Konrad Mönks, Andreas Bernthaler, Irmgard Mühlberger, Bernd Mayer, Rainer Oberbauer and Paul Perco
Background: High-throughput Omics technologies aimed at characterizing the molecular profile of diseases together with massive scientific literature on drugs and clinical trials opened the way for matching molecular profiles and drug mode of action in the realm of drug repositioning. We developed a computational analysis workflow for linking molecular targets, drugs, and diseases, and exemplified this approach for the immunosuppressive drug mycophenolate mofetil (MMF). Methods and Results: We first established a molecular MMF footprint consisting of deregulated Omics features from two transcriptomics datasets as well as from molecular features associated with MMF based on literature search methods. This footprint, consisting of 170 unique features, was used to identify diseases of relevance to MMF in the scientific literature using Medical Subject Heading (MeSH) terms. A disease enrichment score was calculated for each disease in the MeSH hierarchy, with highly ranked diseases being potentially associated to MMF. Diseases currently mentioned in clinical trials on MMF were used to validate our approach. The area under the curve was 0.78 when using the disease enrichment scores in order to discriminate between diseases currently in clinical trials and diseases not addressed by MMF with sensitivity and specificity values of 0.38 and 0.96 respectively. Among those diseases in clinical trials showing high scores were kidney diseases, multiple sclerosis, and systemic lupus erythematosus. Conclusion: We identified a significant recovery of drug-associated diseases for the example case of MMF solely utilizing a molecular profile of the drug mode of action. The approach furthermore provided hypotheses on further diseases approachable by the given drug.
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