A Bayesian phase I/II platform design for co-developing drug combination therapies for multiple indications

Rongji Mu, Jin Xu, Rui Tang, Scott Kopetz, Ying Yuan

Research output: Contribution to journalArticlepeer-review

Abstract

There is a growing trend to combine a new targeted or immunotherapy agent with the cancer-specific standard of care to treat different types of cancers. We propose a master-protocol-based, Bayesian phase I/II platform design to co-develop combination (BPCC) therapies in multiple indications. Under the BPCC design, only a single master protocol is needed, and the combined drug is evaluated in different indications in a concurrent or staggered fashion. For each indication, we jointly model dose-toxicity and -efficacy relationships and employ Bayesian hierarchical models to borrow information across them for more efficient indication-specific decision-making. To account for the characteristic of targeted or immunotherapy agents that their efficacy may not monotonically increase with the dose, and often plateau at high doses, we use the utility to quantify the risk-benefit tradeoff of the treatment. At each interim, we update the toxicity and efficacy model, as well as the estimate of the utility, based on the observed data across indications to inform the indication-specific decision of dose escalation and de-escalation and identify the optimal biological dose for each indication. Simulation study shows that the BPCC design has desirable operating characteristics, and that it provides an efficient approach to accelerate the development of combination therapies.

Original languageEnglish (US)
Pages (from-to)374-389
Number of pages16
JournalStatistics in Medicine
Volume41
Issue number2
DOIs
StatePublished - Jan 30 2022

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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