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Translation of Innovative Designs Into Phase I Trials

André Rogatko, David Schoeneck, William Jonas, Mourad Tighiouart, Fadlo R. Khuri, Alan Porter

From the Winship Cancer Institute at Emory University; Technology Policy and Assessment Center, Georgia Tech; and Search Technology Inc, Atlanta, GA

Address reprint requests to André Rogatko, PhD, Winship Cancer Institute, 1365-B Clifton Rd NE, Room B4109, Atlanta, GA 30322; e-mail: Andre_rogatko{at}emory.org

Purpose: Phase I clinical trials of new anticancer therapies determine suitable doses for further testing. Optimization of their design is vital in that they enroll cancer patients whose well-being is distinctly at risk. This study examines the effectiveness of knowledge transfer about more effective statistical designs to clinical practice.

Methods: We examined abstract records of cancer phase I trials from the Science Citation Index database between 1991 and 2006 and classified them into clinical (dose-finding trials) and statistical trials (methodologic studies of dose-escalation designs). We then mapped these two sets by tracking which trials adopted new statistical designs.

Results: One thousand two hundred thirty-five clinical and 90 statistical studies were identified. Only 1.6% of the phase I cancer trials (20 of 1,235 trials) followed a design proposed in one of the statistical studies. These 20 clinical studies showed extensive lags between publication of the statistical paper and its translation into a clinical paper. These 20 clinical trials followed Bayesian adaptive designs. The remainder used variations of the standard up-and-down method.

Conclusion: A consequence of using less effective designs is that more patients are treated with doses outside the therapeutic window. Simulation studies have shown that up-and-down designs treated only 35% of patients at optimal dose levels versus 55% for Bayesian adaptive designs. This implies needless loss of treatment efficacy and, possibly, lives. We suggest that regulatory agencies (eg, US Food and Drug Administration) should proactively encourage the adoption of statistical designs that would allow more patients to be treated at near-optimal doses while controlling for excessive toxicity.

Supported in part by grants from Georgia Cancer Coalition, Distinguished Cancer Clinicians and Scientists Program (A.R., F.R.K.), Robert W. Woodruff Health Sciences Center Fund (A.R.), and NIH/NCI Grants No. 1 P01 CA116676 (F.R.K., A.R., M.T.) and P20 CA103735 (A.R., F.R.K.). A.R. is a Georgia Cancer Coalition Distinguished Scholar; F.R.K. is a Georgia Cancer Coalition Distinguished Clinician.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.