Complex Nitrosamines
Understanding the mutagenic potential of structurally-complex (API-like) nitrosamines.
- A collaboration facilitated by Lhasa Limited, in partnership with the pharmaceutical industry, which enables pre-competitive data sharing.
- Partners share Ames data on structurally-complex (e.g. API-like) nitrosamines, which is input by Lhasa (in an anonymised form) into a shared database to enable read across and reduce duplicate testing.
- The consortium also aims to use the data to improve structure activity relationships (SARs) for complex nitrosamines - identifying trends in compound complexity versus mutagenic potential.
Related pages
Insight
The complex nitrosamines data sharing initiative launched in 2021, in response to the nitrosamine impurity crisis within the pharmaceutical industry. Since then, it has been reported that API-like nitrosamines are less likely to be Ames-positive, and where carcinogenicity data exists for the Ames-positive nitrosamines, they are of lower potency than small nitrosamines.
Anecdotally, less than 50% nitrosamine positivity has been observed, compared to more than 80% for small nitrosamines. The hypothesis is that this discrepancy can be explained mechanistically (i.e. steric hindrance, alternative metabolic routes, higher molecular weight and variations in other physicochemical properties). To test this hypothesis, we need to generate a sufficiently large dataset and conduct systematic review.
The following points provide a greater insight to the complex nitrosamine data sharing initiative:
- Data shared: results are primarily from Ames mutagenicity assays performed on API-like nitrosamines.
- Data can be presented to regulatory bodies. To assist with submissions, or when simply querying the information held within the database, members of the group can request the raw data via Lhasa Limited.
- Lhasa Limited acts as an honest broker and expertly curates the data from participating organisations. This allows a level of anonymity, whereby the submitting companies are known to each other, but who submitted what is known to Lhasa Limited only.
- Lhasa Limited, with supported consortium members, are going to investigate nitrosamine SAR results.
Benefits
Members of the Complex Nitrosamines group include prominent members of the pharmaceutical industry and we are always happy to hear from interested parties. The benefits that can be achieved by joining the data sharing consortium include:
- Advancing the knowledge of complex nitrosamines: By participating in this consortium, you are actively helping the progression towards more accurate predictions for this chemical class of high concern. The shared data can be used to build and validate new SAR models for the prediction of the potential mutagenic activity of nitrosamines.
- Time and Cost Savings: The sharing of data regarding complex nitrosamines can help reduce testing burden by avoiding duplicate testing across within companies. Ames data acts as an early predictor for genotoxicity, potentially avoiding the need for costly experiments later in development.
- Supporting Expert Review under ICH M7: The high-quality data found within the nitrosamine database lends itself to the ICH M7 workflow, allowing you to retrieve relevant information quickly and potentially avoid the need for costly experiments.
- Utilise Proprietary Data: Joining this data sharing venture gives you the chance to make use of existing information that is not in the public domain.
- Access High-Quality Data: The systematic peer-review process guarantees a high-quality dataset that you can be confident using.
Why Choose Lhasa?
Lhasa Limited - an honest broker and trusted holder of data
Lhasa is a not-for-profit organisation and believes that shared knowledge can lead to shared progress. Recognised as the original "Honest Broker", Lhasa Limited has repeatedly been trusted with proprietary data and this can be seen with our involvement in other collaborative data sharing projects.
Get Involved
To find out more about data sharing opportunities at Lhasa Limited or to join the complex nitrosamine data sharing initiative, please get in touch.
Recommended References
- Cross K, Ponting DJ (2021) ‘Developing structure-activity relationships for N-nitrosamine activity’ Computational Toxicology, Volume 20, November 2021, 100186. https://doi.org/10.1016/j.comtox.2021.100186
