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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,

The use of in silico prediction systems has become an essential part of regulatory submissions when assessing the genotoxic potential of chemicals. Guidelines such as those from the EMA, US FDA, and ICH highlight their importance. Most notably, the ICH M7 guidance explicitly requires the application of two complementary quantitative/qualitative structure-activity relationship ((Q)SAR) systems to