Antitumor efficacy testing in rodents

Journal of the National Cancer Institute

Volumn 100, Issue 21, 2008, Pages 1500-1510

  Hollingshead, Melinda G ^a,b  

^a NATIONAL CANCER INSTITUTE   (United States)

^b Fairview Center   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANTINEOPLASTIC AGENT; TAMOXIFEN; TEMOZOLOMIDE; TOPOTECAN;

ANTINEOPLASTIC ACTIVITY; DRUG EFFICACY; DRUG MECHANISM; HUMAN; MOUSE; NONHUMAN; NOTE; PRIORITY JOURNAL; RODENT; TREATMENT PLANNING; TUMOR GROWTH; TUMOR MODEL; TUMOR XENOGRAFT;

ANIMALS; ANTINEOPLASTIC AGENTS; BIOLOGICAL AVAILABILITY; DACARBAZINE; DATA INTERPRETATION, STATISTICAL; DRUG APPROVAL; DRUG DESIGN; DRUG SCREENING ASSAYS, ANTITUMOR; DRUGS, INVESTIGATIONAL; ENDPOINT DETERMINATION; HUMANS; KAPLAN-MEIERS ESTIMATE; MICE; NEOPLASMS; RANDOM ALLOCATION; RATS; RESEARCH DESIGN; RODENTIA; SAMPLE SIZE; TAMOXIFEN; TOPOTECAN; TRANSPLANTATION, HETEROLOGOUS; UNITED STATES; VEHICLES;

EID: 55749094502     PISSN: 00278874     EISSN: 14602105     Source Type: Journal    
DOI: 10.1093/jnci/djn351     Document Type: Note

References (49)

1. American Cancer Society. Cancer Facts and Figures 2008. Atlanta, GA: American Cancer Society; 2008.
2. Venkatesh S, Lipper RA. Role of the development scientist in compound lead selection and optimization. J Pharm Sci. 2000;89(2):145-154.
3. Emanuel EJ, Schnipper LE, Kamin DY, Levinson J, Lichter AS. The costs of conducting clinical research. J Clin Oncol. 2003;21(22):4145-4150.
4. DiMasi JA, Hansen RW, Grabowski HG. The price of innovation: new estimates of drug development costs. J Health Econ. 2003;22(2):151-185.
5. Hermiston TW, Kirn DH. Genetically based therapeutics for cancer: similarities and contrasts with traditional drug discovery and development. Mol Ther. 2005;11(4):496-507.
6. Burchill SA. What do, can and should we learn from models to evaluate potential anticancer agents? Future Med. 2006;2(2):201-211.
7. Plowman J, Dykes DJ, Hollingshead M, Simpson-Herren L, Alley MC. Human tumor xenograft models in NCI drug development. In: Teicher B, ed. Anticancer Drug Development Guide: Preclinical Screening, Clinical Trials and Approval. Totowa, NJ: Humana Press; 1997:101-125.
8. Suggitt M, Bibby MC. 50 years of preclinical anticancer drug screening: empirical to target-driven approaches. Clin Cancer Res. 2005;11(3):971-981.
9. Skipper HE. Improvement of the model system. Cancer Res. 1969;29(12):2329-2333.
10. Teicher BA. Tumor models for efficacy determination. Mol Cancer Ther. 2006;5(10):2435-2443.
11. Alley MC, Hollingshead MG, Dykes DJ, Waud WR. Human tumor xenograft models in NCI drug development. In: Teicher BA, Andrews PA, eds. Cancer Drug Discovery and Development: Anticancer Drug Development Guide: Preclinical Screening, Clinical Trials, and Approval. 2nd ed. Totowa, NJ: Humana Press, Inc; 2004:125-152.
12. Fogh J, Giovanella BC, eds. The Nude Mouse in Experimental and Clinical Research. Vol. 1. New York, NY: Academic Press; 1978.
13. Fogh J, Giovanella BC, eds. The Nude Mouse in Experimental and Clinical Research. Vol. 2. New York, NY: Academic Press; 1982.
14. Peterson JK, Houghton PJ. Integrating pharmacology and in vivo cancer models in preclinical and clinical drug development. Eur J Cancer. 2004;40(6):837-844.
15. Kelland LR. "Of mice and men": values and liabilities of the athymic nude mouse model in anticancer drug development. Eur J Cancer. 2004;40(6):827-836.
16. Voskoglou-Nomikos T, Pater JL, Seymour L. Clinical predictive value of the in vitro cell line, human xenograft, and mouse allograft preclinical cancer models. Clin Cancer Res. 2003;9(11):4227-4239.
17. Kerbel RS. Human tumor xenografts as predictive preclinical models for anticancer drug activity in humans. Cancer Biol Ther. 2003;2:(4 suppl):S134-S139.
18. Johnson JI, Decker S, Zaharevitz D, et al. Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trials. Br J Cancer. 2001;84(10):1424-1431.
19. Luo FR, Yang Z, Camuso A, et al. Dasatinib(BMS-354825) pharmacokinetics and pharmacodynamic biomarkers in animal models predict optimal clinical exposure. Clin Cancer Res. 2006;12(23):7180-7186.
20. Carter CA, Chen C, Brink C, et al. Sorafenib is efficacious and tolerated in combination with cytotoxic or cytostatic agents in preclinical models of human non-small cell lung carcinoma. Cancer Chemother Pharmacol. 2007;59(2):183-195.
21. Lee FYF, Borzilleri R, Fairchild CR, et al. BMS-247550: a novel epothilone analog with a mode of action similar to paclitaxel but possessing superior antitumor efficacy. Clin Cancer Res. 2001;7(5):1429-1437.
22. Man S, Bocci G, Francia G, et al. Antitumor effects in mice of low-dose (metronomic) cyclophosphamide administered continuously through the drinking water. Cancer Res. 2002;62(10):2731-2735.
23. Chow LQM, Eckhardt SG. Sunitinib: from rational design to clinical efficacy. J Clin Oncol. 2007;25(7):884-896.
24. Newell DR. Flasks, fibres, and flanks - preclinical tumour models for predicting clinical antitumour activity. Br J Cancer. 2001;84(10):1289-1290.
25. Kerbel RS. What is the optimal rodent model for anti-tumor drug testing? Cancer Metastasis Rev. 1999;17(3):301-304.
26. Rocchetti M, Simeioni M, Pesenti E, De Nicolao G, Poggesi I. Predicting the active doses in human from animal studies: a novel approach in oncology. Eur J Cancer. 2007;43(12):1862-1868.
27. Dixit R, Boelsterli UA. Healthy animals and animal models of human disease(s) in safety assessment of human pharmaceuticals, including therapeutic antibodies. Drug Discov Today. 2007;12(7/8):336-342.
28. Houghton PJ, Morton CL, Tucker C, et al. The pediatric preclinical testing program: description of models and early testing results. Pediatr Blood Cancer. 2007;49(7):928-940.
29. Eastman A, Perez RP. New targets and challenges in the molecular therapeutics of cancer. Br J Clin Pharmacol. 2006;62(1):5-14.
30. Talmadge JE, Singh RK, Fidler IJ, Raz A. Murine models to evaluate novel and conventional therapeutic strategies for cancer. Am J Pathol. 2007;170(3):793-804.
31. The Cancer Genome Project. Wellcome Trust Sanger Institute Web site. http://www.sanger.ac.uk/genetics/CGP. Accessed September 15, 2008.
32. Ellison G, Klinowska T, Westwood RFR, Docter E, French T, Fox JC. Further evidence to support the melanocytic origin of MDA-MB-435. Mol Pathol. 2002;55(5):294-299.
33. Sellappan S, Grijalva R, Zhou X, et al. Lineage infidelity of MDA-MB-435 cells. Cancer Res. 2004;64(10):3479-3485.
34. Rae JM, Creighton CJ, Meck JM, Haddad BR, Johnson MD. MDA-MB-435 cells are derived from M14 melanoma cells - a loss for breast cancer, but a boon for melanoma research. Breast Cancer Res Treat. 2007;104(1):13-19.
35. Goodman J, Walsh V. The Story of Taxol: Nature and Politics in the Pursuit of an Anti-cancer Drug. New York, NY: Cambridge University Press; 2001.
36. Eiseman JL, Eddington ND, Leslie J, et al. Plasma pharmacokinetics and tissue distribution of paclitaxel in CD2F1 mice. Cancer Chemother Pharmacol. 1994;34(6):465-471.
37. Kinders RJ, Hollingshead M, Parchment RE, et al. Preclinical modeling of a phase 0 clinical trial protocol. J Clin Oncol. 2007;25:(18 suppl):14058.
38. Flaherty KT, O'Dwyer PJ. Conventional design and novel strategies in the era of targeted therapies. In: Teicher BA, Andrews PA, eds. Cancer Drug Discovery and Development: Anticancer Drug Development Guide: Preclinical Screening, Clinical Trials, and Approval. 2nd ed. Totowa, NJ: Humana Press Inc; 2004:363-380.
39. Kakeji Y, Teicher BA. Preclinical studies of the combination of angiogenic inhibitors with cytotoxic agents. Invest New Drugs. 1997;15(1):39-48.
40. Waud WR, Gilbert KS, Shepherd RV, Montgomery JA, Secrist JA III. Preclinical antitumor activity of 4′-thio-β-D- arabinofuranosylcytosine (4′-thio-ara-C). Cancer Chemother Pharmacol. 2003;51(5):422-426.
41. Corbett TH, White K, Polin L, et al. Discovery and preclinical antitumor efficacy evaluations of LY32262 and LY33169. Invest New Drugs. 2003;21(1):33-45.
42. Martin DS, Stolfi RL, Sawyer RC. Commentary on "clinical predictivity of transplantable tumor systems in the selection of new drugs for solid tumors: rationale for a three-stage strategy". Cancer Treat Rep. 1984;68(11):1317-1318.
43. Stolfi RL, Stolfi LM, Sawyer RC, Martin DS. Chemotherapeutic evaluation using clinical criteria in spontaneous, autochthonous murine breast tumors. J Natl Cancer Inst. 1988;80(1):52-55.
44. Bouhemad B, Zhang M, Lu Q, Rouby J-J. Clinical review: bedside lung untrasound in critical care practice. Crit Care. 2007;11(1):205-213.
45. Snedecor GW, Cochran WG. Statistical Methods. 7th ed. Ames, IA: The Iowa State University Press; 1980.
46. Motulsky H. Intuitive Biostatistics. New York, NY: Oxford University Press; 1995.
47. Bland M. An Introduction to Medical Statistics. New York, NY: Oxford University Press; 2000.
48. Knight W. Wm. Knight's Public Domain Tables and Programs: Department of Mathematics and Statistics, University of New Brunswick Web site. http://www.math.unb.ca/~knight/utility/index.html. Accessed September 15, 2008.
49. Baker AF, Dragovich T, Ihle NT, Williams R, Fenoglio-Preiser C, Powis G. Stability of phosphoprotein as a biological marker of tumor signaling. Clin Cancer Res. 2005;11(12):4338-4340.