Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay

Jia, Xuelian; Xu, Wen; Russo, Daniel P.; Aleksunes, Lauren M.; Zhu, Hao

doi:10.1016/j.jhazmat.2022.129193

Cited by 23 publications

(18 citation statements)

References 89 publications

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“…Golbraikh et al , indicated that the CCR of reliable QSAR models should be at least 0.65. Previous studies have shown that the same or similar CCR value thresholds are acceptable for reliable QSAR models. − Therefore, a CCR value of 0.65 was used as the cutoff value to select the QSAR models in this study.…”

Section: Resultsmentioning

confidence: 99%

Data-Driven Quantitative Structure–Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure

Chung

Russo

Wang

et al. 2023

Environ. Sci. Technol.

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Traditional methodologies for assessing chemical toxicity are expensive and time-consuming. Computational modeling approaches have emerged as low-cost alternatives, especially those used to develop quantitative structure–activity relationship (QSAR) models. However, conventional QSAR models have limited training data, leading to low predictivity for new compounds. We developed a data-driven modeling approach for constructing carcinogenicity-related models and used these models to identify potential new human carcinogens. To this goal, we used a probe carcinogen dataset from the US Environmental Protection Agency’s Integrated Risk Information System (IRIS) to identify relevant PubChem bioassays. Responses of 25 PubChem assays were significantly relevant to carcinogenicity. Eight assays inferred carcinogenicity predictivity and were selected for QSAR model training. Using 5 machine learning algorithms and 3 types of chemical fingerprints, 15 QSAR models were developed for each PubChem assay dataset. These models showed acceptable predictivity during 5-fold cross-validation (average CCR = 0.71). Using our QSAR models, we can correctly predict and rank 342 IRIS compounds’ carcinogenic potentials (PPV = 0.72). The models predicted potential new carcinogens, which were validated by a literature search. This study portends an automated technique that can be applied to prioritize potential toxicants using validated QSAR models based on extensive training sets from public data resources.

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Section: Resultsmentioning

confidence: 99%

Data-Driven Quantitative Structure–Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure

Chung

Russo

Wang

et al. 2023

Environ. Sci. Technol.

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“…An individual AOP may focus on a specific pathway, where mechanistically linked events proceed to a toxicity effect in a unidirectional and linear way. , In a mechanistic model for hepatotoxicity predictions, the toxicity potential of a chemical is assessed on the basis of whether it (1) possesses certain structural alerts and (2) activates the antioxidant response element (ARE) pathway, an oxidative stress-related key event . This is a decision rule-based model, where chemicals that satisfy both two conditions are predicted as toxic, and chemicals that dissatisfy both two conditions are predicted as nontoxic.…”

Section: Toxicological Knowledge In Guiding the Design Of Iml Modelsmentioning

confidence: 99%

“…163,171 hepatotoxicity predictions, the toxicity potential of a chemical is assessed on the basis of whether it (1) possesses certain structural alerts and (2) activates the antioxidant response element (ARE) pathway, an oxidative stress-related key event. 172 This is a decision rule-based model, where chemicals that satisfy both two conditions are predicted as toxic, and chemicals that dissatisfy both two conditions are predicted as nontoxic. Chemicals that possess the identified structural alerts are suspected to be metabolized into reactive intermediates (i.e., MIE), which can trigger oxidative stress in the liver, thereby forming a plausible pathway that leads to hepatotoxicity.…”

Section: Toxicological Knowledge In Guiding the Design Of Iml Modelsmentioning

confidence: 99%

Advancing Computational Toxicology by Interpretable Machine Learning

Jia

Wang

Zhu

2023

Environ. Sci. Technol.

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Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML-and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

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“…461 The AOP strategy was implemented into a computational framework that depicts a pathway from structural alerts in which oxidative stress is a key event and hepatotoxicity is the adverse outcome. 459 This oxidative stress hepatotoxicity model can predict the hepatotoxicity potential of new compounds and infer mechanisms involved in toxicity. Although the concept of AOPs in nanotoxicology is still in a preliminary stage, there were previous studies to construct AOPs with direct relevance for NMs.…”

Section: Mechanism-driven Modeling For Predictive Nanotoxicologymentioning

confidence: 99%

“…Since its establishment in 2010, the adverse outcome pathway (AOP) framework has provided mechanism-driven extrapolations of toxicity of new materials, including NMs. ,− The AOP framework is a theoretical concept that describes a cascade of measurable key events linking a molecular initiating event (MIE) to an adverse outcome (AO) through intermediate key events (Figure D). Currently, AOP-based mechanism-driven modeling is being developed for various types of toxicities such as endocrine disruption, neurotoxicity, growth impairment, hepatotoxicity, acute inhalation toxicity, and skin sensitization . The AOP strategy was implemented into a computational framework that depicts a pathway from structural alerts in which oxidative stress is a key event and hepatotoxicity is the adverse outcome .…”

Section: Unraveling Quantitative Nanostructure–toxicity Relationships...mentioning

confidence: 99%

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

et al. 2023

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Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)–toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

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Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay

Cited by 23 publications

References 89 publications

Data-Driven Quantitative Structure–Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure

Data-Driven Quantitative Structure–Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure

Advancing Computational Toxicology by Interpretable Machine Learning

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

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