Machine learning-assisted exploration of multidrug-drug administration regimens for organoid arrays.

Journal: Science advances
Published Date: Jul 30, 2025

Abstract

Combination therapies enhance the therapeutic effect of cancer treatment; however, identifying effective interdependent doses, durations, and sequences of multidrug administration regimens is a time- and labor-intensive task. Here, we integrated machine learning, automation, and large microfluidic arrays of cancer spheroids or patient-derived organoids formed in a tissue-mimetic hydrogel to achieve notable acceleration of the discovery of effective multidrug administration regimens. For the clinically approved drug combination, we found a sequential administration regimen leading to a substantial reduction in the total drug dose, in comparison with concurrent drug supply, both at comparable drug efficacy. For the drugs that are currently under clinical development, we found a synergistic effect of concurrently administered drugs and showed that the synergy diminishes for the sequential drug supply. The developed strategy holds promise for the discovery of effective combination therapies for advanced cancer treatment, including personalized chemotherapies.

Authors

  • Ilya Yakavets
    Department of Chemistry, University of Toronto, 80 Saint George Street, Toronto, ON M5S 3H6, Canada.
  • Sina Kheiri
    Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, Ontario M5S 3G8, Canada.
  • Jennifer Cruickshank
    Princess Margaret Cancer Centre, University Health Network, 610 University Avenue, Toronto, ON M5G 2C1, Canada.
  • Riley J Hickman
    Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada.
  • Faeze Rakhshani
    Department of Chemistry, University of Toronto, 80 Saint George Street, Toronto, ON M5S 3H6, Canada.
  • Matteo Aldeghi
    Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3H6, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada.
  • Ella M Rajaonson
    Chemical Physics Theory Group, Department of Chemistry, University of Toronto, 80 Saint George street, Toronto, ON M5S 3H6, Canada.
  • Edmond W K Young
    Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada; http://ibmt.mie.utoronto.ca/. Electronic address: eyoung@mie.utoronto.ca.
  • Alán Aspuru-Guzik
    Departments of Chemistry, Computer Science, University of Toronto St. George Campus Toronto ON Canada.
  • David W Cescon
    Princess Margaret Cancer Centre, University Health Network, 610 University Avenue, Toronto, ON M5G 2C1, Canada.
  • Eugenia Kumacheva
    Department of Chemistry, University of Toronto, 80 Saint George Street, Toronto, ON M5S 3H6, Canada.

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