cosmostox.eu
COSMOS - Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety
http://www.cosmostox.eu/about/partners
School of Pharmacy and Chemistry, Liverpool John Moores University, Liverpool, England. The Liverpool School of Pharmacy at Liverpool John Moores University is the second oldest School of Pharmacy in the United Kingdom (founded in 1849), the University itself has over 24, 000 students and is one of the largest Universities in the UK. Liverpool John Moores University. Commission of the European Communities Directorate General, Joint Research Centre, IHCP, Ispra, Italy. Within the COSMOS project, the JRC i...
cosmostox.eu
COSMOS - Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety
http://www.cosmostox.eu/misc/imprint
Dr Andrea Richarz Project manager. Liverpool John Moores University. School of Pharmacy and Biomolecular Sciences. Liverpool, L3 3AF. Creative concept and web hosting:. School of Computing Informatics and Media. Bradford, West Yorkshire, BD7 1DP. E-mail: webmaster@inf.brad.ac.uk. Painting the future animal-free safety assessment of chemical substances: Achievements of SEURAT-1. Held on 4 December 2015 in Brussels. More information available from the SEURAT-1 website. Held on 9 September 2015 in Liverpool!
cosmostox.eu
COSMOS - Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety
http://www.cosmostox.eu/about/about
The COSMOS Project is a unique collaboration addressing the safety assessment needs of the cosmetics industry, without the use of animals. The main aim of COSMOS is to develop freely available (open access and/or source) tools and workflows to predict the safety to humans following the use of cosmetic ingredients. This will be achieved using computational tools such as applying thresholds of toxicological concern (TTC),. Data and physiologically-based pharmacokinetic (PBPK) modelling. Data and modelling:...
cosmostox.eu
COSMOS - Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety
http://www.cosmostox.eu/links/alternatives
Alternatives to Animal Testing. European Commission and Cosmetics Europe. Alternatives to Animal Testing. EURL-ECVAM: European Union Reference Laboratory for alternatives to animal testing. EPAA: European Partnership for Alternative Approaches to Animal Testing. Ecopa: European consensus-platform for alternatives. AltTox.org: Non-animal Methods for Toxicity Testin. Altweb: the Alternatives to Animal Testing Web Site. CAAT: Johns Hopkins University Center for Alternatives to Animal Testing. COSMOS had a s...
cosmostox.eu
COSMOS - Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety
http://www.cosmostox.eu/publications/printed
Fratev F, Tsakovska I, Al Sharif M, Mihaylova E, Pajeva I (2015) Structural and dynamical insight into PPARγ antagonism: in silico study of the ligand-receptor interactions of non-covalent antagonists. Int. J. Mol. Sci. 16(7): 15405-15424. Schultz TW, Amcoff P, Berggren E, Gautier F, Klaric M, Knight DJ, Mahony C, Schwarz M, White A, Cronin MTD (2015) A strategy for structuring and reporting a read-across prediction of toxicity. Reg. Toxicol. Pharmacol. 72: 586 601. Gajewska M, Worth A, Urani C, Briesen ...
cosmostox.eu
COSMOS - Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety
http://www.cosmostox.eu/about/seurat
COSMOS is one of seven projects forming the SEURAT-1. SEURAT is a European research initiative with the long-term goal of achieving “Safety Evaluation Ultimately Replacing Animal Testing”. In a first step, SEURAT-1 (Towards the replacement of. Repeated dose systemic toxicity testing) will iteratively develop an innovative concept for repeated dose systemic toxicity testing based on the research work carried out by six research projects:. Stem Cells for Relevant efficient extended and normalized Toxicology.
cosmostox.eu
COSMOS - Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety
http://www.cosmostox.eu/what/COSMOSdb
In Vitro In Vivo Extrapolations. The COSMOS Database v1.0 is freely available. For a link to COSMOS DB, which you can access after free registration with your email address. The COSMOS DB leaflet can be downloaded here. More than 80,000 chemical records with more than 40,000 unique structures. Name, CAS number, IDs. One substances or a list of chemicals. Chemical structure exact, substructure and similarity search. Sketch your own query or enter SMILES. The recording of the webinar is available here.
cosmostox.eu
COSMOS - Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety
http://www.cosmostox.eu/what/knime
In Vitro In Vivo Extrapolations. Computational Workflows for Toxicity Prediction. Open and flexible platforms to capture modelling processes are required to support data capture, storage and retrieval, links of chemistry to pathways through Adverse Outcome Pathways (AOPs) as well as transparent modelling to evaluate the safety of chemicals to humans. Enoch SJ, Ellison CM, Schultz TW, Cronin MTD (2011) A review of the electrophilic reaction chemistry involved in covalent protein binding relevant to toxici...
cosmostox.eu
COSMOS - Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety
http://www.cosmostox.eu/publications/posters
Richarz AN, Alov P, Ellison CM, Enoch SJ, Kovarich S, Madden JC, Meinl T, Paini A, Palczewska A, Sala Benito JV, Steinmetz FP, Cronin MTD (2014) KNIME workflows to predict ADMET properties to support chemical safety assessment. UK-QSAR and ChemoInformatics Group Spring Meeting, 21 May 2015, Leeds, England. Richarz AN, Bienfait B, Enoch SJ, Schwab C, Cronin MT, Yang C (2015) Cheminformatics approaches to tailor in silico profilers for refined category formation to support chemical safety assessment. S...
