Nano-QSAR to predict cytotoxicity of metal and metal oxide nanoparticles

Abstract:
The ModelRX case study was designed to cover the important area of generating and applying predictive models, and more specifically QSAR models in hazard assessment endorsed by different regulations, as completely in silico alternatives to animal testing and useful also in early research when no data is available for a compound. The QSAR development process schematically presented in Figure 1 begins by obtaining a training data set from an OpenRiskNet data source. A model can then be trained with OpenRiskNet modelling tools and the resulting models are packaged into a container, documented and ontologically annotated. To assure the quality of the models, they are validated using OECD guidelines (Jennings et al. 2018). Prediction for new compounds can be obtained using a specific model or a consensus of predictions of all models. This case study will present this workflow with the example of blood-brain-barrier (BBB) penetration, for which multiple models were generated using tools from OpenRiskNet consortium and associated partners used individually as well as in a consensus approach using Dempster-Shafer theory (Park et al. 2014; Rathman et al. 2018).

Abstract:
The OpenRiskNet project (https://openrisknet.org/) is funded by the H2020-EINFRA-22-2016 Programme and its main objective is the development of an open e-infrastructure providing data and software resources and services to a variety of industries requiring risk assessment (e.g. chemicals, cosmetic ingredients, pharma or nanotechnologies). The concept of case studies was followed in order to test and evaluate proposed solutions and is described in https://openrisknet.org/e-infrastructure/development/case-studies/. Two case studies, namely ModelRX and RevK, focus on modelling within risk assessment. The ModelRX – Modelling for Prediction or Read Across case study provides computational methods for predictive modelling and support of existing data suitability assessment. It supports final risk assessment by providing calculations of theoretical descriptors, gap filling of incomplete datasets. computational modelling (QSAR) and predictions of adverse effects. Services are offered through Jaqpot (UI/API), JGU WEKA (API), Lazar (UI) and Jupyter & Squonk Notebooks. In the RevK – Reverse dosimetry and PBPK prediction case study, physiologically based pharmacokinetic (PBPK) models are made accessible for the purpose of risk assessment-relevant scenarios. The PKSim software, the httk R package and custom-made PBPK models have been integrated. RevK offers services through Jaqpot (UI/API).