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Data‐Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events
Author(s) -
Chiang AP,
Butte AJ
Publication year - 2009
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1038/clpt.2008.274
Subject(s) - clinical pharmacology , modalities , drug reaction , data science , molecular pharmacology , field (mathematics) , drug discovery , computer science , drug development , translational research , computational biology , bioinformatics , medicine , pharmacology , drug , engineering ethics , biology , social science , engineering , sociology , pathology , mathematics , receptor , pure mathematics
The dangers of serious adverse drug reactions (SADRs) are well known to clinicians, pharmacologists, and the lay public. Efforts to elucidate the molecular mechanisms behind SADRs have made significant progress through genetics and gene expression measurements. However, as the field of pharmacology adopts the same novel higher‐density measurement modalities that have proven successful in other areas of biology, one wonders whether there can be more ways to benefit from the explosion of data created by these tools. The development of analytic tools and algorithms to interpret these biological data to create tools for medicine is central to the field of translational bioinformatics. In this review we introduce some of the types of SADR predictors that are required, and we discuss several databases that are publicly available for the study of SADRs, ranging from clinical to molecular measurements. We also describe recent examples of how bioinformatics methods coupled with data repositories can advance the science of SADRs. Clinical Pharmacology & Therapeutics (2009); 85 , 3, 259–268 doi: 10.1038/clpt.2008.274