Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses.

Journal: Progress in neuro-psychopharmacology & biological psychiatry
PMID: 34320403

Abstract

Zebrafish (Danio rerio) are rapidly emerging in biomedicine as promising tools for disease modelling and drug discovery. The use of zebrafish for neuroscience research is also growing rapidly, necessitating novel reliable and unbiased methods of neurophenotypic data collection and analyses. Here, we applied the artificial intelligence (AI) neural network-based algorithms to a large dataset of adult zebrafish locomotor tracks collected previously in a series of in vivo experiments with multiple established psychotropic drugs. We first trained AI to recognize various drugs from a wide range of psychotropic agents tested, and then confirmed prediction accuracy of trained AI by comparing several agents with known similar behavioral and pharmacological profiles. Presenting a framework for innovative neurophenotyping, this proof-of-concept study aims to improve AI-driven movement pattern classification in zebrafish, thereby fostering drug discovery and development utilizing this key model organism.

Authors

  • Dmitrii V Bozhko
    ZebraML, Inc., Houston, TX, USA.
  • Vladislav O Myrov
    ZebraML, Inc., Houston, TX, USA.
  • Sofia M Kolchanova
    ZebraML, Inc., Houston, TX, USA.
  • Aleksandr I Polovian
    ZebraML, Inc., Houston, TX, USA.
  • Georgii K Galumov
    ZebraML, Inc., Houston, TX, USA.
  • Konstantin A Demin
    Institite of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia; Almazov National Medical Research Center, St. Petersburg, Russia; Neurobiology Program, Sirius University, Sochi, Russia.
  • Konstantin N Zabegalov
    Institite of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia; Ural Federal University, Ekaterinburg, Russia; Neurobiology Program, Sirius University, Sochi, Russia; Group of Preclinical Bioscreening, Granov Russian Research Center of Radiology and Surgical Technologies, Ministry of Healthcare of Russian Federation, Pesochny, Russia.
  • Tatiana Strekalova
    Maastricht University, Maastricht, Netherlands; Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine and Department of Normal Physiology, Sechenov Moscow State Medical University, Moscow, Russia.
  • Murilo S de Abreu
    Bioscience Institute, University of Passo Fundo, Passo Fundo, Brazil; Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
  • Elena V Petersen
    Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
  • Allan V Kalueff
    School of Pharmacy, Southwest University, Chongqing, China; Ural Federal University, Ekaterinburg, Russia; ZENEREI, LLC, Slidell, LA, USA; Group of Preclinical Bioscreening, Granov Russian Research Center of Radiology and Surgical Technologies, Ministry of Healthcare of Russian Federation, Pesochny, Russia. Electronic address: avkalueff@gmail.com.

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