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Innovate

Discover the latest innovations in computer-aided reasoning and information systems.

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Advancing drug discovery though data-driven federated learning

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 […]

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Blog image for how to overcome challenges in forced degradation studies with Zeneth

How to overcome the critical challenges faced in forced degradation studies

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,

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Why prevalence shifts matter in machine learning

Why prevalence shifts matter in machine learning

In machine learning, the validation of binary classifiers, algorithms that categorise data into two classes, is essential. However, a frequently overlooked issue is prevalence shift, which occurs when two test datasets have different rates of positive instances. This phenomenon can distort performance metrics, leading to incorrect model evaluations. Understanding the problem Prevalence shift fundamentally affects

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2024 Lhasa Publication Award Winners

Celebrating excellence, our top publications of 2024

We recently invited our scientific community to vote for their favourite Lhasa 2024 publication across 3 categories: Toxicology (excluding nitrosamines), Toxicology (specifically nitrosamines) and Chemistry. These awards not only recognise exceptional contributions by Lhasa scientists, but also highlight impactful research that drives progress in our fields and informs the direction of future publications.  The winners

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Navigating nitrosamine impurities with Lhasa

Navigating nitrosamine impurities: An in silico approach to risk assessment

In the ever-evolving landscape of pharmaceutical development, ensuring the safety and efficacy of medications is paramount. One of the emerging challenges in this field is the detection and management of nitrosamine impurities—potentially carcinogenic compounds that can form during drug manufacturing. To address this, having a robust workflow for nitrosamine impurity risk assessments is essential. In

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Sarah Nexus frequently asked questions

Everything you need to know about Sarah Nexus

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

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10 FAQs About Mirabilis: Pharmaceutical Impurity Control Using In Silico Tools

Identification and control of impurities in active pharmaceutical ingredients (APIs) and pharmaceutical drug products is critical in drug development. Mirabilis is our innovative solution designed to address this challenge. In this post, we’ll answer ten frequently asked questions about Mirabilis.   What is Mirabilis? Mirabilis is an in silico tool developed by Lhasa Limited that

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A step towards animal-free developmental and reproductive toxicity safety assessments

Developmental and reproductive toxicity (DART) is a critical area of toxicology that is required to assess the impact of new chemicals on adult fertility and embryo development. Industries across the globe rely on DART assessments to safeguard human health, adhering to regulatory standards like those outlined in the ICH S5 (R3) guideline. However, the current

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10 frequently asked questions about Derek Nexus, answered

For over four decades, at Lhasa Limited we have been developing scientific software solutions that enable scientists to make informed decisions on the safety of drugs, chemicals and cosmetics. Our journey advanced significantly with the creation of Derek Nexus, an in silico expert knowledge-based toxicity prediction tool. As regulatory landscapes evolved and the push for

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Adverse Outcome Pathways; AOP; AOPs; Carcinogenicity; Expert Review; Computational Toxicology; New Approach Methodologies; Weight-of-Evidence; WoE, ICH S1B

Embracing new approach methodologies in carcinogenicity assessments

Do you need a transparent, scientifically robust, and reproducible weight-of-evidence approach for assessing for carcinogenicity? Then this scientific publication is for you. We are excited to share “Structuring Expert Review Using AOPs: Enabling Robust Weight-of-Evidence Assessments for Carcinogenicity Under ICH S1B(R1)”, which has been published in Computational Toxicology, as part of our Lhasa special issue,

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