Abstract
To assess the toxicity of new chemicals and drugs, regulatory agencies require in vivo testing for many toxic endpoints, resulting in millions of animal experiments conducted each year. However, following the Replace, Reduce, Refine (3R) principle, the development and optimization of alternative methods, in particular in silico methods, has been put into focus in the recent years. It is generally acknowledged that the more complex a toxic endpoint, the more difficult it is to model. Therefore, computational toxicology is shifting from modelling general and complex endpoints to the investigation and modelling of pathways of toxicity and the underlying molecular effects. The U.S. Toxicology in the 21st Century (Tox21) initiative has screened a large library of compounds, including approximately 10K environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects, and made the results publicly available. Through the Tox21 Data Challenge, the consortium has established a platform for computational toxicologists to develop and validate their predictive models. Here, we present a fast and successful method for the prediction of different outcomes of the nuclear receptor and stress response pathway screening from the Tox21 Data Challenge 2014. The method is based on the combination of molecular similarity calculations and a naïve Bayes machine learning algorithm and has been implemented as a KNIME pipeline. Molecules are represented as binary vectors consisting of a concatenation of common two-dimensional molecular fingerprint types with topological compound properties. The prediction method has been optimized individually for each modelled target and evaluated in a cross-validation as well as with the independent Tox21 validation set. Our results show that the method can achieve good prediction accuracies and rank among the top algorithms submitted to the prediction challenge, indicating its broad applicability in toxicity prediction.
Highlights
The U.S Toxicology in the twenty-first century (Tox21) initiative has been established in 2008 with the vision to support the transformation of toxicology into a predictive science (Krewski et al, 2010)
Another current focus of the U.S Toxicology in the twenty-first century initiative (Tox21) screening is aromatase, an enzyme involved in the conversion of androgen to estrogen and a target of endocrine disrupting chemicals (Chen et al, 2014), as well as the aryl hydrocarbon receptor (AhR), a nuclear receptor involved in the mediation of tumorgenesis induced by dioxin (Murray et al, 2014)
The Tox21 Data Challenge 2014 consisted of the prediction of 12 different screening outcomes: the activation or inhibition of nuclear receptors AhR, PPARγ, aromatase, estrogen receptors (ER) and androgen receptors (AR) as well as the effect on stress response pathways consisting of the activation of the antioxidant response element (ARE), heat shock response (HSE) and p53 signaling, the disruption of mitochondrial membrane potential (MMP) and the induction of genotoxicity (ATAD5)
Summary
The U.S Toxicology in the twenty-first century (Tox21) initiative has been established in 2008 with the vision to support the transformation of toxicology into a predictive science (Krewski et al, 2010). Bisphenol A and its derivatives have been shown to exhibit a promiscuous binding behavior involving, for instance, estrogen receptors (ER), androgen receptors (AR) and peroxisome proliferator-activated receptors (PPAR) of the γ subtype (Delfosse et al, 2014), all of which are subject of the Tox screening Another current focus of the Tox screening is aromatase, an enzyme involved in the conversion of androgen to estrogen and a target of endocrine disrupting chemicals (Chen et al, 2014), as well as the aryl hydrocarbon receptor (AhR), a nuclear receptor involved in the mediation of tumorgenesis induced by dioxin (Murray et al, 2014). Recent studies have shown that the impairment of mitochondrial function is associated with drug-induced adverse effects on the liver and cardiovascular system (Nadanaciva and Will, 2011; AtteneRamos et al, 2015)
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