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

Artificial Intelligence (AI) has become a powerful force within the pharmaceutical industry, catalysed by the challenging lengthy and costly drug development process. To maximize the impact of AI, it is critical to have access to enough good quality data to allow machine learning algorithms to extract relevant knowledge and produce valuable models.

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.

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.

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.

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.

There is no perfect formula when it comes to trying to organise any kind of work trip. Normally, you have to engage through a series of mental gymnastics to try and balance semi-quantifiable concepts such as ‘return on investment’ and ‘value’.

Let’s take ‘value’ for instance. Is the value of the trip based on a new sale? The time spent out of the office? Establishing a personal contact to secure existing business? Ensuring that the product that you’re supporting is used and understood to the maximum capacity?

Now balance that with travelling halfway across the planet, rather than a quick hop on the Eurostar.

Read more about our trip to India earlier this year.

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 creation of great software cannot be achieved using a single pre-determined set of rules, if it could, everyone would follow those rules and it would be straightforward. 

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.