Advancing the science of a read-across framework for evaluation of data-poor chemicals incorporating systematic and new approach methods

Lizarraga, Lucina E.; Suter, Glenn W.; Lambert, Jason C.; Patlewicz, Grace; Zhao, Jay; Dean, Jeffry L.; Kaiser, Phillip

doi:10.1016/j.yrtph.2022.105293

Cited by 12 publications

(7 citation statements)

References 48 publications

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“…Here, structural similarity was applied to identify the chemicals most likely to bind to and exert effects on receptors such as retinoic acid, vitamin D, and pparda , as suggested from our transcriptomic analysis. The chemical fingerprints approach used here, and other similar ones also based on structural similarity of the compounds [ 87 ], may be useful to suggest potential mechanisms of action of a compound, and it could aid in focusing on specific components of a mixture to reduce the experimental testing load. Thus, using chemoinformatic data has major potential implications in reducing specifically animal testing, while dealing with the tremendous task of evaluating risks caused by mixtures.…”

Section: Discussionmentioning

confidence: 99%

A realistic mixture of ubiquitous persistent organic pollutants affects bone and cartilage development in zebrafish by interaction with nuclear receptor signaling

Guerrero-Limón,

Zappia,

Muller

2024

PLoS ONE

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"Persistent organic pollutants (POPs)" have a plethora of deleterious effects on humans and the environment due to their bioaccumulative, persistent, and mimicking properties. Individually, each of these chemicals has been tested and its effects measured, however they are rather found as parts of complex mixtures of which we do not fully grasp the extent of their potential consequences. Here we studied the effects of realistic, environmentally relevant mixtures of 29 POPs on cartilage and bone development using zebrafish as a model species. We observed developmental issues in cartilage, in the form of diverse malformations such as micrognathia, reduced size of the Meckel’s and other structures. Also, mineralized bone formation was disrupted, hence impacting the overall development of the larvae at later life stages. Assessment of the transcriptome revealed disruption of nuclear receptor pathways, such as androgen, vitamin D, and retinoic acid, that may explain the mechanisms of action of the compounds within the tested mixtures. In addition, clustering of the compounds using their chemical signatures revealed structural similarities with the model chemicals vitamin D and retinoic acid that can explain the effects and/or enhancing the phenotypes we witnessed. Further mechanistic studies will be required to fully understand this kind of molecular interactions and their repercussions in organisms. Our results contribute to the already existing catalogue of deleterious effects caused by exposure to POPs and help to understand the potential consequences in at risk populations.

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Section: Discussionmentioning

confidence: 99%

A realistic mixture of ubiquitous persistent organic pollutants affects bone and cartilage development in zebrafish by interaction with nuclear receptor signaling

Guerrero-Limón,

Zappia,

Muller

2024

PLoS ONE

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“…Furthermore, Lizarraga et al [ 32 ] recently introduced a modified, revised read-across framework based on key principles, evaluation experience, and new approach methodologies (NAMs) data. The revised read-across framework particularly emphasizes evaluating biological similarity as a means of identifying groups of similar substances.…”

Section: Discussionmentioning

confidence: 99%

Developing a Novel Read-Across Concept for Ecotoxicological Risk Assessment of Phosphate Chemicals: A Case Study

Lee,

Ok,

Moon

et al. 2024

Toxics

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This study introduces a novel concept approach for a read-across assessment, considering species sensitivity differences among phosphate chemicals within structurally similar compound groups. Twenty-five organic chemicals, with a log Kow of 5 or less, were categorized into three functional groups based on acetylcholinesterase (AChE) inhibition as a specific mode of action (MOA). The short-term aquatic toxicity data (LC50) for fish, crustaceans, and insects were collected from the U.S. EPA Ecotoxicology (ECOTOX) Knowledgebase. A geometric mean calculation method was applied for multiple toxic endpoints. Performance metrics for the new read-across concept, including correlation coefficient, bias, precision, and accuracy, were calculated. Overall, a slightly higher overestimation (49.2%) than underestimation (48.4%) in toxicity predictions was observed in two case studies. In Case study I, a strong positive correlation (r = 0.93) between the predicted and known toxicity values of target chemicals was observed, while in Case study II, with limited information on species and their ecotoxicity, showed a moderate correlation (r = 0.75). Overall, the bias and precision for Case study I were 0.32 ± 0.01, while Case study II showed 0.65 ± 0.06; however, the relative bias (%) increased from 37.65% (Case study I) to 91.94% (Case study II). Bland–Altman plots highlight the mean differences of 1.33 (Case study I) and 1.24 (Case study II), respectively. The new read-across concept, focusing on AChE inhibition and structural similarity, demonstrated good reliability, applicability, and accuracy with minimal bias. Future studies are needed to evaluate various types of chemical substances, diverse modes of action, functional groups, toxic endpoints, and test species to ensure overall comprehensiveness and robustness in toxicity predictions.

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“…The plot of ARKA_1 vs ARKA_2 can not only identify potential activity cliffs but can also help one to understand the similar types of chemicals that are grouped in a cluster -a basic form of Read-Across. Additionally, it is also possible to identify the chemical nature and possible adverse outcome pathways (AOPs) of the close congeners using the concepts of Read-Across [50,51] and quantitative read-across structureactivity relationship (q-RASAR) [26,52]. This approach can not only be used in assessing https://doi.org/10.26434/chemrxiv-2024-jqkjv ORCID: https://orcid.org/0000-0003-4486-8074 Content not peer-reviewed by ChemRxiv.…”

Section: Limitations and Future Prospectsmentioning

confidence: 99%

ARKA: A framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data

Banerjee,

Roy

2024

Preprint

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Toxicity assessment of environmental chemicals is an integral aspect of assessing the sustainability of flora and fauna constituting the aquatic and terrestrial ecosystems. A wide variety of living organisms are constantly being exposed to these chemicals, most of which generate toxic effects. Due to the lack of experimental toxicity data of environmental chemicals, there arises a need to fill data gaps by in silico approaches. One of the most commonly used in silico approaches for toxicity assessment of small datasets is the Quantitative Structure-Activity Relationship (QSAR), which generates predictive models for the efficient prediction of query compounds. However, the predictions from these models are often erroneous for some compounds, and the reliability of the predictions from QSARs derived from small datasets is often questionable from a statistical point of view. This is due to the presence of a larger number of descriptors as compared to the number of training compounds, which reduces the degree of freedom of the developed model. To reduce the overall prediction error for a particular QSAR model, we have proposed here the computation of the novel Arithmetic Residuals in K-groups Analysis (ARKA) descriptors. We have reduced the number of modeling descriptors, keeping the entire chemical space and preventing the loss of chemical information. We have used here five representative environmentally relevant endpoints (skin sensitization, earthworm toxicity, milk/plasma partitioning, algal toxicity, and rodent carcinogenicity of hazardous chemicals) with graded responses to which the ARKA framework was applied for classification modeling. On comparing the performance of the models generated using conventional QSAR descriptors and the ARKA descriptors, the prediction quality of the models derived from ARKA descriptors was found much better than the models derived from QSAR descriptors signifying the potential of ARKA descriptors in ecotoxicological classification modeling of small data sets. For the ease of users, a Java-based expert system has been developed that computes the ARKA descriptors from the input of QSAR descriptors.

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Advancing the science of a read-across framework for evaluation of data-poor chemicals incorporating systematic and new approach methods

Cited by 12 publications

References 48 publications

A realistic mixture of ubiquitous persistent organic pollutants affects bone and cartilage development in zebrafish by interaction with nuclear receptor signaling

A realistic mixture of ubiquitous persistent organic pollutants affects bone and cartilage development in zebrafish by interaction with nuclear receptor signaling

Developing a Novel Read-Across Concept for Ecotoxicological Risk Assessment of Phosphate Chemicals: A Case Study

ARKA: A framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data

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