Innovate

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. Updated 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 […]

In May, Lhasa hosted a webinar titled; The Application of Adverse Outcome Pathways (AOPs) for Risk Assessment. In case you missed this event, Senior Scientist, Alun Myden has provided an overview of the presentation and the work which Lhasa is doing in this area. An Introduction to AOPs AOPs are a formalised approach to documenting mechanisms

The concept of pre-competitive collaborations and data sharing is currently in sharp focus with the current global collaborative efforts to combat Coronavirus, where the sharing of scientific knowledge is both urgent and critical for us to have a better chance of tackling the global pandemic. From the inception of Lhasa Limited in 1983, we’ve sought

Coronaviruses and SARS-CoV-2 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

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. Despite the availability

Human Review of In Silico Predictions of Toxicity: Some Key Questions for Consideration When Making An Assessment The speed by which models for toxicity prediction can be built and the accuracy with which they make predictions, have both improved greatly in recent years as a result of advances in technology, mechanistic understanding and access to

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

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