Skip to main content
SpringerLink
Log in

Preclinical Models for Anticancer Drug Development

  • Chapter
  • First Online:
Principles of Anticancer Drug Development

Part of the book series: Cancer Drug Discovery and Development ((CDD&D))

Abstract

New molecules under consideration as novel cancer therapeutics face a number of challenges in their development path. Most prominent among these is that in contrast to therapeutic areas such as infectious diseases or inflammation, where a variety of model systems are predictive of clinical success assuming that pharmaceutical features of the candidate molecules can be properly designed or modified, such models are lacking in oncology. Thus, an overriding goal of a cancer drug development pathway should be to “fail fast” structures that have low likelihood of success, and “advance smartly” structures that will achieve the desired goal, which is ultimately the evolution of a successful registration strategy. This chapter will touch on the various types of yardsticks that can be applied throughout a drug’s development path to achieve these goals.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kuh HJ, Jang SH, Wientjes MG, et al: Determinants of paclitaxel penetration and accumulation in human solid tumor. J Pharmacol Exp Ther 290:871–80, 1999.

    PubMed  CAS  Google Scholar 

  2. Shepherd FA, Rodrigues Pereira J, Ciuleanu T, et al: Erlotinib in previously treated non-small-cell lung cancer. N Engl J Med 353:123–32, 2005.

    PubMed  CAS  Google Scholar 

  3. Tan AR, Headlee D, Messmann R, et al: Phase I clinical and pharmacokinetic study of flavopiridol administered as a daily 1-hour infusion in patients with advanced neoplasms. J Clin Oncol 20:4074–82, 2002.

    PubMed  CAS  Google Scholar 

  4. Demo SD, Kirk CJ, Aujay MA, et al: Antitumor activity of PR-171, a novel irreversible inhibitor of the proteasome. Cancer Res 67:6383–91, 2007.

    PubMed  CAS  Google Scholar 

  5. Dowsett M, Nicholson RI, Pietras RJ: Biological characteristics of the pure antiestrogen fulvestrant: overcoming endocrine resistance. Breast Cancer Res Treat 93 (Suppl 1):S11–8, 2005.

    PubMed  CAS  Google Scholar 

  6. Janus A, Robak T, Smolewski P: The mammalian target of the rapamycin (mTOR) kinase pathway: its role in tumourigenesis and targeted antitumour therapy. Cell Mol Biol Lett 10:479–98, 2005.

    PubMed  CAS  Google Scholar 

  7. Lipinski CA, Lombardo F, Dominy BW, et al: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26, 2001.

    PubMed  CAS  Google Scholar 

  8. Newman DJ, Cragg GM: Natural products as sources of new drugs over the last 25 years.J Nat Prod 70:461–77, 2007.

    PubMed  CAS  Google Scholar 

  9. Cragg GM, Boyd MR, Cardellina JH 2nd, et al: Ethnobotany and drug discovery: the experience of the US National Cancer Institute. Ciba Found Symp 185:178–90, 1994 (discussion 190–6).

    PubMed  CAS  Google Scholar 

  10. Chen YH, Lipes BD, Kenan DJ, et al: Identification of recombinant antibodies against multiple distinct toll-like receptors by homolog mining a single immune scFv phage library.J Immunol Methods 340(2):144–53, 2009.

    PubMed  CAS  Google Scholar 

  11. Wang B, Chen YB, Ayalon O, et al: Human single-chain Fv immunoconjugates targeted to a melanoma-associated chondroitin sulfate proteoglycan mediate specific lysis of human melanoma cells by natural killer cells and complement. Proc Natl Acad Sci USA 96:1627–32, 1999.

    PubMed  CAS  Google Scholar 

  12. Strome SE, Sausville EA, Mann D: A mechanistic perspective of monoclonal antibodies in cancer therapy beyond target-related effects. Oncologist 12:1084–95, 2007.

    PubMed  CAS  Google Scholar 

  13. Foss FM, Bacha P, Osann KE, et al: Biological correlates of acute hypersensitivity events with DAB(389)IL-2 (denileukin diftitox, ONTAK) in cutaneous T-cell lymphoma: decreased frequency and severity with steroid premedication. Clin Lymphoma 1:298–302, 2001.

    PubMed  CAS  Google Scholar 

  14. Sahdev S, Khattar SK, Saini KS: Production of active eukaryotic proteins through bacterial expression systems: a review of the existing biotechnology strategies. Mol Cell Biochem 307:249–64, 2008.

    PubMed  CAS  Google Scholar 

  15. Waehler R, Russell SJ, Curiel DT: Engineering targeted viral vectors for gene therapy. Nat Rev Genet 8:573–87, 2007.

    PubMed  CAS  Google Scholar 

  16. Aurigemma R, Tomaszewski JE, Ruppel S et al: Regulatory aspects in the development of gene therapy, in Curiel DT, Douglas J: Cancer Gene Therapy, Humana Press, Totowa NJ, 2005, pp 441–472.

    Google Scholar 

  17. Chabner BA, Roberts TG Jr. Timeline: Chemotherapy and the war on cancer. Nat Rev Cancer 5:65–72, 2005.

    PubMed  CAS  Google Scholar 

  18. Waud WR: Murine L1210 and P388 leukemias, in Teicher BA & Andrews PA: Anticancer Drug Development Guide Preclinical Screening, Clinical Trials, and Approval, 2nd ed. Humana Press, Totowa, NJ, 2004, pp 79–97.

    Google Scholar 

  19. Boyd MR: The NCI human tumor cell line (60 Cell)screen, in Teicher BA & Andrews PA: Anticancer Drug Development Guide Preclinical Screening, Clinical Trials, and Approval, 2nd ed. Humana Press, Totowa, NJ, 2004, pp 41–61.

    Google Scholar 

  20. Druker BJ, Tamura S, Buchdunger E, et al: Effects of a selective inhibitor of the Abl tyrosine kinase on the growth of Bcr-Abl positive cells. Nat Med 2:561–6, 1996.

    PubMed  CAS  Google Scholar 

  21. Pegram MD, Konecny G, Slamon DJ: The molecular and cellular biology of HER2/neu gene amplification/overexpression and the clinical development of herceptin (trastuzumab) therapy for breast cancer. Cancer Treat Res 103:57–75, 2000.

    PubMed  CAS  Google Scholar 

  22. Baumann CK, Castiglione-Gertsch M: Estrogen receptor modulators and down regulators: optimal use in postmenopausal women with breast cancer. Drugs 16:2335–53, 2007.

    Google Scholar 

  23. Taplin ME: Drug insight: role of the androgen receptor in the development and progression of prostate cancer. Nat Clin Pract Oncol 4:236–44, 2007.

    PubMed  CAS  Google Scholar 

  24. Kim DH, Sabatini DM: Raptor and mTOR: subunits of a nutrient-sensitive complex. Curr Top Microbiol Immunol 279:259–70, 2004.

    PubMed  CAS  Google Scholar 

  25. Schellens JH: Capecitabine. Oncologist 12:152–5, 2007.

    PubMed  CAS  Google Scholar 

  26. Weinstein IB, Joe A: Oncogene addiction. Cancer Res 68:3077–80, 2008.

    PubMed  CAS  Google Scholar 

  27. Reddy A, Kaelin WG Jr.: Using cancer genetics to guide the selection of anticancer drug targets. Curr Opin Pharmacol 2:366–73, 2002.

    PubMed  CAS  Google Scholar 

  28. Davies H, Bignell GR, Cox C, et al: Mutations of the BRAF gene in human cancer. Nature 417:949–54, 2002.

    PubMed  CAS  Google Scholar 

  29. Willmore-Payne C, Holden JA, Tripp S, et al: Human malignant melanoma: detection of BRAF- and c-kit-activating mutations by high-resolution amplicon melting analysis. Hum Pathol 36:486–93, 2005.

    PubMed  CAS  Google Scholar 

  30. Aina OH, Liu R, Sutcliffe JL, et al: From combinatorial chemistry to cancer–targeting peptides. Mol Pharm 4:631–51, 2007.

    PubMed  CAS  Google Scholar 

  31. Messeguer A, Cortés N: Combinatorial chemistry in cancer research. Clin Transl Oncol 9:83–92, 2007.

    PubMed  CAS  Google Scholar 

  32. Tan DS: Current progress in natural product-like libraries for discovery screening. Comb Chem High Throughput Screen 7:631–43, 2004.

    PubMed  CAS  Google Scholar 

  33. Schepartz SA: Introduction and Historical Background, in Foye WO: Cancer Chemotherapeutic Agents, American Chemical Society, Washington DC 1995, pp 1–7.

    Google Scholar 

  34. Elion GB, Hitchings GH: Metabolic basis for the actions of analogs of purines and pyrimidines. Adv Chemother 2:91–177, 1965.

    PubMed  CAS  Google Scholar 

  35. Grever MR, Schepartz SA, Chabner BA: The National Cancer Institute: cancer drug discovery and development program. Semin Oncol 19:622–38, 1992.

    PubMed  CAS  Google Scholar 

  36. Corbett T, Polin L, LoRusso P et al: In vivo methods for screening and preclinical testing, in Teicher BA & Andrews PA: Anticancer Drug Development Guide Preclinical Screening, Clinical Trials, and Approval, 2nd ed. Humana Press, Totowa, NJ 2004, pp 99–123.

    Google Scholar 

  37. Shoemaker RH: The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 6:813–23, 2006.

    PubMed  CAS  Google Scholar 

  38. Paull KD, Hamel E, Malspeis, L: Prediction of biochemical mechanism of action from the in vitro antitumor screen of the national cancer institute, in Foye WO: Cancer Chemotherapeutic Agents, American Chemical Society, Washington, DC, 1995 pp 9–45.

    Google Scholar 

  39. Weinstein JN, Kohn KW, Grever MR, et al: Neural computing in cancer drug development: predicting mechanism of action. Science 258:447–51, 1992.

    PubMed  CAS  Google Scholar 

  40. Scherf U, Ross DT, Waltham M et al: A gene expression database for the molecular pharmacology of cancer. Nat Genet 24:236–44, 2000.

    PubMed  CAS  Google Scholar 

  41. Rabow AA, Shoemaker RH, Sausville EA et al: Mining the National Cancer Institute’s tumor-screening database: identification of compounds with similar cellular activities. J Med Chem 45:818–40, 2002.

    PubMed  CAS  Google Scholar 

  42. Zaharevitz DW, Holbeck SL, Bowerman C, et al: COMPARE: a web accessible tool for investigating mechanisms of cell growth inhibition. J Mol Graph Model 20:297–303, 2002.

    PubMed  CAS  Google Scholar 

  43. Keinan S, Paquette WD, Skoko JJ, et al: Computational design, synthesis and biological evaluation of para-quinone-based inhibitors for redox regulation of the dual-specificity phosphatase Cdc25B. Org Biomol Chem 6:3256–63, 2008.

    PubMed  CAS  Google Scholar 

  44. Bender A, Bojanic D, Davies JW et al: Which aspects of HTS are empirically correlated with downstream success? Curr Opin Drug Discov Devel 11:327–37, 2008.

    PubMed  CAS  Google Scholar 

  45. Hancock CN, Macias A, Lee EK et al: Identification of novel extracellular signal-regulated kinase docking domain inhibitor. J Med Chem 48:4586–95, 2005.

    PubMed  CAS  Google Scholar 

  46. Fischer PM: Computational chemistry approaches to drug discovery in signal transduction. Biotechnol J 3:452–70, 2008.

    PubMed  CAS  Google Scholar 

  47. Alonso H, Bliznyuk AA, Gready JE: Combining docking and molecular dynamic simulations in drug design. Med Res Rev 26:531–68, 2006.

    PubMed  CAS  Google Scholar 

  48. Kirchmair J, Distinto S, Schuster D, et al: Enhancing drug discovery through in silico screening: strategies to increase true positives retrieval rates. Curr Med Chem 15:2040–53, 2008.

    PubMed  CAS  Google Scholar 

  49. Rowlands MG, Newbatt YM, Prodromou C, et al: High-throughput screening assay for inhibitors of heat-shock protein 90 ATPase activity. Anal Biochem 327:176–83, 2004.

    PubMed  CAS  Google Scholar 

  50. Jobson AG, Cardellina JH 2nd, Scudiero D, et al: Identification of a Bis-guanylhydrazone [4,4′-Diacetyldiphenylurea-bis(guanylhydrazone); NSC 109555] as a novel chemotype for inhibition of Chk2 kinase. Mol Pharmacol 72:876–84, 2007.

    PubMed  CAS  Google Scholar 

  51. Wendt MD, Shen W, Kunzer A, et al: Discovery and structure-activity relationship of antagonists of B-cell lymphoma 2 family proteins with chemopotentiation activity in vitro and in vivo.J Med Chem 49:1165–81, 2006.

    PubMed  CAS  Google Scholar 

  52. Rapisarda A, Uranchimeg B, Scudiero DA et al: Identification of small molecule inhibitors of hypoxia-inducible factor 1 transcriptional activation pathway. Cancer Res 62:4316–24, 2002.

    PubMed  CAS  Google Scholar 

  53. Bommi-Reddy A, Almeciga I, Sawyer J, et al: Kinase requirements in human cells: III. Altered kinase requirements in VHL−/− cancer cells detected in a pilot synthetic lethal screen. Proc Natl Acad Sci USA 105:16484–9, 2008.

    PubMed  CAS  Google Scholar 

  54. Kaelin WG Jr.: The concept of synthetic lethality in the context of anticancer therapy. Nat Rev Cancer 5:689–98, 2005.

    PubMed  CAS  Google Scholar 

  55. Webb CP, Hose CD, Koochekpour S, et al: The geldanamycins are potent inhibitors of the hepatocyte growth factor/scatter factor-met-urokinase plasminogen activator-plasmin proteolytic network. Cancer Res 60:342–9, 2000.

    PubMed  CAS  Google Scholar 

  56. Simon JA, Bedalov A: Yeast as a model system for anticancer drug discovery. Nat Rev Cancer 4:481–92, 2004.

    PubMed  CAS  Google Scholar 

  57. Liu X, Kramer JA, Swaffield JC, et al: Development of a highthroughput yeast-based assay for detection of metabolically activated genotoxins. Mutat Res 653:63–9, 2008.

    PubMed  CAS  Google Scholar 

  58. Tran TC, Sneed B, Haider J, et al: Automated, quantitative screening assay for antiangiogenic compounds using transgenic zebrafish. Cancer Res 67:11386–92, 2007.

    PubMed  CAS  Google Scholar 

  59. van Haaften G, Romeijn R, Pothof J, et al: Identification of conserved pathways of DNA-damage response and radiation protection by genome-wide RNAi. Curr Biol 16:1344–50, 2006.

    PubMed  Google Scholar 

  60. 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 84:1424–31, 2001.

    PubMed  CAS  Google Scholar 

  61. Hollingshead MG, Alley MC, Kaur G, et al: NCI specialized procedures in preclinical drug evaluations, in Teicher BA & Andrews PA: Anticancer Drug Development Guide Preclinical Screening, Clinical Trials, and Approval, 2nd ed. Humana Press, Totowa, NJ, 2004,pp 153–182.

    Google Scholar 

  62. Hamburger AW, Salmon SE: Primary bioassay of human tumor stem cells. Science 197:461–3, 1977.

    PubMed  CAS  Google Scholar 

  63. Chumsri S, Phatak P, Edelman MJ et al: Cancer stem cells and individualized therapy. Cancer Genomics Proteomics 4:165–74, 2007.

    PubMed  CAS  Google Scholar 

  64. Fiebig HH, Maier A, Burger AM: Clonogenic assay with established human tumour xenografts: correlation of in vitro to in vivo activity as a basis for anticancer drug discovery. Eur J Cancer 40:802–20, 2004.

    PubMed  CAS  Google Scholar 

  65. Schirner M, Hoffmann J, Menrad A, Schneider MR: Antiangiogenic chemotherapeutic agents: characterization in comparison to their tumor growth inhibition in human renal cell carcinoma models. Clin Cancer Res 4:1331–6, 1998.

    PubMed  CAS  Google Scholar 

  66. Taraboletti G, Giavazzi R: Modelling approaches for angiogenesis. Eur J Cancer 40:881–9, 2004.

    PubMed  CAS  Google Scholar 

  67. Auerbach R, Lewis R, Shinners B, et al: Angiogenesis assays: a critical overview. Clin Chem 49:32–40, 2003.

    PubMed  CAS  Google Scholar 

  68. Zogakis TG, Costouros NG, Kruger EA, et al: Microarray gene expression profiling of angiogenesis inhibitors using the rat aortic ring assay. Biotechniques 33: 664–6, 668, 2002.

    Google Scholar 

  69. Chou TC, Talalay P: Quantitative analysis of dose–effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Adv Enzyme Regul 22:27–55, 1984.

    PubMed  CAS  Google Scholar 

  70. Teicher BA: Preclinical models for combination therapy, in Teicher BA & Andrews PA: Anticancer Drug Develoopment Guide Preclinical Screening, Clinical Trials, and Approval, 2nd ed. Humana Press, Totowa, NJ, 2004, pp 213–242.

    Google Scholar 

  71. Decker S, Sausville EA: Preclinical modeling of combination treatments: fantasy or requirement? Ann N Y Acad Sci 1059:61–9, 2005.

    PubMed  CAS  Google Scholar 

  72. Hollingshead MG: Antitumor efficacy testing in rodents. J Natl Cancer Inst 100:1500–10, 2008.

    PubMed  CAS  Google Scholar 

  73. Tan M, Fang HB, Tian GL, et al: Small-sample inference for incomplete longitudinal data with truncation and censoring in tumor xenograft models. Biometrics 58:612–20, 2002.

    PubMed  Google Scholar 

  74. Alley MC, Hollingshead M, Dykes DJ, et al: Human tumor xenograft models in nci drug development, in Teicher BA & Andrews PA: Anticancer Drug Development Guide Preclinical Screening, Clinical Trials, and Approval, 2nd ed. Humana Press, Totowa, NJ 2004 pp 125–152.

    Google Scholar 

  75. Fiebig HH, Dengler WA, Roth T: Human tumor xenografts: predictivity, characterization and discovery, in Fiebig HH and Burger AM: Relevance of Tumor Models for Antiocancer Development. vol 54, Karger, Contributions to Oncology Basel, 1999, pp 29–50.

    Google Scholar 

  76. Kelland LR: Of mice and men: values and liabilities of the athymic nude mouse model in anticancer drug development. Eur J Cancer 40:827–36, 2004.

    PubMed  CAS  Google Scholar 

  77. Fichtner I, Becker M, Zeisig R, et al: In vivo models for endocrine-dependent breast carcinomas: special considerations of clinical relevance. Eur J Cancer 40:845–51, 2004.

    PubMed  CAS  Google Scholar 

  78. Hollingshead MG, Bonomi CA, Borgel SD, et al: A potential role for imaging technology in anticancer efficacy evaluations. Eur J Cancer 40:890–8, 2004.

    PubMed  CAS  Google Scholar 

  79. Hoffman RM: The multiple uses of fluorescent proteins to visualize cancer in vivo. Nat Rev Cancer 5:796–806, 2005.

    PubMed  CAS  Google Scholar 

  80. Sahai E: Illuminating the metastatic process. Nat Rev Cancer 7:737–49, 2007.

    PubMed  CAS  Google Scholar 

  81. Bibby MC: Orthotopic models of cancer for preclinical drug evaluation: advantages and disadvantages. Eur J Cancer 40:852–7, 2004.

    PubMed  CAS  Google Scholar 

  82. Hollingshead MG, Alley MC, Camalier RF et al: In vivo cultivation of tumor cells in hollow fibers. Life Sci 57:131–141, 1995.

    PubMed  CAS  Google Scholar 

  83. Decker S, Hollingshead M, Bonomi CA, et al: The hollow fibre model in cancer drug screening: the NCI experience. Eur J Cancer 40:821–6, 2004.

    PubMed  CAS  Google Scholar 

  84. Zabludoff SD, Deng C, Grondine MR, et al: AZD7762, a novel checkpoint kinase inhibitor, drives checkpoint abrogation and potentiates DNA-targeted therapies. Mol Cancer Ther 7:2955–66, 2008.

    PubMed  CAS  Google Scholar 

  85. 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 9:4227–39, 2003.

    PubMed  Google Scholar 

  86. Mi Z, Burke TG: Marked interspecies variations concerning the interactions of camptothecin with serum albumins: a frequency-domain fluorescence spectroscopic study. Biochemistry 33:12540-5, 1994.

    PubMed  CAS  Google Scholar 

  87. Thompson J, Stewart CF, Houghton PJ: Animal models for studying the action of topoisomerase I targeted drugs. Biochim Biophys Acta 1400:301–19, 1998.

    PubMed  CAS  Google Scholar 

  88. Ishii S, Nagasawa M, Kariya Y, et al: Selective cytotoxic activity of brefeldin A against human tumor cell lines. J Antibiot (Tokyo) 42:1877–8, 1989.

    Google Scholar 

  89. Volpe DA, Tomaszewski JE, Parchment RE, et al: Myelotoxic effects of the bifunctional alkylating agent bizelesin on human, canine and murine myeloid progenitor cells. Cancer Chemother Pharmacol 39:143–9, 1996.

    PubMed  CAS  Google Scholar 

  90. Saito A, Yamashita T, Mariko Y, et al: A synthetic inhibitor of histone deacetylase, MS-27-275, with marked in vivo antitumor activity against human tumors. Proc Natl Acad Sci USA 96:4592–7, 1999.

    PubMed  CAS  Google Scholar 

  91. Ryan QC, Headlee D, Acharya M, et al: Phase I and pharmacokinetic study of MS-275, a histone deacetylase inhibitor, in patients with advanced and refractory solid tumors or lymphoma. J Clin Oncol 23:3912–22, 2005.

    PubMed  CAS  Google Scholar 

  92. Gupta RS, Chopra A, Stetsko DK: Cellular basis for the species differences in sensitivity to cardiac glycosides (digitalis). J Cell Physiol 127:197–206, 1986.

    PubMed  CAS  Google Scholar 

  93. Peterson JK, Houghton PJ: Integrating pharmacology and in vivo cancer models in preclinical and clinical drug development. Eur J Cancer 40:837–44, 2004.

    PubMed  CAS  Google Scholar 

  94. 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 12:7180–6, 2006.

    PubMed  CAS  Google Scholar 

  95. Adams J, Palombella VJ, Sausville EA, et al: Proteasome inhibitors: a novel class of potent and effective antitumor agents. Cancer Res 59:2615–22, 1999.

    PubMed  CAS  Google Scholar 

  96. Orlowski RZ, Stinchcombe TE, Mitchell BS, et al: Phase I trial of the proteasome inhibitor PS-341 in patients with refractory hematologic malignancies. J Clin Oncol 20: 4420–7, 2002.

    PubMed  CAS  Google Scholar 

  97. Omer CA, Chen Z, Diehl RE, et al: Mouse mammary tumor virus-Ki-rasB transgenic mice develop mammary carcinomas that can be growth-inhibited by a farnesyl:protein transferase inhibitor. Cancer Res 60:2680–8, 2000.

    PubMed  CAS  Google Scholar 

  98. Nørgaard P, Law B, Joseph H, et al: Treatment with farnesyl-protein transferase inhibitor induces regression of mammary tumors in transforming growth factor (TGF) alpha and TGF alpha/neu transgenic mice by inhibition of mitogenic activity and induction of apoptosis. Clin Cancer Res 5:35–42, 1999.

    PubMed  Google Scholar 

  99. Hansen K, Khanna C: Spontaneous and genetically engineered animal models; use in preclinical cancer drug development. Eur J Cancer 40:858–80, 2004.

    PubMed  CAS  Google Scholar 

  100. Cheng KC, Li C, Uss AS: Prediction of oral drug absorption in humans – from cultured cell lines and experimental animals. Expert Opin Drug Metab Toxicol 4:581–90, 2008.

    PubMed  CAS  Google Scholar 

  101. Dorr RT: Cytoprotective agents for anthracyclines. Semin Oncol 23(4 Suppl 8):23–34, 1996.

    PubMed  CAS  Google Scholar 

  102. Dorn A, Hermann F, Ebneth A, et al: Evaluation of a high-throughput fluorescence assay method for HERG potassium channel inhibition. J Biomol Screen 10:339–47, 2005.

    PubMed  CAS  Google Scholar 

  103. Joseph EK, Chen X, Bogen O, et al: Oxaliplatin acts on IB4-positive nociceptors to induce an oxidative stress-dependent acute painful peripheral neuropathy. J Pain 9:463-72, 2008.

    PubMed  CAS  Google Scholar 

  104. Cheung C, Gonzalez FJ: Humanized mouse lines and their application for prediction of human drug metabolism and toxicological risk assessment. J Pharmacol Exp Ther 327:288–99, 2008.

    PubMed  CAS  Google Scholar 

  105. Farmer H, McCabe N, Lord CJ, et al: Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434:917–21, 2005.

    PubMed  CAS  Google Scholar 

  106. Taniguchi T, Tischkowitz M, Ameziane N, et al: Disruption of the Fanconi anemia-BRCA pathway in cisplatin-sensitive ovarian tumors. Nat Med 9:568–74, 2003.

    PubMed  CAS  Google Scholar 

  107. Boss DS, Olmos RV, Sinaasappel M, et al: Application of PET/CT in the development of novel anticancer drugs. Oncologist 13:25–38, 2008.

    PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Marlene and Stewart Greenebaum Cancer Center, University of Maryland, 22 S. Greene Street, Room S9D07, Baltimore, MD, 21201, USA

    Edward A. Sausville

Authors
  1. Edward A. Sausville
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Edward A. Sausville .

Editor information

Editors and Affiliations

  1. Dept. Biostatistics, Bioinformatics &, Epidemiology, Medical University of South Carolina, Jonathan Lucas St. 86, Charleston, 29466, South Carolina, USA

    Elizabeth Garrett-Mayer

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Sausville, E.A. (2011). Preclinical Models for Anticancer Drug Development. In: Garrett-Mayer, E. (eds) Principles of Anticancer Drug Development. Cancer Drug Discovery and Development. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7358-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-4419-7358-0_4

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-7357-3

  • Online ISBN: 978-1-4419-7358-0

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation