Nitrites in Excipients data release: Supporting more scientifically driven nitrosamine risk assessments
17 May 2022
Lhasa Limited is pleased to announce the release of the 2022.1 Nitrites in Excipients database.
03 May 2022
Effiris provides secondary pharmacology models, which have learnt from the in-house confidential data of pharmaceutical collaborators, while preserving all confidential aspects of the compounds used. As a result, Effiris overcomes one of the main challenges facing model development in drug discovery - accessing and utilising high-quality proprietary data.
The aim of the Federated Learning Hackathon - expected to launch within Q2 2022 - is to produce a federated Effiris model which outperforms the models trained in-house using only private proprietary data, at each of the participating pharmaceutical organisations. A diverse panel of nine secondary pharmacology endpoints of high interest to the pharmaceutical industry have been prioritised for the hackathon initiative...
Read the full article for more information.
29 April 2022
Due to the active consortium and successful member data donations into the database, Lhasa is pleased to announce that the Elemental Impurities Database is now open for all to join.
27 April 2022
It is with great excitement that Lhasa Limited announces the launch of free tool, Wiki Kaptis.
Wiki Kaptis is the result of an ongoing collaboration between Lhasa Limited and AOP-Wiki.
Access Wiki Kaptis for free via this article.
28 March 2022
Lhasa Limited is excited to announce the launch of an ICH S1 consortium. The Lhasa-led consortium consists of several industry partners – all with a common goal of supporting meeting ICH S1 and reducing animal testing using Adverse Outcome Pathways (AOPs) within Kaptis.
24 March 2022
Lhasa Limited is pleased to announce the release of Nexus 2.5, which incorporates new Derek Nexus knowledge relating to skin sensitisation, and updates to the Sarah Nexus training set to enable informed decision making on chemical safety.
AstraZeneca, Novartis, Pfizer and Teva join new Lhasa Limited data sharing initiative to better understand mutagenic potential of complex nitrosamines
09 March 2022
We are delighted to announce the launch of our new complex nitrosamine data sharing initiative. The project currently holds 4 respected members within the pharmaceutical industry: AstraZeneca, Novartis, Pfizer and Teva, with 5 additional pharmaceutical companies in the process of joining.
Lhasa Limited releases skin sensitisation defined approach ITSv1 1.0 - providing hazard and potency skin sensitisation predictions without the use of animal tests
14 February 2022
Lhasa’s new web application ‘skin sensitisation defined approach ITSv1 1.0’, combines an in silico prediction from Derek Nexus with results from two OECD-validated in chemico/in vitro assays (DPRA and h-CLAT) to provide hazard and potency skin sensitisation predictions, without the use of animal tests.
10 February 2022
Lhasa Limited is delighted to announce the release of Vitic 2022.1.
Over the past 12 months, a focus for Scientists at Lhasa Limited has been on developing genotoxicity and carcinogenicity endpoints in Vitic. Vitic aims to provide a wealth of high-quality data to support chemical risk assessments. Within this article, we explore some of the key improvements in the 2022.1 Vitic data release.
Lhasa Limited announces the release of Effiris 2.2 - providing accurate secondary pharmacology predictions for novel compounds
17 January 2022
Effiris 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?...