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! 

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 article, we discuss how Derek Nexus and Sarah Nexus meet the 5 OECD principles.

A key requirement of ICH M7 is to use two complementary (Q)SAR methodologies, one expert rule-based and the second statistical-based. This blog article recognises the benefits provided when in silico systems are integrated to provide a full ICH M7 assessment in one environment.

Discover how Lhasa Limited can help make mutagenicity assessment a simpler task, in this article!

The speed by which models for toxicity prediction can be built and the accuracy with which they can make predictions, have both improved greatly in recent years as a result of advances in technology, mechanistic understanding and access to data.

The saying goes, “It takes a village to raise a child”. This sentiment can be translated to the work we do at Lhasa Limited, particularly to the work which we put into building Adverse Outcome Pathways (AOPs). To echo the sentiment in Chris Barber’s previous blog post, The challenge of building QSAR models; it is easy to build an AOP, harder to build a useful AOP, and as for building an AOP you can trust? It takes a team.

Read about how we are building adverse outcome pathways (AOPs) at Lhasa in this blog post written by Dr. Susanne Stalford, Senior Scientist at Lhasa.