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Optimizing immunotherapy drug dose and schedule is crucial. This study proposes a tailored strategy using immune response, efficacy, and toxicity data for biomarker subgroups, improving clinical trial decision-making.

Keywords:
Bayesian adaptive designbiomarkerimmunotherapyoptimal treatment regimephase I/II trialrisk‐benefit tradeoffsubgroups

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Area of Science:

  • Immunotherapy
  • Clinical Trial Design
  • Biostatistics

Background:

  • Drug dose and schedule significantly impact immunotherapy efficacy by modulating immune responses.
  • Integrating immune response measures into clinical trials aids decision-making due to their link with efficacy and toxicity.
  • Biomarker data can identify patient subgroups with improved treatment efficacy.

Purpose of the Study:

  • To develop a dose-schedule optimization strategy for immunotherapy clinical trials.
  • To tailor treatment optimization to specific biomarker-defined patient subgroups.
  • To leverage immune response, toxicity, and efficacy data for enhanced decision-making.

Main Methods:

  • A joint modeling framework was developed to simultaneously evaluate immune response, toxicity, and efficacy.
  • Utility functions were elicited to capture patient-specific risk-benefit tradeoffs.
  • Parsimonious yet flexible models were employed to handle small sample sizes in early-phase trials.

Main Results:

  • The proposed joint modeling framework enables information sharing across different outcome types and patient subgroups.
  • Simulation studies demonstrated desirable operating characteristics for the dose-schedule optimization design.
  • The approach effectively informs dose-schedule optimization tailored to biomarker subgroups.

Conclusions:

  • A novel dose-schedule optimization strategy enhances immunotherapy clinical trial design.
  • Tailoring treatment based on biomarker subgroups and integrated outcome modeling improves risk-benefit assessment.
  • The proposed methods are suitable for early-phase trials with limited sample sizes.