AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology
This record is part of
a dataset collected by the EU Commission in June-September 2018
. Some of these links will therefore die out with time.
Please see the
overview of courses maintained by ETPLAS
or contact Norecopa for more information.
Owner/Developer: Joint Research Centre (EURL ECVAM) (JRC (EURL ECVAM))
24 September 2015
Invited international experts in computational modeling, toxicology, and risk assessment from academia, government, and the private sector came together to explore the scientific opportunities and stimulate greater communication and collaboration between the AOP development and computational modeling communities. Modelers were introduced to the principles and practice of AOP description and invited to think about how this systematic organization of knowledge could aid model development. A number of case examples exploring the different ways models and AOPs could be integrated to address regulatory challenges were discussed. Issues related to regulatory uptake and application were considered. Finally, the group discussed ways to further engage the modeling community in the endeavor of AOP-informed predictive toxicology.
Optional / Voluntary
Researchers, Regulators and policy-makers
Academia, Industry, Governmental bodies
Continuing Professional Development, University (Doctoral education), Postdoctoral (teaching and research)
Partial coverage (e.g. a module)
No species is addressed specifically
|Course level on animal species:||
Basic course, Advanced course
|Details on the topic or technology covered:||
HOW CAN AOPs INFORM COMPUTATIONAL MODEL DEVELOPMENT?
CASE EXAMPLES OF AOP-INFORMED COMPUTATIONAL PREDICTION MODELS
ENSURING AOP INFORMED PREDICTIVE MODELS ARE FIT FOR REGULATORY PURPOSES
Test guidelines (OECD, ISO, etc.)
Did you find what you were looking for?Yes, I found it! No, I did not!
Thanks for your feedback! Please note that we cannot respond unless you supply your email address.
What are you looking for?
Please give us your feedback so we can improve the information on the page. Thank you in advance for your help. Please add your email address if you would like a reply.Please contact us by email if you have any questions.