AAPS PharmSci 360 2023

American Association of Pharmaceutical Scientists (AAPS) 2023 PharmSci 360

Join Enthought at the 2023 American Association of Pharmaceutical Scientists (AAPS) PharmSci 360 on October 22-25 in Orlando. AAPS is one of the world’s largest scientific organizations focused on advancing how pharmaceutical scientists develop products and therapies that improve global health. At the annual PharmSci 360 event, pharmaceutical leaders in R&D from industry and academia convene to learn about what’s the newest advancements in scientific research and technology.

Please come say hello!



Enthought at the Expo (Booth #3210) | 

Enthought | 2023 PharmSci 360Visit us at the Enthought booth (#3210) at the Expo at the Orange County Convention Center! We’re so excited to meet everyone in person. Come chat about how AI and machine learning-driven solutions can automate and accelerate drug discovery and development. AND get a very special giveaway! Let us know you’re coming by here. Hours of the Expo are:

  • Monday, Oct 23, 8:30 AM – 5:00 PM
  • Tuesday, Oct 24, 8:30 AM – 5:00 PM
  • Wednesday, Oct 25, 8:30 AM – 1:30 PM


Session with Dr. Jim Corson | Automated Analysis of Organoid Culture Development

Jim Corson, Ph.DOur own Dr. Jim Corson, Vice President of Life Science Solutions, will be speaking on Wednesday, October 25th. Dr. Corson holds a Ph.D. in neuroscience and oversees solutions employing data modeling, computational and analytics workflows, and AI/ML for CROs and biotechnology and biopharmacology companies. During his time as a neurophysiologist, Corson focused on the development and malleability of sensory system synaptic circuitry.

Automated Analysis of Organoid Culture Development
Session: State-of-the-Art Tools for Basic Research and Early-Stage Drug Discovery
Wednesday, October 25, 2023 | 9:00am-9:30am
309 AB West
Session Link


Organoids are three-dimensional models of an organ or tissue that is grown in vitro from stem cells or tissue-specific progenitor cells. Given this structure, the generation and cultivation of these structures can be a complex and labor-intensive process. The analysis of organoid culture health often requires the expertise and intuition of a senior researcher, inherently making the process unscalable and variable between researchers. Rather than relying solely on researcher experience and intuition, we believe that it should be codified into an automated process that can enable companies to scale their R&D workforce. Here, using a combination of computer vision and machine learning, we developed a technique that automatically classifies the health of organoid cultures and recommends when the researcher should passage the organoids. Careful tracking and data recording throughout the culturing process will allow researchers to not only optimize the machine learning model and computer vision algorithms to the organoid model in question, but also enable that information to be integrated into the experiments performed with the organoid cultures. This will allow researchers to investigate any effect that the culture process may have on the experimental results.


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