Drug repurposing and adverse event prediction using high‐throughput literature analysis

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.

Accessibility of trial reports for drugs stalling in development: a systematic assessment of registered trials

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RNAi Validation of Pancreatic Cancer Antigens Identified by Cell Surface Proteomics

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Development of an Evaluation Method for a Driver's Cognitive Workload Using ECG Signal

“…AUC 분석은 <Figure 3>과 같이 1-specificity(x 축; false alarm rate)와 sensitivity(y축; hit rate)의 관계를 나타내는 ROC curve 아래의 면적을 계산하여 신호판별 성능을 평가한다 (Deftereos et al, 2011;Hand and Till, 2001;Swets, 1998 mative; AUC ≤ 0.5), 약간 정확(slightly accurate; 0.5 < AUC ≤ 0.7), 정확(moderate accurate; 0.7 < AUC ≤ 0.9), 매우 정확(highly accurate; 0.9 < AUC < 1), 그리고 완벽(perfect inspection; AUC = 1)의 5가지 등급으로 판별 성능을 분류하고 있다 (Greiner et al, 2000;Swets, 1988). 한편, 의학 분야에서는 AUC 값이 0.8 미만 이면 정상인과 환자를 적합하게 구분하지 못하는 것으로 평가 하고 있다 (Park and Moon, 2005).…”