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Systems Medicine in Pharmaceutical Research and Development.

Lars Kuepfer1,2, Andreas Schuppert3

  • 1Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany.

Methods in Molecular Biology (Clifton, N.J.)
|December 18, 2015
PubMed
Summary

Drug development costs are rising due to complex diseases. Systems medicine, using mechanistic and data-driven modeling, offers a solution to manage complexity and reduce pharmaceutical R&D expenses.

Keywords:
Data miningData-driven modelingHybrid modelingMechanistic modelingPharmaceutical R&DPharmacodynamicsPhysiologically based pharmacokinetics (PBPK)

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Area of Science:

  • Pharmacological Research
  • Systems Medicine
  • Computational Biology

Background:

  • Drug development is increasingly costly and time-consuming, with costs nearing one billion US dollars and timelines exceeding ten years.
  • Rising disease complexity drives higher pharmaceutical research and development expenses.
  • Systems medicine offers a framework to manage complexity and control escalating costs in drug discovery.

Purpose of the Study:

  • To explore the application of mechanistic and data-driven modeling in pharmaceutical research.
  • To identify the requirements for future integrated hybrid modeling technologies.
  • To outline industrial application areas for current and future modeling techniques.

Main Methods:

  • Mechanistic modeling, representing biological mechanisms with explicit equations.
  • Data-driven modeling, utilizing advanced statistical and machine learning methods.
  • Discussion of integrated hybrid modeling technologies.

Main Results:

  • Mechanistic modeling provides insights into biological mechanisms but has limitations in predicting outcomes from cell to patient.
  • Data-driven modeling compensates for mechanistic modeling's limitations.
  • Hybrid modeling approaches are essential for future pharmaceutical R&D progress.

Conclusions:

  • Integrated hybrid modeling technologies are crucial for advancing pharmaceutical research and development.
  • Systems medicine, through advanced modeling, is key to controlling complexity and costs.
  • Further technological development is needed to fully realize the benefits of hybrid modeling.