Mining the National Cancer Institute Anticancer Drug Discovery Database: cluster analysis of ellipticine analogs with p53-inverse and central nervous system-selective patterns of activity - PubMed (original) (raw)
Mining the National Cancer Institute Anticancer Drug Discovery Database: cluster analysis of ellipticine analogs with p53-inverse and central nervous system-selective patterns of activity
L M Shi et al. Mol Pharmacol. 1998 Feb.
Abstract
The United States National Cancer Institute conducts an anticancer drug discovery program in which approximately 10,000 compounds are screened every year in vitro against a panel of 60 human cancer cell lines from different organs. To date, approximately 62,000 compounds have been tested in the program, and a large amount of information on their activity patterns has been accumulated. For the current study, anticancer activity patterns of 112 ellipticine analogs were analyzed with the use of a hierarchical clustering algorithm. A dramatic coherence between molecular structures and their activity patterns could be seen from the cluster tree: the first subgroup (compounds 1-66) consisted principally of normal ellipticines, whereas the second subgroup (compounds 67-112) consisted principally of N2-alkyl-substituted ellipticiniums. Almost all apparent discrepancies in this clustering were explainable on the basis of chemical transformation to active forms under cell culture conditions. Correlations of activity with p53 status and selective activity against cells of central nervous system origin made this data set of special interest to us. The ellipticiniums, but not the ellipticines, were more potent on average against p53 mutant cells than against p53 wild-type ones (i.e., they seemed to be "p53-inverse") in this short term assay. This study strongly supports the hypothesis that "fingerprint" patterns of activity in the National Cancer Institute in vitro cell screening program encode incisive information on the mechanisms of action and other biological behaviors of tested compounds. Insights gained by mining the activity patterns could contribute to our understanding of anticancer drugs and the molecular pharmacology of cancer.
Similar articles
- Mining the NCI anticancer drug discovery databases: genetic function approximation for the QSAR study of anticancer ellipticine analogues.
Shi LM, Fan Y, Myers TG, O'Connor PM, Paull KD, Friend SH, Weinstein JN. Shi LM, et al. J Chem Inf Comput Sci. 1998 Mar-Apr;38(2):189-99. doi: 10.1021/ci970085w. J Chem Inf Comput Sci. 1998. PMID: 9538518 - Mining and visualizing large anticancer drug discovery databases.
Shi LM, Fan Y, Lee JK, Waltham M, Andrews DT, Scherf U, Paull KD, Weinstein JN. Shi LM, et al. J Chem Inf Comput Sci. 2000 Mar-Apr;40(2):367-79. doi: 10.1021/ci990087b. J Chem Inf Comput Sci. 2000. PMID: 10761142 - Mutant p53-dependent growth suppression distinguishes PRIMA-1 from known anticancer drugs: a statistical analysis of information in the National Cancer Institute database.
Bykov VJ, Issaeva N, Selivanova G, Wiman KG. Bykov VJ, et al. Carcinogenesis. 2002 Dec;23(12):2011-8. doi: 10.1093/carcin/23.12.2011. Carcinogenesis. 2002. PMID: 12507923 - Panel of human cancer cell lines provides valuable database for drug discovery and bioinformatics.
Yamori T. Yamori T. Cancer Chemother Pharmacol. 2003 Jul;52 Suppl 1:S74-9. doi: 10.1007/s00280-003-0649-1. Epub 2003 Jun 18. Cancer Chemother Pharmacol. 2003. PMID: 12819939 Review.
Cited by
- Karyotypic "state" as a potential determinant for anticancer drug discovery.
Roschke AV, Lababidi S, Tonon G, Gehlhaus KS, Bussey K, Weinstein JN, Kirsch IR. Roschke AV, et al. Proc Natl Acad Sci U S A. 2005 Feb 22;102(8):2964-9. doi: 10.1073/pnas.0405578102. Epub 2005 Feb 9. Proc Natl Acad Sci U S A. 2005. PMID: 15703300 Free PMC article. - Pharmacological activation of p53 in cancer cells.
Athar M, Elmets CA, Kopelovich L. Athar M, et al. Curr Pharm Des. 2011;17(6):631-9. doi: 10.2174/138161211795222595. Curr Pharm Des. 2011. PMID: 21391904 Free PMC article. Review. - Therapeutic targeting of p53: all mutants are equal, but some mutants are more equal than others.
Sabapathy K, Lane DP. Sabapathy K, et al. Nat Rev Clin Oncol. 2018 Jan;15(1):13-30. doi: 10.1038/nrclinonc.2017.151. Epub 2017 Sep 26. Nat Rev Clin Oncol. 2018. PMID: 28948977 Review. - Characterization and optimization of a novel protein-protein interaction biosensor high-content screening assay to identify disruptors of the interactions between p53 and hDM2.
Dudgeon DD, Shinde SN, Shun TY, Lazo JS, Strock CJ, Giuliano KA, Taylor DL, Johnston PA, Johnston PA. Dudgeon DD, et al. Assay Drug Dev Technol. 2010 Aug;8(4):437-58. doi: 10.1089/adt.2010.0281. Assay Drug Dev Technol. 2010. PMID: 20662736 Free PMC article.
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources
Research Materials
Miscellaneous