Adverse Outcome Pathway (AOP) Informed Modeling of Aquatic Toxicology: QSARs, Read-Across, and Interspecies Verification of Modes of Action

Ellison, Claire M.; Piechota, Przemyslaw; Madden, Judith C.; Enoch, Steven J.; Cronin, Mark T.D.

doi:10.1021/acs.est.5b05918

Cited by 38 publications

(22 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, specific endpoints/biomarkers measured by toxicologists are significant factors that affect the sensitivity of species. In several studies, both biochemical or molecular responses [8,46,47] and mode of action-related endpoints [48][49][50][51] were emphasized, especially for EDCs, in PNEC derivation and ecological risk assessment.…”

Section: Discussionmentioning

confidence: 99%

Applying adverse outcome pathways and species sensitivity–weighted distribution to predicted‐no‐effect concentration derivation and quantitative ecological risk assessment for bisphenol A and 4‐nonylphenol in aquatic environments: A case study on Tianjin City, China

Wang

Zong

et al. 2017

Enviro Toxic and Chemistry

View full text Add to dashboard Cite

Adverse outcome pathways (AOPs) are a novel concept that effectively considers the toxic modes of action and guides the ecological risk assessment of chemicals. To better use toxicity data including biochemical or molecular responses and mechanistic data, we further developed a species sensitivity-weighted distribution (SSWD) method for bisphenol A and 4-nonylphenol. Their aquatic predicted-no-effect concentrations (PNECs) were derived using the log-normal statistical extrapolation method. We calculated aquatic PNECs of bisphenol A and 4-nonylphenol with values of 4.01 and 0.721 µg/L, respectively. The ecological risk of each chemical in different aquatic environments near Tianjin, China, a coastal municipality along the Bohai Sea, was characterized by hazard quotient and probabilistic risk quotient assessment techniques. Hazard quotients of 7.02 and 5.99 at 2 municipal sewage sites using all of the endpoints were observed for 4-nonylphenol, which indicated high ecological risks posed by 4-nonylphenol to aquatic organisms, especially endocrine-disrupting effects. Moreover, a high ecological risk of 4-nonylphenol was indicated based on the probabilistic risk quotient method. The present results show that combining the SSWD method and the AOP concept could better protect aquatic organisms from adverse effects such as endocrine disruption and could decrease uncertainty in ecological risk assessment. Environ Toxicol Chem 2018;37:551-562. © 2017 SETAC.

show abstract

Section: Discussionmentioning

confidence: 99%

Applying adverse outcome pathways and species sensitivity–weighted distribution to predicted‐no‐effect concentration derivation and quantitative ecological risk assessment for bisphenol A and 4‐nonylphenol in aquatic environments: A case study on Tianjin City, China

Wang

Zong

et al. 2017

Enviro Toxic and Chemistry

View full text Add to dashboard Cite

show abstract

“…The relative similarity of acute aquatic potency within groups of compounds acting by a similar mechanism of action, especially non-polar narcosis has been known for many years 98 and can be applied to successful QSAR development. 29 Similarly, the need to take care for other mechanisms of action, particular those associated with species-specific metabolism 99 or reactivity 100 is important and can be related to the individual AOPs. Whilst generalist extrapolation approaches may work for lethal potency, more sophisticated modelling will be required for receptor mediated responses.…”

Section: Interspecies Relationships Of Toxicity and Sensitivitymentioning

confidence: 99%

“…21 In addition, the use of AOPs to support computational modelling deriving structural alerts [25][26][27][28] for toxicity prediction or as part of a grouping strategy leading to read-across, and for QSAR development, is well established. 29 Other computational approaches, beyond the ab initio risk assessment consideration, that utilise and extend the AOP framework are the development of Integrated Assessment and Testing Approaches (IATAs). 13,18,[30][31][32][33][34][35][36][37][38] The Links Between in Silico Models and the Different Steps of an AOP Figure 1 illustrates that models, or in silico approaches, may potentially be utilised at all stages of the AOP to provide knowledge and information (it should be noted that the structure of the generic AOP shown in Figure 1 is illustrative only and many AOPs do not include exposure or proceed to the ecosystem level).…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Adverse Outcome Pathways and Chemistry-BasedIn SilicoModels to Predict Toxicity

Cronin

RicharzAndrea-Nicole

2017

Applied In Vitro Toxicology

View full text Add to dashboard Cite

The current landscape of Adverse Outcome Pathways (AOPs) provides a means of organising information relating to the adverse effects elicited following exposure to chemicals. As such, AOPs are an excellent driver for the development and application of in silico models for predictive toxicology allowing for the direct relationship between chemistry and adverse effects to be established. Information may be extracted from AOPs to support the creation of (quantitative) structure-activity relationships ((Q)SARs) as well as to increase confidence in grouping and read-across. Any part of an AOP can be linked to these various types of in silico models. There is, however, an emphasis on using information from known Molecular Initiating Events (MIEs) to create models including 2D and 3D structural alerts, SARs and QSARs. MIEs can be classified according to the nature of the interaction e.g. covalent reactivity, oxidative stress, phototoxicity, chronic receptor mediated, acute enzyme inhibition, unspecific, physical and other effects. Different types of MIEs require different approaches to their in silico modelling. Modelling Key Events and Key Event Relationships is useful if they represent the rate limiting step or key determinant of toxicity. Modelling of metabolism and chemical interactions will become part of AOP networks, which are also driving species-specific extrapolation and respective adaptation of models. With more information and data being captured, in silico approaches will increasingly support the application of knowledge from AOPs to build weight of evidence and support risk assessment, e.g. in the context of Integrated Assessment and Testing Approaches (IATAs).

show abstract

“…In this context transport has usually been quantified by descriptors for hydrophobicity and interaction by descriptors for electrophilicity or more specific interaction such as receptor binding . Many QSAR models for acute aquatic toxicity have been developed on a mechanistic basis, currently more commonly referred to through Adverse Outcome Pathways …”

Section: Resultsmentioning

confidence: 99%

Interpretation of QSAR Models: Mining Structural Patterns Taking into Account Molecular Context

Matveieva

Cronin

Polishchuk

2018

Molecular Informatics

Self Cite

View full text Add to dashboard Cite

The study focused on QSAR model interpretation. The goal was to develop a workflow for the identification of molecular fragments in different contexts important for the property modelled. Using a previously established approach -Structural and physicochemical interpretation of QSAR models (SPCI) -fragment contributions were calculated and their relative influence on the compounds' properties characterised. Analysis of the distributions of these contributions using Gaussian mixture modelling was performed to identify groups of compounds (clusters) comprising the same fragment, where these fragments had substantially different contributions to the property studied. SMARTSminer was used to detect patterns discriminating groups of compounds from each other and visual inspection if the former did not help. The approach was applied to analyse the toxicity, in terms of 40 hour inhibition of growth, of 1984 compounds to Tetrahymena pyriformis. The results showed that the clustering technique correctly identified known toxicophoric patterns: it detected groups of compounds where fragments have specific molecular context making them contribute substantially more to toxicity. The results show the applicability of the interpretation of QSAR models to retrieve reasonable patterns, even from data sets consisting of compounds having different mechanisms of action, something which is difficult to achieve using conventional pattern/data mining approaches.

show abstract

Adverse Outcome Pathway (AOP) Informed Modeling of Aquatic Toxicology: QSARs, Read-Across, and Interspecies Verification of Modes of Action

Cited by 38 publications

References 68 publications

Applying adverse outcome pathways and species sensitivity–weighted distribution to predicted‐no‐effect concentration derivation and quantitative ecological risk assessment for bisphenol A and 4‐nonylphenol in aquatic environments: A case study on Tianjin City, China

Applying adverse outcome pathways and species sensitivity–weighted distribution to predicted‐no‐effect concentration derivation and quantitative ecological risk assessment for bisphenol A and 4‐nonylphenol in aquatic environments: A case study on Tianjin City, China

Relationship Between Adverse Outcome Pathways and Chemistry-BasedIn SilicoModels to Predict Toxicity

Interpretation of QSAR Models: Mining Structural Patterns Taking into Account Molecular Context

Contact Info

Product

Resources

About