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Mount Sinai Journal of Medicine A Journal of Translational and Personalized Medicine
Article . 2010 . Peer-reviewed
License: Wiley Online Library User Agreement
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Systems Pharmacology

Authors: Aislyn D W, Boran; Ravi, Iyengar;

Systems Pharmacology

Abstract

AbstractWe examine how physiology and pathophysiology are studied from a systems perspective, using high‐throughput experiments and computational analysis of regulatory networks. We describe the integration of these analyses with pharmacology, which leads to new understanding of drug action and enables drug discovery for complex diseases. Network studies of drug‐target relationships can serve as an indication on the general trends in the approved drugs and the drug‐discovery progress. There is a growing number of targeted therapies approved and in the pipeline, which meets a new set of problems with efficacy and adverse effects. The pitfalls of these mechanistically based drugs are described, along with how a systems view of drug action is increasingly important to uncover intricate signaling mechanisms that play an important part in drug action, resistance mechanisms, and off‐target effects. Computational methodologies enable the classification of drugs according to their structures and to which proteins they bind. Recent studies have combined the structural analyses with analysis of regulatory networks to make predictions about the therapeutic effects of drugs for complex diseases and possible off‐target effects. Mt Sinai J Med 77:333–344, 2010. © 2010 Mount Sinai School of Medicine

Related Organizations
Keywords

Prescription Drugs, Pharmacogenetics, Drug Design, Systems Biology, Mutation, Computational Biology, Humans, Signal Transduction

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
44
Top 10%
Top 10%
Top 10%
bronze