By Abie Ekangaki, Ph.D., Vice President, Statistical Consulting
Over the past decade, significant advances have improved our understanding of the genetic and molecular mechanisms that lead to cancer. And yet, a recent review of the oncology drugs approved by the U.S. Food and Drug Administration (FDA) on the basis of response rate showed only 10 percent of these therapies demonstrate an overall survival benefit.1
With an etiology that involves an array of genetic interactions and dysfunction across multiple systems, cancer is one of the most scientifically complex and dynamic diseases. This complexity makes the design of oncology clinical trials, especially early-stage studies, challenging.
With the emergence of personalized medicine, we are seeing a shift in how early-phase oncology trials are conducted, including a growing number of Phase 1 trials reporting preliminary response rates. This shift is due in part to an increase in adaptive trial designs that seek to limit the number of patients exposed to ineffective doses or treatments while accelerating the timeline to the detection of efficacy signals.
In this white paper, we address clinical trials in personalized medicine and explore the expanding role of adaptive trial designs in Phase 1 and Phase 2 oncology studies.