Optimal Design Developed to Help Find Best Dose for Cancer Patients


Research advances personalized cancer therapy.

Research advances personalized cancer therapy.

Cancer treatments are now more personalized than ever with the advent of a new 2-cycle dose-finding method for cancer patients.

The technique, developed by statisticians, helps doctors optimize the dose of a new cancer treatment patients receive in phase 1/2 clinical trials. Juhee Lee, assistant professor of applied mathematics and statistics at the University of California at Santa Cruz, presented the “Optimal Two-Cycle Dose-Finding Design” she developed alongside Peter F. Thall, professor of biostatistics at The University of Texas MD Anderson Cancer Center in Houston; Peter Meuller, professor of mathematics at The University of Texas at Austin; and Yuan Ji, director of the Program for Computational Genomics and Medicine Research Institute at NorthShore University Health System in Chicago.

Anytime an experimental anticancer treatment is developed, it needs to be tested to see how it affects humans. The only way to do this is by using it to treat actual cancer patients.

During the phase 1/2 trial, a sequence of small cohorts with 2 to 3 patients are given varying doses of the experimental treatment. When the clinical outcomes of each group are observed, their data are added to the accumulated dose-outcome data from all previous patients and this data is used to choose the best dose for the next group.

When the trial is completed, the final best dose is selected to treat future patients.

“Medical treatment often involves multiple cycles of therapy. Physicians routinely choose a patient’s treatment in each cycle adaptively based on the patient’s history of treatments and clinical outcomes,” Less said. “In such settings, a patient’s therapy is not one treatment, but rather a sequence of treatments that each is chosen using an adaptive algorithm of the general form ‘observe, treat, observe, treat, and so forth.”

Based on results determined in earlier trials, each new patient’s first dose is chosen using “adaptive” rules in a dose-finding trial. Because conventional designs disregard the patient’s cycle 1 dose and outcomes when they choose the patient’s cycle 2 dose, the physician must choose each patient’s cycle 2 dose informally based on his or her intuition.

However, this method can prove to be detrimental to both patient and physician as it can lead to bad decision-making.

The Optimal Two-Cycle Dose-Finding Design was motivated by this issue. The new design is the first of its kind to deal with the problem of optimizing each patient’s dose levels in two cycles in phase 1/2 cancer clinical trials. Detailed computer simulations have shown the two-cycle design often is 30 to 35 percent better than conventional methods in terms of how well it performs in choosing the best dose levels for patients.

The Optimal Two-Cycle Dose-Finding Design is a clear example of “personalized medicine” as it uses each patient’s cycle 1 data to set a dose level to give that patient in the second cycle of therapy. Since it also utilizes data outcome from others participating in the trial, the design is adaptive in two ways. The method can also be used when treating diseases other than cancer including rapid treatment of stroke or optimizing successive doses of a drug to control pain after surgery.

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