Abstract
An open-source implementation of a previously published integrated testing strategy (ITS) for skin sensitization using a Bayesian network has been developed using R, a free and open-source statistical computing language. The ITS model provides probabilistic predictions of skin sensitization potency based on in silico and in vitro information as well as skin penetration characteristics from a published bioavailability model (Kasting et al., 2008). The structure of the Bayesian network was designed to be consistent with the adverse outcome pathway published by the OECD (Jaworska et al., 2011, 2013). In this paper, the previously published data set (Jaworska et al., 2013) is improved by two data corrections and a modified application of the Kasting model. The new data set implemented in the original commercial software package and the new R version produced consistent results. The data and a fully documented version of the code are publicly available (http://ntp.niehs.nih.gov/go/its).
Highlights
Toxicity testing in the 21st century is purposefully transitioning from traditional disease-related observations in animal models towards the use of mechanism-based outcomes from cell-based assays and in silico models
It is unlikely that a single assay or in silico model will provide sufficient information on the risk or hazard posed by a chemical
The structure of the Bayesian network was designed to be consistent with the adverse outcome pathway (AOP) for substances that initiate the skin sensitization process by covalently binding to skin proteins (Jaworska et al, 2011, 2013)
Summary
Summary An open source implementation of a previously published integrated testing strategy (ITS) for skin sensitization using a Bayesian network has been developed using R, a free and open source statistical computing language. The structure of the Bayesian network was designed to be consistent with the adverse outcome pathway published by the OECD (Jaworska et al, 2011, 2013). The integrated testing strategy (ITS) using a Bayesian network for skin sensitization was previously developed using commercial software (Jaworska et al, 2011, 2013). The structure of the Bayesian network was designed to be consistent with the adverse outcome pathway (AOP) for substances that initiate the skin sensitization process by covalently binding to skin proteins (Jaworska et al, 2011, 2013).
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