Periodic Reporting for period 1 - OBERON (An integrative strategy of testing systems for identification of EDs related to metabolic disorders)
Reporting period: 2019-01-01 to 2020-06-30
The main objective of the OBERON project is to develop a new battery of tests for the detection of endocrine disruptors having an impact on metabolic disorders, without the use of animal experimentation. Based on the concept of integrated approach for testing and assessment (IATA), OBERON will combine 1) experimental methods, (2) high throughput omics technologies, 3) epidemiology and human biomonitoring and 4) advanced computational models on functional endpoints related to metabolism.
Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far
Taking advantage of the expertise diversity of the 11 partners from the OBERON project, existing and newly generated data will be integrated to develop test systems to detect endocrine disruptors related to metabolism. Several existing EU-cohorts are analyzed, and cellular models, zebrafish models and computational approaches are used. During the first period, we started the establishment and harmonization of several experimental tests across the different EU laboratories. Some experimental results have been already produced, and a network science-based model was established, but the COVID-19 crisis slowed down the full project. All these results will be used for computational modeling to create the final tiered tests, and to develop new adverse outcome pathway (AOPs) for metabolism. During this first period, expected deliverables including ethics, and publications were achieved.
Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)
In epidemiological studies on the metabolic effects of EDs, we will not only use traditional phenotypic markers but also a set of relevant molecular markers to improve the links between exposure and effects. A variety of non-animal experimental approaches with a large set of biological outcomes will be integrated using computational approaches to further improve the value and predictive capacity of these sets of tests. Improving the quality of the testing strategy will prevent the dissemination of toxic compounds and will have health, social and economic benefits.