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WIREs Syst Biol Med
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Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms

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Small molecules are indispensable to modern medical therapy. However, their use may lead to unintended, negative medical outcomes commonly referred to as adverse drug reactions (ADRs). These effects vary widely in mechanism, severity, and populations affected, making ADR prediction and identification important public health concerns. Current methods rely on clinical trials and postmarket surveillance programs to find novel ADRs; however, clinical trials are limited by small sample size, whereas postmarket surveillance methods may be biased and inherently leave patients at risk until sufficient clinical evidence has been gathered. Systems pharmacology, an emerging interdisciplinary field combining network and chemical biology, provides important tools to uncover and understand ADRs and may mitigate the drawbacks of traditional methods. In particular, network analysis allows researchers to integrate heterogeneous data sources and quantify the interactions between biological and chemical entities. Recent work in this area has combined chemical, biological, and large‐scale observational health data to predict ADRs in both individual patients and global populations. In this review, we explore the rapid expansion of systems pharmacology in the study of ADRs. We enumerate the existing methods and strategies and illustrate progress in the field with a model framework that incorporates crucial data elements, such as diet and comorbidities, known to modulate ADR risk. Using this framework, we highlight avenues of research that may currently be underexplored, representing opportunities for future work. WIREs Syst Biol Med 2016, 8:104–122. doi: 10.1002/wsbm.1323 This article is categorized under: Biological Mechanisms > Chemical Biology Analytical and Computational Methods > Computational Methods Translational, Genomic, and Systems Medicine > Translational Medicine
A data model framework illustrates how diverse data types can form a complete profile of an individual's adverse drug reaction (ADR) risk. Many types of data from various fields investigate individual aspects of ADR risk, including microbiome, metabolome, lifestyle, nutrition, and the genome. Each of these contributes important information on an individual's ADR risk. Achieving precision medicine requires integrating these diverse data and the application of statistical modeling techniques to predict an individual's overall ADR risk for a given drug.
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Bipartite graph of data sources used in the literature for systems biology of adverse drug reactions (ADRs). We surveyed data sources used in previous studies and whether or not those datasets were used in combination or not (singleton nodes in Figure ). Edges in the graph represent datasets used in combination by the same publication. Edge thickness indicates the number of publications using that particular dataset combination. Node size is based on the degree of the node, and color indicates the closeness centrality. Figure illustrates that some datasets are used together often, while others are rarely used in combination. This helps indicate areas of opportunity for future systems biology researchers interested in using novel or underutilized data sources.
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Adverse drug reactions (ADRs) can occur among certain individuals due to diverse disruptions in the drug's mechanism of action. Some examples of these disruptions include, genetic/microbiome‐related (Figure (a)), dietary or lifestyle dependent (Figure (b)) or driven by a patient's comorbidities (Figure (c)).
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Translational, Genomic, and Systems Medicine > Translational Medicine
Analytical and Computational Methods > Computational Methods
Biological Mechanisms > Chemical Biology

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