QSAR Model for Androgen Receptor Antagonism - Data from CHO Cell Reporter Gene Assays

Jensen, Gunde Egeskov; Nikolov, Nikolai Georgiev; Dreisig, Karin; Vinggaard, Anne Marie; Russel, Jay; Niemelä,

doi:10.4172/2157-7536.s2-006

Cited by 3 publications

(2 citation statements)

References 57 publications

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“…Although the above described AA models all use CP, they are only partly directly comparable as underlying data, techniques and/or features differ (see Table 4). Note that some other QSAR models for AA have been published, based on similar data, using random forest, deep learning [53], and the Case Ultra system [54]. Since set-up and reported performance measures differ from this CP study, they can not directly be compared.…”

Section: Conformal Predictors-validation Of Aa Modelmentioning

confidence: 98%

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: Conformal Predictors-validation Of Aa Modelmentioning

confidence: 98%

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

“…A run of a (Q)SAR model for androgen receptor (AR) antagonism on 37,917 EINECS (European Inventory of Existing Commercial Chemical Substances) substances found that 9.2% (3,488) were predicted to antagonize the AR, i.e. having anti-androgenic properties (Jensen et al, 2012). It should be taken into consideration that the model focus on substances interacting with the AR, and does not include anti-androgenic substances acting through other mechanisms of action (e.g.…”

Section: Leadmentioning

confidence: 99%

The Cost of Inaction

Olsson¹

2014

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Prediction ofin vitroandin vivooestrogen receptor activity using hierarchical clustering

Martin

2016

SAR and QSAR in Environmental Research

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In this study, hierarchical clustering classification models were developed to predict in vitro and in vivo oestrogen receptor (ER) activity. Classification models were developed for binding, agonist, and antagonist in vitro ER activity and for mouse in vivo uterotrophic ER binding. In vitro classification models yielded balanced accuracies ranging from 0.65 to 0.85 for the external prediction set. In vivo ER classification models yielded balanced accuracies ranging from 0.72 to 0.83. If used as additional biological descriptors for in vivo models, in vitro scores were found to increase the prediction accuracy of in vivo ER models. If in vitro activity was used directly as a surrogate for in vivo activity, the results were poor (balanced accuracy ranged from 0.49 to 0.72). Under-sampling negative compounds in the training set was found to increase the coverage (fraction of chemicals which can be predicted) and increase prediction sensitivity.

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QSAR Model for Androgen Receptor Antagonism - Data from CHO Cell Reporter Gene Assays

Cited by 3 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

The Cost of Inaction

Prediction ofin vitroandin vivooestrogen receptor activity using hierarchical clustering

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