Setaria Features and Benefits
Setaria has various features and benefits. To find out more about how Setaria can help you, please get in touch.
Facilitates ICH M7 Compliance
By bringing together predictions from Derek and Sarah Nexus, toxicity data from Vitic and in-house sources, and expert commentary, Setaria centralises all the knowledge and data that is necessary to generate an ICH M7 classification for impurities.
Reduces Duplication of Effort
Setaria enhances the visibility of (Q)SAR predictions and expert conclusions across an organisation, ensuring that repeat assessments of the same compounds can be avoided.
Rapid Data Retrieval
Quickly access data and export relevant information in a format suitable for regulatory submission. Setaria is designed to deliver fast and efficient access to data and knowledge, enabling quick decision making. All compounds present in Setaria are searchable by structure, CAS number, or name and results can be refined by study result, (Q)SAR prediction, or assigned ICH M7 class.
Supports Lower Risk Drug Development
Knowledge and data from previous development projects can be leveraged by medicinal chemists to support drug design through understanding the risk associated with specific areas of chemical space.
Enhances Risk Management Across Discovery Programs
Individual compounds can be associated with one or more ‘Discovery Programs’, allowing risk management plans to be developed prior to further testing.
Archive of Historical Assessments
Setaria supports informed decision making by providing an audit trail for historical (Q)SAR assessments, enabling users to track and understand former assessments.
Through the provision of intuitive editing tools, Setaria can be moulded to fit the different needs of individual organisations, allowing the user to populate their own customised schema. This ensures that search options and compound identifiers, are consistent with in-house naming conventions, facilitating company-wide understanding and ease of use.
Assessing In Silico Performance
The integration of (Q)SAR predictions and experimental data allow for the rapid validation of in silico performance, for in-house chemical space.