[Copenhague, Denmark] – [Sept 11th 2024] – Two rising stars in toxicology, Thiago Marques Pedro and Xia Yu, received top honors at Eurotox 2024 for their outstanding work in New Approach Methodologies (NAMs), as recognized by the European Society for Toxicology In Vitro (ESTIV).
Thiago Marques Pedro earned the ESTIV Best Oral Presentation award for his research titled “Using a Machine Learning Framework to Improve the Efficiency of Mitochondrial Toxicity Screening by Guiding Compound Selection.” This innovative work explores how machine learning can optimize the identification of potentially toxic compounds, significantly enhancing the drug development process and reducing reliance on traditional animal testing.
Xia Yu was awarded the ESTIV Best Poster Award for her research, “Evaluation of Real-Life PFAS Mixture Toxicity and Impact on 3D Placenta Spheroid Model.” This study delved into the concerning impact of PFAS, a group of persistent environmental pollutants, on placental health. Using a realistic mixture of PFAS found in human placentas, Yu demonstrated the detrimental effects on cell viability and key placental functions like hormone production and cell invasion. This research provides crucial evidence for stronger risk assessments of chemical mixtures, particularly concerning pregnancy outcomes.
Shaping the Future of Toxicology
“We are committed to promoting research in New Approach Methodologies and supporting early career scientists and we are incredibily proud for these 2 young scientists for their excellent work in toxicology research” shared Helena Kandorava, president of ESTIV Society. “The work of Thiago Marques Pedro and Xia Yu exemplifies the power of NAMs to provide human-relevant data, leading to safer products and a deeper understanding of environmental pollutants.”
Joining the Movement
Interested in contributing to the exciting evolution of toxicological research? Learn more about ESTIV membership and how to become involved in this vibrant scientific community by visiting: https://www.estiv.org/membership/