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PKBOIN - 12:一种纳入药代动力学结果以寻找最佳生物学剂量的贝叶斯最优区间I/II期设计。

PKBOIN-12: A Bayesian Optimal Interval Phase I/II Design Incorporating Pharmacokinetics Outcomes to Find the Optimal Biological Dose.

作者信息

Sun Hao, Tu Jieqi

机构信息

Global Biometrics & Data Sciences, Bristol Myers Squibb, Lawrenceville, New Jersey, USA.

Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois Chicago, Chicago, Illinois, USA.

出版信息

Pharm Stat. 2025 Mar-Apr;24(2):e2444. doi: 10.1002/pst.2444. Epub 2024 Oct 24.

Abstract

Immunotherapies and targeted therapies have gained popularity due to their promising therapeutic effects across multiple treatment areas. The focus of early phase dose-finding clinical trials has shifted from finding the maximum tolerated dose (MTD) to identifying the optimal biological dose (OBD), which aims to balance the toxicity and efficacy outcomes, thus optimizing the risk-benefit trade-off. These trials often collect multiple pharmacokinetics (PK) outcomes to assess drug exposure, which has shown correlations with toxicity and efficacy outcomes but has not been utilized in the current dose-finding designs for OBD selection. Moreover, PK outcomes are usually available within days after initial treatment, much faster than toxicity and efficacy outcomes. To bridge this gap, we introduce the innovative model-assisted PKBOIN-12 design, which enhances BOIN12 by integrating PK information into both the dose-finding algorithm and the final OBD determination process. We further extend PKBOIN-12 to TITE-PKBOIN-12 to address the challenges of late-onset toxicity and efficacy outcomes. Simulation results demonstrate that PKBOIN-12 more effectively identifies the OBD and allocates a greater number of patients to it than BOIN12. Additionally, PKBOIN-12 decreases the probability of selecting inefficacious doses as the OBD by excluding those with low drug exposure. Comprehensive simulation studies and sensitivity analysis confirm the robustness of both PKBOIN-12 and TITE-PKBOIN-12 in various scenarios.

摘要

免疫疗法和靶向疗法因其在多个治疗领域具有显著的治疗效果而受到广泛关注。早期剂量探索性临床试验的重点已从寻找最大耐受剂量(MTD)转向确定最佳生物学剂量(OBD),其目的是平衡毒性和疗效结果,从而优化风险效益比。这些试验通常会收集多个药代动力学(PK)结果以评估药物暴露情况,虽然已表明其与毒性和疗效结果相关,但在当前用于选择OBD的剂量探索设计中尚未得到应用。此外,PK结果通常在初始治疗后的数天内即可获得,比毒性和疗效结果快得多。为弥合这一差距,我们引入了创新的模型辅助PKBOIN-12设计,该设计通过将PK信息整合到剂量探索算法和最终OBD确定过程中,对BOIN12进行了改进。我们进一步将PKBOIN-12扩展为TITE-PKBOIN-12,以应对迟发性毒性和疗效结果带来的挑战。模拟结果表明,与BOIN12相比,PKBOIN-12能更有效地识别OBD,并将更多患者分配至该剂量组。此外,PKBOIN-12通过排除药物暴露量低的剂量,降低了将无效剂量选为OBD的概率。全面的模拟研究和敏感性分析证实了PKBOIN-12和TITE-PKBOIN-12在各种情况下的稳健性。

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