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Drug-target and disease networks: polypharmacology in the post-genomic era.

Masoudi-Nejad A, Mousavian Z, Bozorgmehr JH - In Silico Pharmacol (2013)

Bottom Line: Due to the laborious and costly experimental process of drug-target interaction prediction, in silico prediction could be an efficient way of providing useful information in supporting experimental interaction data.An important notion that has emerged in post-genomic drug discovery is that the large-scale integration of genomic, proteomic, signaling and metabolomic data can allow us to construct complex networks of the cell that would provide us with a new framework for understanding the molecular basis of physiological or pathophysiological states.The future goal, therefore, is to create a computational platform that integrates genome-scale metabolic pathway, protein-protein interaction networks, gene transcriptional analysis in order to build a comprehensive network for multi-target multi-drug discovery.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.

ABSTRACT
With the growing understanding of complex diseases, the focus of drug discovery has shifted away from the well-accepted "one target, one drug" model, to a new "multi-target, multi-drug" model, aimed at systemically modulating multiple targets. Identification of the interaction between drugs and target proteins plays an important role in genomic drug discovery, in order to discover new drugs or novel targets for existing drugs. Due to the laborious and costly experimental process of drug-target interaction prediction, in silico prediction could be an efficient way of providing useful information in supporting experimental interaction data. An important notion that has emerged in post-genomic drug discovery is that the large-scale integration of genomic, proteomic, signaling and metabolomic data can allow us to construct complex networks of the cell that would provide us with a new framework for understanding the molecular basis of physiological or pathophysiological states. An emerging paradigm of polypharmacology in the post-genomic era is that drug, target and disease spaces can be correlated to study the effect of drugs on different spaces and their interrelationships can be exploited for designing drugs or cocktails which can effectively target one or more disease states. The future goal, therefore, is to create a computational platform that integrates genome-scale metabolic pathway, protein-protein interaction networks, gene transcriptional analysis in order to build a comprehensive network for multi-target multi-drug discovery.

No MeSH data available.


Polypharmacology in the post-genomic era using pharmacological space.
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Fig1: Polypharmacology in the post-genomic era using pharmacological space.

Mentions: Drug discovery also can be influenced by the exploitation of “omics” data. Due to the advances in genomics, transcriptomics, proteomics and metabolomics, large amounts of data have been provided on drug-target interactions. To speed up the drug development process, the information flow must be analyzed by more effective tools in the early stages of drug discovery pipeline. The emerging field of “systems pharmacology”, which relies on “omics” technologies, can explain both therapeutic and adverse effects of drugs through analyses at multiple scales of biological networks (Figure 1).Figure 1


Drug-target and disease networks: polypharmacology in the post-genomic era.

Masoudi-Nejad A, Mousavian Z, Bozorgmehr JH - In Silico Pharmacol (2013)

Polypharmacology in the post-genomic era using pharmacological space.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230718&req=5

Fig1: Polypharmacology in the post-genomic era using pharmacological space.
Mentions: Drug discovery also can be influenced by the exploitation of “omics” data. Due to the advances in genomics, transcriptomics, proteomics and metabolomics, large amounts of data have been provided on drug-target interactions. To speed up the drug development process, the information flow must be analyzed by more effective tools in the early stages of drug discovery pipeline. The emerging field of “systems pharmacology”, which relies on “omics” technologies, can explain both therapeutic and adverse effects of drugs through analyses at multiple scales of biological networks (Figure 1).Figure 1

Bottom Line: Due to the laborious and costly experimental process of drug-target interaction prediction, in silico prediction could be an efficient way of providing useful information in supporting experimental interaction data.An important notion that has emerged in post-genomic drug discovery is that the large-scale integration of genomic, proteomic, signaling and metabolomic data can allow us to construct complex networks of the cell that would provide us with a new framework for understanding the molecular basis of physiological or pathophysiological states.The future goal, therefore, is to create a computational platform that integrates genome-scale metabolic pathway, protein-protein interaction networks, gene transcriptional analysis in order to build a comprehensive network for multi-target multi-drug discovery.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.

ABSTRACT
With the growing understanding of complex diseases, the focus of drug discovery has shifted away from the well-accepted "one target, one drug" model, to a new "multi-target, multi-drug" model, aimed at systemically modulating multiple targets. Identification of the interaction between drugs and target proteins plays an important role in genomic drug discovery, in order to discover new drugs or novel targets for existing drugs. Due to the laborious and costly experimental process of drug-target interaction prediction, in silico prediction could be an efficient way of providing useful information in supporting experimental interaction data. An important notion that has emerged in post-genomic drug discovery is that the large-scale integration of genomic, proteomic, signaling and metabolomic data can allow us to construct complex networks of the cell that would provide us with a new framework for understanding the molecular basis of physiological or pathophysiological states. An emerging paradigm of polypharmacology in the post-genomic era is that drug, target and disease spaces can be correlated to study the effect of drugs on different spaces and their interrelationships can be exploited for designing drugs or cocktails which can effectively target one or more disease states. The future goal, therefore, is to create a computational platform that integrates genome-scale metabolic pathway, protein-protein interaction networks, gene transcriptional analysis in order to build a comprehensive network for multi-target multi-drug discovery.

No MeSH data available.