Broad toxicology profiling takes traditionally place at the interface between discovery and development when a potential drug candidate is selected. However, it would be both time- and cost-wise better if mechanism (target)-related toxicity and compound-chemistry related toxicity is addressed earlier, when discussions on novel drug targets take place and compound series are identified and optimized. As the traditional in-vivo and in-vitro toxicity testing is rather low-throughput, they can’t be used in these early stages of the drug discovery process. Therefore a paradigm shift in toxicity testing needs to take place to move to high-throughput cell-based assays to reveal key pathways and proteins linked with toxicity end points. I will present some explorations and case studies where both transcriptional profiling and imaging techniques are explored to flag early potential toxicity issues already during the drug development process where the findings could still influence the final candidate selection.
About the Presenter: Bie Verbist
Bie Verbiststudied medicinal chemistry at KU Leuven, Belgium and finished PhD in 2005 on the design and synthesis of potential β‐turn mimetics in the group of Prof.Dr.G.Hoornaert. Following this, she started as a post-doc at Johnson & Johnson Pharmaceutical Research and Development in Beerse, Belgium where she was involved in the design, synthesis and validation of new biological entities within the therapeutic areas pain and internal medicine, for three years. Afterwards, she went back to university to follow a one-year MaNaMa in statistical data analysis. In 2011, after a short period of working as a scientific collaborator at Ghent University on qPCR data, she started a second PhD to search for low-frequency variants in viral populations using Illumina deep sequencing technologies under supervision of Prof.Dr. O. Thas and in close collaboration with Johnson & Johnson Pharmaceutical Research and Development in Beerse, Belgium. In 2014, Bie joined Johnson & Johnson as a Principal Biostatistician in the non-clinical statistics department to support oncology projects within discovery with a focus on omics data analysis.
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