Modeling of non-steroidal anti-inflammatory drug effect within signaling pathways and miRNA-regulation pathways

Jian Li1, Ulrich R Mansmann

  • 1Institute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-University Munich, Munich, Germany. lijian@ibe.med.uni-muenchen.de

Plos One
|August 23, 2013
PubMed

Insights

Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) show anti-tumor effects but have side effects. This study developed a systems biology model to predict individual cancer cell responses to NSAIDs, aiding personalized medicine and biomarker discovery.

Area of Science:

  • Systems biology
  • Computational oncology
  • Pharmacogenomics

Background:

  • Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) exhibit anti-tumor properties across various cancers.
  • Prolonged NSAID use is linked to significant side effects, necessitating personalized treatment approaches.
  • Developing individualized NSAID therapies is crucial for advancing personalized medicine in oncology.

Purpose of the Study:

  • To construct a systems biology-based molecular model (NSAID model) integrating the cyclooxygenase (COX) pathway and related signaling networks.
  • To incorporate four cancer hallmarks into the model to represent tumorigenesis aspects.
  • To develop a Flux-Comparative-Analysis (FCA) method for predicting individual cellular drug responsiveness.

Main Methods:

  • Constructed a molecular model of the COX pathway and associated signaling networks.
  • Integrated four cancer hallmarks to reflect tumorigenesis.
  • Developed Flux-Comparative-Analysis (FCA) using Petri nets to model dynamic cellular properties and drug responsiveness.
  • Validated the model using gene expression profiles and drug response data from various tumor types.
  • Investigated synthetic lethality and microRNA (miRNA) biomarker discovery strategies based on the COX pathway.

Main Results:

  • The NSAID model accurately predicts individual cellular responses to therapeutic interventions, including specific drug (NS-398) and gene inhibition (COX-2 siRNA).
  • The model effectively reflects physiological, developmental, and pathological processes.
  • Identified potential oncogenic and tumor-suppressive miRNAs for breast, colon, and lung cancer cell lines.
  • Results align with independent studies on miRNA biomarkers for cancer diagnostics and treatment.

Conclusions:

  • The developed NSAID model, utilizing a systems biology approach, demonstrates predictive power for individual cellular responses to therapeutic interventions.
  • The study highlights the potential of systems biology for personalized medicine, enabling prediction of treatment outcomes.
  • miRNA biomarker discovery at the individual level is feasible, potentially improving cancer diagnostics and treatment strategies.

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