An iterative phase I/II/I clinical trial design incorporating genomic biomarker information
Rudolph S. Parrish1*, Ashok Krishnamurthy2 and Caryn M. Thompson3
1 Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, 485 E. Gray St, Louisville, KY 40202 USA
2 Department of Mathematical and Statistical Sciences, College of Liberal Arts and Sciences, University of Colorado Denver, Denver, CO 80202 USA
3 Elanco R&D Statistics, Eli Lilly and Company, 2001 West Main Street, Greenfield, IN 46140 USA
Abstract
It is believed that most cancer clinical trials involve a heterogeneous group of patients at the molecular level. This heterogeneity is one of the reasons that not all patients with cancer respond to a given drug. In view of this patient heterogeneity, it is clear that a “one size fits all” approach may not be suitable in the drug development process. Therefore, it is important to be able to predict which patients are most likely to benefit from a new drug. This would not only save patients from unnecessary risk of toxicity but might facilitate their receiving beneficial treatment, and it would shorten the time required for drug development and lower associated costs. The goals of this paper are twofold: (1) to investigate statistical issues involved in applying a genomic biomarker classification method to account for patient heterogeneity with respect to toxicity and efficacy response in early phase clinical trials, allowing for the possibility of differing treatment efficacy among subpopulations, and (2) developing a new design approach that incorporates high-dimensional genomic information into phase I/II clinical trials.
We propose to achieve this goal by using a novel compound iterative phase I/II/I clinical trial design which may be conceptualized into three stages. Our results showed that by applying predictive classifiers to determine a maximum tolerated dose (MTD) estimate for each subpopulation we can obtain an acceptably large value of probability of efficacy (i.e., improved response rates) while also controlling the probability of toxicity (i.e., increased safety) for each subpopulation. We concluded that the MTD estimates obtained from our proposed design are more accurate than would be expected from a single phase I trial.
Keywords: Phase I clinical trial, maximum tolerated dose, patient heterogeneity, genomic biomarkers, microarray data
