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Phase I/II dose-finding design for molecularly targeted agent: Plateau determination using adaptive randomization.

Marie-Karelle Riviere1,2, Ying Yuan3, Jacques-Henri Jourdan4

  • 11 INSERM, U1138, Team 22, Centre de Recherche des Cordeliers, Université Paris 5, Université Paris 6, France.

Statistical Methods in Medical Research
|March 19, 2016
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Summary

This study introduces a new Bayesian design for phase I/II clinical trials to find the optimal dose of molecularly targeted agents, balancing safety and efficacy. The method is suitable for drugs where efficacy plateaus at higher doses.

Keywords:
Dose-findingmolecularly targeted agentsoncologyphase Iphase II

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Area of Science:

  • Clinical Pharmacology
  • Biostatistics
  • Oncology Drug Development

Background:

  • Traditional phase I trials assume monotonic dose-toxicity and dose-efficacy relationships.
  • This assumption is inadequate for molecularly targeted agents (e.g., monoclonal antibodies) where efficacy may plateau.
  • The goal for these agents is to identify the optimal dose: the lowest safe dose with maximal efficacy.

Purpose of the Study:

  • To develop a novel Bayesian phase I/II dose-finding design for molecularly targeted agents.
  • To identify the optimal dose, considering both toxicity and a plateauing efficacy profile.
  • To accommodate potential late-onset efficacy using a weighted likelihood approach.

Main Methods:

  • A Bayesian design integrating phase I and II objectives.
  • Utilized a logistic model incorporating a plateau parameter for dose-efficacy.
  • Employed a weighted likelihood approach for late-onset efficacy.
  • Adaptive patient allocation to the estimated optimal dose based on accumulating data.

Main Results:

  • Simulation studies demonstrated favorable operating characteristics of the proposed design.
  • The design effectively balances dose-finding for toxicity and efficacy in the context of plateauing response.
  • The method is applicable to ongoing and future phase I clinical trials lacking alternative designs.

Conclusions:

  • The developed Bayesian design provides a robust framework for optimal dose-finding of molecularly targeted agents.
  • This approach addresses the limitations of conventional methods for drugs with non-monotonic dose-efficacy curves.
  • An accompanying R package (dfmta) is available for practical implementation.