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In this latest blog, we will define the difference between certainty and uncertainty in the ICH M7 guideline for mutagenic impurities. Delving into the power of existing experimental data, we’ll address how Vitic’s expert toxicity database can accelerate the workflow of Classes 1, 2 and 5 in mutagenicity risk assessments. To Toxicologists, the ICH M7 […]

Carcinogenicity assessment is a critical step in drug development. Teams must balance regulatory compliance, development timelines, and ethical testing, often with incomplete datasets. The revised ICH S1B(R1) guideline introduces a weight-of-evidence (WoE) approach that provides a more structured, efficient, and human-relevant path forward. By applying this framework, organisations can benefit from: $2 – 4M cost

In this blog, we explore the value of chemical structure search functionality in Vitic. Structure searching directs our members toward toxicity insights that can help guide their risk and safety assessments. We’ll discuss how chemical structure searches are conducted and the value that a toxicity database like Vitic can bring to decision making. A key

In a recent Lhasa Limited webinar, experts from BfR (the German Federal Institute for Risk Assessment), Syngenta, and Lhasa came together to explore how in silico tools are being used to support genotoxicity assessments. The session delivered a thorough overview of how these advanced computational methods are applied in both regulatory and industry settings, focusing

The ICH M7(R2) Guideline (2023) continues to recommend the bacterial reverse mutation assay (Ames test), conducted in accordance with OECD 471, as a primary tool for assessing the mutagenic potential of pharmaceutical impurities, including nitrosamines. While the Ames test is generally predictive of rodent carcinogenicity, concerns remain about its sensitivity under standard conditions, particularly when

A new paper, “Data-driven federated learning in drug discovery with knowledge distillation”, was recently published in Nature Machine Intelligence, the leading journal for Machine Learning and AI. It explores the potential for federated learning to advance drug discovery through secure, collaborative research, representing a major step forward in privacy-preserving machine learning.   What is Federated

At Lhasa we know that there are many challenges involved in carrying out risk assessments for drug substances and drug products, to ultimately ensure a safe and effective product for patients. Forced degradation studies are essential in pharmaceutical development to assess drug stability and ensure product quality and safety. These studies help identify degradation pathways,