Academic journal article Environmental Health Perspectives

Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across

Academic journal article Environmental Health Perspectives

Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across

Article excerpt

Introduction

There are currently over 100,000 chemicals available on the market that lack toxicity information, comprising roughly 90% of the 140,000 consumer products in use (Hartung and Rovida 2009; Judson et al. 2009). Traditional toxicology evaluations require the use of animal models for testing new compounds. However, these animal models are costly and time-consuming, and they raise ethical concerns regarding the well-being of animals (Hartung 2017). Under this paradigm, generating substantial toxicity data for a limited number of compounds could take years, and it would be financially impossible to test all the available compounds using animal testing protocols (Hartung 2016). In 2007, the National Research Council Committee on Toxicity Testing and Assessment of Environmental Agents addressed this issue by proposing a new framework to accurately and more quickly evaluate the health risks due to environmental chemical exposures (National Research Council 2007). This federal effort stressed the importance of integrating/establishing the use of computational and in vitro-based alternative methods for chemical risk evaluation. One such alternative, called read-across, relies on using toxicity information from structurally similar compounds to estimate the toxicity of untested compounds (Patlewicz et al. 2014; Wang et al. 2012). This strategy can be used to fill toxicity data gaps for untested chemicals and has been implemented by various regulatory agencies (Hartung 2016). Previous read-across studies relied solely on chemical structure similarity searching (Enoch et al. 2008; Hewitt et al. 2010; Koleva et al. 2008; Luechtefeld et al. 2016; Wu et al. 2013). However, this type of read-across is not applicable for compounds with unique chemical structures and can be confounded by "activity cliffs" (i.e., structurally similar compounds with distinctly different toxicity characteristics) (Maggiora 2006). More recently, efforts to include biological information as a basis for similarity in read-across approaches have started (Zhu et al. 2016). Previous studies using biological data for chemical toxicity evaluations were mostly based on in-house biological data and were limited to specific mechanisms (Judson et al. 2015; Kleinstreuer et al. 2017). This paper addresses the challenge of identifying and integrating biological data from various resources into read-across modeling.

In vitro high-throughput screening (HTS) is capable of rapidly testing large numbers of chemicals to study their effects on molecular targets using whole-cell and cell-free assays. Because of their relatively low cost and high-throughput, efforts such as the Toxicity Testing in the 21st Century (Tox21) program have focused on the application of HTS techniques as the basis for chemical hazard assessment (Attene-Ramos et al. 2013). The direct result of these efforts is a rapidly growing amount of in vitro bioassay data being generated for thousands of chemicals and stored in databases accessible to public users, allowing for new statistical and computational techniques to be developed. The impact of such large publicly available databases for chemical toxicity evaluation is profound, with several projects having successfully used HTS data to better evaluate chemicals for potential hazards (Browne et al. 2015; Hartung 2016; Kim et al. 2016; Kleinstreuer et al. 2017; Low et al. 2013; Zhang et al. 2014a; Zhu et al. 2014). However, rapidly changing public data sources represent a dynamic data landscape, and integrating such data to chemical information for toxicity evaluation is an area that remains largely unexplored. Development of automated computational methods to deeply exploit this rich and dynamic data landscape to establish predictive nonanimal toxicity models is needed.

Acute oral toxicity testing is conducted to determine the immediate health effects of an orally administered chemical substance and is expressed in terms of the lethal dosage that kills 50% of the population ([LD. …

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