Bio: Dr Jin Li has over 18 years of experience at Unilever and is currently a senior safety scientist in Safety, Environmental and Regulatory Sciences in the United Kingdom. His main responsibility at Unilever is to develop and apply non-animal approaches to chemical safety risk assessment, focusing on 1) integrating in silico, in vitro, and in vivo multiscale data for chemical safety assessments, and 2) developing novel NAMs-based next-generation risk assessment (NGRA) approaches for human health and environmental safety. He has also supported the submission of safety dossiers on Unilever products to Chinese authorities for many years. Since 2011, Jin has collaborated with scientists globally from the UK, EU, USA, and China, organizing and chairing scientific dialogues and workshops to advance risk-based non-animal safety assessments. This initiative included establishing the Chinese Society of Toxicology Alternative Development Award (2016-2027) in partnership with the Chinese Society of Toxicology (CST). Meanwhile, he has led Unilever's non-animal R&D programs in China for a decade. Jin serves as a council member of the CST, a standing committee member of the Chinese Society of Toxicological Alternatives and Translational Toxicology (TATT), and a member of the Chinese Society of Computational Toxicology and Food Toxicology Safety of the CST. He holds a first degree in Computer Science from China and obtained a Ph.D. from the University of Essex in the UK.

Abstract: Skin Sensitization Risk Assessment – Integrated Chemical Environment (SARA-ICE) is a Bayesian statistical model that estimates a human-relevant metric of skin sensitizer potency. This metric, termed ED01, is the dose with a 1% chance of human skin sensitization. SARA-ICE accounts for input data variability and explicitly quantifies uncertainty in estimating the ED01. Two versions of SARA-ICE have been developed. One, the SARA-ICE defined approach (DA), is under evaluation by the OECD for inclusion in Test Guideline 497, Defined Approaches for Skin Sensitization, with Guideline updates underway. This DA allows derivation of a point-of-departure (POD) and here we show how it may be applied in a case study. Further to this DA, a SARA-ICE ‘Extended’ Model is shared which allows derivation of UN Globally Harmonized System of Classification and Labelling of Chemicals (GHS) hazard and potency categorizations. The models do not require a specific dataset to make predictions, thus allowing for iterative and flexible application based on available data. Recently, a web application has been developed, allowing local installation and making SARA-ICE DA and extended versions available to all stakeholders. This presentation will highlight the differences in the models, their evaluation and application.