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As a member-based organisation, anticipating and supporting the needs of our members is critical. 

For many years the Carcinogenic Potency Database (CPDB)¹, created by Lois Gold and her team, was an important source of long-term carcinogenicity study data. However, as the database had stopped being updated from 2007, Lhasa moved to safeguard the data by providing ongoing access through a freely available interface; the Lhasa Carcinogenicity Database (LCDB)². The LCDB was released in 2016 and has since been updated with additional data from the National Toxicology Program (NTP)³, increasing the data set to 7,745 studies covering 1,726 chemical substances. A recent update has also facilitated greater ease of access by enabling substructure and similarity-based structure queries.

The speed by which models for toxicity prediction can be built and the accuracy with which they can make predictions, have both improved greatly in recent years as a result of advances in technology, mechanistic understanding and access to data.

The saying goes, “It takes a village to raise a child”. This sentiment can be translated to the work we do at Lhasa Limited, particularly to the work which we put into building Adverse Outcome Pathways (AOPs). To echo the sentiment in Chris Barber’s previous blog post, The challenge of building QSAR models; it is easy to build an AOP, harder to build a useful AOP, and as for building an AOP you can trust? It takes a team.

Read about how we are building adverse outcome pathways (AOPs) at Lhasa in this blog post written by Dr. Susanne Stalford, Senior Scientist at Lhasa.

Key to the regulatory acceptance of purge arguments is the conservatism within the purge factor scoring system, which for reactivity purge has been demonstrated across a number of publications and is consistently implemented across the pharmaceutical industry through the development of a collective understanding of and how they should be applied.

Likewise, when volatility purge scoring is relatively simple to understand and apply, particularly using the scoring thresholds originally proposed by Teasdale et al.

However, through our interactions with Mirabilis users, regulators, and other members of the purge community, it has become increasingly apparent that the same cannot be said of solubility purge scoring. Lhasa have therefore identified the huge value to the community of developing an accepted best practice, incorporating both the application of solubility purge scoring and, where appropriate, provision of a clear justification of the purge factor assigned.

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.

AOPs have the potential to be very powerful for the contextualisation of alternative assays and consequently for the management of alternative testing strategies. Read about what Lhasa is doing in this area in this blog piece written by Alun Myden, Senior Scientist at Lhasa.

Building QSAR models is easy. Building useful QSAR models is harder. Building QSAR models you can trust – well that can be really challenging – and this is where a lot of Lhasa’s effort is dedicated – creating models that can be used with confidence to make big decisions – such as those made during a regulatory submission that can protect human safety through exposure of a compound or its impurities! 

Throughout the history of science, progression has relied upon the generation of new knowledge and  then building on that knowledge in order to gain even greater insight. In order to progress, this knowledge needs to be shared as widely as possible.

An award honouring the scientific contributions and memory of Dr. Richard Williams, has been set up to support early career investigators within industry. Supported by the Industrial Genotoxicology Group (IGG), The Richard Williams Memorial Award will sponsor one individual a year to attend and present at the United Kingdom Environmental Mutagen Society (UKEMS) meeting.

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.

The ICH Q3B (Impurities in New Drug Products) guideline provides guidance on the qualification of impurities in new drug substances produced by chemical syntheses. This article discusses how Zeneth, Lhasa Limited’s expert decision support software for predicting the forced degradation of organic compounds, can help to satisfy ICH Q3B.