Use of in silico methods (QSARs) for aquatic toxicology – the basics and applications
Here is what you will learn
This webinar will provide an introduction to in silico methods (QSARs) for aquatic toxicology, including:
- Introduction to in silico methods as an alternative to in vivo testing
- QSAR models and their development
- Structural alerts for toxicity
- In silico models for acute toxicity prediction
- In silico models for bioconcentration prediction
- The use of in silico models in a regulatory context
Webinar duration: Approximately one hour, allowing time for questions and answers at the end of the training session
To ensure the most effective training with optimal involvement from the attendees, and encourage attendees to ask questions during the training, places are strictly limited to 30 delegates per webinar.
All registrants to the training webinar will receive a copy of all slides and access to a recording of the webinar for your personal review.
Dr. Irene McGrath
Irene studied Analytical Science at Dublin City University before completing a Ph.D. in Chemistry.
She has spent over 26 years managing Regulatory Departments and working as a consultant for the regulation of plant protection products, biocides, plant biostimulants and fertilisers in the EU.
Irene manages the regulatory team of consultants at Kerona Scientific in Ireland, working with clients worldwide.
Dr. Neus Sanchez Rodriguez
Neus obtained a B.Sc. in Biological Sciences and M.Sc. in Aquaculture from the University of Valencia in Spain, before completing her Ph.D. on the development of computational approaches (e.g. QSARs, structural alerts) and in vitro methods to assess the toxicity and bioaccumulation of organic chemicals in aquatic organisms at John Moores University in Liverpool (UK).
Neus began working as a regulatory ecotoxicologist for the UK’s Chemicals Regulation Division, where she assessed the ecotoxicity of plant protection products and biocides.
Neus is an expert in the risk assessments for the ecotoxicology section and the prediction of toxicity endpoints for use in a regulatory context.