Sarah Nexus, a New (Q)SAR Approach for the Prediction of Mutagenicity Will Facilitate the Analysis of Impurities in Pharmaceuticals Under the Proposed ICH M7 Guidelines

LEEDS, England, June 6, 2013 /PRNewswire/ --

Lhasa Limited, a leading global supplier of knowledge based software and associated databases for use in toxicity, metabolism and the related sciences, today announces the development of a new product: Sarah Nexus, a statistical (Q)SAR methodology for the prediction of mutagenicity from chemical structure.

Under the proposed ICH M7 Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk the absence of structural alerts from two complementary predictive methodologies (expert rule based and statistical) and an expert review will be sufficient to conclude that impurities in pharmaceuticals are of no mutagenic concern. The expert rule based approach of Derek Nexus combined with the statistical approach of Sarah Nexus will enable organisations to meet the requirements of ICH M7.

The OECD member countries have agreed a set of principles against which (Q)SAR models can be evaluated as fit for regulatory purposes (http://www.oecd.org/env/ehs/risk-assessment/validationofqsarmodels.htm). Sarah Nexus exceeds these four core principles by extending the concept of applicability domain, or chemical space in which the model can be considered valid, by providing a level of confidence (expected accuracy) for every prediction.  

David Watson, CEO of Lhasa, commented: "The prediction of the mutagenic potential of impurities in pharmaceuticals for regulatory submission is a key requirement for our members. Adding the statistical approach of Sarah Nexus to the world leading expert rule based approach of Derek Nexus will enable our members to seamlessly meet the in silico requirements of ICH M7."

Chris Barber, Lhasa's Director of Science, added: "Statistical systems have often disappointed users by making predictions that are either unclear or unsupported. Such 'black box' predictions do not provide experts with sufficient information to make confident decisions. Sarah Nexus combines our expertise in mutagenicity with novel machine-learning algorithms to provide predictions that are supported by estimates of accuracy, clear explanations of how they are derived and direct access to the underlying supporting data."

Sarah Nexus uses the data held within Vitic, Lhasa's custom database, thereby uniquely benefiting from this large peer-reviewed and expert-curated mutagenicity dataset.

Lhasa Ltd will formally introduce Sarah Nexus and present the scientific basis of the software at the 6^th Annual Genotoxic Impurities Meeting in Berlin Germany, 19^th-20^th June 2013. For a demonstration of this new addition to Lhasa Limited's portfolio, please visit our booth at the 6^th Annual Genotoxic Impurities meeting in Berlin, Germany, 19-20 June 2013 and/or the 13^th International Congress of Toxicology in Seoul, South Korea, 30^th June to 4^th July 2013 (booth D-13).

About Lhasa Limited

Lhasa Limited is a not-for-profit organisation that facilitates collaborative data sharing projects in the pharmaceutical, cosmetics and chemistry-related industries. A pioneer in the production of knowledge-based systems for forward thinking scientists, Lhasa limited continues to draw on over 25 years of experience to create user-friendly, state of the art in silico prediction and database systems.

We believe in 'Shared Knowledge, Shared Progress'.  Our not-for-profit, member driven status is designed to facilitate collaborative working and confidential data sharing between organisations. We run collaborative projects with industry, academia and regulatory bodies to continually enhance all our products.

Lhasa's products include Derek Nexus for predicting toxicity, Vitic Nexus for managing chemical information, Meteor Nexus for predicting metabolic fate and Zeneth for predicting forced degradation pathways.

For further information on Lhasa Limited and Sarah Nexus contact info@lhasalimited.org or call +44-113-394-6020.