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Metabolically re-modeling the drug pipeline.

Matthew A Oberhardt1, Keren Yizhak, Eytan Ruppin

  • 1School of Computer Sciences, Tel Aviv University, Tel Aviv 69978, Israel; The Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.

Current Opinion in Pharmacology
|June 5, 2013
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Summary

Constraint-based modeling (CBM) using genome-scale metabolic models (GSMMs) offers a powerful approach to integrate omics data for drug discovery. GSMMs aid in predicting drug targets, lead compounds, and toxicity, potentially revolutionizing R&D strategies.

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Area of Science:

  • Computational systems biology
  • Metabolic modeling

Background:

  • Rising drug development costs necessitate innovative R&D strategies.
  • Systems biology integrates omics data into the drug pipeline.
  • Constraint-based modeling (CBM) utilizes genome-scale metabolic models (GSMMs).

Purpose of the Study:

  • Review current applications of GSMMs in drug discovery.
  • Highlight GSMMs' role in predicting drug targets and lead compounds.
  • Discuss GSMMs for predicting drug toxicity and selectivity.

Main Methods:

  • Integration of diverse omics datasets using GSMMs.
  • Application of CBM for computational analysis.
  • Review of existing literature on GSMMs in drug discovery.

Main Results:

  • GSMMs facilitate the prediction of novel drug targets and lead compounds.
  • GSMMs show potential in predicting drug toxicity and target selectivity.
  • Current successes and limitations of GSMMs in drug discovery are identified.

Conclusions:

  • GSMMs are valuable tools for interpreting omics data in drug discovery.
  • Further development of GSMMs can significantly impact future R&D pipelines.
  • GSMMs offer a vision for re-modeling the drug discovery process.