Data curation to support toxicity assessments using the Integrated Chemical Environment

Daniel, Amber; Choksi, Neepa; Abedini, Jaleh; Bell, Shannon; Ceger, Patricia; Cook, Bethany; Karmaus, Agnes L.; Rooney, John P.; To, Kimberly T; Allen, David; Kleinstreuer, Nicole

doi:10.3389/ftox.2022.987848

Cited by 16 publications

(9 citation statements)

References 9 publications

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“…The DASS App provides user-friendly, open access to validated non-animal testing strategies from international OECD guidelines and U.S. federal policies, advancing adoption across the scientific and regulatory communities and enabling consistent and reproducible computational data integration. Further development of the DASS App is ongoing to include evaluation of DA results against reference data from the Integrated Chemical Environment and facilitate physicochemical applicability domain analyses [20]. Efforts at the OECD to include assays analogous to the DPRA, KS, or h-CLAT, or other in silico methods, as alternative information sources within the DAs are ongoing.…”

Section: Discussionmentioning

confidence: 99%

Computational application of internationally harmonized defined approaches to skin sensitization: DASS App

To,

Strickland,

Reinke

et al. 2024

BMC Bioinformatics

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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).

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Section: Discussionmentioning

confidence: 99%

Computational application of internationally harmonized defined approaches to skin sensitization: DASS App

To,

Strickland,

Reinke

et al. 2024

BMC Bioinformatics

Self Cite

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“…When reflecting on the value of PIP, it is hard not to see the horizontal and democratic structure of our shared scientific enterprise as a detriment that leads to staggering inefficiencies. 30 In many ways, a "dictatorial" and hierarchical system in scientific data sharing would better systematize and organize all available information around a single standard, joining smallerscope efforts (such as PIP or PPDB, the Pesticide Properties Database) 41 with larger endeavors (e.g., the U.S. EPA's CompTox Chemicals Dashboard or The Integrated Chemical Environment developed by NICEATM) 47 into a single platform. That is not to say there is no value in PIP as a standalone tool, but its utility is mostly as part of a weight-ofevidence scheme along with other sources of information.…”

Section: ■ Methodsmentioning

confidence: 99%

Pesticide Indirect Photodegradation Database: A Data-Sharing Platform for Screening Existing and Discovering New Agrochemicals

Stickelman,

Clay,

Kostal

2023

ACS Chem. Health Saf.

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The ability to control and tune physicochemical properties that underscore chemical behavior in living systems and the environment is at the "heart" of green chemistry. This is especially true for chemical classes designed a priori to be biologically active, such as pesticides, where the chance of unintended adverse outcomes is high. We recently proposed a design-vectoring framework, leveraging validated computational models of ecotoxicity and indirect photodegradation as a useful, quasisystems-based tool for screening existing and designing new agrochemicals. Here, we describe the development of a database that integrates our models, which link structural and substructural features to process metrics, and corresponding predicated data for all agrochemicals with photodegradable cores on the U.S. Environmental Protection Agency's registry (785 compounds and over 18,000 pairwise interactions with chromophoric dissolved organic matter, CDOM). The database is searchable by structural and nonstructural identifiers (e.g., chemical class, oxidizable core, physicochemical and electronic properties, etc.) to aid in chemical selection, hazard, and alternative assessment. Crucially, it can be easily updated and augmented to aid in interactive datasharing across industry, government, and academia. The overarching goal of this project is to spur grander efforts in systems-based design of pesticides that would see this platform paired with target-based computational methods and incorporated into the discovery phase of new product development across industry sectors.

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“…The Integrated Chemical Environment (ICE: https://ice.ntp.niehs.nih.gov/ ) is an example of a suite of models, tools, and high-quality datasets that are designed to democratize access to AI-enabled computational toxicology resources across the scientific community (Abedini et al 2021 ; Daniel et al 2022 ). The ICE database is organized by toxicity endpoint and mechanism, and uses standardized terminology, units, and formatting to adhere to FAIR principles, and is programmatically accessible via a REST API.…”

Section: The Example Of the Integrated Chemical Environment (Ice) As ...mentioning

confidence: 99%

Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*

Kleinstreuer,

Hartung

2024

Arch Toxicol

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The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has the potential to transform chemical safety evaluation. Toxicology has evolved from an empirical science focused on observing apical outcomes of chemical exposure, to a data-rich field ripe for AI integration. The volume, variety and velocity of toxicological data from legacy studies, literature, high-throughput assays, sensor technologies and omics approaches create opportunities but also complexities that AI can help address. In particular, machine learning is well suited to handle and integrate large, heterogeneous datasets that are both structured and unstructured—a key challenge in modern toxicology. AI methods like deep neural networks, large language models, and natural language processing have successfully predicted toxicity endpoints, analyzed high-throughput data, extracted facts from literature, and generated synthetic data. Beyond automating data capture, analysis, and prediction, AI techniques show promise for accelerating quantitative risk assessment by providing probabilistic outputs to capture uncertainties. AI also enables explanation methods to unravel mechanisms and increase trust in modeled predictions. However, issues like model interpretability, data biases, and transparency currently limit regulatory endorsement of AI. Multidisciplinary collaboration is needed to ensure development of interpretable, robust, and human-centered AI systems. Rather than just automating human tasks at scale, transformative AI can catalyze innovation in how evidence is gathered, data are generated, hypotheses are formed and tested, and tasks are performed to usher new paradigms in chemical safety assessment. Used judiciously, AI has immense potential to advance toxicology into a more predictive, mechanism-based, and evidence-integrated scientific discipline to better safeguard human and environmental wellbeing across diverse populations.

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Data curation to support toxicity assessments using the Integrated Chemical Environment

Cited by 16 publications

References 9 publications

Computational application of internationally harmonized defined approaches to skin sensitization: DASS App

Computational application of internationally harmonized defined approaches to skin sensitization: DASS App

Pesticide Indirect Photodegradation Database: A Data-Sharing Platform for Screening Existing and Discovering New Agrochemicals

Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*

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