Towards Structural Systems Pharmacology to Study Complex Diseases and Personalized Medicine: e1003554

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which cont...

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Veröffentlicht in:PLoS computational biology 2014-05, Vol.10 (5)
Hauptverfasser: Xie, Lei, Ge, Xiaoxia, Tan, Hepan, Xie, Li, Zhang, Yinliang, Hart, Thomas, Yang, Xiaowei, Bourne, Philip E
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container_title PLoS computational biology
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creator Xie, Lei
Ge, Xiaoxia
Tan, Hepan
Xie, Li
Zhang, Yinliang
Hart, Thomas
Yang, Xiaowei
Bourne, Philip E
description Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.
doi_str_mv 10.1371/journal.pcbi.1003554
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However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.</description><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1003554</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Disease ; Environmental factors ; Epigenetics ; Genes ; Genomes ; Genomics ; Pharmacology ; Precision medicine ; Proteins ; Signal transduction ; Statistical models ; Studies</subject><ispartof>PLoS computational biology, 2014-05, Vol.10 (5)</ispartof><rights>2014 Xie et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Xie L, Ge X, Tan H, Xie L, Zhang Y, et al. 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subjects Disease
Environmental factors
Epigenetics
Genes
Genomes
Genomics
Pharmacology
Precision medicine
Proteins
Signal transduction
Statistical models
Studies
title Towards Structural Systems Pharmacology to Study Complex Diseases and Personalized Medicine: e1003554
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