Archive

Many readers of this blog will be aware of the threat posed by potentially mutagenic impurities within drug substance synthesis. Such impurities often arise from reagents which are critical to the synthesis therefore this impurity-related threat is unavoidable.

As such, there is a need to assess the risk posed by mutagenic impurities.

Many 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!

Discover a day in the life of a Mirabilis Research Scientist at Lhasa Limited! 

Hello! I’m Pearl and I work in Lhasa’s Marketing Team.

My role as a Marketing Executive at Lhasa Limited is a varied one to say the least. Disciplines involved in the role include email marketing, content marketing, strategy, design, website management, paid media, copy writing, event management and so much more. That’s why I love it so much!

A day in the life of a Software Delivery Team Leader at Lhasa Limited.

In this blog piece, we explore in more detail, the features of the recent release and how they will benefit LCDB users.

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

For many years the Carcinogenic Potency Database (CPDB)¹, created by Lois Gold and her team, was an important source of long-term carcinogenicity study data. However, as the database had stopped being updated from 2007, Lhasa moved to safeguard the data by providing ongoing access through a freely available interface; the Lhasa Carcinogenicity Database (LCDB)². The LCDB was released in 2016 and has since been updated with additional data from the National Toxicology Program (NTP)³, increasing the data set to 7,745 studies covering 1,726 chemical substances. A recent update has also facilitated greater ease of access by enabling substructure and similarity-based structure queries.

In this blog piece, we explore how Derek Nexus performs when evaluated against 7 different in silico skin sensitisation models.

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.