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In his blog “Human review of in silico predictions of toxicity”, Principal Scientist, Alex Cayley, outlined the reasons why expert review is essential when using in silico tools to assess toxicity, particularly in the ICH M7 use case where expert review of two complementary in silico systems is required [1].

In this post we discuss how consideration of the user’s journey led to Lhasa tackling the ICH M7 use case with a brand-new automated workflow, now available to Derek and Sarah users in Nexus 2.3. I’ll also show some worked examples of results from the new system.

Coronaviruses are a large family of viruses which can affect humans or other species and can range anywhere from a mild illness such as the common cold to more severe illnesses such as Severe Acute Respiratory Syndrome (SARS). Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

The recent discovery of N-nitrosamine (nitrosamine) impurities in several marketed pharmaceuticals has led to a requirement for further investigation into nitrosamine mutagenic and carcinogenic activity. Regulatory requirements mean that marketing authorisation holders for human medicines, containing chemically synthesised active substances, must review their medicines for the possible presence of nitrosamines and test all products at risk. Risk-based approaches to prioritise evaluations and subsequent confirmatory testing may be used. These assessments must be completed by October 2020, generating a significant challenge for the pharmaceutical industry.

Derek Nexus contains alerts which predict the potential toxicity of a compound, this article explains how Lhasa scientists create alerts which are trustworthy and accurate.

One of the most important ingredients for an effective team is trust, and a critical phase of team building is starting to develop that sense of trust – to be strong has to be earnt and regularly reinforced; it is rarely simply given! The same is true when using in silico models and by mirroring the natural approaches – the intuitive tests that we apply either consciously or subconsciously - we can understand how and when a model can be effectively used.