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Methods Mol Biol. 2016;1386:87-104. doi: 10.1007/978-1-4939-3283-2_6.

Systems Medicine in Pharmaceutical Research and Development.

Author information

1
Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany.
2
Institute of Applied Microbiology, RWTH Aachen University, Aachen, Germany.
3
Lehrstuhl für datenbasierte Modellierung in CES, Joint Research Center for Computational Biomedicine, AICES, RWTH Aachen University, Augustinerbach 2, Aachen, 52062, Germany. schuppert@aices.rwth-aachen.de.

Abstract

The development of new drug therapies requires substantial and ever increasing investments from the pharmaceutical company. Ten years ago, the average time from early target identification and optimization until initial market authorization of a new drug compound took more than 10 years and involved costs in the order of one billion US dollars. Recent studies indicate even a significant growth of costs in the meanwhile, mainly driven by the increasing complexity of diseases addressed by pharmaceutical research.Modeling and simulation are proven approaches to handle highly complex systems; hence, systems medicine is expected to control the spiral of complexity of diseases and increasing costs. Today, the main focus of systems medicine applications in industry is on mechanistic modeling. Biological mechanisms are represented by explicit equations enabling insight into the cooperation of all relevant mechanisms. Mechanistic modeling is widely accepted in pharmacokinetics, but prediction from cell behavior to patients is rarely possible due to lacks in our understanding of the controlling mechanisms. Data-driven modeling aims to compensate these lacks by the use of advanced statistical and machine learning methods. Future progress in pharmaceutical research and development will require integrated hybrid modeling technologies allowing realization of the benefits of both mechanistic and data-driven modeling. In this chapter, we sketch typical industrial application areas for both modeling techniques and derive the requirements for future technology development.

KEYWORDS:

Data mining; Data-driven modeling; Hybrid modeling; Mechanistic modeling; Pharmaceutical R&D; Pharmacodynamics; Physiologically based pharmacokinetics (PBPK)

PMID:
26677181
DOI:
10.1007/978-1-4939-3283-2_6
[Indexed for MEDLINE]

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