model provided accurate estimates of internal and external exposure and that the in vitro PoD was relevant in the safety assessment of both chemicals. When plasma concentrations estimated from applications of 0.1% and 0.02% daidzein were used to calculate bioactivity exposure ratios, values were >1, indicating a good margin between exposure and concentrations causing adverse effects. In conclusion, this case study highlights the use of NAMs in a 10-step tiered workflow to conclude that the highest safe concentration of daidzein in a body lotion is 0.1%.

AdmetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties.

Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play a crucial role in drug discovery and chemical safety assessment. Built on the achievements of admetSAR and its successor, admetSAR2.0, this paper introduced the new version of the series, admetSAR3.0, as a comprehensive platform for chemical ADMET assessment, including search, prediction and optimization modules. In the search module, admetSAR3.0 hosted over 370000 high-quality experimental ADMET data for 104652 unique compounds, and supplemented chemical structure similarity search function to facilitate read-across. In the prediction module, we introduced comprehensive ADMET endpoints and two new sections for environmental and cosmetic risk assessments, empowering admetSAR3.0 to provide prediction for 119 endpoints, more than double numbers compared to the previous version. Furthermore, the advanced multi-task graph neural network framework offered robust and reliable support for ADMET prediction. In particular, a module named ADMETopt was added to automatically optimize the ADMET properties of query molecules through transformation rules or scaffold hopping. Finally, admetSAR3.0 provides user-friendly interfaces for multiple types of input data, such as SMILES string, chemical structure and batch molecule file, and supports various output types, including digital, chart displays and file downloads. In summary, admetSAR3.0 is anticipated to be a valuable and powerful tool in drug discovery and chemical safety assessment at http://lmmd.ecust.edu.cn/admetsar3/.

A Chemical Safety Assessment of Lyocell-Based Activated Carbon Fiber with a High Surface Area through the Evaluation of HCl Gas Adsorption and Electrochemical Properties

This study investigates lyocell-based activated carbon fibers (ACFs) for their suitability in adsorbing and electrochemically detecting toxic HCl gas. ACFs were prepared via steam activation, varying temperature (800–900 °C) and time (40–240 min) to assess their adsorption and sensing capabilities. The adjustment of activation temperature and reaction time aimed to regulate the uniformity of the pore structure and pore size of the active reaction area, as well as the number of reaction sites in the ACFs. Optimal ACFs were achieved at 900 °C for 50 min, exhibiting the highest specific surface area (1403 m2/g) and total pore volume (0.66 cm3/g). Longer reaction times resulted in pore formation and disorder, reducing mechanical strength. The ACFs prepared under optimal conditions demonstrated a rapid increase in resistance during sensor measurement, indicating a significant sensitivity to HCl gas. These findings suggest the potential of ACFs for efficient HCl gas adsorption (1626.20 mg/g) and highlight the importance of activation parameters in tailoring their properties for practical applications.