A modelling approach to explore some hypotheses of the failure of neuroprotective trials in ischemic stroke patients

Marie-Aimée Dronne1, Emmanuel Grenier, Guillemette Chapuisat

  • 1Université de Lyon, Lyon, F-69003, France. md@upcl.univ-lyon1.fr

Insights

Differences in brain composition between rodents and humans may explain why neuroprotective drugs fail in clinical trials. Mathematical modeling suggests glial cell and white matter proportions impact drug efficacy for ischemic stroke treatment.

Area of Science:

  • Neuroscience
  • Computational Biology
  • Pharmacology

Background:

  • Ischemic stroke is a leading cause of death with limited treatment options.
  • Numerous neuroprotective drugs show promise in animal models but fail in human clinical trials.
  • Differences in brain structure between humans and rodents are hypothesized to cause this discrepancy.

Purpose of the Study:

  • To investigate the impact of white matter and glial cell proportions on neuroprotective drug efficacy.
  • To develop and utilize a mathematical model simulating early ischemic stroke pathophysiology in human and rodent brains.

Main Methods:

  • Developed a two-scale mathematical model using ordinary differential equations.
  • Created two model versions differing in white matter and glial cell proportions for human and rodent brains.
  • Conducted in silico experiments with neuroprotective drugs, including a sodium channel blocker.

Main Results:

  • Simulations showed significantly higher penumbra recovery in the rodent model compared to the human model using a sodium channel blocker.
  • Similar discrepancies in recovery rates were observed with other tested neuroprotective agents.
  • The proportion of glial cells and white matter influenced the simulated efficacy of neuroprotective drugs.

Conclusions:

  • Glial cell and white matter proportions are critical factors influencing neuroprotective drug efficacy in ischemic stroke.
  • These histological and morphological differences between rodent and human brains likely contribute to the failure of neuroprotective agents in clinical trials.
  • Mathematical modeling provides valuable insights into the translational gap between preclinical and clinical stroke research.

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