Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Envisioning the future of early anticancer drug development

Abstract

The development of novel molecularly targeted cancer therapeutics remains slow and expensive with many late-stage failures. There is an urgent need to accelerate this process by improving early clinical anticancer drug evaluation through modern and rational trial designs that incorporate predictive, pharmacokinetic, pharmacodynamic, pharmacogenomic and intermediate end-point biomarkers. In this article, we discuss current approaches and propose strategies that will potentially maximize benefit to patients and expedite the regulatory approvals of new anticancer drugs.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: The shifting focus of old versus new Phase I clinical trial designs.
Figure 2: Updating the pharmacological audit trail.
Figure 3: Future clinical track for early-phase clinical trials.

References

  1. Stratton, M. R., Campbell, P. J. & Futreal, P. A. The cancer genome. Nature 458, 719–724 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. Vogelstein, B. & Kinzler, K. W. Cancer genes and the pathways they control. Nature Med. 10, 789–799 (2004).

    CAS  PubMed  Article  Google Scholar 

  3. Collins, I. & Workman, P. New approaches to molecular cancer therapeutics. Nature Chem. Biol. 2, 689–700 (2006).

    CAS  Article  Google Scholar 

  4. Iorns, E., Lord, C. J., Turner, N. & Ashworth, A. Utilizing RNA interference to enhance cancer drug discovery. Nature Rev. Drug Discov. 6, 556–568 (2007).

    CAS  Article  Google Scholar 

  5. Taube, S. E. et al. A perspective on challenges and issues in biomarker development and drug and biomarker codevelopment. J. Natl. Cancer Inst. 101, 1453–1463 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  6. McDermott, U. & Settleman, J. Personalized cancer therapy with selective kinase inhibitors: an emerging paradigm in medical oncology. J. Clin. Oncol. 27, 5650–5659 (2009).

    CAS  PubMed  Article  Google Scholar 

  7. Janne, P. A., Gray, N. & Settleman, J. Factors underlying sensitivity of cancers to small-molecule kinase inhibitors. Nature Rev. Drug Discov. 8, 709–723 (2009).

    CAS  Article  Google Scholar 

  8. Workman, P. & de Bono, J. Targeted therapeutics for cancer treatment: major progress towards personalised molecular medicine. Curr. Opin. Pharmacol. 8, 359–362 (2008).

    CAS  PubMed  Article  Google Scholar 

  9. DiMasi, J. A. & Grabowski, H. G. Economics of new oncology drug development. J. Clin. Oncol. 25, 209–216 (2007).

    PubMed  Article  Google Scholar 

  10. Reichert, J. M. & Wenger, J. B. Development trends for new cancer therapeutics and vaccines. Drug Discov. Today 13, 30–37 (2008).

    CAS  PubMed  Article  Google Scholar 

  11. DiMasi, J. A., Feldman, L., Seckler, A. & Wilson, A. Trends in risks associated with new drug development: success rates for investigational drugs. Clin. Pharmacol. Ther. 87, 272–277.

  12. Kelloff, G. J. & Sigman, C. C. New science-based endpoints to accelerate oncology drug development. Eur. J. Cancer 41, 491–501 (2005).

    PubMed  Article  Google Scholar 

  13. Simon, R. The use of genomics in clinical trial design. Clin. Cancer Res. 14, 5984–5993 (2008).

    PubMed  Article  Google Scholar 

  14. Sarker, D. & Workman, P. Pharmacodynamic biomarkers for molecular cancer therapeutics. Adv. Cancer Res. 96, 213–268 (2007).

    CAS  PubMed  Article  Google Scholar 

  15. Adjei, A. A. What is the right dose? The elusive optimal biologic dose in Phase I clinical trials. J. Clin. Oncol. 24, 4054–4055 (2006).

    CAS  PubMed  Article  Google Scholar 

  16. Goulart, B., Roberts, T. & Clark, J. Utility and costs of surrogate endpoints (SEs) and biomarkers in Phase I oncology trials. J. Clin. Oncol. 22, (Suppl. 14), 6012 (abstract) (2004).

    Article  Google Scholar 

  17. Yap, T. A. et al. Phase I trial of the irreversible ErbB1 (EGFR) and ErbB2 (HER2) kinase inhibitor BIBW 2992 in patients with advanced solid tumors. J. Clin. Oncol. (in the press).

  18. Huang, R. S. & Ratain, M. J. Pharmacogenetics and pharmacogenomics of anticancer agents. CA Cancer J. Clin. 59, 42–55 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  19. Walko, C. M. & McLeod, H. Pharmacogenomic progress in individualized dosing of key drugs for cancer patients. Nature Clin. Pract. Oncol. 6, 153–162 (2009).

    CAS  Article  Google Scholar 

  20. Workman, P. Challenges of PK/PD measurements in modern drug development. Eur. J. Cancer 38, 2189–2193 (2002).

    CAS  PubMed  Article  Google Scholar 

  21. Workman, P. How much gets there and what does it do? The need for better pharmacokinetic and pharmacodynamic endpoints in contemporary drug discovery and development. Curr. Pharm. Des 9, 891–902 (2003).

    CAS  PubMed  Article  Google Scholar 

  22. Workman, P. Auditing the pharmacological accounts for Hsp90 molecular chaperone inhibitors: unfolding the relationship between pharmacokinetics and pharmacodynamics. Mol. Cancer Ther. 2, 131–138 (2003).

    CAS  PubMed  Article  Google Scholar 

  23. Banerji, U. et al. Phase I pharmacokinetic and pharmacodynamic study of 17-allylamino, 17-demethoxygeldanamycin in patients with advanced malignancies. J. Clin. Oncol. 23, 4152–4161 (2005).

    CAS  PubMed  Article  Google Scholar 

  24. Banerji, U. et al. Pharmacokinetic–pharmacodynamic relationships for the heat shock protein 90 molecular chaperone inhibitor 17-allylamino, 17-demethoxygeldanamycin in human ovarian cancer xenograft models. Clin. Cancer Res. 11, 7023–7032 (2005).

    CAS  PubMed  Article  Google Scholar 

  25. Attard, G., Reid, A. H., Olmos, D. & de Bono, J. S. Antitumor activity with CYP17 blockade indicates that castration-resistant prostate cancer frequently remains hormone driven. Cancer Res. 69, 4937–4940 (2009).

    CAS  PubMed  Article  Google Scholar 

  26. Attard, G. et al. Phase I clinical trial of a selective inhibitor of CYP17, abiraterone acetate, confirms that castration-resistant prostate cancer commonly remains hormone driven. J. Clin. Oncol. 26, 4563–4571 (2008).

    CAS  PubMed  Article  Google Scholar 

  27. Yap, T. A., Carden, C. P., Attard, G. & de Bono, J. S. Targeting CYP17: established and novel approaches in prostate cancer. Curr. Opin. Pharmacol. 8, 449–457 (2008).

    CAS  PubMed  Article  Google Scholar 

  28. Farmer, H. et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434, 917–921 (2005).

    CAS  Article  PubMed  Google Scholar 

  29. Fong, P. C. et al. Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers. N. Engl. J. Med. 361, 123–134 (2009).

    CAS  PubMed  Article  Google Scholar 

  30. Fong, P. C. et al. Poly(ADP)-ribose polymerase inhibition: frequent durable responses in BRCA carrier ovarian cancer correlating with platinum-free interval. J. Clin. Oncol. 20 Apr 2010 (doi: JCO.2009.26.9589v1).

  31. Mandrekar, S. J. & Sargent, D. J. Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges. J. Clin. Oncol. 27, 4027–4034 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  32. Slamon, D. J. et al. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235, 177–182 (1987).

    CAS  PubMed  Article  Google Scholar 

  33. Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344, 783–792 (2001).

    CAS  PubMed  Article  Google Scholar 

  34. Baselga, J. et al. Objective response rate in a Phase II multicenter trial of pertuzumab (P), a HER2 dimerization inhibiting monoclonal antibody, in combination with trastuzumab (T) in patients (pts) with HER2-positive metastatic breast cancer (MBC) which has progressed during treatment with, T.. J. Clin. Oncol. 25, (Suppl. 18), 1004 (abstract) (2007).

    Google Scholar 

  35. Vogel, C. L. et al. A Phase II study of trastuzumab-DM1 (T-DM1), a HER2 antibody–drug conjugate (ADC), in patients (pts) with HER2+ metastatic breast cancer (MBC): final results. J. Clin. Oncol. 27 (Suppl. 15), 1017 (abstract) (2009).

    Google Scholar 

  36. Spector, N. L. et al. EGF103009, a Phase II trial of lapatinib monotherapy in patients with relapsed/refractory inflammatory breast cancer (IBC): clinical activity and biologic predictors of response. J. Clin. Oncol. 24 (Suppl. 18S), 502 (abstract) (2006).

    Google Scholar 

  37. Talpaz, M. et al. Imatinib induces durable hematologic and cytogenetic responses in patients with accelerated phase chronic myeloid leukemia: results of a Phase 2 study. Blood 99, 1928–1937 (2002).

    CAS  PubMed  Article  Google Scholar 

  38. Kwak, E. et al. Clinical activity observed in a Phase I dose escalation trial of an oral c-met and ALK inhibitor, PF-02341066. J. Clin. Oncol. 27 (Suppl. 15), 3509 (abstract) (2009).

    Google Scholar 

  39. Flaherty, K. et al. Phase I study of PLX4032: proof of concept for V600E BRAF mutation as a therapeutic target in human cancer. J. Clin. Oncol. 27 (Suppl. 15), 9000 (abstract) (2009).

    Google Scholar 

  40. Amado, R. G. et al. Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J. Clin. Oncol. 26, 1626–34 (2008).

    CAS  PubMed  Article  Google Scholar 

  41. Thatcher, N. et al. Gefitinib plus best supportive care in previously treated patients with refractory advanced non-small-cell lung cancer: results from a randomised, placebo-controlled, multicentre study (Iressa survival evaluation in lung cancer). Lancet 366, 1527–1537 (2005).

    CAS  PubMed  Article  Google Scholar 

  42. Takano, T. et al. EGFR mutations predict survival benefit from gefitinib in patients with advanced lung adenocarcinoma: a historical comparison of patients treated before and after gefitinib approval in Japan. J. Clin. Oncol. 26, 5589–5595 (2008).

    CAS  PubMed  Article  Google Scholar 

  43. Van Cutsem, E. et al. Randomized Phase III study of irinotecan and 5-FU/FA with or without cetuximab in the first-line treatment of patients with metastatic colorectal cancer (mCRC): the CRYSTAL trial. J. Clin. Oncol. 25 (Suppl. 18), 4000 (abstract) (2007).

    Google Scholar 

  44. Van Cutsem, E. et al. Cetuximab and chemotherapy as initial treatment for metastatic colorectal cancer. N. Engl. J. Med. 360, 1408–1417 (2009).

    CAS  PubMed  Article  Google Scholar 

  45. Wilhelm, S. M. et al. Preclinical overview of sorafenib, a multikinase inhibitor that targets both Raf and VEGF and PDGF receptor tyrosine kinase signaling. Mol. Cancer Ther. 7, 3129–3140 (2008).

    CAS  PubMed  Article  Google Scholar 

  46. Wilhelm, S. M. et al. BAY 43–9006 exhibits broad spectrum oral antitumor activity and targets the RAF/MEK/ERK pathway and receptor tyrosine kinases involved in tumor progression and angiogenesis. Cancer Res. 64, 7099–7109 (2004).

    CAS  PubMed  Article  Google Scholar 

  47. Hoering, A., Leblanc, M. & Crowley, J. J. Randomized Phase III clinical trial designs for targeted agents. Clin. Cancer Res. 14, 4358–4367 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. Yap, T. A. et al. Targeting the PI3K–AKT–mTOR pathway: progress, pitfalls, and promises. Curr. Opin. Pharmacol. 8, 393–412 (2008).

    CAS  PubMed  Article  Google Scholar 

  49. Workman, P. Clarke, P. A., Raynaud, F. I. & van Montfort, R. L. Drugging the PI3 kinome : from chemical tools to drugs in the clinic. Cancer Res. 70, 2146–2157 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. Comoglio, P. M., Giordano, S. & Trusolino, L. Drug development of MET inhibitors: targeting oncogene addiction and expedience. Nature Rev. Drug Discov. 7, 504–516 (2008).

    CAS  Article  Google Scholar 

  51. Yap, T. A. & de Bono, J. S. Targeting the HGF/c-Met axis: state of play. Mol. Cancer Ther. 9, 1077–1079 (2010).

    CAS  PubMed  Article  Google Scholar 

  52. Burzykowski, T. & Buyse, M. Surrogate threshold effect: an alternative measure for meta-analytic surrogate endpoint validation. Pharm. Stat. 5, 173–186 (2006).

    PubMed  Article  Google Scholar 

  53. de Bono, J. S. et al. Circulating tumor cells predict survival benefit from treatment in metastatic castration-resistant prostate cancer. Clin. Cancer Res. 14, 6302–6309 (2008).

    CAS  PubMed  Article  Google Scholar 

  54. Hou, J. M. et al. Evaluation of circulating tumor cells and serological cell death biomarkers in small cell lung cancer patients undergoing chemotherapy. Am. J. Pathol. 175, 808–816 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. Hodgson, D. R. et al. Circulating tumour-derived predictive biomarkers in oncology. Drug Discov. Today 15, 98–101 (2010).

    CAS  PubMed  Article  Google Scholar 

  56. Yerushalmi, R., Woods, R., Ravdin, P. M., Hayes, M. M. & Gelmon, K. A. Ki67 in breast cancer: prognostic and predictive potential. Lancet Oncol. 11, 174–183 (2010).

    CAS  PubMed  Article  Google Scholar 

  57. Cristofanilli, M. et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N. Engl. J. Med. 351, 781–791 (2004).

    CAS  PubMed  Article  Google Scholar 

  58. Cohen, S. J. et al. Relationship of circulating tumor cells to tumor response, progression-free survival, and overall survival in patients with metastatic colorectal cancer. J. Clin. Oncol. 26, 3213–3221 (2008).

    PubMed  Article  Google Scholar 

  59. Attard, G. et al. Characterization of, ERG, AR and PTEN gene status in circulating tumor cells from patients with castration-resistant prostate cancer. Cancer Res. 69, 2912–2918 (2009).

    CAS  PubMed  Article  Google Scholar 

  60. Maheswaran, S. et al. Detection of mutations in EGFR in circulating lung-cancer cells. N. Engl. J. Med. 359, 366–377 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. Frese, K. K. & Tuveson, D. A. Maximizing mouse cancer models. Nature Rev. Cancer 7, 645–658 (2007).

    CAS  Article  Google Scholar 

  62. Raynaud, F. I. et al. Biological properties of potent inhibitors of class I phosphatidylinositide 3-kinases: from PI-103 through PI-540, PI-620 to the oral agent GDC-0941. Mol. Cancer Ther. 8, 1725–1738 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. Guillard, S. et al. Molecular pharmacology of phosphatidylinositol 3-kinase inhibition in human glioma. Cell Cycle 8, 443–453 (2009).

    CAS  PubMed  Article  Google Scholar 

  64. Sarker, D. et al. A phase I study evaluating the pharmacokinetics (PK) and pharmacodynamics (PD) of the oral pan-phosphoinositide-3 kinase (PI3K) inhibitor GDC-0941. J. Clin. Oncol. 27 (Suppl. 15), 3358 (abstract) (2009).

    Google Scholar 

  65. Wagner, A. et al. A first-in-human phase I study to evaluate the pan-PI3K inhibitor GDC-0941 administered QD or BID in patients with advanced solid tumors. J. Clin. Oncol. 27 (Suppl. 15), 3501 (abstract) (2009).

    Google Scholar 

  66. Banerji, U., de Bono, J., Judson, I., Kaye, S. & Workman, P. Biomarkers in early clinical trials: the committed and the skeptics. Clin. Cancer Res. 14, 2512 (2008).

    PubMed  Article  Google Scholar 

  67. Ratain, M. J. & Glassman, R. H. Biomarkers in Phase I oncology trials: signal, noise, or expensive distraction? Clin. Cancer Res. 13, 6545–6548 (2007).

    CAS  PubMed  Article  Google Scholar 

  68. Goulart, B. H. et al. Trends in the use and role of biomarkers in Phase I oncology trials. Clin. Cancer Res. 13, 6719–6726 (2007).

    CAS  PubMed  Article  Google Scholar 

  69. Sawyers, C. L. The cancer biomarker problem. Nature 452, 548–552 (2008).

    CAS  PubMed  Article  Google Scholar 

  70. Park, J. W. et al. Rationale for biomarkers and surrogate end points in mechanism-driven oncology drug development. Clin. Cancer Res. 10, 3885–3896 (2004).

    CAS  PubMed  Article  Google Scholar 

  71. Eisenhauer, E. A., O'Dwyer, P. J., Christian, M. & Humphrey, J. S. Phase I clinical trial design in cancer drug development. J. Clin. Oncol. 18, 684–692 (2000).

    CAS  PubMed  Article  Google Scholar 

  72. Verweij, J., Eskens, F. & de Jonge, M. The multi-institutional Phase I study: disadvantages without advantages? J. Clin. Oncol. 26, 1915–1916 (2008).

    PubMed  Article  Google Scholar 

  73. Dowlati, A. et al. Multi-institutional Phase I trials of anticancer agents. J. Clin. Oncol. 26, 1926–1931 (2008).

    PubMed  Article  Google Scholar 

  74. Thomas, R. K. et al. High-throughput oncogene mutation profiling in human cancer. Nature Genet. 39, 347–351 (2007).

    CAS  Article  PubMed  Google Scholar 

  75. Yap, T. A., Carden, C. P. & Kaye, S. B. Beyond chemotherapy: targeted therapies in ovarian cancer. Nature Rev. Cancer 9, 167–181 (2009).

    CAS  Article  Google Scholar 

  76. Dancey, J. E., Espinoza-Delgado, I., Papaconstantinou, A., Saunders, J. & Rubinstein, L. Safety, efficacy and efficiency of Phase 1 single agent trials using the accelerated titration (ATD) versus modified Fibonacci designs (STD) in 20th Annual AACR–NCI–EORTC International Conference: Molecular Targets and Cancer Therapeutics, A98 (abstract) (American Association for Cancer Research, Boston, 2009).

    Google Scholar 

  77. Iasonos, A., Wilton, A. S., Riedel, E. R., Seshan, V. E. & Spriggs, D. R. A comprehensive comparison of the continual reassessment method to the standard 3 + 3 dose escalation scheme in Phase I dose-finding studies. Clin. Trials 5, 465–477 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  78. O'Quigley, J., Pepe, M. & Fisher, L. Continual reassessment method: a practical design for Phase 1 clinical trials in cancer. Biometrics 46, 33–48 (1990).

    CAS  PubMed  Article  Google Scholar 

  79. Sleijfer, S. & Wiemer, E. Dose selection in Phase I studies: why we should always go for the top. J. Clin. Oncol. 26, 1576–1578 (2008).

    PubMed  Article  Google Scholar 

  80. Booth, C. M. et al. Endpoints and other considerations in Phase I studies of targeted anticancer therapy: recommendations from the task force on Methodology for the Development of Innovative Cancer Therapies (MDICT). Eur. J. Cancer 44, 19–24 (2008).

    CAS  PubMed  Article  Google Scholar 

  81. Propper, D. J. et al. Use of positron emission tomography in pharmacokinetic studies to investigate therapeutic advantage in a Phase I study of 120-hour intravenous infusion XR5000. J. Clin. Oncol. 21, 203–210 (2003).

    CAS  PubMed  Article  Google Scholar 

  82. Turk, D. & Szakacs, G. Relevance of multidrug resistance in the age of targeted therapy. Curr. Opin. Drug Discov. Devel 12, 246–252 (2009).

    CAS  PubMed  Google Scholar 

  83. Workman, P. et al. Minimally invasive pharmacokinetic and pharmacodynamic technologies in hypothesis-testing clinical trials of innovative therapies. J. Natl. Cancer Inst. 98, 580–598 (2006).

    CAS  PubMed  Article  Google Scholar 

  84. Haluska, P. et al. Phase I dose escalation study of the anti insulin-like growth factor-I receptor monoclonal antibody CP-751871 in patients with refractory solid tumors. Clin. Cancer Res. 13, 5834–5840 (2007).

    CAS  PubMed  Article  Google Scholar 

  85. Takimoto, C. H. Maximum tolerated dose: clinical endpoint for a bygone era? Target Oncol. 4, 143–147 (2009).

    PubMed  Article  Google Scholar 

  86. Tutt, A. et al. Phase II trial of the oral PARP inhibitor olaparib in BRCA-deficient advanced breast cancer. J. Clin. Oncol. 27 (Suppl. 18), CRA501 (abstract) (2009).

    Article  Google Scholar 

  87. Ratain, M. J. & Sargent, D. J. Optimising the design of Phase II oncology trials: the importance of randomisation. Eur. J. Cancer 45, 275–280 (2009).

    PubMed  Article  Google Scholar 

  88. Seymour, L. et al. The design of Phase II clinical trials testing cancer therapeutics: consensus recommendations from the clinical trial design task force of the national cancer institute investigational drug steering committee. Clin. Cancer Res. 16, 1764–1769.

    Article  CAS  Google Scholar 

  89. Tang, H. et al. Comparison of error rates in single-arm versus randomized Phase II cancer clinical trials. J. Clin. Oncol. 28, 1936–1941.

  90. Rubinstein, L., Crowley, J., Ivy, P., Leblanc, M. & Sargent, D. Randomized Phase II designs. Clin. Cancer Res. 15, 1883–1890 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  91. Workman, P. & Travers, J. Cancer: drug-tolerant insurgents. Nature 464, 844–845 (2010).

    CAS  PubMed  Article  Google Scholar 

  92. Taylor, I. W. et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nature Biotechnol. 27, 199–204 (2009).

    CAS  Article  Google Scholar 

  93. Schadt, E. E., Friend, S. H. & Shaywitz, D. A. A network view of disease and compound screening. Nature Rev. Drug Discov. 8, 286–295 (2009).

    CAS  Article  Google Scholar 

  94. Gutman, S. & Kessler, L. G. The US Food and Drug Administration perspective on cancer biomarker development. Nature Rev. Cancer 6, 565–571 (2006).

    CAS  Article  Google Scholar 

  95. Sarker, D., Pacey, S. & Workman, P. Use of pharmacokinetic/pharmacodynamic biomarkers to support rational cancer drug development. Biomarkers Med. 1, 399–417 (2007).

    CAS  Article  Google Scholar 

  96. Clarke, P. A. et al. Gene expression profiling of human colon cancer cells following inhibition of signal transduction by 17-allylamino-17-demethoxygeldanamycin, an inhibitor of the hsp90 molecular chaperone. Oncogene 19, 4125–4133 (2000).

    CAS  PubMed  Article  Google Scholar 

  97. Hostein, I., Robertson, D., DiStefano, F., Workman, P. & Clarke, P. A. Inhibition of signal transduction by the Hsp90 inhibitor 17-allylamino-17-demethoxygeldanamycin results in cytostasis and apoptosis. Cancer Res. 61, 4003–4009 (2001).

    CAS  PubMed  Google Scholar 

  98. Tan, D. S. et al. Biomarker-driven early clinical trials in oncology: a paradigm shift in drug development. Cancer J. 15, 406–420 (2009).

    CAS  PubMed  Article  Google Scholar 

  99. Ashworth, A. A synthetic lethal therapeutic approach: poly(ADP) ribose polymerase inhibitors for the treatment of cancers deficient in DNA double-strand break repair. J. Clin. Oncol. 26, 3785–3790 (2008).

    CAS  PubMed  Article  Google Scholar 

  100. Audeh, M. et al. Phase II trial of the oral PARP inhibitor olaparib (AZD2281) in BRCA-deficient advanced ovarian cancer. J. Clin. Oncol. 27 (Suppl. 15), 5500 (abstract) (2009).

    Google Scholar 

  101. Kurzrock, R. et al. Project Zero Delay: a process for accelerating the activation of cancer clinical trials. J. Clin. Oncol. 27, 4433–4440 (2009).

    PubMed  Article  Google Scholar 

  102. Parulekar, W. R. & Eisenhauer, E. A. Phase I trial design for solid tumor studies of targeted, non-cytotoxic agents: theory and practice. J. Natl. Cancer Inst. 96, 990–997 (2004).

    CAS  PubMed  Article  Google Scholar 

  103. Kummar, S., Gutierrez, M., Doroshow, J. H. & Murgo, A. J. Drug development in oncology: classical cytotoxics and molecularly targeted agents. Br. J. Clin. Pharmacol. 62, 15–26 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  104. Le Tourneau, C., Lee, J. J. & Siu, L. L. Dose escalation methods in Phase I cancer clinical trials. J. Natl. Cancer Inst. 101, 708–720 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  105. Cannistra, S. A. Challenges and pitfalls of combining targeted agents in Phase I studies. J. Clin. Oncol. 26, 3665–3667 (2008).

    CAS  PubMed  Article  Google Scholar 

  106. Krop, I. E. et al. Phase I study of trastuzumab–DM1, an HER2 antibody–drug conjugate, given every 3 weeks to patients with HER2-positive metastatic breast cancer. J. Clin. Oncol. 28, 2698–2704 (2010).

    CAS  PubMed  Article  Google Scholar 

Download references

Acknowledgements

The Drug Development Unit of the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research is supported in part by a programme grant from Cancer Research UK. Support was also provided by the Experimental Cancer Medicine Centre (to The Institute of Cancer Research) and the National Institute for Health Research Biomedical Research Centre (jointly to the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research). T.A.Y. is a Cancer Research UK Clinical Research Fellow and P.W. is a Cancer Research UK Life Fellow.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Johann S. de Bono.

Ethics declarations

Competing interests

All the authors are employees of the Institute of Cancer Research, which has a commercial interest in the development of inhibitors of HSP90, PI3K, AKT, BRAF, PARP, CYP17, CDK and chromatin-modifying enzymes. The authors have potentially relevant commercial interactions with Vernalis Ltd, Novartis, Piramed Pharma (acquired by Roche), Astex Therapeutics, AstraZeneca, GSK, Cougar Biotechnology Inc. (acquired by Johnson & Johnson), Merck Serona and Cyclacel Pharmaceuticals.

Related links

Related links

DATABASES

clinicaltrials.gov

NCT00638690

National Cancer Institute Drug Dictionary

5-fluorouracil

abiraterone acetate

cetuximab

FOLFIRI regimen

GDC-0941

gefitinib

imatinib

irinotecan

lapatinib

olaparib

panitumumab

pertuzumab

PF-02341066

PLX4032

sorafenib

tanespimycin

trastuzumab

trastuzumab-DM1

Glossary

Biologically active dose range

The range of drug doses required to result in the modulation of the cellular target of the drug to produce its expected effect.

Continual reassessment method

This tool uses statistical modelling and is employed in dose-finding clinical trials to estimate the dose at which the desired toxicity level can be expected to minimize risk of toxicity to patients.

Maximum tolerated dose

The highest dose of a drug or treatment that does not cause unacceptable side effects.

Pharmacodynamics

The relationship between drug concentration and its biological effects (what the drug does to the body).

Pharmacogenetics

This term was coined in 1959 and represents the study of genetic factors that influence response to drugs and chemicals18.

Pharmacogenomics

Recent advances and improvements in large genome-scale sequencing and bioinformatic tools for processing data have led to the transition of pharmacogenetics to pharmacogenomics, which involves studies of the entire spectrum of genes in the human genome18.

Pharmacokinetics

The concentration of drugs in the body over a period of time, including the processes by which drugs are absorbed, distributed in the body, localized in tissues, metabolized and excreted (what the body does to the drug).

Predictive biomarker

Any measurement associated with response to or lack of response to a particular therapy.

Response Evaluation Criteria In Solid Tumours

A set of published rules that define when cancer patients improve (respond), stay the same (stable) or worsen (progress) during treatments.

Single-arm Phase II trial

A trial that demonstrates the safety and activity of a drug in a selected group of patients. This is in contrast to randomized clinical trials, which involve the random allocation of different treatments (including placebo) to patients in different groups.

Surrogate threshold effect

The minimum treatment effect on the surrogate end point necessary to predict a non-zero effect on the true end point.

Synthetic lethality

In genetics, a phenomenon in which the combination of two otherwise non-lethal mutations results in a non-viable cell.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Yap, T., Sandhu, S., Workman, P. et al. Envisioning the future of early anticancer drug development. Nat Rev Cancer 10, 514–523 (2010). https://doi.org/10.1038/nrc2870

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrc2870

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing