NOV 05, 2020 11:00 AM EST

Image and Data Processing for HCS toxicology

  • CEO & Cofounder, Core Life Analytics
      David Egan is the co-founder and CEO of Core Life Analytics, a Netherlands-based company that helps biologists to analyze their own data. Born in Carrickerry, Ireland he completed his undergraduate training in Industrial Chemistry at the University of Limerick. He received his Ph.D. in 1997 from Cornell University Graduate School of Medical Sciences in New York, NY. His graduate thesis was carried out in the laboratory of Dr. Kathleen Scotto at Memorial Sloan Kettering Cancer Center.
      It was during a post-doctoral fellowship with Dr. Ron Evans at The Salk Institute in la Jolla, CA that David became interested in the use of high throughput technologies for drug discovery. This led to a position at OSI Pharmaceuticals in 2001 and, after a return to Europe in 2003, subsequent positions establishing and managing academic automation facilities at the Netherlands Cancer Institute in Amsterdam, and the University Medical Center Utrecht.
      In Utrecht, while managing the Cell Screening Core at the Department of Cell Biology that he encountered the challenges of data analytics in high throughput image-based screening. This led to the development of the StratoMineR platform with Wienand Omta and the subsequent establishment of Core Life Analytics, which was co-founded with Wienand in 2016.


    The pharmaceuticals and biotechnology industries have made substantial progress in reducing the number of drugs that fail in clinical trials due to safety. This has been due, in part, to an improvement in the ability to identify problematic compounds at an earlier stage in the drug discovery process, using in vitro methods. One technology that is increasingly being used in this effort is high content analysis, (HCA).

    This educational session will serve as an introduction to the field of HCA for toxicology. we will review how HCA can increase the efficiency of tox screening through the multiplexing of assays. There will be a special focus on how HCA, when combined with various forms of Artificial Intelligence, can be used for predictive toxicology. We will discuss how the technologies have been applied in various disease fields and the challenges associated with the implementation of these methods.


    Learning Objectives: 

    • Get an overview of the application of HCA for in vitro toxicology 
    • Understand how the combination of HCA artifical intelligence can be beneficial for predictive toxicology 
    • Gain insights into the challenges associated with the implementation of these methods

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