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Sharing Ames mutagenicity results for common intermediates and compounds containing functional groups of interest.

The mutagenic status of process intermediates is of paramount importance under the ICH M7 guideline. However, Ames mutagenicity studies for common intermediates can be a costly process when companies work separately. We recognise that more can be achieved through collaboration.

Our solution

Launched in 2006, the Intermediates data sharing initiative is a collaboration of pharmaceutical organisations sharing data on Ames mutagenicity results for production intermediates.

While the main focus is on Ames data, the scope has been extended to consider other in vitro and in vivo genotoxicity data to put the Ames test results into perspective.

Members can submit data from their internal portfolio and generate new data as part of the directed testing of commercially available compounds, matching substructures prioritised by our scientists and members to improve structure-activity relationship (SAR) mutagenicity predictions.

The Vitic Intermediates data sharing initiative furthers the development of in silico predictive tools, helping you to enhance your risk assessment workflow.

Key highlights

Mange expert review under ICH M7
The high-quality data found within the Intermediates database lends itself to the ICH M7 workflow, allowing you to retrieve relevant information quickly and potentially avoid the need for costly experiments.

Access peer reviewed data
Through regular contributions from members, the Intermediates database is constantly expanding. The systematic peer-review process guarantees a high-quality standardised dataset that you can be confident using.

Reduce unnecessary testing
The sharing of data regarding common intermediates can act as an early predictor for genotoxicity, helping you to potentially avoid costly experiments later in development.

Regulatory support


The ability to perform substructure or similarity searches facilitates read-across approaches and supports the required in silico analyses to identify potentially mutagenic impurities. Data can be presented to regulators.

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