Cefic-lri Programme | European Chemical Industry Council

Projects

ECO26-RAD: Adapt SimpleTreat for simulating behavior of chemical substances during industrial sewage treatment

REACH Guidance for Chemical Safety Assessment makes use of mathematical simulation of the retention of chemical substances during waste water treatment. REACH R-16 describes the use of the model SimpleTreat, developed by Struijs and co-workers at RIVM, which simulates the […]

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ECO22-IRAS: Advancing the use of passive sampling in risk assessment and management of contaminated sediments: an inter-laboratory comparison study on measurements of freely dissolved (bioavailable) concentrations using different passive sampling formats

The main objective of this study is to map the state of the art in passive sampling sediments and to investigate which measures we can take to reduce any inter-method and -laboratory variation, in order to increase confidence in the […]

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Q3-UMA: Sound Science: Selective citation in science based decision-making

The European Chemical Industry Council has provided funding to Maastricht University for a research project on Research Integrity by exploring one of the most suspected contributors to questionable research practices: selective citation. It is generally accepted that scientific papers should […]

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EEM9.3-IC: Linking LRI AMBIT Chemoinformatic system with the IUCLID Substance database to Support Read-across of Substance endpoint data and Category formation

A major hurdle in using chemoinformatic systems is the lack of easily accessible data on substance identity including structures and composition e.g. constituents, impurities and additives as well as high quality, detailed endpoint data of chemical substances. This project is […]

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AIMT4-UM: DECO2: Moving from DECO towards OECD

The aim of this project is to establish a framework for tools usable in integrated approaches. For this, efforts in our previous DECO project (CEFIC-LRI AIMT3 grant) will be advanced. DECO focused on the prediction of in vivo repeated dose […]

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C3-ED: A comprehensive Epigenomic profile of liver tissue from Rat and Mouse

This project will apply a comprehensive strategy to characterise and curate genome wide methylomes in liver tissues from rats and mouse. This will involve correlation of the profiles generated with public data sets for RNA expression (transcriptomics) and histone modification profiles […]

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B15-UCRAN: Developing a robust method of allocating efficiency measures to regulatory instruments in the chemicals industry

This project aims to improve the depth and availability of knowledge and understanding of the specific factors affecting the efficiency of risk management measure (RMM) to control human (worker and consumer) and environmental exposures that may result from the use […]

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N4-WAG: Science-based grouping of nanoparticles for industrial application of safe-by-design

Current practice is that Regulators evaluate the risk from nanoparticles along the whole value chain, from manufacture through application in products to end of life disposal. This project must provide clear input to the evaluation of nanomaterial value chains. Two […]

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EMSG58-MNEGRI: Human adverse health effects of endocrine active substances: assessment of the quality of individual epidemiological studies and of the overall mechanistic and epidemiologic evidence

The project's objective is to develop a systematic evaluation scheme to assess the quality and reliability of the epidemiological evidence for endocrine active substances to exert adverse effects based on an endocrine mode of action. This scheme is then to […]

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ECO24-UFZ: Computer based prediction of the formation of Non-Extractable Residues (NER) of xenobiotics and their metabolites in soils and sediments with regard to their environmental hazard

The objective of this project is to define alerts from (sub)structures of environmental chemicals to predict the formation of non-extractable residues (NER) and their metabolites in soils and sediments. The intention is to provide a computerized tool to predict whether […]

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