Ingo Vogt1, Jeanette Prinz1, Mónica Campillos1. 1. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany ; German Center for Diabetes Research, 85764 Neuherberg, Germany.
Abstract
BACKGROUND: The incomplete understanding of disease causes and drug mechanisms of action often leads to ineffective drug therapies or side effects. Therefore, new approaches are needed to improve treatment decisions and to elucidate molecular mechanisms underlying pathologies and unwanted drug effects. METHODS: We present here the first analysis of phenotypically related drug-disease pairs. The phenotypic similarity between 4,869 human diseases and 1,667 drugs was evaluated using an ontology-based semantic similarity approach to compare disease symptoms with drug side effects. We assessed and visualized the enrichment over random of clinical and molecular relationships among drug-disease pairs that share phenotypes using lift plots. To determine the associations between drug and disease classes enriched among phenotypically related pairs we employed a network-based approach combined with Fisher's exact test. RESULTS: We observed that molecularly and clinically related (for example, indication or contraindication) drugs and diseases are likely to share phenotypes. An analysis of the relations between drug mechanisms of action (MoAs) and disease classes among highly similar pairs revealed known and suspected MoA-disease relationships. Interestingly, we found that contraindications associated with high phenotypic similarity often involve diseases that have been reported as side effects of the drug, probably due to common mechanisms. Based on this, we propose a list of 752 precautions or potential contraindications for 486 drugs. CONCLUSIONS: Phenotypic similarity between drugs and diseases facilitates the proposal of contraindications and the mechanistic understanding of diseases and drug side effects.
BACKGROUND: The incomplete understanding of disease causes and drug mechanisms of action often leads to ineffective drug therapies or side effects. Therefore, new approaches are needed to improve treatment decisions and to elucidate molecular mechanisms underlying pathologies and unwanted drug effects. METHODS: We present here the first analysis of phenotypically related drug-disease pairs. The phenotypic similarity between 4,869 human diseases and 1,667 drugs was evaluated using an ontology-based semantic similarity approach to compare disease symptoms with drug side effects. We assessed and visualized the enrichment over random of clinical and molecular relationships among drug-disease pairs that share phenotypes using lift plots. To determine the associations between drug and disease classes enriched among phenotypically related pairs we employed a network-based approach combined with Fisher's exact test. RESULTS: We observed that molecularly and clinically related (for example, indication or contraindication) drugs and diseases are likely to share phenotypes. An analysis of the relations between drug mechanisms of action (MoAs) and disease classes among highly similar pairs revealed known and suspected MoA-disease relationships. Interestingly, we found that contraindications associated with high phenotypic similarity often involve diseases that have been reported as side effects of the drug, probably due to common mechanisms. Based on this, we propose a list of 752 precautions or potential contraindications for 486 drugs. CONCLUSIONS: Phenotypic similarity between drugs and diseases facilitates the proposal of contraindications and the mechanistic understanding of diseases and drug side effects.
Authors: Muhammed A Yildirim; Kwang-Il Goh; Michael E Cusick; Albert-László Barabási; Marc Vidal Journal: Nat Biotechnol Date: 2007-10 Impact factor: 54.908
Authors: Akio Ohta; Elieser Gorelik; Simon J Prasad; Franca Ronchese; Dmitriy Lukashev; Michael K K Wong; Xiaojun Huang; Sheila Caldwell; Kebin Liu; Patrick Smith; Jiang-Fan Chen; Edwin K Jackson; Sergey Apasov; Scott Abrams; Michail Sitkovsky Journal: Proc Natl Acad Sci U S A Date: 2006-08-17 Impact factor: 11.205
Authors: Esther Zorio; Juan Gilabert-Estellés; Francisco España; Luis A Ramón; Raul Cosín; Amparo Estellés Journal: Curr Med Chem Date: 2008 Impact factor: 4.530
Authors: Monika Marcinkowska; Adam Bucki; Joanna Sniecikowska; Agnieszka Zagórska; Nikola Fajkis-Zajączkowska; Agata Siwek; Monika Gluch-Lutwin; Paweł Żmudzki; Magdalena Jastrzebska-Wiesek; Anna Partyka; Anna Wesołowska; Michał Abram; Katarzyna Przejczowska-Pomierny; Agnieszka Cios; Elżbieta Wyska; Kamil Mika; Magdalena Kotańska; Paweł Mierzejewski; Marcin Kolaczkowski Journal: J Med Chem Date: 2021-08-26 Impact factor: 7.446
Authors: Khader Shameer; Benjamin S Glicksberg; Rachel Hodos; Kipp W Johnson; Marcus A Badgeley; Ben Readhead; Max S Tomlinson; Timothy O'Connor; Riccardo Miotto; Brian A Kidd; Rong Chen; Avi Ma'ayan; Joel T Dudley Journal: Brief Bioinform Date: 2018-07-20 Impact factor: 11.622
Authors: Damian Szklarczyk; Alberto Santos; Christian von Mering; Lars Juhl Jensen; Peer Bork; Michael Kuhn Journal: Nucleic Acids Res Date: 2015-11-20 Impact factor: 16.971