ETRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events.

Humans are exposed to low levels of N-nitrosamines via different sources. N-Nitrosamines have recently been detected as impurities in various marketed drugs and they are known mutagenic carcinogens belonging to the cohort of concern as referred to in the ICH M7 guideline. Despite their well-known mutagenic properties, there is ongoing discussion on the suitability of the bacterial reverse mutation assay and using induced rat liver S9 as the external source of metabolism to detect their mutagenic potential. Therefore, we have investigated the mutagenic potential of N-nitrosodimethylamine, N-nitrosodiethylamine, N-nitrosodipropylamine, and N-nitrosodibutylamine in vitro under various conditions. Our work showed that the bacterial reverse mutation assay applying plate incorporation or preincubation protocols and using Salmonella typhimurium strains TA100 and TA1535 and E. coli WP2 uvrA is suitable to predict the mutagenicity of n-nitrosamines in the presence of phenobarbital/β-naphthoflavone induced rat liver S9.

The virtual control group (VCG) concept was originally developed in the IMI2 project eTRANSAFE, using data of control animals which pharmaceutical companies have accrued over decades from animal toxicity studies. This control data could be repurposed to create virtual control animals to reduce or replace concurrent controls in animal studies. Initial work demonstrated the general feasibility of the VCG concept, but implementation requires significant further collaborative efforts. The new Innovative Health Initiative (IHI) project VICT3R aims to address these challenges and to obtain regulatory acceptance for the VCG concept. To achieve these goals, VICT3R will build a database comprising high-quality, standardized, and duly annotated control animal data from past and forthcoming toxicity studies. The VICT3R project will create workflows and computational tools to generate adequate VCGs based on statistical and artificial intelligence (AI) approaches. The validity, reproducibility, and robustness of the resulting VCGs will be assessed by comparing the performance of their use with that of real control groups.