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Adaptive clinical trials in oncology


Modern oncology drug development faces challenges very different from those of the past and it must adapt accordingly. The size and expense of phase III clinical trials continue to increase, but the success rate remains unacceptably low. Adaptive trial designs can make development more informative, addressing whether a drug is safe and effective while showing how it should be delivered and to whom. An adaptive design is one in which the accumulating data are used to modify the trial's course. Adaptive designs are ideal for addressing many questions at once. For example, a single trial might identify the appropriate patient population, dose and regimen, and therapeutic combinations, and then switch seamlessly into a phase III confirmatory trial. Adaptive designs rely on information, including from patients who have not achieved the trial's primary end point. Longitudinal models of biomarkers (including tumor burden assessed via imaging) enable predictions of primary end points. Taking a Bayesian perspective facilitates building an efficient and accurate trial, including using longitudinal information. A wholly new paradigm for drug development exemplifying personalized medicine is evinced by an adaptive trial called I-SPY2, in which drugs from many companies are evaluated in the same trial—a phase II screening process.

Key Points

  • Adaptive clinical trial designs can make oncology drug development more informative, more accurate, and shorter

  • Adaptive designs are ideal for addressing many questions at once; a single trial might identify the appropriate patient population, dose and regimen, and therapeutic combinations

  • Seamless phase I–II and phase II–III trials can shorten the duration of a drug's development

  • The potential benefits of adaptive designs are greatest in complicated settings exemplified by personalized medical research

  • Modeling longitudinal information from individual patients is an important aspect of adaptive clinical trials in oncology

  • A Bayesian statistical approach facilitates building complicated but maximally informative clinical trials

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Figure 1: Possible dose combinations in a seamless phase I–II trial addressing agents A and B given concurrently and sequentially.33
Figure 2: A seamless phase II–III trial to evaluate two experimental drugs alone and in combination.
Figure 3: Therapeutic regimens followed in I-SPY2.


  1. Bio/BioMedTracker. Clinical Trial Success Rates Study [online], (2011).

  2. Davies, E. K., Glick, M., Harrison, K. N. & Richards, W. G. Pattern recognition and massively distributed computing. J. Comput. Chem. 23, 1544–1550 (2002).

    Article  CAS  Google Scholar 

  3. McCarthy, M. Number of cancer drugs in development rises in the USA. Lancet Oncol. 2, 256 (2001).

    Article  Google Scholar 

  4. SME News. PhRMA report indicates high number of cancer drugs in clinical trials [online], (2011).

  5. FDA. FDA's Critical Path Initiative [online], (2011).

  6. FDA. Critical Path Opportunities Report [online], (2006).

  7. FDA. FDA Unveils Critical Path Opportunities List Outlining Blueprint To Modernizing Medical Product Development by 2010 Biomarker Development and Clinical Trial Design Greatest Areas for Impact [online], (2006).

  8. FDA. Guidance for Industry. Adaptive Design Clinical Trials for Drugs and Biologics [online], (2010).

  9. Committee For Medicinal Products For Human Use (CHMP). Reflection Paper On Methodological Issues In Confirmatory Clinical Trials Planned With An Adaptive Design [online], (2007).

  10. NIH News. NIH and FDA announce awards to advance regulatory science [online], (2010).

  11. ScienceInsider. FDA's $25 Million Pitch for Improving Drug Regulation [online], (2010).

  12. Woodcock, J., Griffin, J. P. & Behrman, R. E. Development of novel combination therapies. N. Engl. J. Med. 17, 985–987 (2011).

    Article  Google Scholar 

  13. FDA. Food And Drug Administration Modernization Act Of 1997 [online], (1997).

  14. FDA. Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials [online], (2010).

  15. Johns Hopkins University. Can Baysian approaches to studying new treatments improve regulatory decision making? [online], (2004).

  16. Gaydos, B. et al. Good practices for adaptive clinical trials in pharmaceutical product development. Drug Inf. J. 43, 539–556 (2009).

    Article  Google Scholar 

  17. DeMets, D. L. & Califf, R. M. A historical perspective on clinical trials innovation and leadership: where have the academics gone? JAMA 305, 713–714 (2011).

    Article  CAS  Google Scholar 

  18. Biswas, S., Liu, D. D., Lee, J. J. & Berry, D. A. Bayesian clinical trials at the University of Texas, M. D. Anderson Cancer Center. Clin. Trials 6, 205–216 (2009).

    Article  Google Scholar 

  19. Jennison, C. & Turnbull, B. W. Group Sequential Methods with Applications to Clinical Trials (Chapman & Hall/CRC, Boca Raton, 1999).

    Google Scholar 

  20. US National Library of Medicine.[online], (2011).

  21. Broglio, K. R. & Berry, D. A. Detecting an overall survival benefit that is derived from progression-free survival. J. Natl Cancer Inst. 101, 1642–1649 (2009).

    Article  Google Scholar 

  22. Bowater, R. J., Bridge, L. J. & Lilford, R. J. The relationship between progression-free and post-progression survival in treating four types of metastatic cancer. Cancer Lett. 262, 48–53 (2008).

    Article  CAS  Google Scholar 

  23. Berry, D. Discussion of: Prediction of survival benefits from progression-free survival in patients with advanced non-small cell cancer: evidence from a pooled analysis of 2,838 patients randomized in 7 trials [abstract 8019], by Marc, E. Buyse. Presented at the 44th ASCO Annual Meeting (2008).

  24. Buyse, M. et al. Progression-free survival is a surrogate for survival in advanced colorectal cancer. J. Clin. Oncol. 25, 5218–5224 (2007).

    Article  CAS  Google Scholar 

  25. Burzykowski, T. et al. Evaluation of tumor response, disease control, progression-free survival, and time to progression as potential surrogate end points in metastatic breast cancer. J. Clin. Oncol. 26, 1987–1992 (2008).

    Article  CAS  Google Scholar 

  26. Buyse, M. E. et al. Prediction of survival benefits from progression-free survival in patients with advanced non small cell lung cancer: evidence from a pooled analysis of 2,838 patients randomized in 7 trials [abstract]. J. Clin. Oncol. 26 (Suppl.), a8019 (2008).

    Article  Google Scholar 

  27. Berry, D. A. Bayesian clinical trials. Nat. Rev. Drug Discov. 5, 27–36 (2006).

    Article  CAS  Google Scholar 

  28. Berry, D. A. in Holland-Frei Cancer Medicine 8th edn Ch. 35 (eds Hong, W. K. et al.) 446–463 (People's Medical Publishing House, Shelton, 2010).

    Google Scholar 

  29. Berry, D. A. Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis. Clin. Trials 2, 295–300 (2005).

    Article  Google Scholar 

  30. Berry, D. A. Statistics: A Bayesian Perspective (Duxbury Press, Belmont, 1996).

    Google Scholar 

  31. Berry, S. M., Carlin, B. P., Lee, J. J. & Muller, P. Bayesian Adaptive Methods for Clinical Trials (CRC Press, New York, 2010).

    Book  Google Scholar 

  32. Muss, H. B. et al. Adjuvant chemotherapy in older women with early-stage breast cancer. N. Engl. J. Med. 360, 2055–2065 (2009).

    Article  CAS  Google Scholar 

  33. Huang, X., Biswas, S., Oki, Y., Issa, J. P. & Berry, D. A. A parallel phase I/II clinical trial design for combination therapies. Biometrics 63, 429–436 (2007).

    Article  CAS  Google Scholar 

  34. Thall, P. F., Millikan, R. E., Mueller, P. & Lee, S. J. Dose-finding with two agents in phase I oncology trials. Biometrics 59, 487–496 (2003).

    Article  Google Scholar 

  35. US National Library of Medicine.[online], (2011).

  36. FDA. Guidance for Clinical Trial Sponsors. Establishment and Operation of Clinical Trial Data Monitoring Committees [online], (2006).

  37. Barker, A. D. et al. I-SPY 2: An adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin. Pharmacol. Ther. 86, 97–100 (2009).

    Article  CAS  Google Scholar 

  38. Winslow, R. A New Rx for Medicine. Wall Street Journal [online], (2010).

    Google Scholar 

  39. FDA. Driving Biomedical Innovation: Initiatives for Improving Products for Patients [online], (2011).

  40. Breast Cancer I-SPY-2 Trial. Treatment Phase [online], (2011).

  41. Quintiles. Quintiles to Support Innovative I-Spy 2 Breast Cancer Clinical Trial [online], (2011).

  42. Foundation for the National Institutes of Health. FNIH Announces the First Annual U.S.–Russia Scientific Forum [online], (2011).

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D. A. Berry declares that he is a stock holder at Berry Consultants. Berry Consultants designs adaptive Bayesian clinical trials in all therapeutic areas for pharmaceutical and medical device companies, and for US NIH co-operative groups.

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Berry, D. Adaptive clinical trials in oncology. Nat Rev Clin Oncol 9, 199–207 (2012).

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