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A new computational workflow to guide personalized drug therapy.

Simone Pernice1, Alessandro Maglione2, Dora Tortarolo3

  • 1Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy; CINI Infolife laboratory, Turin, Italy.

Journal of Biomedical Informatics
|November 20, 2023
PubMed
Summary

GreatNectorworkflow, a computational tool, analyzes patient data to predict disease dynamics and stratify individuals for personalized medicine. It identified T-cell changes in Multiple Sclerosis patients, distinguishing responders from non-responders.

Keywords:
Computational modelsLongitudinal dataMultiple Sclerosis

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Area of Science:

  • Computational biology
  • Systems biology
  • Precision medicine

Background:

  • Personalized medicine relies on computational models for analyzing complex, heterogeneous patient data.
  • Patient stratification is crucial for effective precision medicine due to individual variability and environmental factors.

Purpose of the Study:

  • To present GreatNectorworkflow as a tool for analyzing longitudinal patient data and simulating patient-specific disease dynamics.
  • To enable patient stratification for tailored interventions.

Main Methods:

  • GreatNectorworkflow integrates CONNECTOR for longitudinal data analysis and GreatMod, a quantitative modeling framework based on Petri Nets, for unsupervised subject stratification.
  • The framework combines data-driven analysis with quantitative modeling for disease dynamics simulation.

Main Results:

  • Longitudinal data from Multiple Sclerosis patients revealed distinct T-cell dynamics post-alemtuzumab treatment, differentiating responders from non-responders.
  • Non-responders showed increased Th17 concentration around 36 months.
  • GreatNectorworkflow stratified patients into three distinct model meta-patients.

Conclusions:

  • GreatNectorworkflow effectively stratifies patients based on their disease dynamics.
  • The identified patient subgroups provide insights for developing patient-tailored interventions in precision medicine.