shared knowledge shared progress
New data released into our Nitrites in Excipients DatabaseLhasa Limited releases new data to Nitrites in Excipients project users.
14 December 2021
08 December 2021
07 October 2021
20 tips for nitrosamine impurity assessmentMany readers will be aware of the risk posed by nitrosamine impurities, as an ongoing pharmaceutical industry hot topic. In July 2021 ANVISA published their guidance for assessment and control of potentially carcinogenic nitrosamines in active pharmaceutical ingredients and drug products. To discuss this topic, we invited knowledgeable speakers from ANVISA, Industry - GSK and Libbs Farmacêutica - and Lhasa to give their perspectives on this topic. This article details the top 20 take away points from the workshop for quick and easy consumption - enjoy!
05 August 2021
08 April 2021
25 January 2021
Lhasa Limited announces the release of Effiris 2.2 - providing accurate secondary pharmacology predictions for novel compoundsEffiris delivers state-of-the-art secondary pharmacology models, where each model is able to learn from the private data of all pharmaceutical collaborators. As a result, Effiris overcomes one of the main challenges facing model development in drug discovery - accessing and utilising high-quality proprietary data.
Prior to this release, Effiris already offered model building – the ability to build teacher models in Effiris using in-house confidential data, privacy-preserving data sharing – the production of label files which extract knowledge while preserving all confidential aspects of the compounds used, and hybrid model building – collating Effiris member’s confidential dataset labels with the shared knowledge available in Effiris and publicly available data, in order to generate more comprehensive dataset to build a hybrid models.
What can you expect from Effiris 2.2?...
17 January 2022
Predicting N-nitrosamine carcinogenic potency using structure-activity relationships (SARs)This blog article introduces an insightful publication by Lhasa Principal Scientist, Dr. David Ponting and Dr. Kevin Cross from Instem, which investigates whether improvements in structure-activity relationships can more accurately predict N-nitrosamine carcinogenic potency.
17 December 2021
November 2021Mirabilis
November 2021Derek Nexus
November 2021Elemental Impurities
Lhasa Limited announces the release of Effiris 2.2 - providing accurate secondary pharmacology predictions for novel compoundsEffiris delivers state-of-the-art secondary pharmacology models, where each model is able to learn from the private data of all pharmaceutical collaborators. As a result, Effiris overcomes one of the main challenges facing model development in drug discovery - accessing and utilising high-quality proprietary data.
Prior to this release, Effiris already offered model building – the ability to build teacher models in Effiris using in-house confidential data, privacy-preserving data sharing – the production of label files which extract knowledge while preserving all confidential aspects of the compounds used, and hybrid model building – collating Effiris member’s confidential dataset labels with the shared knowledge available in Effiris and publicly available data, in order to generate more comprehensive dataset to build a hybrid models.
What can you expect from Effiris 2.2?...
17 January 2022
14 December 2021
08 December 2021
Predicting N-nitrosamine carcinogenic potency using structure-activity relationships (SARs)This blog article introduces an insightful publication by Lhasa Principal Scientist, Dr. David Ponting and Dr. Kevin Cross from Instem, which investigates whether improvements in structure-activity relationships can more accurately predict N-nitrosamine carcinogenic potency.
17 December 2021
30 November 2021
16 November 2021
Clarke G; Shannon-Little AL; Williams RV;Derek Nexus, Lhasa Limited's exert knowledge-based toxicity prediction software, generates negative predictions for endpoints, such as Ames mutagenicity, to assist with regulatory submission. This infographic describes how go about understanding a negative prediction in Derek Nexus.
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