Lhasa Limited is pleased to announce the release of Zeneth 10.0.0, featuring multiple updates including differing prediction types: Structure-based or nitrosamine risk assessment.
Zeneth is a cutting-edge in silico tool that considers the reaction conditions used in forced degradation studies and predicts theoretical degradation products that can arise for a given API or drug product. One use case is within exploratory formulation departments to identify potential degradation problems with a drug at an early stage in drug development. This aids future decisions around experimental requirements as well as excipient selection. Zeneth can predict for nitrosating pathways, allowing the potential formation of nitrosamines as degradation products to be identified.
This release introduces differing prediction types for the first time. Structure-based prediction is the default prediction within Zeneth, where query compounds and any excipients are processed against reaction conditions input by the user, to generate any possible degradation pathways.
The new nitrosamine risk assessment evaluates a query compound and relevant excipients under a set of reaction conditions known to cause nitrosation reactions. In this case, the excipients and condition sets are pre-populated to predict reactions that the query compound could undergo, eliminating human error in the nitrosamine risk assessment workflow.
Further updates in this release include:
- The ability to run multiple API structure predictions simultaneously.
- Thirteen new transformations and seven new excipients added to knowledge base.
- Additional experimental evidence from forced degradation studies donated by members.
- The ability to perform a substructure search on degradants.
- The addition of a chromophore predictor to transformations, to influence the likelihood score of degradants.
- An updated user interface and usability.
Contact Lhasa for further information on how Zeneth can support your drug development workflow.
The predictive capabilities of Zeneth are outlined in an open access article we recently published in OPRD.