Literature DB >> 16264434

Combinatorial pharmacogenetics.

Russell A Wilke1, David M Reif, Jason H Moore.   

Abstract

Combinatorial pharmacogenetics seeks to characterize genetic variations that affect reactions to potentially toxic agents within the complex metabolic networks of the human body. Polymorphic drug-metabolizing enzymes are likely to represent some of the most common inheritable risk factors associated with common 'disease' phenotypes, such as adverse drug reactions. The relatively high concordance between polymorphisms in drug-metabolizing enzymes and clinical phenotypes indicates that research into this class of polymorphisms could benefit patients in the near future. Characterization of other genes affecting drug disposition (absorption, distribution, metabolism and elimination) will further enhance this process. As with most questions concerning biological systems, the complexity arises out of the combinatorial magnitude of all the possible interactions and pathways. The high-dimensionality of the resulting analysis problem will often overwhelm traditional analysis methods. Novel analysis techniques, such as multifactor dimensionality reduction, offer viable options for evaluating such data.

Entities:  

Mesh:

Substances:

Year:  2005        PMID: 16264434     DOI: 10.1038/nrd1874

Source DB:  PubMed          Journal:  Nat Rev Drug Discov        ISSN: 1474-1776            Impact factor:   84.694


  40 in total

1.  Mapping genes that predict treatment outcome in admixed populations.

Authors:  T M Baye; R A Wilke
Journal:  Pharmacogenomics J       Date:  2010-10-05       Impact factor: 3.550

2.  Machine learning for detecting gene-gene interactions: a review.

Authors:  Brett A McKinney; David M Reif; Marylyn D Ritchie; Jason H Moore
Journal:  Appl Bioinformatics       Date:  2006

3.  Summarizing drug information in Medline citations.

Authors:  Marcelo Fiszman; Thomas C Rindflesch; Halil Kilicoglu
Journal:  AMIA Annu Symp Proc       Date:  2006

Review 4.  Therapeutic drug monitoring and pharmacogenetic tests as tools in pharmacovigilance.

Authors:  Eveline Jaquenoud Sirot; Jan Willem van der Velden; Katharina Rentsch; Chin B Eap; Pierre Baumann
Journal:  Drug Saf       Date:  2006       Impact factor: 5.606

Review 5.  Identifying genetic risk factors for serious adverse drug reactions: current progress and challenges.

Authors:  Russell A Wilke; Debbie W Lin; Dan M Roden; Paul B Watkins; David Flockhart; Issam Zineh; Kathleen M Giacomini; Ronald M Krauss
Journal:  Nat Rev Drug Discov       Date:  2007-11       Impact factor: 84.694

6.  The Pathway Less Traveled: Moving from Candidate Genes to Candidate Pathways in the Analysis of Genome-Wide Data from Large Scale Pharmacogenetic Association Studies.

Authors:  R A Wilke; R K Mareedu; J H Moore
Journal:  Curr Pharmacogenomics Person Med       Date:  2008

7.  Integrated analysis of genetic and proteomic data identifies biomarkers associated with adverse events following smallpox vaccination.

Authors:  D M Reif; A A Motsinger-Reif; B A McKinney; M T Rock; J E Crowe; J H Moore
Journal:  Genes Immun       Date:  2008-10-16       Impact factor: 2.676

8.  Assessment of a pharmacogenomic marker panel in a polypharmacy population identified from electronic medical records.

Authors:  Matthew T Oetjens; Joshua C Denny; Marylyn D Ritchie; Niloufar B Gillani; Danielle M Richardson; Nicole A Restrepo; Jill M Pulley; Holli H Dilks; Melissa A Basford; Erica Bowton; Dan R Masys; Russell A Wilke; Dan M Roden; Dana C Crawford
Journal:  Pharmacogenomics       Date:  2013-05       Impact factor: 2.533

9.  Genetic variation in the UGT1A locus is associated with simvastatin efficacy in a clinical practice setting.

Authors:  Otito F Iwuchukwu; QiPing Feng; Wei-Qi Wei; Lan Jiang; Min Jiang; Hua Xu; Joshua C Denny; Russell A Wilke; Ronald M Krauss; Dan M Roden; C Michael Stein
Journal:  Pharmacogenomics       Date:  2014-11       Impact factor: 2.533

Review 10.  Bioinformatics challenges for genome-wide association studies.

Authors:  Jason H Moore; Folkert W Asselbergs; Scott M Williams
Journal:  Bioinformatics       Date:  2010-01-06       Impact factor: 6.937

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.