Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and

Yong Zang1,2, Beibei Guo3, Yingjie Qiu1

  • 1Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA.

PubMed

Insights

Innovative phase I-II clinical trial designs optimize doses for targeted cancer therapies and immunotherapies. These adaptive designs use efficacy and toxicity data to find the best biological dose, maximizing patient benefit.

Area of Science:

  • Oncology
  • Clinical Trial Design
  • Pharmacology

Background:

  • Targeted agents and immunotherapies represent a paradigm shift in cancer treatment.
  • The "more is better" principle is often inapplicable to these novel therapies due to complex mechanisms.
  • Optimizing dosage is crucial for maximizing therapeutic effects while managing toxicity.

Purpose of the Study:

  • To review innovative phase I-II clinical trial designs for targeted agents and immunotherapies.
  • To examine designs that adaptively determine patient doses based on accumulating efficacy and toxicity data.
  • To identify optimal biological doses that maximize overall therapeutic effect.

Main Methods:

  • Review of innovative phase I-II clinical trial designs, categorized into efficacy-driven, utility-based, and multi-endpoint designs.
  • Analysis of dose-outcome models, optimal biological dose definitions, dose-finding algorithms, and software implementation.
  • Illustration with two real-world phase I-II trial examples (EffTox and ISO designs).

Main Results:

  • Discussion of three main categories of adaptive phase I-II designs.
  • Presentation of dose-outcome models, dose-finding algorithms, and software tools for each design type.
  • Exemplification of design application through EffTox and ISO trials.

Conclusions:

  • Adaptive phase I-II designs are essential for optimizing novel cancer therapies.
  • These designs effectively utilize accumulated data to determine optimal biological doses.
  • A classification tree is provided to guide the selection of appropriate trial designs.

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