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A mechanistic framework for integrating chemical structure and high-throughput screening results to improve toxicity predictions
Citation:
Nelms, M., C. Mellor, S. Enoch, R. Judson, G. Patlewicz, A. Richard, J. Madden, M. Cronin, AND S. Edwards. A mechanistic framework for integrating chemical structure and high-throughput screening results to improve toxicity predictions. Computational Toxicology. Elsevier B.V., Amsterdam, Netherlands, 8:1-12, (2018). https://doi.org/10.1016/j.comtox.2018.08.003
Impact/Purpose:
Adverse Outcome Pathways (AOPs) can inform risk assessment by providing a structured description of the mechanisms underlying chemical toxicity. This can provide a framework for integrating toxicity information from high throughput, alternative methods with traditional toxicology and epidemiology results. As the point within the AOP at which the chemistry and biology interact, the molecular initiating event (MIE) can accommodate both biological data and chemical structure/physicochemical information. Together, these data can be utilised to define chemical clusters based on a specific MIE and shared structural features. In vitro data can be used in conjunction with in silico information to assist with chemical toxicity predictions in instances when data are limited. Through an iterative process, the biological information can highlight structurally similar chemical clusters, which can then be used to identify structural features that are important for triggering an MIE. By doing this the chemical structure information can improve our confidence in the high throughput assay results in addition to providing information about chemicals for which no data exist. This approach can be easily adapted to fit in an Integrated Approach to Testing and Assessment (IATA) where the minimum number of toxicity tests are performed based on the results from earlier toxicity predictions.
Description:
Adverse Outcome Pathways (AOPs) establish a connection between a molecular initiating event (MIE) and an adverse outcome. Detailed understanding of the MIE provides the ideal data for determining chemical properties required to elicit the MIE. This study utilized high-throughput screening data from the ToxCast program, coupled with chemical structural information, to generate chemical clusters using three similarity methods pertaining to nine MIEs within an AOP network for hepatic steatosis. Three case studies demonstrate the utility of the mechanistic information held by the MIE for integrating biological and chemical data. Evaluation of the chemical clusters activating the glucocorticoid receptor identified activity differences in chemicals within a cluster. Comparison of the estrogen receptor results with previous work showed that bioactivity data and structural alerts can be combined to improve predictions in a customizable way where bioactivity data are limited. The aryl hydrocarbon receptor (AHR) highlighted that while structural data can be used to offset limited data for new screening efforts, not all ToxCast targets have sufficient data to define robust chemical clusters. In this context, an alternative to additional receptor assays is proposed where assays for proximal key events downstream of AHR activation could be used to enhance confidence in active calls. These case studies illustrate how the AOP framework can support an iterative process whereby in vitro toxicity testing and chemical structure can be combined to improve toxicity predictions. In vitro assays can inform the development of structural alerts linking chemical structure to toxicity. Consequently, structurally related chemical groups can facilitate identification of assays that would be informative for a specific MIE. Together, these activities form a virtuous cycle where the mechanistic basis for the in vitro results and the breadth of the structural alerts continually improve over time to better predict activity of chemicals for which limited toxicity data exist.
URLs/Downloads:
DOI: A mechanistic framework for integrating chemical structure and high-throughput screening results to improve toxicity predictions