Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms

• Published in: Wiley interdisciplinary reviews. Systems biology and medicine Vol. 8; no. 2; p. 104
• Main Authors: Boland, Mary Regina, Jacunski, Alexandra, Lorberbaum, Tal, Romano, Joseph D, Moskovitch, Robert, Tatonetti, Nicholas P
• Format: Journal Article
• Language: English
• Published: United States 01.03.2016
• Subjects: Animals; Drug-Related Side Effects and Adverse Reactions - metabolism; Drug-Related Side Effects and Adverse Reactions - pathology; Drug-Related Side Effects and Adverse Reactions - physiopathology; Humans; Systems Biology - methods
• ISSN: 1939-005X
• DOI: 10.1002/wsbm.1323

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.