COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals.

The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.

Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study.

The OECD has proposed five principles for validation of QSAR models used for regulatory purposes. Here we present a case study investigating how these principles can be applied to models based on Kohonen and counter propagation neural networks. The study is based on a counter propagation network model that has been built using toxicity data in fish fathead minnow for 541 compounds. The study demonstrates that most, if not all, of the OECD criteria may be met when modeling using this neural network approach.

Study of a toxin-alkaline phosphatase conjugate for the development of an immunosensor for tetrodotoxin determination.

This paper describes a direct competitive immunoenzymatic spectrophotometric assay (ELISA) for tetrodotoxin (TTX) determination and the adaptation of this method for use in an electrochemical assay format. The novelty of this work involves the use of the antigen labelled with alkaline phosphatase (AP); this conjugate was prepared in our laboratory as there is no commercially available conjugate of any kind for TTX. The new conjugate was characterized in terms of its affinity for the specific antibody as well as the residual concentration and the residual activity of the enzyme (AP) incorporated as label. The proposed method based on the new conjugate showed satisfactory results for TTX determination: for the spectrophotometric method the dynamic range was 4-15 ng mL(-1) with a limit of detection (LOD) of 2 ng mL(-1) (R=0.9247), whereas for the electrochemical protocol the dynamic range was 2-50 ng mL(-1) and the LOD was 1 ng mL(-1).