PLOS Computational Biology: Towards Structural Systems Pharmacology to Study Complex Diseases and Personalized Medicine
Towards Structural Systems Pharmacology to Study Complex Diseases and Personalized Medicine
Lei Xie mail,
Xiaoxia Ge,
Hepan Tan,
Li Xie,
Yinliang Zhang,
Thomas Hart,
Xiaowei Yang,
Philip E. Bourne
Published: May 15, 2014
DOI: 10.1371/journal.pcbi.1003554
Abstract
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.
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Citation: Xie L, Ge X, Tan H, Xie L, Zhang Y, et al. (2014) Towards Structural Systems Pharmacology to Study Complex Diseases and Personalized Medicine. PLoS Comput Biol 10(5): e1003554. doi:10.1371/journal.pcbi.1003554
Editor: Ruth Nussinov, National Cancer Institute, United States of America and Tel Aviv University, Israel, United States of America
Published: May 15, 2014
Copyright: © 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.
Funding: This work was funded by CUNY Research Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.