Collaborations & Projects

Collaborations

U.S. FDA

The U.S. FDA Center for Food Safety and Applied Nutrition (CFSAN) and MN-AM collaborated and jointly developed the food additives knowledge base CERES (Chemical Evaluation and Risk Estimation System) of the Office of Food Safety for pre-market review as well as post-market monitoring of food ingredients and packaging materials.

US FDA CFSAN →

CEPOS Insilico

CEPOS Insilico GmbH, located in Germany, and MN-AM share complementory technology for mutual benefit for their software platforms and systems. Thus, both partners are able to offer better products, services, and solutions to their customers and end users.

CEPOS Insilico →

Mario Negri Institute for Pharmacological Research

The department of Environmental Health Sciences at the Mario Negri Institute, Milan, Italy and MN-AM are collaborating in a series of projects in the area of chemical safety and risk assessment.

Environmental Health Sciences →

KODE Chemoinformatics

KODE Chemoinformatics (KODE S.r.l.), located in Italy, and MN-AM are technology partners collaborating in a couple of publicly-funded and private projects to leaverage complementary software applications, workflows and methods to the next level of performance.

KODE Chemoinformatics →

The Ohio State University

The Department of Chemical and Biomolecular Engineering (CBE) of the Ohio State University and MN-AM collaborate in many different areas of computational chemistry and toxicology such as molecular informatics, statistical modelling and data analysis, experimental design, QSAR modelling and read-across efforts, AI, Bayesian network approaches, and computational risk assessment of chemical-induced toxicity.

OSU CBE →

Liverpool John Moores University

The research group of Prof. Mark Cronin at the School of Pharmacy and Biomolecular Sciences of the Liverpool John Moores University in UK and MN-AM are collaborating in different areas of computational chemistry and toxicology.

LJMU →

National Institute of Health Sciences

The Division of Genetics and Mutagenesis in the National Institute of Health Sciences (NIHS), Tokyo, Japan is collaborating with MN-AM.

NIHS →

European Commission, Joint Research Centre (JRC)

The EURL ECVAM group of Joint Research Centre (JRC) in Ispra, Italy, the European Commission’s science and knowledge service, is collaborating with MN-AM in different areas of computational chemistry and toxicology.

JRC →

Cosmetics Europe

MN-AM collaborates with Cosmetics Europe (CE) to scope, develop, and maintain an informatics platform (CE-TOXGPS) to complement predictive toxicology initiatives at Cosmetics Europe. The CE-TOXGPS system is based on the ChemTunes·ToxGPS® technology of MN-AM and will allow for the integration, visualisation and analysis of “traditional” in vivo data along with New Approach Methodologies (NAMs) and data including the results of in silicoin vitro, HTS, and molecular biology (and other relevant) assays.

Cosmectics Europe →

Foundation of the Korea Cosmetic Industry Institute (KCII)

MN-AM collaborates with the Foundation of the Korea Cosmetic Industry Institute (KCII) to provide, develop, and maintain the Cosmetic Ingredients Prediction System (KCII CSP). The KCII CSP system is based on the ChemTunes·ToxGPS® technology of MN-AM to support the chemical safety and risk assessment of cosmetic ingredients for the members of the foundation.

KCII →

Health Canada

MN-AM collaborates with Health Canada to explore chemoinformatics approaches for predictive toxicology and New Approach Methodologies (NAMs).

Health Canada →

Projects

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ONTOX Project

ONtology-driven and artificial intelligence-based repeated dose TOXicity testing of chemicals for next generation risk assessment

The goal of the ONTOX consortium is to provide a functional and sustainable solution for advancing human risk assessment of chemicals without the use of animals in line with the principles of 21st century toxicity testing and next generation risk assessment.

New Approach Methodologies (NAMs) will be developed applying computational systems based on cutting-edge artificial intelligence (AI) and will be primarily fed by available biological / mechanistic, toxicological / epidemiological, physico-chemical and kinetic data. Data will be consecutively integrated in physiological maps, quantitative adverse outcome pathway networks and ontology frameworks. Data gaps, as identified by AI, will be filled by targeted state-of-the-art in vitro and in silico testing.

ONTOX started in May 2021 and will run until April 2026.

ONTOX is funded by the Horizon 2020 research and innovation programme of the European Union under the grant agreement no. 963845.

Learn more →

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COSMOS Project

Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety

COSMOS was one of seven projects forming the SEURAT-1 cluster, SEURAT being a European research initiative of the EU FP7 and Cosmetics Europe with the long-term goal of achieving “Safety Evaluation Ultimately Replacing Animal Testing”.

COSMOS was a unique collaboration addressing the safety assessment needs of the cosmetics industry, without the use of animals. The main aim of COSMOS was to develop freely available tools and workflows to predict the safety to humans following the use of cosmetic ingredients.

The COSMOS project ran from January 2011 to December 2015.

COSMOS was jointly funded by the European Commission (through the 7th Framework Programme, grant agreement no. 266835) and the European trade association for the cosmetic, toiletry and perfumery industry (Cosmetics Europe).

Altamira was an US and Molecular Networks an EU partner of the COSMOS project. Together with the COSMOS project coordinator LJMU (Liverpool John Moores University), MN-AM secured, maintains, and further develops the major results and outcomes of COSMOS through the public COSMOS NG platform and organizes regular COSMOS NG forum meetings.

COSMOS NG includes a toxicity database and in silico tools serving as a public safety assessment knowledge hub with qualified data.

Learn more →

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