Predicting Organ Toxicity Using <i>in Vitro</i> Bioactivity Data and Chemical Structure

Liu, Jie; Patlewicz, Grace; Williams, Antony J.; Thomas, Russell S.; Shah, Imran

doi:10.1021/acs.chemrestox.7b00084

Cited by 58 publications

(44 citation statements)

References 57 publications

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“…Thus, if, in future, sufficient standard toxicity data will be available for model training, the introduced pipeline has the potential to become even more powerful. Also, information about the compound's bioavailability and in vitro to in vivo translation of the assays would be of high interest [10,[86][87][88]. According to Grenet et al [87], it seems to be more challenging to predict long-term in vivo endocrine disruption, compared to predicting short-term in vivo endocrine effects.…”

Section: Knowtox-case Studymentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Knowtox-case Studymentioning

confidence: 99%

“…cell cycle, steroid receptors, and cytotoxicity. ToxCast has since been used: to develop QSAR models [7][8][9]; to generate biological fingerprints for in vivo endpoint predictions [10]; to decipher adverse outcome pathways [7,11]; and as a basis for read-across [12][13][14].…”

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

View full text Add to dashboard Cite

show abstract

“…have the potential to provide useful data to reduce the uncertainty associated with some aspect of the read‐across. Current available NAM data are particularly helpful in addressing toxicodynamic questions or uncertainties in read‐across . However, expansion of methods and tools to help directly address toxicokinetics is needed.…”

Section: Application Of Read‐across In a Regulatory Contextmentioning

confidence: 99%

Relevance and Application of Read‐Across – Mini Review of European Consensus Platform for Alternatives and Scandinavian Society for Cell Toxicology 2017 Workshop Session

Stuard

Heinonen

2018

Basic Clin Pharma Tox

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Structure activity relationship (SAR)-based read-across is an effective approach for addressing data gaps in human health risk assessment for 'data-poor' chemicals. In read-across, available data on chemical structural analogues are used to predict the toxicity potential of the data-poor chemical. This approach has long been recognized by regulatory agencies and used by industry to evaluate the hazards of chemicals for which there are limited direct data. Construction of a scientifically robust SAR-based read-across hazard assessment is a complex and iterative process involving multiple considerations in each step. Traditional in vivo data generated using regulatory guideline compliant study designs typically forms the basis for read-across assessments. Recently, however, new data streams have been explored and incorporated to enhance read-across predictivity. These in vitro and omics data streams may be used in different ways involving identification of hazards or to provide insight into modes of action for observed toxicological responses. These 'new approach methods' can also enable comparison of biological responses across analogues or a category of structurally related chemicals in order to establish a pattern of biological similarity in addition chemical similarity and/or to help address potency differences across a category. The purpose of this workshop session was to inform on practical considerations in conducting SAR-based read-across assessments and to review recent activities related to application of new approach methods to the practice of read-across.

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“…Hundreds of NAM are available to researchers, some highly complex, such as microphysiological systems (Marx et al 2016 ), others being inexpensive and allowing high throughput (Adler et al 2011 ; Bal-Price et al 2018 ; Judson et al 2017 ; Leist et al 2012b ; Liu et al 2017 ; Richard et al 2016 ; Zimmer et al 2012 ). However, the assembly of such NAM to batteries is demanding, and the use across multiple laboratories in coordinated research activities is particularly challenging (Aschner et al 2017 ; Behl et al 2015 , 2019 ; Jacobs et al 2016 ; Jaworska et al 2015 ; Judson et al 2017 ; Legradi et al 2018 ; Li et al 2017 ; Sonneveld et al 2011 ; Thomas et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

The EU-ToxRisk method documentation, data processing and chemical testing pipeline for the regulatory use of new approach methods

et al. 2020

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Hazard assessment, based on new approach methods (NAM), requires the use of batteries of assays, where individual tests may be contributed by different laboratories. A unified strategy for such collaborative testing is presented. It details all procedures required to allow test information to be usable for integrated hazard assessment, strategic project decisions and/or for regulatory purposes. The EU-ToxRisk project developed a strategy to provide regulatorily valid data, and exemplified this using a panel of > 20 assays (with > 50 individual endpoints), each exposed to 19 well-known test compounds (e.g. rotenone, colchicine, mercury, paracetamol, rifampicine, paraquat, taxol). Examples of strategy implementation are provided for all aspects required to ensure data validity: (i) documentation of test methods in a publicly accessible database; (ii) deposition of standard operating procedures (SOP) at the European Union DB-ALM repository; (iii) test readiness scoring accoding to defined criteria; (iv) disclosure of the pipeline for data processing; (v) link of uncertainty measures and metadata to the data; (vi) definition of test chemicals, their handling and their behavior in test media; (vii) specification of the test purpose and overall evaluation plans. Moreover, data generation was exemplified by providing results from 25 reporter assays. A complete evaluation of the entire test battery will be described elsewhere. A major learning from the retrospective analysis of this large testing project was the need for thorough definitions of the above strategy aspects, ideally in form of a study pre-registration, to allow adequate interpretation of the data and to ensure overall scientific/toxicological validity.

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Predicting Organ Toxicity Using in Vitro Bioactivity Data and Chemical Structure

Cited by 58 publications

References 57 publications

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

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

Relevance and Application of Read‐Across – Mini Review of European Consensus Platform for Alternatives and Scandinavian Society for Cell Toxicology 2017 Workshop Session

The EU-ToxRisk method documentation, data processing and chemical testing pipeline for the regulatory use of new approach methods

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