A side effect resource to capture phenotypic effects of drugs

By Michael Kuhn, Monica Campillos, Ivica Letunic, Lars Juhl Jensen, Peer Bork. 2010

907 citations recorded by the source corpus.

The molecular understanding of phenotypes caused by drugs in humans is essential for elucidating mechanisms of action and for developing personalized medicines. Side effects of drugs (also known as adverse drug reactions) are an important source of human phenotypic information, but so far research on this topic has been hampered by insufficient accessibility of data. Consequently, we have developed a public, computer‐readable side effect resource (SIDER) that connects 888 drugs to 1450 side effect terms. It contains information on frequency in patients for one‐third of the drug–side effect pairs. For 199 drugs, the side effect frequency of placebo administration could also be extracted. We illustrate the potential of SIDER with a number of analyses. The resource is freely available for academic research at http://sideeffects.embl.de.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

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Citations in the corpus

The SIDER database of drugs and side effects (2015, background): “To make ADRs amendable to academic research in a simple way, we developed SIDER (‘Side Effect Resource’) in 2010 when no such resource was freely available for academic researchers (7).”
STITCH 3: zooming in on protein–chemical interactions (2011, methodology): “For this reason, we include links to the following databases in the chemical pop-up window: PubChem (23,24), ChEMBL (1), DrugBank (12), KEGG (17) and the SIDER database of drug side effects (25).”
Extracting research-quality phenotypes from electronic health records to support precision medicine (2015, methodology): “This approach has led to several previously unavailable resources, such as SIDER [137] and MEDI [138-140].”
ADRTrace: Detecting Expected and Unexpected Adverse Drug Reactions from User Reviews on Social Media Sites (2013, methodology): “We used SIDER 2 [3], a resource containing known ADRs, to determine whether ADRs were expected or unexpected.”
Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects (2015, background): “SIDER is a side effect database of drugs containing information on market medicines and their recorded adverse drug reactions 21 .”
Data-Driven Prediction of Drug Effects and Interactions (2012, methodology): “Nonetheless, recent work in chemical biology has used the SIDER (a text-mined database of drug package inserts) to good effect ( 12 , 13 , 20 ).”
Molecular Systems Biology Peer Review Process File Indi: a Computational Framework for Inferring Drug Interactions and Their Associated Recommendations Transaction Report (positive): “Drug side effects were obtained from SIDER (Kuhn et al, 2010).”
ChemProt-3.0: a global chemical biology diseases mapping (2016, positive): “Clinical information from the Anatomical Therapeutic Chemical Classification System (23) developed by the World Health Organization, as well as side effect data from Sider 2 were also integrated (24).”
Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, background): “Shared resources for biomedical relation extraction Utility category Data sources Terminology/Ontology/Knowledge Base GO [161], UMLS [58], MeSH [162], Wordnet [118], Verbnet [163], Biothesaurus [122], PDB [101], Uniprot [121], SIDER [164], DrugBank [165], HIVDB [166], RegaDB [167], Entrez Gene [153]”
BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (2016, methodology): “Side-effect data of drugs were downloaded from SIDER database (version: Oct. 17, 2012) 45 and known drug-ATC code associations from DrugBank (down-loaded on 20150726) and KEGG (version: July 31, 2014) 46 .”
Portable Automatic Text Classification for Adverse Drug Reaction Detection via Multi-corpus Training (2014, methodology): “We added additional terms from SIDER II [48] and the Consumer Health Vocabulary Initiative 19 [49] — which include colloquialisms.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “The side-effect database, SIDER (http://sideeffects.embl.de; Kuhn et al., 2010) considerably enhanced side-effect network studies.”
A heuristic approach to determine an appropriate number of topics in topic modeling (2015, methodology): “The second dataset was retrieved from the publicly available SIDER2 database ( http://sideeffects.embl.de ) [13].”