Literature DB >> 18206287

Genomic and genetic biomarkers of toxicity.

Donna L Mendrick1.   

Abstract

Biomarkers in general use today are diagnostic in that they reflect an organ's ongoing dysfunction or damage. Unfortunately, in some cases the change in the biomarker occurs too late for effective medical intervention such as seen with acute renal failure. New biomarkers of toxicity are needed to (a) alert physicians of subtle changes prior to organ dysfunction or damage to enable preventive measures and (b) predict, prior to human exposure, if a drug is likely to induce toxicity in many patients or in specific individuals. Microarray technologies can move biomarker discovery forward at an unforeseen speed as tens of thousands of genes and genetic variants can be monitored simultaneously in one biological specimen. Pharmacogenomics, herein defined as the study of a drug's effect on gene expression, can be used to discover biomarkers in solid tissues or peripheral blood cells that are altered in animals or individuals following drug exposure. Pharmacogenetics, herein defined as the study of genetic factors that affect drug response, can be employed to identify individuals whose genetic make-up suggests they would respond poorly to a particular drug. Biomarkers discovered with these approaches can result in genomic/genetic tests, protein assays or other analytical tests. Examples of such are provided with a discussion of the unresolved issues that inhibit the use of toxicity biomarkers such as biomarker validation, reimbursement of clinical tests and patient privacy.

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Year:  2007        PMID: 18206287     DOI: 10.1016/j.tox.2007.11.013

Source DB:  PubMed          Journal:  Toxicology        ISSN: 0300-483X            Impact factor:   4.221


  9 in total

Review 1.  Blood transcriptomics: applications in toxicology.

Authors:  Pius Joseph; Christina Umbright; Rajendran Sellamuthu
Journal:  J Appl Toxicol       Date:  2013-03-01       Impact factor: 3.446

Review 2.  Oxygenomics in environmental stress.

Authors:  H Sone; H Akanuma; T Fukuda
Journal:  Redox Rep       Date:  2010       Impact factor: 4.412

3.  Glycocapture-assisted global quantitative proteomics (gagQP) reveals multiorgan responses in serum toxicoproteome.

Authors:  Bingyun Sun; Angelita G Utleg; Zhiyuan Hu; Shizhen Qin; Andrew Keller; Cynthia Lorang; Li Gray; Amy Brightman; Denis Lee; Vinita M Alexander; Jeffrey A Ranish; Robert L Moritz; Leroy Hood
Journal:  J Proteome Res       Date:  2013-04-11       Impact factor: 4.466

4.  Blood gene expression markers to detect and distinguish target organ toxicity.

Authors:  Christina Umbright; Rajendran Sellamuthu; Shengqiao Li; Michael Kashon; Michael Luster; Pius Joseph
Journal:  Mol Cell Biochem       Date:  2009-09-26       Impact factor: 3.396

Review 5.  The current status of biomarkers for predicting toxicity.

Authors:  Sarah Campion; Jiri Aubrecht; Kim Boekelheide; David W Brewster; Vishal S Vaidya; Linnea Anderson; Deborah Burt; Edward Dere; Kathleen Hwang; Sara Pacheco; Janani Saikumar; Shelli Schomaker; Mark Sigman; Federico Goodsaid
Journal:  Expert Opin Drug Metab Toxicol       Date:  2013-08-21       Impact factor: 4.481

6.  Identification of discriminating biomarkers for human disease using integrative network biology.

Authors:  Joel T Dudley; Atul J Butte
Journal:  Pac Symp Biocomput       Date:  2009

7.  Predictive Power Estimation Algorithm (PPEA)--a new algorithm to reduce overfitting for genomic biomarker discovery.

Authors:  Jiangang Liu; Robert A Jolly; Aaron T Smith; George H Searfoss; Keith M Goldstein; Vladimir N Uversky; Keith Dunker; Shuyu Li; Craig E Thomas; Tao Wei
Journal:  PLoS One       Date:  2011-09-15       Impact factor: 3.240

8.  Identification and categorization of liver toxicity markers induced by a related pair of drugs.

Authors:  Ching-Wei Chang; Frederick A Beland; Wade M Hines; James C Fuscoe; Tao Han; James J Chen
Journal:  Int J Mol Sci       Date:  2011-07-15       Impact factor: 5.923

Review 9.  Applications of high-throughput genomics to antiviral research: evasion of antiviral responses and activation of inflammation during fulminant RNA virus infection.

Authors:  John C Kash
Journal:  Antiviral Res       Date:  2009-04-16       Impact factor: 5.970

  9 in total

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