Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks.

Journal: Pharmaceutical research
PMID: 40244511

Abstract

OBJECTIVE: Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However, significant manual distillation of the literature is needed in order to construct a QSP model. Parameters may need to be fit, and simplifying assumptions of the model need to be made. In this work, we apply Universal Physics-Informed Neural Networks (UPINNs) to learn unknown components of various differential equations that model chemotherapy pharmacodynamics.

Authors

  • Lena Podina
    David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
  • Ali Ghodsi
    Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
  • Mohammad Kohandel
    Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada.

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