Cefic-lri Programme | European Chemical Industry Council

AIMT8: Prediction of systemic toxicity after repeated exposure by new approach methodologies (NAMs) – is prediction of STOR-RE classification possible?

Principal Investigator

Dr Sylvia Escher
Chemical Risk Assessment
Nikolai-Fuchs Straβe 1
30625 Hannover
Tel: + 49 511 5350 330


Dr Chihae Yang, Molecular Networks GmbH (MN), Erlangen, DE,
Prof. Dr. Mark Cronin, Liverpool John Moores University (LJMU), Liverpool, UK, m.t.cronin@livjm.ac.uk


This project aims to assess the ability of currently available new approach methodologies (NAM) to predict STOT-RE categories of a range of chemicals for which both in vitro and in vivo data are available and that can be profiled in silico. This could challenge the current belief that in order to be useful, new methods must predict all outcomes of an animal study, both the NOAEL (quantitatively) and the observed apical findings (qualitatively). In case that this paradigm is not true, the outcome of this project could facilitate the acceptance of the alternative methods such as in vitro assays, as well as computational approaches, for safety and regulatory decision making.

This project consists of five work packages (WPs) which cover the following main objectives:

WP 1 data selection and STOT assignment (ITEM): For the purpose of this project we will need compounds with high quality in vivo data as well as in vitro data. First we will therefore identify compounds with in vivo data from high quality databases, e.g. ToxRef DB, RepDose DB, HESS DB and Cosmos DB. These compounds will be mapped against the standardised in vitro data from ToxCast/Tox21. In a next step the in vivo data e.g. LOAEL values will be used to assign each chemical to its appropriate STOT-RE category.

WP2 machine learning techniques (MN). A range of statistical and machine learning techniques will be selected to assess the ability of the in vitro and in silico data (collectively the NAMs data) to predict the STOT-RE classification and to create suitable data structures for the techniques. Use the machine learning techniques to investigate the ability of the in silico/in vitro data to predict the STOT-RE classification.

WP3 Prediction of STOT-RE classification by in vitro assays (LJMU). Finally determine the optimal data and techniques to predict the STOT-RE classification from WP2. Include exemplarily (Q)IVIVE if necessary to obtain useful results. Analyse mechanistic links between the in vitro results and the in vivo apical endpoint leading to STOT-RE classification.

WP4 Guidance on integration of NAM data for classification or derivation of point of departure/reference dose from in vivo studies (lead ITEM): Examine whether guidance can be given on the derivation of reference doses for risk assessment based on NAMs. Assess whether different ways of expressing the hazard potential of chemicals can be developed which are more suited to New Approach Methodology.

WP5 Project management and dissemination of results (lead ITEM): the results of this project will be published e.g. at relevant international congresses like EUROTOX 2018 or SOT 2018. Further a publication in a peer-reviewed journal is foreseen.

Advances to Current State of the Art

The application of NAM (New Approach Methodology) in risk assessment is an area of intensive research. It is common understanding that single e.g. cell based in vitro assays may not be able to assess complex endpoints such as systemic toxicity of a compound after repeated long term exposure at relatively low levels.

Objective of the research:

The project will advance on the understanding of the use of NAMs by analysing a different way of predicting systemic toxicity after repeated exposure. It will evaluate whether in vitro methods will be able to predict STOT-RE classification of compounds. STOT-RE classification is based on the NOAEL of the in vivo study and does not consider the type of effect or the affected organ. If prediction of STOT-RE classification by NAMs is possible, this result will contribute to a paradigm shift in risk assessment and will also facilitate the use of NAMs in prioritization and labelling as well as later on in risk and safety assessment.

Timeline: January 2017 > July 2018

LRI funding: € 80 000

Cefic-Lri Programme Responsible Care

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