Using Molecular Initiating Events to Develop a Structural Alert Based Screening Workflow for Nuclear Receptor Ligands Associated with Hepatic Steatosis

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.

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“…At this point, some of the large data compilations (e.g. ChEMBL, Pub-Chem) may be relevant to assist in the interpretation of models (61,65). …”

Section: An Example Of In Silico Modelling: Development Of Structuralmentioning

confidence: 99%

In SilicoPrediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

et al. 2017

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“…[20][21] When associated with chemical structure, these models, also called in silico approaches, can provide a direct linkage between chemistry and adverse effect leveraging the content of the AOP to support the meaning and interpretation of the model. [22][23] In silico models for toxicity prediction vary from structural alerts derived from structureactivity relationships (SARs) through to quantitative structure-activity relationships (QSARs) which are suitable for the prediction of potency. 24 This paper explores the linkage of these models, as well as grouping and read-across, to AOPs.…”

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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“…Besides, SA also can be used in other fields such as cosmetic research and environmental protection. Several groups of SAs have been reported for different toxic endpoints (Amberg et al, ; Benigni & Bossa, ; Li et al, ; Li et al, , ; Li et al, ; Mellor, Steinmetz, & Cronin, ).…”

Section: Methodsmentioning

confidence: 99%

In silico prediction of drug‐induced rhabdomyolysis with machine‐learning models and structural alerts

Cui

Liu

Zhang

et al. 2019

J of Applied Toxicology

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Drug-induced rhabdomyolysis (DIR) is a serious adverse reaction and can be fatal. In the present study, we focused on the modeling and understanding of the molecular basis of DIR of small molecule drugs. A series of machine-learning models were developed using an Online Chemical Modeling Environment platform with a diverse dataset. A total of 80 machine-learning models were generated. Based on the topperforming individual models, a consensus model was also developed. The consensus model was available at https://ochem.eu/model/32214665, and the individual models can be accessed with the corresponding model IDs on the website. Furthermore, we also analyzed the difference of distributions of eight key physicochemical properties between rhabdomyolysis-inducing drugs and non-rhabdomyolysisinducing drugs. Finally, structural alerts responsible for DIR were identified from fragments of the Klekota-Roth fingerprints.

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Using Molecular Initiating Events to Develop a Structural Alert Based Screening Workflow for Nuclear Receptor Ligands Associated with Hepatic Steatosis

Cited by 53 publications

References 39 publications

In SilicoPrediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

In SilicoPrediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

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

In silico prediction of drug‐induced rhabdomyolysis with machine‐learning models and structural alerts

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