Sarah Features and Benefits
Sarah Nexus has various features and benefits. To find out more about how Sarah Nexus can help you, please get in touch.
Expert ICH M7 Support
Sarah predictions are accepted by regulators under the ICH M7 guideline. The M7 prediction functionality allows for simultaneous compound processing in Sarah and Derek against the mutagenicity in vitro endpoint, fulfilling both the expert rule-based and statistical-based predictions required under ICH M7.
Rapid Mutagenicity Assessment
Sarah can swiftly provide single or batch predictions for the mutagenicity of query compounds.
Extensive Coverage of Chemical Space
“Out of Domain” predictions are of little value when conducting expert review and add additional complexity to this process. Sarah’s large, high-quality training set ensures maximum coverage of chemical space thus minimising problematic “Out of Domains”.
Accurate Predictions for Proprietary Compounds
Sarah has been extensively evaluated using pharmaceutical proprietary data sets and delivers high performance (Barber et al. 2015).
The Sarah model is built upon a large, high-quality dataset that has been curated by Lhasa experts. Structures have been standardised and conflicting data points have been solidly assessed meaning you can be confident that the predictions are based on scientifically robust data.
Predictions are clearly represented and supported by a measure of confidence, the fragments on which the predictions are based and relevant examples from the Sarah training set ordered by structural similarity to the query compound. This high level of transparency facilitates the expert review process.
Wealth of Supporting Information
In order to aid expert review, Sarah incorporates additional data such as strain information, similar compounds which were not included in the model, and references. This additional information is available for single predictions, batch predictions, and batch validations.
Sarah has default prediction settings that have been designed to ensure the best balance between sensitivity and specificity for use under the ICH M7 guideline. However, it is recognised that users may want to change this approach for their particular need and Sarah’s intuitive prediction setup options allow you to tailor the prediction accordingly.
Sarah incorporates a reporting framework that allows (.doc .pdf .xlsx and .sdf) file export. Report templates are fully customisable by the end user ensuring that you can provide the right information at the right time.
A user-editable classification is derived from the predictions provided by Derek, Sarah and any relevant experimental information included.
Barber et al. (2015). ‘Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained’, Regulatory Toxicology and Pharmacology, vol. 76, April, p. 7-20. http://dx.doi.org/10.1016/j.yrtph.2015.12.006