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. 2015 Feb;97(2):151-8.
doi: 10.1002/cpt.2. Epub 2014 Dec 20.

Systems Pharmacology Augments Drug Safety Surveillance

Free PMC article

Systems Pharmacology Augments Drug Safety Surveillance

T Lorberbaum et al. Clin Pharmacol Ther. .
Free PMC article


Small molecule drugs are the foundation of modern medical practice, yet their use is limited by the onset of unexpected and severe adverse events (AEs). Regulatory agencies rely on postmarketing surveillance to monitor safety once drugs are approved for clinical use. Despite advances in pharmacovigilance methods that address issues of confounding bias, clinical data of AEs are inherently noisy. Systems pharmacology-the integration of systems biology and chemical genomics-can illuminate drug mechanisms of action. We hypothesize that these data can improve drug safety surveillance by highlighting drugs with a mechanistic connection to the target phenotype (enriching true positives) and filtering those that do not (depleting false positives). We present an algorithm, the modular assembly of drug safety subnetworks (MADSS), to combine systems pharmacology and pharmacovigilance data and significantly improve drug safety monitoring for four clinically relevant adverse drug reactions.

Conflict of interest statement

The authors declare that they have no conflict of interest.


Figure 1
Figure 1
Overview of Modular Assembly of Drug Safety Subnetworks (MADSS). Orange boxes indicate data sources used in this analysis. Gray boxes indicate additional data sources not used in this study but supported by the method. Beginning with a human protein-protein interaction network (interactome) built from such data as experimental evidence, metabolic pathway databases, text mining, and interactions predicted from co-expression data, we isolate all medium-confidence interactions and above. Seed proteins with demonstrated genetic links to the adverse event (AE) are subsequently annotated. We next apply four adapted network analysis functions to score all proteins in the interactome on their connectivity to the seed set. Proteins with high scores embody an AE neighborhood (gray dotted circle); drugs targeting proteins in this subnetwork are predicted to elicit AEs. We assign positive and negative control drugs to their highest-scoring target. We then combine the four AE neighborhoods (one for each pairwise network function) by training a random forest classifier to generate a subnetwork (SubNet) model (red dotted circle). We integrate MWAS and systems pharmacology (SubNet) models using a logistic regression classifier to predict drug safety.
Figure 2
Figure 2
Systems pharmacology data significantly improve drug safety predictions. (A) Receiver operating characteristic (ROC) curve showing performance of pharmacovigilance statistics (MWAS) alone, systems pharmacology (SubNet) alone, and MWAS+SubNet for four adverse events (AEs) combined. The true positive rate, or sensitivity, is plotted against the false positive rate, or 1-specificity. Area under the ROC curve (AUROC) is indicated in parentheses; an AUROC of 0.50 is equivalent to random classification and 1 represents perfect classification. MWAS+SubNet performs significantly better than MWAS alone. (B) ROC curves demonstrating performance for individual AEs: gastrointestinal bleeding (Gastro), acute liver failure (Liver), acute myocardial infarction (MI), and acute kidney failure (Kidney). AUROCs for MWAS alone (black), SubNet alone (red), and MWAS+SubNet (green) are indicated. (C) Quantification of classifier performance using the commonly applied metrics of F1 score (measuring classifier precision and recall), AUROC, and accuracy.
Figure 3
Figure 3
Network flow representation of acute myocardial infarction AE neighborhood. Red triangles represent drug classes. Blue nodes with red borders are high-scoring drug targets; red nodes are seed proteins. Blue nodes in the center represent intermediates linking drug targets to seeds. Intermediate node size and edge thickness are representative of the number of shortest paths traveling through them. The AE neighborhood for MI constructed using MADSS is enriched for genes involved in cAMP biosynthesis and inflammatory response.

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