Vitic NexusVitic Nexus is the next generation chemical database and information management system, offering researchers and scientists rapid access to searchable toxicological information. Early review of Vitic Nexus is the quick, inexpensive way to identify potentially toxic chemicals, and therefore reject unsuitable drug candidates.
Vitic Nexus is your trusted toxicity expert and management system; containing data implemented by scientists at Lhasa Limited, who continually work on the toxicity database with current toxicological knowledge.ZenethZeneth is an expert, knowledge-based software that gives you accurate forced degradation predictions quickly. Zeneth is the perfect cost-effective solution for scientists who need to understand the forced degradation pathways of organic compounds.
Zeneth is your trusted degradation expert system. It is based on data implemented by scientists at Lhasa Limited, who continually work on the transformation knowledge base with current transformation knowledge.
This FAQs section details frequently asked questions about the ICH M7 guidelines. For FAQs on Lhasa products, please refer to the relevant product area.
Can you provide suggested settings for use in Sarah Nexus?
Yes we have recommended settings for the of Sarah.
The user is able to change the settings within Sarah. This gives the user some flexibility in how the predictions are derived in order to best match their use-case. For example if harsh triaging was desired to answer the question “do any of my molecules have potentially activating features?”, then the user could select ‘conservative’ whereby any activating fragments would trigger a positive prediction irrespective of the presence of deactivating features. Similarly if only very confident predictions are wanted, then the user can raise the equivocal threshold (level below which the weight of evidence is not high enough to make a call).
However, for typical use, and particularly for regulatory submission, we recommend that the settings for both equivocal threshold and sensitivity are both set at 8% and the reasoning is set at weighted. This then provides the best compromise between sensitivity and specificity and also provides the best overall accuracy. We have done extensive analyses using a number of proprietary datasets to derive these settings.at Lhasa Limited SOT.