Automatic filtering and substantiation of drug safety signals

Abstract

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions.

Figure 1. Schematic representation of the signal substantiation process.

The signal substantiation process involves the automatic search for evidences that support the causal inference of the potential signal. A. Signal substantiation through proteins. The profile of targets of the drug and its metabolites is obtained by in silico profiling methods (Drug-Target-Profile). The profile of proteins associated with the clinical event is obtained by mining DisGeNET (Event-Protein Profile). The profiles are compared to find proteins in common in both profiles (Drug-Event Linking Proteins). The evidences that support the association of the drug and event with the Drug-Event Linking proteins are explored to determine if they support the causal inference of the signal. B. Signal substantiation through pathways. Proteins in the Drug-Target-Profile and in the Event-Protein Profile are searched in The Human Protein Atlas database to determine if they are expressed in the same tissue and cell type. Proteins that share expression at both levels (tissue and cell type) are used to query Reactome database, and pathways that contain at least one protein from the Drug-Target-Profile and one protein from the Event-Protein Profile are retrieved. Then, these pathways are explored to determine if they support the causal inference of the signal.

Figure 2. Cytoscape graph for QTPROL-haloperidol.

The results of the ADR-S workflow can be visualized as a graph in which the nodes are proteins, compounds and clinical events. A: Detail of the network depicting the haloperidol targets, the proteins associated with QTPROL and the connection between them. The proteins encoded by the genes KCNH1, KCNH2 and CACNA1C constitute Drug-Event linking proteins between haloperidol and the terms corresponding to QTPROL. B: Detail of the targets of haloperidol, showing the adrenergic receptors (light blue) and the drug transporter encoded by the gene ABCB1 (purple). In both graphs, the multiple edges between two nodes represent different evidences for the corresponding association between the nodes.