QSAR model for predicting cell viability of human embryonic kidney cells exposed to SiO₂ nanoparticles

A predictive model for the viability (%) of cultured human embryonic kidney cells (HEK293) exposed to 20 and 50 nm silica nanoparticles was built using 'optimal descriptors' as mathematical functions of size, concentration and exposure time. The calculation was carried out with CORAL software (http://www.insilico.eu/coral/) on five random splits of combined systems (particle size-particle concentration-cell exposure time) into training, calibration, and validation sets. The R(2) values of the best models were above 0.68. The average statistical quality of the model for the viability (%) of HEK293 exposed to different concentrations of silica nanoparticles measured by MTT assay is satisfactory.

The eNanoMapper database for nanomaterial safety information

BACKGROUND

The NanoSafety Cluster, a cluster of projects funded by the European Commision, identified the need for a computational infrastructure for toxicological data management of engineered nanomaterials (ENMs). Ontologies, open standards, and interoperable designs were envisioned to empower a harmonized approach to European research in nanotechnology. This setting provides a number of opportunities and challenges in the representation of nanomaterials data and the integration of ENM information originating from diverse systems. Within this cluster, eNanoMapper works towards supporting the collaborative safety assessment for ENMs by creating a modular and extensible infrastructure for data sharing, data analysis, and building computational toxicology models for ENMs.

RESULTS

The eNanoMapper database solution builds on the previous experience of the consortium partners in supporting diverse data through flexible data storage, open source components and web services. We have recently described the design of the eNanoMapper prototype database along with a summary of challenges in the representation of ENM data and an extensive review of existing nano-related data models, databases, and nanomaterials-related entries in chemical and toxicogenomic databases. This paper continues with a focus on the database functionality exposed through its application programming interface (API), and its use in visualisation and modelling. Considering the preferred community practice of using spreadsheet templates, we developed a configurable spreadsheet parser facilitating user friendly data preparation and data upload. We further present a web application able to retrieve the experimental data via the API and analyze it with multiple data preprocessing and machine learning algorithms.

CONCLUSION

We demonstrate how the eNanoMapper database is used to import and publish online ENM and assay data from several data sources, how the "representational state transfer" (REST) API enables building user friendly interfaces and graphical summaries of the data, and how these resources facilitate the modelling of reproducible quantitative structure-activity relationships for nanomaterials (NanoQSAR).