Green Toxicology: Connecting Green Chemistry and Modern Toxicology

Krebs, Johanna; McKeague, Maureen

doi:10.1021/acs.chemrestox.0c00260

Cited by 16 publications

(6 citation statements)

References 82 publications

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“…68 Preventing regrettable substitutions is a considerable challenge for scientists globally. Green chemistry, 69,70 and intelligent toxicology 71,72 can provide a safe way forward as elaborated in a proposed general framework to avoid regrettable substitutions in Figure 2. Green chemistry can enable the design and production of least toxic/safe alternative chemicals based on 12 green principles, 70 thus eliminating hazardous waste.…”

mentioning

confidence: 99%

Alternative Plasticizers As Emerging Global Environmental and Health Threat: Another Regrettable Substitution?

Qadeer

Kirsten

Ajmal

et al. 2022

Environ. Sci. Technol.

104

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P lasticizers are synthetic chemicals that are commonly used in polyvinyl chloride (PVC) based products, food packaging, children's toys, medical devices, and adhesives. There are about 30 000 chemicals can potentially be utilized as plasticizers. 1 Phthalate plasticizers are a commonly utilized compound, comprising up to 85% of the total plasticizers in the market. 1 Phthalate plasticizers have been regarded as hazardous compounds due to numerous reports based on its toxicological effects, including bioaccumulation potential, endocrine disruption, carcinogenicity, and developmental defects. 1−4 These findings resulted in global regulation measures and control of typical phthalate plasticizers 5−7 and the introduction and mass production of alternative plasticizers (APs), including but not limited to adipates, benzoates, phosphate esters, citrates, sebacates, terephthalates, trimellitates, cyclohexane dicarboxylic acids, and biobased alternatives. 5,8 The shift to incorporate APs without completely understanding their toxicities may have similar detrimental impacts, akin to phthalate plasticizers.Regrettable substitution has an extensive historical narrative and is still a common occurrence (Table 1), resulting in negative repercussions on public health and environment. For instance, Bisphenol A (BPA) (first made in 1891 by Alexander Dianin), which was commonly applied during the production of polycarbonate plastic, was replaced with various bisphenols (BPS, BPP, BPZ, and BPF, to name a few) 9 due to many reports of its toxicities (e.g., neurocognitive disabilities. reproductive, and developmental defects). 9,10 Subsequently, substitute bisphenols (BPS, BPP, BPZ) have been found to have similar toxicities (endocrine disruptor) or, in some cases, worse. 11,12 The well-known toxic chemical DDT (dichlorodiphenyltrichloroethane)defects includes developmental issues, endocrine disruption and cancer to name a fewwas replaced with

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mentioning

confidence: 99%

Alternative Plasticizers As Emerging Global Environmental and Health Threat: Another Regrettable Substitution?

Qadeer

Kirsten

Ajmal

et al. 2022

Environ. Sci. Technol.

104

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“…Such animal-based experiments have raised ethical and economic concerns in the research community. Recently, the concept of next-generation risk assessment (NGRA) has emerged, which integrates new approach methodologies (NAMs) such as in silico and in vitro approaches − for making health and safety decisions without the need for in vivo data.…”

Section: Introductionmentioning

confidence: 99%

Advancing Toxicity Predictions: A Review on in Vitro to in Vivo Extrapolation in Next-Generation Risk Assessment

Han,

Li,

Yang

et al. 2024

Environ. Health

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As a key step in next-generation risk assessment (NGRA), in vitro to in vivo extrapolation (IVIVE) aims to mobilize a mechanism-based understanding of toxicology to translate bioactive chemical concentrations obtained from in vitro assays to corresponding exposures likely to induce bioactivity in vivo. This conversion can be achieved via physiologically-based toxicokinetic (PBTK) models and machine learning (ML) algorithms. The last 5 years have witnessed a period of rapid development in IVIVE, with the number of IVIVE-related publications increasing annually. This Review aims to (1) provide a comprehensive overview of the origin of IVIVE and initiatives undertaken by multiple national agencies to promote its development; (2) compile and sort out IVIVE-related publications and perform a clustering analysis of their high-frequency keywords to capture key research hotspots; (3) comprehensively review PBTK and ML model-based IVIVE studies published in the last 5 years to understand the research directions and methodology developments; and (4) propose future perspectives for IVIVE from two aspects: expanding the scope of application and integrating new technologies. The former includes focusing on metabolite toxicity, conducting IVIVE studies on susceptible populations, advancing ML-based quantitative IVIVE models, and extending research to ecological effects. The latter includes combining systems biology, multiomics, and adverse outcome networks with IVIVE, aiming at a more microscopic, mechanistic, and comprehensive toxicity prediction. This Review highlights the important value of IVIVE in NGRA, with the goal of providing confidence for its routine use in chemical prioritization, hazard assessment, and regulatory decision making.

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“…In 2008, Toxicity Testing in the 21st Century: A Vision and A Strategy demonstrated a paradigm shift in toxicity testing from in vivo experiments to cell-based in vitro experiments . Based on the toxicological mechanisms, the high-throughput in vitro assays detect the molecular and cellular bioactivities of compound–protein interactions, − avoiding ethical issues and accelerating toxicity testing for chemicals. , Nevertheless, in vitro experiments are also unable to provide toxicological information for all commercial chemicals. Currently, faced with the issues of in vivo and in vitro assays, regulatory agencies around the world have called for the development and use of in silico methods.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Environmental Toxicology: Current Progress and Open Challenges

Tan,

Jin,

Fang

et al. 2023

ACS EST Water

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Ubiquitous chemicals in the environment may pose a threat to human health and the ecosystem, so comprehensive toxicity information must be obtained. Due to the inability of traditional experimental methods to meet the needs of toxicity testing of a large number of chemicals, in vivo and in vitro assays have been shifted to a new paradigm, computer-assisted virtual screening. However, the commonly used virtual screening techniques, including read-across and machine learning-based quantitative structure−activity relationship (QSAR), have limitations in assessing complicated, high-dimensional, and multimodal bioactivity data. In these cases, deep learning (DL) has emerged as a desirable solution for the application of QSARs in toxicity prediction. Therefore, this paper introduces and discusses (a) architectures of six commonly used DL algorithms (fully connected neural network, convolutional neural network, recurrent neural network, long short-term memory, graph neural network, and generative adversarial network), (b) the application scenarios of six DL algorithms, e.g., toxicity prediction and data generation, and (c) challenges and future trends of DLs in toxicity prediction. We believe that by consolidating toxicological mechanisms and DL algorithms, this survey can help readers to build prediction models with excellent performance while promoting further discussions of the fusion of environmental toxicology and DL.

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Green Toxicology: Connecting Green Chemistry and Modern Toxicology

Cited by 16 publications

References 82 publications

Alternative Plasticizers As Emerging Global Environmental and Health Threat: Another Regrettable Substitution?

Alternative Plasticizers As Emerging Global Environmental and Health Threat: Another Regrettable Substitution?

Advancing Toxicity Predictions: A Review on in Vitro to in Vivo Extrapolation in Next-Generation Risk Assessment

Deep Learning in Environmental Toxicology: Current Progress and Open Challenges

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