Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. Mar-Apr 2016;8(2):104-22.
doi: 10.1002/wsbm.1323. Epub 2015 Nov 12.

Systems Biology Approaches for Identifying Adverse Drug Reactions and Elucidating Their Underlying Biological Mechanisms

Free PMC article

Systems Biology Approaches for Identifying Adverse Drug Reactions and Elucidating Their Underlying Biological Mechanisms

Mary Regina Boland et al. Wiley Interdiscip Rev Syst Biol Med. .
Free PMC article


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.


Figure 1
Figure 1. 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 1A), dietary or lifestyle dependent (Figure 1B) or driven by a patient's comorbidities (Figure 1C).
Figure 2
Figure 2. Bipartite graph of data sources used in the literature for systems biology of adverse drug reactions
We surveyed data sources used in previous studies and whether or not those datasets were used in combination or not (singleton nodes in Figure 2). 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 2 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 under-utilized data sources.
Figure 3
Figure 3. A Data Model Framework Illustrates How Diverse Data Types can form a Complete Profile of an Individual's Adverse Drug Reaction Risk
Many types of data form various fields investigate the individual aspects of ADR risk including: microbiome, metabolome, lifestyle, nutrition and the genome. Each of these contributes important information on an individual's adverse drug reaction (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.

Similar articles

See all similar articles

Cited by 15 articles

See all "Cited by" articles

Publication types

MeSH terms