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WIREs Syst Biol Med
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Drug repurposing and adverse event prediction using high‐throughput literature analysis

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Abstract Drug repurposing is the process of using existing drugs in indications other than the ones they were originally designed for. It is an area of significant recent activity due to the mounting costs of traditional drug development and scarcity of new chemical entities brought to the market by bio‐pharmaceutical companies. By selecting drugs that already satisfy basic toxicity, ADME and related criteria, drug repurposing promises to deliver significant value at reduced cost and in dramatically shorter time frames than is normally the case for the drug development process. The same process that results in drug repurposing can also be used for the prediction of adverse events of known or novel drugs. The analytics method is based on the description of the mechanism of action of a drug, which is then compared to the molecular mechanisms underlying all known adverse events. This review will focus on those approaches to drug repurposing and adverse event prediction that are based on the biomedical literature. Such approaches typically begin with an analysis of the literature and aim to reveal indirect relationships among seemingly unconnected biomedical entities such as genes, signaling pathways, physiological processes, and diseases. Networks of associations of these entities allow the uncovering of the molecular mechanisms underlying a disease, better understanding of the biological effects of a drug and the evaluation of its benefit/risk profile. In silico results can be tested in relevant cellular and animal models and, eventually, in clinical trials. WIREs Syst Biol Med 2011 3 323–334 DOI: 10.1002/wsbm.147 This article is categorized under: Analytical and Computational Methods > Computational Methods

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(a) Concept A is related to concept B, as reported in one set of papers (indicated by the gray ellipse on the left), while B is related to C according to another set of papers (indicated by the purple ellipse on the right). Although A is not known to be directly related to C one can infer an indirect relation through B. As A and C are known beforehand, this is a closed discovery process. (b) Concept A is related to concept B, and through that to C, D, etc. Furthermore, A is related to F through E. In this open discovery process, the target concepts are not known beforehand, and their selection is part of the process output.

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Receiver operating characteristic curve (ROC) analysis of the overall performance of Clinical Outcome Search Space (COSS) in classifying drugs, according to their propensity to produce a predefined set of adverse drug reactions (ADRs). The area under the curve (AUC) value is 0.75 (95% CI 0.70–0.79).

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The Information Extraction and Ranking algorithms implemented in Biovista's Clinical Outcome Search Space (COSS) platform.

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