Quantifying drug tissue biodistribution by integrating high content screening with deep-learning analysis.

Journal: Scientific reports
PMID: 32873881

Abstract

Quantitatively determining in vivo achievable drug concentrations in targeted organs of animal models and subsequent target engagement confirmation is a challenge to drug discovery and translation due to lack of bioassay technologies that can discriminate drug binding with different mechanisms. We have developed a multiplexed and high-throughput method to quantify drug distribution in tissues by integrating high content screening (HCS) with U-Net based deep learning (DL) image analysis models. This technology combination allowed direct visualization and quantification of biologics drug binding in targeted tissues with cellular resolution, thus enabling biologists to objectively determine drug binding kinetics.

Authors

  • Zhuyin Li
    Bristol-Myers Squibb Company, Lawrenceville, New Jersey.
  • Youping Xiao
    Information Technology for R&D, Bristol-Myers Squibb, Princeton, NJ, USA.
  • Jia Peng
    Department of computed tomography, The Affiliated Zhongshan City Hospital of Sun Yat-sen University, PR China.
  • Darren Locke
    Translational Medicine, Bristol-Myers Squibb, Princeton, NJ, USA.
  • Derek Holmes
    Immunoscience Biology Discovery, Bristol-Myers Squibb, Princeton, NJ, USA.
  • Lei Li
    Department of Thoracic Surgery, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.
  • Shannon Hamilton
    Lead Discovery and Optimization, Bristol-Myers Squibb, 3551 Lawrenceville Road, Princeton, NJ, 08540, USA.
  • Erica Cook
    Lead Discovery and Optimization, Bristol-Myers Squibb, 3551 Lawrenceville Road, Princeton, NJ, 08540, USA.
  • Larnie Myer
    Lead Discovery and Optimization, Bristol-Myers Squibb, 3551 Lawrenceville Road, Princeton, NJ, 08540, USA.
  • Dana Vanderwall
    Information Technology for R&D, Bristol-Myers Squibb, Princeton, NJ, USA.
  • Normand Cloutier
    Information Technology for R&D, Bristol-Myers Squibb, Princeton, NJ, USA.
  • Akbar M Siddiqui
    Information Technology for R&D, Bristol-Myers Squibb, Princeton, NJ, USA.
  • Paul Whitehead
    Information Technology for R&D, Bristol-Myers Squibb, Princeton, NJ, USA.
  • Richard Bishop
    Information Technology for R&D, Bristol-Myers Squibb, Princeton, NJ, USA.
  • Lei Zhao
    Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China.
  • Mary Ellen Cvijic
    Bristol-Myers Squibb Company, Lawrenceville, New Jersey.

Keywords

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