Metabolomics as read-across tool: A case study with phenoxy herbicides

Ravenzwaay, Bennard van; Sperber, Saskia; Lemke, O.; Fabian, Eric; Faulhammer, Frank; Kamp, Hennicke; Mellert, W.; Strauss, Volker; Strigun, Alexander; Peter, Erik; Spitzer, Michael; Walk, T.

doi:10.1016/j.yrtph.2016.09.013

Cited by 54 publications

(51 citation statements)

References 20 publications

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“…Van Ravenzwaay et al published an example on alternative in vivo data, which illustrates the value of metabolomics data for the substantiation of a read-across approach (van Ravenzwaay et al 2016). The phenoxy-carboxylic acid herbicide 2-(4-chloro-2-methylphenoxy)propionic acid (MCPP) was selected as the target substance and 2-methyl-4-chlorophenoxyacetic acid (MCPA) and 2-(2,4-dichlorophenoxy propionic acid) (2,4-DPA) as structurally closely related source substances.…”

Section: Read-across Examples and Challengesmentioning

confidence: 99%

Towards grouping concepts based on new approach methodologies in chemical hazard assessment: the read-across approach of the EU-ToxRisk project

et al. 2019

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Read-across is one of the most frequently used alternative tools for hazard assessment, in particular for complex endpoints such as repeated dose or developmental and reproductive toxicity. Read-across extrapolates the outcome of a specific toxicological in vivo endpoint from tested (source) compounds to "similar" (target) compound(s). If appropriately applied, a read-across approach can be used instead of de novo animal testing. The read-across approach starts with structural/ physicochemical similarity between target and source compounds, assuming that similar structural characteristics lead to similar human hazards. In addition, similarity also has to be shown for the toxicokinetic and toxicodynamic properties of the grouped compounds. To date, many read-across cases fail to demonstrate toxicokinetic and toxicodynamic similarities. New concepts, in vitro and in silico tools are needed to better characterise these properties, collectively called new approach methodologies (NAMs). This white paper outlines a general read-across assessment concept using NAMs to support hazard characterization of the grouped compounds by generating data on their dynamic and kinetic properties. Based on the overarching read-across hypothesis, the read-across workflow suggests targeted or untargeted NAM testing also outlining how mechanistic knowledge such as adverse outcome pathways (AOPs) can be utilized. Toxicokinetic models (biokinetic and PBPK), enriched by in vitro parameters such as plasma protein binding and hepatocellular clearance, are proposed to show (dis)similarity of target and source compound toxicokinetics. Furthermore, in vitro to in vivo extrapolation is proposed to predict a human equivalent dose, as potential point of departure for risk assessment. Finally, the generated NAM data are anchored to the existing in vivo data of source compounds to predict the hazard of the target compound in a qualitative and/ or quantitative manner. To build this EU-ToxRisk read-across concept, case studies have been conducted and discussed with the regulatory community. These case studies are briefly outlined.

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Section: Read-across Examples and Challengesmentioning

confidence: 99%

Towards grouping concepts based on new approach methodologies in chemical hazard assessment: the read-across approach of the EU-ToxRisk project

et al. 2019

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“…Read-across is a common, often manual, approach in toxicology [12,15,16], based on the assumption that similar molecules can evoke similar toxic effects. Missing information on query chemicals' properties may be gathered by reading across information from very similar molecules.…”

Section: Introductionmentioning

confidence: 99%

“…Using different molecular encodings and diverse similarity measures, computers can search through large compound databases to identify the most similar compounds and-given a decent similaritytransfer knowledge to a query compound. Prerequisite for successful read-across is a robust and reproducible test system of the underlying experimental data [16], i.e. a standardised assay set-up to ensure comparable read-outs.…”

Section: Introductionmentioning

confidence: 99%

KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development

Morger¹,

Mathea²,

Achenbach³

et al. 2020

J Cheminform

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Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process.

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“…1 The development of the toxicological pathway concept, not least stimulated by the 2007 National Academies of Sciences, Engineering, and Medicine report, 2 has seen a growth in the number of assays and hence data available, e.g. those directly associated with pathways such as from high throughput / content assays such as Tox21 / ToxCast 3 and omics [4][5][6] or those that may be applied indirectly from existing in vivo and other data. [7][8][9] These assays have provided an explosion in the number of pathway related data available which could form the basis of either strategies to predict toxicity, or be used to develop computational models.…”

Section: Introductionmentioning

confidence: 99%

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

Cronin

RicharzAndrea-Nicole

2017

Applied In Vitro Toxicology

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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).

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Metabolomics as read-across tool: A case study with phenoxy herbicides

Cited by 54 publications

References 20 publications

Towards grouping concepts based on new approach methodologies in chemical hazard assessment: the read-across approach of the EU-ToxRisk project

Towards grouping concepts based on new approach methodologies in chemical hazard assessment: the read-across approach of the EU-ToxRisk project

KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development

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

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