Literature DB >> 16370374

Analysis of drug-induced effect patterns to link structure and side effects of medicines.

Anton F Fliri1, William T Loging, Peter F Thadeio, Robert A Volkmann.   

Abstract

The high failure rate of experimental medicines in clinical trials accentuates inefficiencies of current drug discovery processes caused by a lack of tools for translating the information exchange between protein and organ system networks. Recently, we reported that biological activity spectra (biospectra), derived from in vitro protein binding assays, provide a mechanism for assessing a molecule's capacity to modulate the function of protein-network components. Herein we describe the translation of adverse effect data derived from 1,045 prescription drug labels into effect spectra and show their utility for diagnosing drug-induced effects of medicines. In addition, notwithstanding the limitation imposed by the quality of drug label information, we show that biospectrum analysis, in concert with effect spectrum analysis, provides an alignment between preclinical and clinical drug-induced effects. The identification of this alignment provides a mechanism for forecasting clinical effect profiles of medicines.

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Year:  2005        PMID: 16370374     DOI: 10.1038/nchembio747

Source DB:  PubMed          Journal:  Nat Chem Biol        ISSN: 1552-4450            Impact factor:   15.040


  34 in total

1.  Biclustering of adverse drug events in the FDA's spontaneous reporting system.

Authors:  R Harpaz; H Perez; H S Chase; R Rabadan; G Hripcsak; C Friedman
Journal:  Clin Pharmacol Ther       Date:  2010-12-29       Impact factor: 6.875

Review 2.  Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.

Authors:  Peter Csermely; Tamás Korcsmáros; Huba J M Kiss; Gábor London; Ruth Nussinov
Journal:  Pharmacol Ther       Date:  2013-02-04       Impact factor: 12.310

3.  Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature.

Authors:  Rong Xu; QuanQiu Wang
Journal:  J Biomed Inform       Date:  2014-06-10       Impact factor: 6.317

4.  Predicting adverse drug reactions using publicly available PubChem BioAssay data.

Authors:  Y Pouliot; A P Chiang; A J Butte
Journal:  Clin Pharmacol Ther       Date:  2011-05-25       Impact factor: 6.875

5.  The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions.

Authors:  Santiago Vilar; George Hripcsak
Journal:  Brief Bioinform       Date:  2017-07-01       Impact factor: 11.622

6.  Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning.

Authors:  Mei Liu; Ruichu Cai; Yong Hu; Michael E Matheny; Jingchun Sun; Jun Hu; Hua Xu
Journal:  J Am Med Inform Assoc       Date:  2013-12-11       Impact factor: 4.497

7.  Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach.

Authors:  Tianyun Liu; Russ B Altman
Journal:  J Chem Inf Model       Date:  2015-07-16       Impact factor: 4.956

8.  Comparing a knowledge-driven approach to a supervised machine learning approach in large-scale extraction of drug-side effect relationships from free-text biomedical literature.

Authors:  Rong Xu; QuanQiu Wang
Journal:  BMC Bioinformatics       Date:  2015-03-18       Impact factor: 3.169

Review 9.  Data-driven methods to discover molecular determinants of serious adverse drug events.

Authors:  A P Chiang; A J Butte
Journal:  Clin Pharmacol Ther       Date:  2009-01-28       Impact factor: 6.875

10.  A structure-based approach for mapping adverse drug reactions to the perturbation of underlying biological pathways.

Authors:  Izhar Wallach; Navdeep Jaitly; Ryan Lilien
Journal:  PLoS One       Date:  2010-08-23       Impact factor: 3.240

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