Cytotoxic assays for screening anticancer agents

Statistics in Medicine

Volumn 25, Issue 13, 2006, Pages 2323-2339

  Baharith, Lamya A ^a   Al Khouli, Abeer ^a   Raab, Gillian M ^b  

^a KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

^b EDINBURGH NAPIER UNIVERSITY   (United Kingdom)

Author keywords

Bio assay; Cytotoxic agents; Dose response; Non linear random effects

Indexed keywords

ANTINEOPLASTIC AGENT; BUSULFAN; CHLORAMBUCIL; CHLORMETHINE; FLUOROURACIL; GUANAZOLE; MELPHALAN; MERCAPTOPURINE; METHOTREXATE; PIPOBROMAN; THIOTEPA; TIOGUANINE; TRETAMINE;

ANALYTICAL ERROR; ANTINEOPLASTIC ACTIVITY; ARTICLE; BAYES THEOREM; BREAST CANCER; CANCER CELL CULTURE; CANCER GROWTH; CANCER INHIBITION; CENTRAL NERVOUS SYSTEM TUMOR; COLON CANCER; CONCENTRATION RESPONSE; CYTOTOXICITY TEST; DRUG POTENCY; DRUG RESPONSE; DRUG SCREENING; HUMAN; INFORMATION PROCESSING; KIDNEY CANCER; LEUKEMIA CELL LINE; LUNG CANCER; MAXIMUM LIKELIHOOD METHOD; MELANOMA; NONLINEAR SYSTEM; OVARY CANCER; PROSTATE CANCER;

ANTINEOPLASTIC AGENTS; CELL LINE, TUMOR; DOSE-RESPONSE RELATIONSHIP, DRUG; DRUG SCREENING ASSAYS, ANTITUMOR; FEMALE; HUMANS; LEAST-SQUARES ANALYSIS; MALE; NONLINEAR DYNAMICS;

EID: 33745618684     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.2272     Document Type: Article

References (33)

1. Development therapeutics program nci/nih. http://dtp.nci.nih.gov/index.html [Accessed 1st February 2004].
2. Boyd MR, Paull KD, Rubinstein LR. Data display and analysis strategies for the NCI disease-oriented in vitro antitumor drug screen. Proceedings of the 22nd Annual Detroit Oncology Symposium on Anticancer Drug Discovery and Development, Detroit, April 25-27, 1990, 1992.
3. Boyd MR, Paull KD. Some practical considerations and applications of the National Cancer Institute in vitro anticancer drug discovery screen. Drug Development Research 1995; 34:91-109.
4. Weinstein JN, Kohn KW, Grever MR, Viswanadhan VN, Rubinstein LV, Monks A, Scudiero DA, Welch L, Kousoukos AD, Chiausa AJ, Paull KD. Neural computing in cancer drug development: predicting mechanism of action. Science 1992; 258(16):447-451.
5. Weinstein JN, Myers T, Buolamwini K, van Osdol W, Licht J, Viswanadhan VN, Kohn KW, Rubinstein LV, Kousoukos AD, Monks A, Scudiero DA, Anderson NL, Zaharevitz D, Chabner BA, Grever MR, Paull KD. Predictive statistical and artificial intelligence in the U.S. National Cancer Institute's drug discovery program for cancer and aids. Stem Cells 1994; 12:13-22.
6. Kousoukos AD, Rubinstein LV, Faraggi D, Simon RM, Kalyandrug S, Weinstein JN, Kohn KW, Paull KD. Discrimination techniques applied to the NCI in vitro anti-tumour drug screen: predicting biochemical mechanism of action. Statistics in Medicine 1994; 13:719-730.
7. van Osdol WW, Myers TG, Paull KD, Kohn KW, Weinstein JN. Use of Kohonen self-organizing map to study the mechanisms of action of chemotherapeutic agents. Journal of the National Cancer Institute 1994; 86(24):1853-1859.
8. Keskin O, Bahar I, Jernigan RL, Beutler JA, Shoemaker RH, Sausville EA, Covell DG. Characterization of anticancer agents by their growth inhibitory activity and relationships to mechanism of action and structure. Anti-Cancer Drug Design 2000; 15:79-98.
9. Musumarra G, Condorelli DF, Costa AS, Fichera M. A multivariate insight into the in vitro antitumour screen database of the national cancer institute: classification of compounds, similarities among cell lines and the influence of molecular targets. Journal of Computer-Aided Molecular Design 2001; 15:219-234.
10. Ross DT, Scherf U, Eisen MB, Perou CM, Rees C, Spellman P, Iyer V, Jeffrey SS, Van de Rijn M, Waltham M, Pergamenschikov A, Lee JC, Lashkari D, Myers TG, Shalon D, Weinstein JN, Botstein D, Brown PO. Systematic variation in gene expression patterns in human cancer cell lines. Nature Genetics 24(3):227-235.
11. Scherf U, Ross DT, Waltham M, Smith LH, Lee JK, Tanabe L, Kohn KW, Reinhold WC, Myers TG, Andrews DT, Scudiero DA, Eisen MB, Sausville EA, Pommier Y, Botstein D, Brown PO, Weinstein JN. A gene expression database for the molecular pharmacology of cancer. Nature Genetics 2000; 24(3):236-244.
12. Marx KA, O'Neil P, Hoffman P, Ujwal ML. Data mining the NCI cancer cell line compound gi(50) values: identifying quinone subtypes effective against melanoma and leukemia cell classes. Journal of Chemical Information and Computer Sciences 2003; 43(5):1652-1667.
13. Nishizuka S, Charboneau L, Young L, Major S, Reinhold WC, Waltham M, Kouros-Mehr H, Bussey KJ, Lee JK, Espina V, Munson PJ, Petricoin E, Liotta LA, Weinstein JN. Proteomic profiling of the nci-60 cancer cell lines using new high-density reverse-phase lysate microarrays. Proceedings of the National Academy of Sciences of the United States of America 2003; 100(24):14229-14234.
14. Paull KD, Shoemaker RH, Hodes L, Monks A, Scudiero DA, Rubinstein L, Plowman J, Boyd MR. Display and analysis of patterns of differential activity of drug against human tumor cell lines: development of mean graph and compare algorithm. Journal of the National Cancer Institute 1989; 81(14):1088-1092.
15. The compare algorithm. http://dtp.nci.nih.gov/docs/compare/compare.html [Accessed 1st February 2004].
16. Finney DJ. Radioligand assay. Biometrics 1976; 32:721-740.
17. Monks A, Scudiero D, Skehan P, Shoemaker R, Paull K, Vistica D, Hose S, Lagley C, Cronise P, Viagro-Wolfe A, Gray-Goodrich M, Campbell H, Boyd MR. Feasibility of a high-flux anticancer drug screen utilizing a diverse panel of human tumour cell lines. Journal of the National Cancer Institute 1991; 83:236-244.
18. Skehan P, Storing R, Scudiero D, Monks A, McMahon D, Vistica D, Warren JD, Bokesh H, Kenney S, Boyd MR. New colorimetric cytotoxic assay for anticancer drug screening. Journal of the National Cancer Institute 1990; 82(13):1107-1112.
19. Healy MJ. Comment on linear models article. Clinical Chemistry 1980; 26:1227-1228.
20. Dudley RA, Edwards P, Ekins RP, Finney DJ, Mckenzie IG, Raab GM, Rodbard D, Rodgers RPC. Guidelines for immunoassay data-processing. Clinical Chemistry 1985; 31(8):1264-1271.
21. Johnson A, Kotz B, Balakrishnan C. Continuous Univariate Distributions. Wiley: New York, 1994.
22. Chambers J, Hastie T. Statistical Models in S. Wadswarth & Brooks Cole: Belmont, CA, 1992.
23. The R project. http://www.r-project.org/ [Accessed 1st February 2004].
24. Williams DA. Interval estimation of the median lethal dose. Biometrics 1986; 42(3):641-645.
25. Davidian M, Giltinan DM. Nonlinear Models for Repeated Measurement Data. CRC Press: Boca Raton, FL, 1995.
26. Pinheiro JC, Bates DM. Mixed-Effects Models in S and S-PLUS. Springer: New York, 2000.
27. Venables B, Ripley B. Modern Applied Statistics with Splus (4th edn). Springer: New York, 2002.
28. The bugs project:winbugs. http://www.mrc-bsu.cam.ac.uk/bugs/ [Accessed 1st February04].
29. Best NG, Tan KKC, Gilks WR, Spiegelhalter DJ. Estimation of population pharmacokinetics using the gibbs sampler. Journal of Pharmacokinetics and Biopharmaceutics 1995; 23:407-435.
30. Wakefield JC. The bayesian approach to population pharmacokinetic models. Journal of the American Statistical Association 1996; 91:61-76.
31. Lindstrom MJ, Bates DM. Nonlinear mixed-effects models for repeated measures data. Biometrics 1990; 46:673-687.
32. Wolfinger RD, Lin X. Two Taylor-series approximation methods for non-linear mixed models. Computational Statistics and Data Analysis 1997; 25:465-490.
33. Laird NM, Ware JH. Random effects models for longitudinal data. Biometrics 1982; 38:963-974.