SaferWorldbyDesign Webinar: Using Transcriptomics Data in Risk Assessment

Using Transcriptomics Data in Risk Assessment (presented by Thomas Darde)

One of the challenges in toxicology is to be able to extrapolate the results of the different phases of risk analysis from experimental systems to human populations. Animal models, although widely used, often present differences in terms of substance clearance or enzymatic activity. For these practical reasons, but also for ethical, political, and economic reasons, laboratories are being asked to make major efforts to replace these models with other alternatives, reduce their use to a minimum, and refine experimental strategies to minimize the stress and pain of the animals (the “3Rs” principle). Transcriptomics constitutes a promising avenue to address these challenges. After careful planning of exposure conditions and RNA sequencing, the data generated can be used to build more advanced models for predictive toxicology. SciLicium, a young bioinformatics French SME, specializing in toxicogenomics, will present how to generate and use these data in risk assessment.

Toxicogenomics Data Management and Analysis within EU-ToxRisk (presented by Tomaž Mohorič)

Good data management practices adopted within the EU-ToxRisk project allowed us to make laboratory-generated data sets easily available through web requests and hence accessible directly from the browser. In this way, we can create interactive visualizations of processed data sets that give a great user experience and are shareable simply by copying a URL link. Such a notebook was prepared for the transcriptomic data sets generated within the EU-ToxRisk project supporting toxicological mechanistic analysis and risk assessment.

Presenters: 

Thomas Darde (Founder & Bioinformatician, Scilicium)

Over the past few years, he has been called upon to work on multi-technological genomics data (DNA chip, 3 ‘sequencing, Single-cell) on which he has been able to apply various methods of multivariate exploratory analysis (PCA , FMA, clustering) but also machine learning (Machine and Deep Learning). 

Tomaž Mohorič (Computational Scientist, Edelweiss Connect) 

He obtained a Ph.D. in physical chemistry at the University of Ljubljana. During his Ph.D., he has specialised in computer simulations of simple and complex liquids, in particular under non-equilibrium states. One example was the extensive computational studies of microwave heating of water and aqueous solutions. Besides that, he also participated in a multinational team of physicists, who studied by theory and in practice the properties of magnetic colloids under non-equilibrium conditions. After that, he worked as a research scientist in analytics development at Krka d.d., where he gained first-hand experience in the pharmaceutical industry. He joined Edelweiss Connect in 2019 to contribute to the modeling and data analysis for the replacement of animal testing.