AOP173 key event associated pathway predictor – online application for the prediction of benchmark dose lower bound (BMDLs) of a transcriptomic pathway involved in MWCNTs-induced lung fibrosis

Abstract

Nano-QSAR model allows for prediction of the toxicity of materials that have not been experimentally tested before by linking the nano-related structural properties with the biological responses induced by nanomaterials. Prediction of adverse effects caused by substances without having to perform time- and cost-consuming experiments makes QSAR models promising tools for supporting risk assessment. However, very often, newly developed nano-QSAR models are not used in practice due to the complexity of their algorithms, the necessity to have experience in chemoinformatics, and their poor accessibility. In this perspective, the aim of this paper is to encourage developers of the QSAR models to take the effort to prepare user-friendly applications based on predictive models. This would make the developed models accessible to a wider community, and, in effect, promote their further application by regulators and decision-makers. Here, we describe a web-based application that enables to predict the transcriptomic pathway-level response perturbated in the lungs of mice exposed to multiwalled carbon nanotubes. The developed application is freely available at http://aop173-event1.nanoqsar-aop.com/apps/aop_app. It requires only two types of input information related to analyzed nanotubes (their length and diameter) to assess the doses that initiate the inflammation process that may lead to lung fibrosis.

Keywords:

• Nano-QSAR
• multiwalled carbon nanotubes
• nano-AOP
• transcriptomic
• web-based application

Author contributions

K.J., M.G. and F.S developed the application. All authors contributed to the analysis and discussion. K.J., F.S. and M.G. wrote the manuscript. All authors critically revised the manuscript and accepted its final version.

Disclosure statement

No potential conflict of interest was reported by author(s).

Additional information

Funding

The Nano-QSAR-AOP concept and the data required to develop application were funded by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 760813, project title: Physiologically Anchored Tools for Realistic nanOmateriaL hazard aSsessment (PATROLS). UV was also supported by funding from FFIKA, Focused Research Effort on Chemicals in the Working Environment, from the Danish Government.