Moving Beyond 3+3: The Future of Clinical Trial Design

Am Soc Clin Oncol Educ Book. 2021 Jun:41:e133-e144. doi: 10.1200/EDBK_319783.

Abstract

Misgivings have been raised about the operating characteristics of the canonical 3+3 dose-escalation phase I clinical trial design. Yet, the traditional 3+3 design is still the most commonly used. Although it has been implied that adhering to this design is due to a stubborn reluctance to adopt change despite other designs performing better in hypothetical computer-generated simulation models, the continued adherence to 3+3 dose-escalation phase I strategies is more likely because these designs perform the best in the real world, pinpointing the correct dose and important side effects with an acceptable degree of precision. Beyond statistical simulations, there are little data to refute the supposed shortcomings ascribed to the 3+3 method. Even so, to address the unique nuances of gene- and immune-targeted compounds, a variety of inventive phase 1 trial designs have been suggested. Strategies for developing these therapies have launched first-in-human studies devised to acquire a breadth of patient data that far exceed the size of a typical phase I design and blur the distinction between dose selection and efficacy evaluation. Recent phase I trials of promising cancer therapies assessed objective tumor response and durability at various doses and schedules as well as incorporated multiple expansion cohorts spanning a variety of histology or biomarker-defined tumor subtypes, sometimes resulting in U.S. Food and Drug Administration approval after phase I. This article reviews recent innovations in phase I design from the perspective of multiple stakeholders and provides recommendations for future trials.

Publication types

  • Review

MeSH terms

  • Clinical Trials as Topic*
  • Computer Simulation
  • Dose-Response Relationship, Drug
  • Drug-Related Side Effects and Adverse Reactions
  • Humans
  • Neoplasms* / diagnosis
  • Neoplasms* / drug therapy
  • Neoplasms* / epidemiology
  • Research Design