Lhasa Limited shared knowledge shared progress


This is a library of Lhasa's blog articles. 

  • MIT Artificial Intelligence AI Machine Learning Technology Knowledge 1170x500px
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    Effiris: A secondary pharmacology model suite powered by privacy-preserving data sharing

    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.

    21 May 2020

  • Effiris blog image Feb2021
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    How to better anticipate and mitigate adverse drug reactions [an infographic]

    Are you interested in using your data to anticipate and mitigate adverse drug reactions? The Effiris consortium - currently composed of Takeda, GSK and UCB - is working to achieve just that.

    17 February 2021

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    ICH M7 Expert Review in action: New Expert Review functionality in Nexus 2.3 with worked examples

    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.

    16 June 2020

  • Image for solubility purge best practice blog
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    Lhasa Limited collaborates with industry and regulators to define solubility purge best practice

    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.

    29 October 2020

  • Vaccine image rendered
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    Lhasa Limited supports an international initiative in the fight against COVID-19

    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).

    25 June 2020

© 2021 Lhasa Limited | Registered office: Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK Tel: +44 (0)113 394 6020
VAT number 396 8737 77 | Lhasa Limited is registered as a charity (290866)| Company Registration Number 01765239 (England and Wales).

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