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We extend Bayesian Logistic Regression to model the dose-toxicity relationship in the setting of phase I dose-escalation/dose-finding trials for cancer immunotherapies. Immunotherapy drugs are associated with Cytokine Release Syndrome, a systemic immune reaction that can be mitigated when initial lower doses of the drug are administered to generate immune tolerance. This complicates the standard dose-finding problem; we now need to find both the optimal safe dose and the dose regimen that allows patients to quickly and safely reach that dose without CRS. As part of solving this methodological challenge, we show how to (i) jointly model CRS and non-CRS toxicities, and (ii) generalise the BLRM to model dose regimens, in addition to single doses.

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Phase I Dose Escalation Trials in Cancer Immunotherapy: Modifying the Bayesian Logistic Regression Model for Cytokine Release Syndrome

  • 会议论文

  • 发表日期：

    2026年

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  • Matt Chapman-Rounds,
  • Miguel Pereira

摘要

We extend Bayesian Logistic Regression to model the dose-toxicity relationship in the setting of phase I dose-escalation/dose-finding trials for cancer immunotherapies. Immunotherapy drugs are associated with Cytokine Release Syndrome, a systemic immune reaction that can be mitigated when initial lower doses of the drug are administered to generate immune tolerance. This complicates the standard dose-finding problem; we now need to find both the optimal safe dose and the dose regimen that allows patients to quickly and safely reach that dose without CRS. As part of solving this methodological challenge, we show how to (i) jointly model CRS and non-CRS toxicities, and (ii) generalise the BLRM to model dose regimens, in addition to single doses.