Pharmacometrics-Enabled DOse OPtimization (PEDOOP) for seamless phase I-II trials in oncology

被引:1
作者
Yuan, Shijie [1 ]
Huang, Zhanbo [2 ]
Liu, Jiaxin [3 ]
Ji, Yuan [4 ]
机构
[1] Univ Texas Austin, Dept Stat & Data Sci, Austin, TX USA
[2] Fudan Univ, Sch Data Sci, Shanghai, Peoples R China
[3] Cytel Inc, Stat, Shanghai, Peoples R China
[4] Univ Chicago, Dept Publ Hlth Sci, 5841 S Maryland Ave,MC2000, Chicago, IL 60637 USA
关键词
Adaptive randomization; dose finding; optimal biological dose; pharmacodynamics; pharmacokinetics; CONTINUAL REASSESSMENT METHOD; ADAPTIVE RANDOMIZATION; CLINICAL-TRIALS; DESIGN; ESCALATION;
D O I
10.1080/10543406.2024.2364716
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
We consider a dose-optimization design for a first-in-human oncology trial that aims to identify a suitable dose for late-phase drug development. The proposed approach, called the Pharmacometrics-Enabled DOse OPtimization (PEDOOP) design, incorporates observed patient-level pharmacokinetics (PK) measurements and latent pharmacodynamics (PD) information for trial decision-making and dose optimization. PEDOOP consists of two seamless phases. In phase I, patient-level time-course drug concentrations, derived PD effects, and the toxicity outcomes from patients are integrated into a statistical model to estimate the dose-toxicity response. A simple dose-finding design guides dose escalation in phase I. At the end of the phase I dose finding, a graduation rule is used to assess the safety and efficacy of all the doses and select those with promising efficacy and acceptable safety for a randomized comparison against a control arm in phase II. In phase II, patients are randomized to the selected doses based on a fixed or adaptive randomization ratio. At the end of phase II, an optimal biological dose (OBD) is selected for late-phase development. We conduct simulation studies to assess the PEDOOP design in comparison to an existing seamless design that also combines phases I and II in a single trial.
引用
收藏
页数:20
相关论文
共 25 条
[1]  
Abramowitz M., 1968, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, V55
[2]  
Andrew B., 1999, Applied biopharmaceutics pharmacokinetics, V264
[3]   A Bayesian interval dose-finding design addressingOckham's razor: mTPI-2 [J].
Guo, Wentian ;
Wang, Sue-Jane ;
Yang, Shengjie ;
Lynn, Henry ;
Ji, Yuan .
CONTEMPORARY CLINICAL TRIALS, 2017, 58 :23-33
[4]   A parallel phase I/II clinical trial design for combination therapies [J].
Huang, Xuelin ;
Biswas, Swati ;
Oki, Yasuhiro ;
Issa, Jean-Pierre ;
Berry, Donald A. .
BIOMETRICS, 2007, 63 (02) :429-436
[5]   A modified toxicity probability interval method for dose-finding trials [J].
Ji, Yuan ;
Liu, Ping ;
Li, Yisheng ;
Bekele, B. Nebiyou .
CLINICAL TRIALS, 2010, 7 (06) :653-663
[6]  
Lin Ruitao, 2020, JCO Precis Oncol, V4, DOI 10.1200/PO.20.00257
[7]   The i3+3 design for phase I clinical trials [J].
Liu, Meizi ;
Wang, Sue-Jane ;
Ji, Yuan .
JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 2020, 30 (02) :294-304
[8]   Bayesian optimal interval designs for phase I clinical trials [J].
Liu, Suyu ;
Yuan, Ying .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 2015, 64 (03) :507-523
[9]  
Meibohm B, 1997, INT J CLIN PHARM TH, V35, P401
[10]   CONTINUAL REASSESSMENT METHOD - A PRACTICAL DESIGN FOR PHASE-1 CLINICAL-TRIALS IN CANCER [J].
OQUIGLEY, J ;
PEPE, M ;
FISHER, L .
BIOMETRICS, 1990, 46 (01) :33-48