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Sarah Nexus

Statistical-based software for the prediction of mutagenicity

  • Sarah Nexus is a statistical software tool that gives you accurate mutagenicity predictions.
  • Sarah Nexus can be used as part of an ICH M7 workflow.
  • Early, accurate in silico toxicity testing using Sarah Nexus is the quick, inexpensive way to identify potentially toxic chemicals, aiding experts in rejecting unsuitable drug candidates.

Sarah Nexus logo

 Why do you need Sarah Nexus?

General Approach

(Q)SAR Methodology

Advantages of Lhasa’s Methodology


Interpretation of Predictions


Features and Benefits

Sarah Model Builder


Why do you need Sarah Nexus?

The ICH M7 guideline1 proposes that a computational toxicology assessment should be performed using two complementary (Q)SAR methodologies that predict the outcome of a bacterial mutagenicity assay. Specifically, one methodology should be expert rule-based and the second methodology should be statistical-based.

(Q)SAR models utilising these prediction methodologies should also follow the validation principals set forth by the Organisation for Economic Co-operation and Development (OECD)2.
Sarah Nexus and Derek Nexus (the Lhasa Limited expert toxicity prediction tool), in combination, can provide you with the means to meet the computational toxicological assessment requirements of the ICH M7 guidelines from one intuitive interface.

You can assess your potential genotoxic impurities quickly and easily and submit those results to regulators reducing the need for time consuming and expensive in vitro tests.

Both Derek Nexus and Sarah Nexus have been designed independently to meet the OECD validation principles, and both systems can be run from within the same Nexus interface to help simplify your workflow.

The models provide completely independent predictions, with the option to consolidate into a single report.

1) http://www.ich.org/products/guidelines/multidisciplinary/article/multidisciplinary-guidelines.html

2) http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?doclanguage=en&cote=env/jm/mono(2007)2

General Approach

  • Sarah Nexus uses a unique, machine-learning methodology.
  • The query structure is fragmented and the fragments are refined and ranked depending on the similarity of the query to the training set of compounds.
  • Sarah gives an overall transparent prediction supported by a level of confidence and relevant examples.

(Q)SAR Methodology

Sarah Nexus uses a unique, hierarchical, machine-learning methodology to build a model for Ames mutagenicity.

Query structures are fragmented by Sarah and the fragments are reviewed for activity versus inactivity. It further refines these fragments by considering the similarity of the query structure to a training set of compounds.

The fragments are arranged into a network of hypotheses (or nodes) and the fragments which are perceived to be of a greater value contribute to an overall prediction of toxicity. Fragments may be of various sizes and can even overlap, ensuring greater accuracy in predictions. Figures 1-3 highlight a step-by-step guide to the fragmentation process.

The overall prediction is comprised of a conclusion about the Ames mutagenicity of a structure, and a confidence rating in the prediction. In addition, Sarah also provides the fragments on which the prediction is based and relevant examples from the training set, ordered by structural similarity to the query. This high level of transparency facilitates the expert review process.

Advantages of Lhasa’s Methodology

 The advantages of this methodology include:

  • The ability to generate fragments that are contained within the training set molecules, thereby avoiding the bias of models built using pre-determined fragments, which may not reflect the training data.
  • The ability to build a hierarchy of models - some more global and some more local, giving users the best of both worlds.
    • A single global model while having broad coverage, will not be adequately sensitive to local variations (activity cliffs).
    • Local models whilst more accurate for fragments that fall inside their chemical space will be narrower in their scope (applicability domain).
    • Sarah Nexus contains both and will select the most appropriate model for each fragment.
  • Sarah Nexus looks at the information available for each fragment and uses scientifically valid rules to combine these. The relative importance of the contribution of each local model is provided, along with the data that underlies it, thereby providing a very transparent prediction. Furthermore, Sarah Nexus gives a measure of confidence for each prediction it makes. Lhasa believes that this uniquely gives the information that experts need to be able to understand and judge the prediction.


Sarah Nexus provides a confidence score for each prediction along with direct access to supporting data to aid expert analysis. The confidence score is based on each fragment’s contribution to the overall prediction and the weight placed upon each fragment. Lhasa’s analysis shows that the confidence strongly correlates to accuracy (figure 4 - for the full graphic which explains confidence in Sarah, click here). 

For more information on confidence in Sarah Nexus, please view Lhasa’s video on Model Building and Interpreting Confidence, presented by Account Manager Dr Dave Yeo. 



Interpretation of Predictions

When considering the interpretation of a prediction, expert review is very important. This is why Lhasa has worked hard to ensure Sarah Nexus facilitates this by providing sufficient information to support an expert analysis.

Sarah Nexus represents a different approach to ‘black box’ statistical models, where the user can have an understanding of the model’s accuracy against a test set, but can’t assess an individual prediction.

The structural explanation for the prediction provided by Sarah Nexus is conveyed by highlighting those fragment(s) that Sarah considers meaningful. Derek Nexus also highlights fragments of the query compound in order to illustrate the matches to patterns used to hold knowledge within Derek.

The reason that both models highlight structural fragments is the same: to draw the user’s attention to parts of the query compound which influenced the prediction. However, Derek relies on patterns drawn by experts to find these fragments, whereas Sarah identifies those with statistical significance.

Sarah Nexus, like Derek Nexus, presents all the information an expert requires in order to come to an informed decision.

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