Next generation risk assessment (NGRA) aims to enable transparent, reproducible chemical safety assessments based on human-relevant, animal-free new approach methodologies (NAMs). The Alternative Safety Profiling Algorithm (ASPA) was developed within the ASPIS cluster to provide an algorithmic workflow that structures problem formulation, evidence integration, and decision-making across three main pillars – hazard, ADME (toxicokinetics), and exposure. A stakeholder workshop was organized to refine ASPA. Four breakout groups systematically reviewed corresponding workflow sections, identifying strengths, conceptual gaps, and opportunities for harmonization. Across groups, participants endorsed ASPA’s modular, technology-neutral nature and its focus on standardizing processes rather than prescribing specific test batteries. The hazard pillar discussions emphasized a sensitive, hypothesis-generating Tier 1, complemented by a specific, mechanistic Tier 2, capable of deriving points of departure (PoDs). ADME experts supported a physiologically based kinetic (PBK) modelling strategy, advancing from generic towards more complex models, using mechanistic information and experimental data. The exposure group proposed refinements for transparent, tiered exposure modelling, with emphasis on realistic worst-case scenarios and explicit uncertainty communication. Cross-pillar discussions highlighted the importance of feedback loops among all pillars, and the documentation of decision points to achieve consistency and defensi­bility. The workshop outcomes informed three parallel developments: (i) algorithmic refinement and re-design toward the next ASPA version, (ii) the creation of detailed guidance for each building block, and (iii) the establishment of practical case studies to demonstrate workflow implementation. This report already contains a first case study (developmental neurotoxicity assessment of desnitro-imidacloprid). These advances increase the operability, transparency, and regulatory readiness of ASPA.

BACKGROUND:Extraction of toxicological end points from primary sources is a central component of systematic reviews and human health risk assessments. To ensure optimal use of these data, consistent language should be used for end point descriptions. However, primary source language describing treatment-related end points can vary greatly, resulting in large labor efforts to manually standardize extractions before data are fit for use. OBJECTIVES:To minimize these labor efforts, we applied an augmented intelligence approach and developed automated tools to support standardization of extracted information via application of preexisting controlled vocabularies. METHODS:We created and applied a harmonized controlled vocabulary crosswalk, consisting of Unified Medical Language System (UMLS) codes, German Federal Institute for Risk Assessment (BfR) DevTox harmonized terms, and The Organization for Economic Co-operation and Development (OECD) end point vocabularies, to roughly 34,000 extractions from prenatal developmental toxicology studies conducted by the National Toxicology Program (NTP) and 6,400 extractions from European Chemicals Agency (ECHA) prenatal developmental toxicology studies, all recorded based on the original study report language. RESULTS:We automatically applied standardized controlled vocabulary terms to 75% of the NTP extracted end points and 57% of the ECHA extracted end points. Of all the standardized extracted end points, about half (51%) required manual review for potential extraneous matches or inaccuracies. Extracted end points that were not mapped to standardized terms tended to be too general or required human logic to find a good match. We estimate that this augmented intelligence approach saved >350 hours of manual effort and yielded valuable resources including a controlled vocabulary crosswalk, organized related terms lists, code for implementing an automated mapping workflow, and a computationally accessible dataset. DISCUSSION:Augmenting manual efforts with automation tools increased the efficiency of producing a findable, accessible, interoperable, and reusable (FAIR) dataset of regulatory guideline studies. This open-source approach can be readily applied to other legacy developmental toxicology datasets, and the code design is customizable for other study types. https://doi.org/10.1289/EHP13215.

Background Chemically induced skin sensitization, or allergic contact dermatitis, is a common occupational and public health issue. Regulatory authorities require an assessment of potential to cause skin sensitization for many chemical products. Defined approaches for skin sensitization (DASS) identify potential chemical skin sensitizers by integrating data from multiple non-animal tests based on human cells, molecular targets, and computational model predictions using standardized data interpretation procedures. While several DASS are internationally accepted by regulatory agencies, the data interpretation procedures vary in logical complexity, and manual application can be time-consuming or prone to error. Results We developed the DASS App, an open-source web application, to facilitate user application of three regulatory testing strategies for skin sensitization assessment: the Two-out-of-Three (2o3), the Integrated Testing Strategy (ITS), and the Key Event 3/1 Sequential Testing Strategy (KE 3/1 STS) without the need for software downloads or computational expertise. The application supports upload and analysis of user-provided data, includes steps to identify inconsistencies and formatting issues, and provides predictions in a downloadable format. Conclusion This open-access web-based implementation of internationally harmonized regulatory guidelines for an important public health endpoint is designed to support broad user uptake and consistent, reproducible application. The DASS App is freely accessible via https://ntp.niehs.nih.gov/go/952311 and all scripts are available on GitHub ( https://github.com/NIEHS/DASS ).

Over the past several decades, reducing, refining, and replacing animal testing (three R’s) has been a prominent goal in chemical toxicology.1 The STopTox (Systemic and Topical chemical Toxicity) platform was developed for this objective as an innovative in-silico alternative to conventional animal testing for acute systemic and topical toxicity testing.2 STopTox utilizes quantitative structure-activity relationship (QSAR) models to predict the toxicity of chemicals, providing a comprehensive, accessible, and user-friendly tool for hazard identification.2 STopTox models were rigorously validated during its initial development using extensive publicly available data sets, ensuring compliance with the Organisation for Economic Co-operation and Development (OECD) principles. These models boasted high internal accuracy and substantial external predictive power.2,3 Despite these promising results, continued validation with novel compounds is integral in establishing the robustness and reliability needed for STopTox to be used as a substitute for in vivo animal testing. In this research letter, we aim to evaluate the predictive performance of STopTox using independent data sets across the six major endpoints of acute toxicity: acute oral, dermal, and inhalation systemic toxicity, as well as skin sensitization, skin irritation/corrosion, and eye irritation/corrosion through external validation. The outcomes of this validation underscore the potential of STopTox to reliably predict toxicity, thereby supporting STopTox as a reliable regulatory decision-making tool that contributes to reducing animal testing in toxicological assessments.

Additional file 1. Defined approach R functions.

The worm Development and Activity Test (wDAT) measures C. elegans developmental milestone acquisition timing and stage-specific spontaneous locomotor activity (SLA). Previously, the wDAT identified developmental delays and SLA level changes in C. elegans with mammalian developmental toxicants arsenic, lead, and mercury. 5-fluorouracil (5FU), cyclophosphamide (CP), hydroxyurea (HU), and ribavirin (RV) are teratogens that also induce growth retardation in developing mammals. In at least some studies on each of these chemicals, fetal weight reductions were seen at mammalian exposures below those that had teratogenic effects, suggesting that screening for developmental delay in a small alternative whole-animal model could act as a general toxicity endpoint to identify chemicals for further testing for more specific adverse developmental outcomes. Consistent with mammalian developmental effects, 5FU, HU, and RV were associated with developmental delays with the wDAT. Exposures associated with developmental delay induced hypoactivity with 5FU and HU, but slight hyperactivity with RV. CP is a prodrug that requires bioactivation by cytochrome P450s for both therapeutic and toxic effects. CP tests as a false negative in several in vitro assays, and it was also a false negative with the wDAT. These results suggest that the wDAT has the potential to identify some developmental toxicants, and that a positive wDAT result with an unknown may warrant further testing in mammals. Further assessment with larger panels of positive and negative controls will help qualify the applicability and utility of this C. elegans wDAT assay within toxicity test batteries or weight of evidence approaches for developmental toxicity assessment.

Chemically induced skin sensitization is a common occupational and public health issue. Regulatory authorities require an assessment of potential to cause skin sensitization for many chemical products. Defined approaches for skin sensitization (DASS) can identify potential chemical skin sensitizers without using animal tests. While several DASS are internationally accepted by regulatory agencies, the data interpretation procedures vary in logical complexity, and manual application can be time-consuming or prone to error. We developed the DASS App, an open-source web application, to facilitate user application of these regulatory testing strategies without the need for software downloads or computational expertise. The web application is freely available at https://ntp.niehs.nih.gov/go/952311. All scripts are available on GitHub (https://github.com/NIEHS/DASS).

BACKGROUND:Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES:The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS:An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS:The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION:CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

4 David Pamies, Marcel Leist, Sandra Coecke, Gerard Bowe, Dave Allen, Gerhard 5 Gstraunthaler, Anna Bal-Price, Francesca Pistollato, Rob deVries, Thomas Hartung and 6 Glyn Stacey 7 Department of Biomedical Science, University of Lausanne, Lausanne, Vaud, Switzerland; Center for Alternatives to Animal 8 Testing (CAAT) Europe, University of Konstanz, Konstanz, Germany; In vitro Toxicology and Biomedicine, Dept inaugurated by the 9 Doerenkamp-Zbinden Foundation, University of Konstanz, Konstanz, Germany; European Commission Joint Research Centre 10 (JRC), Ispra, Italy; Integrated Laboratory Systems, LLC., Morrisville, NC, USA; Medical University Innsbruck, Department of 11 Physiology, Innsbruck, Austria; Evidence-based Toxicology Collaboration, Johns Hopkins Bloomberg School of Public Health, 12 Baltimore, MD, USA; SYRCLE, Department for Health Evidence, Radboud Institute for Health Sciences, Radboud UMC, Nijmegen, 13 The Netherlands; Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Bloomberg School of Public Health, 14 Baltimore, MD, USA; International Stem Cell Banking Initiative, Barley, Herts, UK; National Stem Cell Resource Centre, Institute 15 of Zoology, Chinese Academy of Sciences, Beijing, China; Innovation Academy for Stem Cell and Regeneration, Chinese Academy 16 of Sciences, Beijing, China 17 18 19 20 Correspondence: Thomas Hartung, MD PhD 21 Johns Hopkins Bloomberg School of Public Health 22 Department of Environmental Health & Engineering 23 Center for Alternatives to Animal Testing (CAAT) 24 615 N Wolfe St, Baltimore, MD 21205, USA 25 (thartun1@jhu.edu) 26 27 28 29

new compounds, and develop predictive models for response in humans. Major accomplishments include the deployment of a comprehensive robotic platform for rapid testing of chemicals in conjunction with the largest collection of environmental chemicals and drugs assembled to date, along with data analysis pipeline and multiple quality-control measures. Deposition into the public domain of the largest-ever dataset has enabled model-building through internal efforts and crowdsourcing. An overview of the Tox21 screening operations setup and accomplishments will be provided, followed by an update on recent efforts to develop models of enhanced biological relevance through stem cell and tissue bioprinting technology development.

The U.S. Environmental Protection Agency (EPA) requires acute dermal systemic toxicity testing for hazard classification and labeling of pesticides to protect human health and the environment during the handling and use of chemicals. This study considered whether acute oral LD50 data could be used to determine EPA acute dermal hazard classifications. Oral and dermal LD50 data were collected for 225 pesticide active ingredients. Two approaches were used to predict dermal hazard classifications. First, oral hazard categories based on oral LD50 were compared to dermal hazard categories based on dermal LD50. Concordance with the reference dermal hazard categories was 65% (146/225), overclassification was 31% (70/225), and underclassification was 4% (9/225). In the second approach, the oral LD50 was used directly to assign the dermal hazard category. Concordance with the reference dermal hazard categories was 43% (96/225), overclassification was 56% (126/225), and underclassification was 1% (3/225). For substances in EPA Category IV the predictivity was 100% (22/22) with either approach. These data suggest that if only acute oral toxicity data are used for predicting both oral and dermal hazards, the dermal acute toxicity of many pesticide actives could be overstated.

High-throughput screening (HTS) assays may provide an efficient way to identify endocrine-active chemicals. However, nominal in vitro assay concentrations of a chemical may not accurately reflect the blood or tissue levels that cause in vivo effects because in vitro assays do not fully account for chemical pharmacokinetics. In this in vitro-to-in vivo extrapolation (IVIVE) study, we used metabolic clearance and plasma protein binding data with population-based pharmacokinetic (PK) models to quantitatively compare in vitro and in vivo dosimetry for reference chemicals estradiol and bisphenol A, and 230 environmental chemicals that potentially interact with the estrogen receptor (ER) pathway. We first determined the point-of-departure (POD) values from an HTS ER transactivation assay, BG1Luc HTS, and then estimated the daily oral equivalent doses (OEDs) in rats and humans that would result in a steady-state in vivo blood concentration equivalent to the POD values. Where available, we compared the OEDs to the lowest effective doses (LEDs) in rat uterotrophic assays and human exposure values. For all the chemicals with in vivo data, OEDs estimated from POD values were lower than the LEDs in rat uterotrophic assays, suggesting that BG1Luc HTS assay may provide a more conservative hazard estimate for use in risk assessment. Further, human exposure values were generally lower than OEDs estimated from the BG1Luc HTS assay across all environmental chemicals with reported exposure information. In cases where the human OEDs are orders of magnitude lower than the estimated exposures, chemical-induced toxicity appears unlikely. Our modeling approach highlights the importance of PK considerations in ranking endocrine-active chemicals based on in vitro HTS assays.

Regulatory authorities require testing to identify substances with the potential to cause allergic contact dermatitis so that appropriate labeling alerts users to the hazard and precautions necessary to minimize exposure. To reduce or eliminate animal use in testing, integrated test strategies (ITS) that combine in silico and in vitro test methods have been proposed. Scientists at the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) and Procter and Gamble (P&G) are developing an opensource version of a previously published ITS for skin sensitization. The original ITS is based on a Bayesian network (BN ITS-2) using in silico and in vitro models that map to the OECD Adverse Outcome Pathway for skin sensitization. BN ITS-2 was developed using a commercial software package. To increase accessibility and algorithmic transparency, NICEATM and P&G developed open-source ITS-2 (OS ITS-2) with tools in R software for building and performing exact inference using a Bayesian network. R versions of widely used algorithms for supervised discretization and latent class learning were substituted for proprietary algorithms. The overall classification accuracies for the OS ITS-2 and the BN ITS-2 were the same, with three compounds misclassified by both methods. Two case studies of representative substances, chlorobenzene and 2-mercaptobenzothiazole, were evaluated using NICEATM’s skin sensitization database, and value of information was assessed for the in vitro assays and in silico inputs. The OS ITS-2 provides availability and transparency, and represents a major step in allowing the ITS to be reproduced and tested, which is essential for use in a regulatory framework. The model is available on the NTP website (http://ntp.niehs.nih.gov/go/its).