Toxicogenomics is a field of science that deals with the collection, interpretation, and storage of information about gene and protein activity within particular cell or tissue of an organism in response to toxic substances. Toxicogenomics combines toxicology with genomics or other high throughput molecular profiling technologies such as transcriptomics, proteomics and metabolomics.[1][2] Toxicogenomics endeavors to elucidate molecular mechanisms evolved in the expression of toxicity, and to derive molecular expression patterns (i.e., molecular biomarkers) that predict toxicity or the genetic susceptibility to it.

In pharmaceutical research toxicogenomics is defined as the study of the structure and function of the genome as it responds to adverse xenobiotic exposure. It is the toxicological subdiscipline of pharmacogenomics, which is broadly defined as the study of inter-individual variations in whole-genome or candidate gene single-nucleotide polymorphism maps, haplotype markers, and alterations in gene expression that might correlate with drug responses (Lesko and Woodcock 2004, Lesko et al. 2003). Though the term toxicogenomics first appeared in the literature in 1999 (Nuwaysir et al.) it was already in common use within the pharmaceutical industry as its origin was driven by marketing strategies from vendor companies. The term is still not universal accepted, and others have offered alternative terms such as chemogenomics to describe essentially the same area (Fielden et al., 2005).

The nature and complexity of the data (in volume and variability) demands highly developed processes of automated handling and storage. The analysis usually involves a wide array of bioinformatics and statistics,[3] regularly involving classification approaches.[4]

In pharmaceutical drug discovery and development toxicogenomics is used to study adverse, i.e. toxic, effects, of pharmaceutical drugs in defined model systems in order to draw conclusions on the toxic risk to patients or the environment. Both the EPA and the U.S. Food and Drug Administration currently preclude basing regulatory decision making on genomics data alone. However, they do encourage the voluntary submission of well-documented, quality genomics data. Both agencies are considering the use of submitted data on a case-by-case basis for assessment purposes (e.g., to help elucidate mechanism of action or contribute to a weight-of-evidence approach) or for populating relevant comparative databases by encouraging parallel submissions of genomics data and traditional toxicologic test results.[5]

Public projects

See also


  1. The National Academies Press: Communicating Toxicogenomics Information to Nonexperts: A Workshop Summary (2005)
  2. ed. by Hisham K. Hamadeh; Cynthia A. Afshari. (2004). Hamadeh HK, Afshari CA, ed. Toxicogenomics: Principles and Applications. Hoboken, NJ: Wiley-Liss. ISBN 0-471-43417-5.
    Omenn GS (November 2004). "Toxicogenomics: Principles and Applications". Environ Health Perspect. 112 (16): A962. PMC 1247673Freely accessible.
  3. Mattes WB, Pettit SD, Sansone SA, Bushel PR, Waters MD (March 2004). "Database development in toxicogenomics: issues and efforts". Environ. Health Perspect. 112 (4): 495–505. doi:10.1289/ehp.6697. PMC 1241904Freely accessible. PMID 15033600.
  4. Ellinger-Ziegelbauer H, Gmuender H, Bandenburg A, Ahr HJ (January 2008). "Prediction of a carcinogenic potential of rat hepatocarcinogens using toxicogenomics analysis of short-term in vivo studies". Mutat. Res. 637 (1–2): 23–39. doi:10.1016/j.mrfmmm.2007.06.010. PMID 17689568.
  5. Corvi R, Ahr HJ, Albertini S, et al. (March 2006). "Meeting Report: Validation of Toxicogenomics-Based Test Systems: ECVAM–ICCVAM/NICEATM Considerations for Regulatory Use". Environ Health Perspect. 114 (3): 420–9. doi:10.1289/ehp.8247. PMC 1392237Freely accessible. PMID 16507466.
  6. Collins BC, Clarke A, Kitteringham NR, Gallagher WM, Pennington SR (October 2007). "Use of proteomics for the discovery of early markers of drug toxicity". Expert Opin Drug Metab Toxicol. 3 (5): 689–704. doi:10.1517/17425255.3.5.689. PMID 17916055.
  7. 1 2 Mattes, William B. (2008). "Public Consortium Efforts in Toxicogenomics". In Mendrick, Donna L.; Mattes, William B. Essential Concepts in Toxicogenomics. Methods in Molecular Biology. 460. pp. 221–238. doi:10.1007/978-1-60327-048-9_11. ISBN 978-1-58829-638-2. PMID 18449490.
  8. Igarashi, Y; Nakatsu, N; Yamashita, T; Ono, A; Ohno, Y; Urushidani, T; Yamada, H (2014). "Open TG-GATEs: A large-scale toxicogenomics database". Nucleic Acids Research. 43 (Database issue): D921–7. doi:10.1093/nar/gku955. PMC 4384023Freely accessible. PMID 25313160.
  9. Dix DJ, Houck KA, Martin MT, Richard AM, Setzer RW, Kavlock RJ (January 2007). "The ToxCast program for prioritizing toxicity testing of environmental chemicals". Toxicol. Sci. 95 (1): 5–12. doi:10.1093/toxsci/kfl103. PMID 16963515.
  10. "Toxicology in the 21st century project"
  11. Abdo, N; Xia, M; Brown, C. C.; Kosyk, O; Huang, R; Sakamuru, S; Zhou, Y. H.; Jack, J; Gallins, P; Xia, K; Li, Y; Chiu, W. A.; Motsinger-Reif, A; Austin, C. P.; Tice, R. R.; Rusyn, I; Wright, F. A. (2015). "Population-Based in Vitro Hazard and Concentration-Response Assessment of Chemicals: The 1000 Genomes High-Throughput Screening Study". Environmental Health Perspectives. 123 (5): 458–66. doi:10.1289/ehp.1408775. PMC 4421772Freely accessible. PMID 25622337.
  12. NIEHS-NCATS-UNC-DREAM Toxicogenetics Challenge!Synapse:syn1761567
  13. "Kernel-based Structural and Pharmacological Analoging" (KSPA) wins prediction of average cytotoxicity at the NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge
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