BIPSE: A biomarker‐based phase I/II design for immunotherapy trials with progression‐free survival endpoint

Abstract

A Bayesian biomarker-based phase I/II design (BIPSE) is presented for immunotherapy trials with a progression-free survival (PFS) endpoint. The objective is to identify the subgroup-specific optimal dose, defined as the dose with the best risk-benefit tradeoff in each biomarker subgroup. We jointly model the immune response, toxicity outcome, and PFS with information borrowing across subgroups. A plateau model is used to describe the marginal distribution of the immune response. Conditional on the immune response, we model toxicity using probit regression and model PFS using the mixture cure rate model. During the trial, based on the accumulating data, we continuously update model estimates and adaptively randomize patients to doses with high desirability within each subgroup. Simulation studies show that the BIPSE design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.

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

“…To better summarize this article, in Figure 5, we present a classification tree depicting all the reviewed designs based on the type of OBD (efficacy-driven or utility-based), the dose-outcome model (model based or model free), the shape of the doseresponse curve (unimodal or plateaued), the secondary efficacy endpoint (immune response, pharmacodynamic, or survival), and the availability of software. It should be noted that there are many other efficacydriven designs, [26][27][28][29][30][31] utility-based designs, [32][33][34][35][36][37][38] or designs incorporating multiple efficacy outcomes [39][40][41][42][43][44][45][46] not reviewed due to the page limits. Besides, many other phase I-II clinical trial designs have been proposed to handle more complicated clinical settings for targeted agents and immunotherapies such as the late-onset outcomes, 24,47,48 drug-drug combination, [49][50][51][52] dose schedule, [53][54][55][56][57][58] and personalized medicine.…”

“…Besides, many other phase I-II clinical trial designs have been proposed to handle more complicated clinical settings for targeted agents and immunotherapies such as the late-onset outcomes, 24,47,48 drug-drug combination, [49][50][51][52] dose schedule, [53][54][55][56][57][58] and personalized medicine. 40,45,46,[59][60][61][62][63][64][65][66] The phase I-II clinical trial designs belong to the class of seamless designs and are dedicated to the early stages of drug development. Many other types of seamless designs, such as the phase II-III design, have also been developed, focusing on the later stage of the drug development, such as the treatment effect confirmation and validation.…”

Biomarker‐based precision dose finding for immunotherapy combined with radiotherapy

“…(2021) presented a design to find the optimal dose within subgroups based on toxicity and efficacy. Guo and Zang (2022a, 2022b) proposed designs for immunotherapy to identify the optimal dose for each subgroup defined by the baseline biomarker measurements. Guo et al.…”

A Bayesian phase I/II design to determine subgroup‐specific optimal dose for immunotherapy sequentially combined with radiotherapy

“…Identifying subgroups requires different modeling strategies. For example, compared with the designs in Guo and Zang [28][29] which model immune response, toxicity, and efficacy, the SIR design in the current paper has an additional layer of model, that is, the latent subgroup membership model. We jointly estimate the subgroup membership variables with other model parameters.…”

“…Lee, Thall, and Msaouel 27 described a design that aims to find the optimal dose within subgroups based on toxicity and efficacy. Guo and Zang 28 proposed a design for immunotherapy with a progression‐free survival endpoint to identify the subgroup‐specific optimal dose. Guo and Zang 29 presented a biomarker‐based design for determining the optimal dose for each subgroup for immunotherapy that jointly models the immune response, toxicity, and efficacy outcomes.…”