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The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The exclusive use of in vivo testing is limited by the time and costs required for performing experiments and by the need to sacrifice a number of animals. (Quantitative) structure–activity relationships [(Q)SAR] proved a valid alternative to reduce and assist in vivo assays for assessing acute toxicological hazard. In the framework of a new international collaborative project, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods and the U.S. Environmental Protection Agency’s National Center for Computational Toxicology compiled a large database of rat acute oral LD50 data, with the aim of supporting the development of new computational models for predicting five regulatory relevant acute toxicity endpoints. In this article, a series of regression and classification computational models were developed by employing different statistical and knowledge-based methodologies. External validation was performed to demonstrate the real-life predictability of models. Integrated modeling was then applied to improve performance of single models. Statistical results confirmed the relevance of developed models in regulatory frameworks, and confirmed the effectiveness of integrated modeling. The best integrated strategies reached RMSEs lower than 0.50 and the best classification models reached balanced accuracies over 0.70 for multi-class and over 0.80 for binary endpoints. Computed predictions will be hosted on the EPA’s Chemistry Dashboard and made freely available to the scientific community.

Changes in gene expression can help reveal the mechanisms of disease processes and the mode of action for toxicities and adverse effects on cellular responses induced by exposures to chemicals, drugs and environment agents. The U.S. Tox21 Federal collaboration, which currently quantifies the biological effects of nearly 10,000 chemicals via quantitative high-throughput screening(qHTS) in in vitro model systems, is now making an effort to incorporate gene expression profiling into the existing battery of assays. Whole transcriptome analyses performed on large numbers of samples using microarrays or RNA-Seq is currently cost-prohibitive. Accordingly, the Tox21 Program is pursuing a high-throughput transcriptomics (HTT) method that focuses on the targeted detection of gene expression for a carefully selected subset of the transcriptome that potentially can reduce the cost by a factor of 10-fold, allowing for the analysis of larger numbers of samples. To identify the optimal transcriptome subset, genes were sought that are (1) representative of the highly diverse biological space, (2) capable of serving as a proxy for expression changes in unmeasured genes, and (3) sufficient to provide coverage of well described biological pathways. A hybrid method for gene selection is presented herein that combines data-driven and knowledge-driven concepts into one cohesive method. Our approach is modular, applicable to any species, and facilitates a robust, quantitative evaluation of performance. In particular, we were able to perform gene selection such that the resulting set of \"sentinel genes\" adequately represents all known canonical pathways from Molecular Signature Database (MSigDB v4.0) and can be used to infer expression changes for the remainder of the transcriptome. The resulting computational model allowed us to choose a purely data-driven subset of 1500 sentinel genes, referred to as the S1500 set, which was then augmented using a knowledge-driven selection of additional genes to create the final S1500+ gene set. Our results indicate that the sentinel genes selected can be used to accurately predict pathway perturbations and biological relationships for samples under study.

Low-cost, high-throughput in vitro bioassays have potential as alternatives to animal models for toxicity testing. However, incorporating in vitro bioassays into chemical toxicity evaluations such as read-across requires significant data curation and analysis based on knowledge of relevant toxicity mechanisms, lowering the enthusiasm of using the massive amount of unstructured public data. We aimed to develop a computational method to automatically extract useful bioassay data from a public repository (i.e., PubChem) and assess its ability to predict animal toxicity using a novel bioprofile-based read-across approach. A training database containing 7,385 compounds with diverse rat acute oral toxicity data was searched against PubChem to establish in vitro bioprofiles. Using a novel subspace clustering algorithm, bioassay groups that may inform on relevant toxicity mechanisms underlying acute oral toxicity were identified. These bioassays groups were used to predict animal acute oral toxicity using read-across through a cross-validation process. Finally, an external test set of over 600 new compounds was used to validate the resulting model predictivity. Several bioassay clusters showed high predictivity for acute oral toxicity (positive prediction rates range from 62-100%) through cross-validation. After incorporating individual clusters into an ensemble model, chemical toxicants in the external test set were evaluated for putative acute toxicity (positive prediction rate equal to 76%). Additionally, chemical fragment -in vitro-in vivo relationships were identified to illustrate new animal toxicity mechanisms. The in vitro bioassay data-driven profiling strategy developed in this study meets the urgent needs of computational toxicology in the current big data era and can be extended to develop predictive models for other complex toxicity end points. https://doi.org/10.1289/EHP3614.

The Adverse Outcome Pathway (AOP) framework is a pivotal tool for organizing mechanistic knowledge and linking it to adverse outcomes of regulatory significance. However, the integration of test method information, particularly New Approach Methods (NAMs), within the central repository for AOP knowledge, (the AOP-Wiki), has been suboptimal, limiting the framework’s utility for regulatory decision-making. The Methods2AOP collaboration, comprised of various international stakeholders, was established to address this gap and enhance the role of test methods within the AOP framework. This paper reviews their work emphasizing the importance of linking detailed test method information and conceptually proposes how it may be included in the AOP knowledgebase in alignment with existing assay documentation standards and governance frameworks. The Methods2AOP collaboration proposes using ontologies to standardize and structure information, thereby facilitating interoperability, enabling reusability, and establishing clear connections between test methods and Key Events (KEs). A conceptual model is presented to demonstrate qualitative similarities between concepts in key event components and structured methods information. The implementation of Methods2AOP recommendations would increase the clarity and transparency of method descriptions, which could support regulatory acceptance and a wider adoption of NAMs. The broad community of stakeholders impacted by this work stands to benefit from the Methods2AOP recommendations through enhanced regulatory decisions, increased visibility and scientific impact, new market opportunities, and the accelerated adoption of NAMs in regulatory affairs. In summary, the Methods2AOP collaboration presents a comprehensive effort to formally standardize the integration of test methods into the AOP framework, thereby fostering a more robust, and transparent system that aligns with the goals of the scientific and regulatory communities.

The Adverse Outcome Pathway (AOP) framework is a pivotal tool for organizing mechanistic knowledge and linking it to adverse outcomes of regulatory significance. However, the integration of test method information, particularly New Approach Methods (NAMs), within the central repository for AOP knowledge, (the AOP-Wiki), has been suboptimal, limiting the framework’s utility for regulatory decision-making. The Methods2AOP collaboration, comprised of various international stakeholders, was established to address this gap and enhance the role of test methods within the AOP framework. This paper reviews their work emphasizing the importance of linking detailed test method information and conceptually proposes how it may be included in the AOP knowledgebase. The Methods2AOP collaboration proposes using ontologies to standardize and structure information, thereby facilitating interoperability, enabling reusability, and establishing clear connections between test methods and Key Events (KEs). A conceptual model is presented to demonstrate qualitative similarities between concepts in key event components and structured methods information. The implementation of Methods2AOP recommendations would increase the clarity and transparency of method descriptions, which could support regulatory acceptance and a wider adoption of NAMs. The broad community of stakeholders impacted by this work stands to benefit from the Methods2AOP recommendations through enhanced regulatory decisions, increased visibility and scientific impact, new market opportunities, and the accelerated adoption of NAMs in regulatory affairs. In summary, the Methods2AOP collaboration presents a comprehensive effort to formally standardize the integration of test methods into the AOP framework, thereby fostering a more robust, and transparent system that aligns with the goals of the scientific and regulatory communities.

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. models built using existing data facilitate rapid acute toxicity predictions without using animals. The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 ( value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [ ( )], and nontoxic chemicals ( ). 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. 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 results. CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for 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.

The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The exclusive use of in vivo testing is limited by the time and costs required for performing experiments and by the need to sacrifice a number of animals. (Quantitative) structure-activity relationships [(Q)SAR] proved a valid alternative to reduce and assist in vivo assays for assessing acute toxicological hazard. In the framework of a new international collaborative project, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods and the U.S. Environmental Protection Agency's National Center for Computational Toxicology compiled a large database of rat acute oral LD50 data, with the aim of supporting the development of new computational models for predicting five regulatory relevant acute toxicity endpoints. In this article, a series of regression and classification computational models were developed by employing different statistical and knowledge-based methodologies. External validation was performed to demonstrate the real-life predictability of models. Integrated modeling was then applied to improve performance of single models. Statistical results confirmed the relevance of developed models in regulatory frameworks, and confirmed the effectiveness of integrated modeling. The best integrated strategies reached RMSEs lower than 0.50 and the best classification models reached balanced accuracies over 0.70 for multi-class and over 0.80 for binary endpoints. Computed predictions will be hosted on the EPA's Chemistry Dashboard and made freely available to the scientific community.