1
|
Wang Z, Zhang J, Xia T, He R, Yan F. A Bayesian phase I-II clinical trial design to find the biological optimal dose on drug combination. J Biopharm Stat 2024; 34:582-595. [PMID: 37461311 DOI: 10.1080/10543406.2023.2236208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 07/09/2023] [Indexed: 05/29/2024]
Abstract
In recent years, combined therapy shows expected treatment effect as they increase dose intensity, work on multiple targets and benefit more patients for antitumor treatment. However, dose -finding designs for combined therapy face a number of challenges. Therefore, under the framework of phase I-II, we propose a two-stage dose -finding design to identify the biologically optimal dose combination (BODC), defined as the one with the maximum posterior mean utility under acceptable safety. We model the probabilities of toxicity and efficacy by using linear logistic regression models and conduct Bayesian model selection (BMS) procedure to define the most likely pattern of dose-response surface. The BMS can adaptively select the most suitable model during the trial, making the results robust. We investigated the operating characteristics of the proposed design through simulation studies under various practical scenarios and showed that the proposed design is robust and performed well.
Collapse
Affiliation(s)
- Ziqing Wang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
| | - Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
| | - Tian Xia
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
| | - Ruyue He
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
| |
Collapse
|
2
|
Xia Q, Takeda K, Yamaguchi Y, Zhang J. A generalized Bayesian optimal interval design for dose optimization in immunotherapy. Pharm Stat 2024; 23:480-494. [PMID: 38295856 DOI: 10.1002/pst.2369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 01/12/2024] [Accepted: 01/21/2024] [Indexed: 07/13/2024]
Abstract
For novel immuno-oncology therapies, the primary purpose of a dose-finding trial is to identify an optimal dose (OD), defined as the tolerable dose having adequate efficacy and immune response under the unpredictable dose-outcome (toxicity, efficacy, and immune response) relationships. In addition, the multiple low or moderate-grade toxicities rather than dose-limiting toxicities (DLTs) and multiple levels of efficacy should be evaluated differently in dose-finding to determine true OD for developing novel immuno-oncology therapies. We proposed a generalized Bayesian optimal interval design for immunotherapy, simultaneously considering efficacy and toxicity grades and immune response outcomes. The proposed design, named gBOIN-ETI design, is model-assisted and easy to implement to develop immunotherapy efficiently. The operating characteristics of the gBOIN-ETI are compared with other dose-finding trial designs in oncology by simulation across various realistic settings. Our simulations show that the gBOIN-ETI design could outperform the other available approaches in terms of both the percentage of correct OD selection and the average number of patients allocated to the OD across various realistic trial settings.
Collapse
Affiliation(s)
- Qing Xia
- Global Biostatistics Science, Amgen Inc, Camarillo, California, USA
| | - Kentaro Takeda
- Data Science, Astellas Pharma Global Development, Inc, Northbrook, Illinois, USA
| | - Yusuke Yamaguchi
- Data Science, Astellas Pharma Global Development, Inc, Northbrook, Illinois, USA
| | - Jun Zhang
- Data Science, Astellas Pharma China, Beijing, China
| |
Collapse
|
3
|
Zang Y, Guo B, Qiu Y, Liu H, Opyrchal M, Lu X. Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies. Clin Trials 2024; 21:298-307. [PMID: 38205644 PMCID: PMC11132954 DOI: 10.1177/17407745231220661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Targeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of "more is better" is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I-II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I-II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I-II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose-outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I-II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.
Collapse
Affiliation(s)
- Yong Zang
- Department of Biostatistics and Health Data Sciences, School of Medicine, Indiana University
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University
| | - Beibei Guo
- Department of Experimental Statistics, Louisiana State University
| | - Yingjie Qiu
- Department of Biostatistics and Health Data Sciences, School of Medicine, Indiana University
| | - Hao Liu
- Department of Biostatistics and Epidemiology, Cancer Institute of New Jersey, Rutgers University
| | | | - Xiongbin Lu
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University
| |
Collapse
|
4
|
Zhang J, Yan F, Wages NA, Lin R. Local continual reassessment methods for dose finding and optimization in drug-combination trials. Stat Methods Med Res 2023; 32:2049-2063. [PMID: 37593951 PMCID: PMC10563380 DOI: 10.1177/09622802231192955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Due to the limited sample size and large dose exploration space, obtaining a desirable dose combination is a challenging task in the early development of combination treatments for cancer patients. Most existing designs for optimizing the dose combination are model-based, requiring significant efforts to elicit parameters or prior distributions. Model-based designs also rely on intensive model calibration and may yield unstable performance in the case of model misspecification or sparse data. We propose to employ local, underparameterized models for dose exploration to reduce the hurdle of model calibration and enhance the design robustness. Building upon the framework of the partial ordering continual reassessment method, we develop local data-based continual reassessment method designs for identifying the maximum tolerated dose combination, using toxicity only, and the optimal biological dose combination, using both toxicity and efficacy, respectively. The local data-based continual reassessment method designs only model the local data from neighboring dose combinations. Therefore, they are flexible in estimating the local space and circumventing unstable characterization of the entire dose-exploration surface. Our simulation studies show that our approach has competitive performance compared to widely used methods for finding maximum tolerated dose combination, and it has advantages over existing model-based methods for optimizing optimal biological dose combination.
Collapse
Affiliation(s)
- Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Nolan A Wages
- Department of Biostatistics, Massey Cancer Center, Virginia Commonwealth University, Richmond, VA , USA
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| |
Collapse
|
5
|
Zhang J, Chen X, Li B, Yan F. A comparative study of adaptive trial designs for dose optimization. Pharm Stat 2023; 22:797-814. [PMID: 37156731 DOI: 10.1002/pst.2306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 04/19/2023] [Accepted: 04/25/2023] [Indexed: 05/10/2023]
Abstract
Recently, the US Food and Drug Administration Oncology Center of Excellence initiated Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development. The agency pointed out that the current paradigm for dose selection-based on the maximum tolerated dose (MTD)-is not sufficient for molecularly targeted therapies and immunotherapies, for which efficacy may not increase after the dose reaches a certain level. In these cases, it is more appropriate to identify the optimal biological dose (OBD) that optimizes the risk-benefit tradeoff of the drug. Project Optimus has spurred tremendous interest and urgent need for guidance on designing dose optimization trials. In this article, we review several representative dose optimization designs, including model-based and model-assisted designs, and compare their operating characteristics based on 10,000 randomly generated scenarios with various dose-toxicity and dose-efficacy curves and some fixed representative scenarios. The results show that, compared with model-based designs, model-assisted methods have advantages of easy-to-implement, robustness, and high accuracy to identify OBD. Some guidance is provided to help biostatisticians and clinicians to choose appropriate dose optimization methods in practice.
Collapse
Affiliation(s)
- Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Xin Chen
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Bosheng Li
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| |
Collapse
|
6
|
Thall PF, Zang Y, Yuan Y. Generalized phase I-II designs to increase long term therapeutic success rate. Pharm Stat 2023; 22:692-706. [PMID: 37038957 PMCID: PMC10524372 DOI: 10.1002/pst.2301] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 03/11/2023] [Accepted: 03/24/2023] [Indexed: 04/12/2023]
Abstract
Designs for early phase dose finding clinical trials typically are either phase I based on toxicity, or phase I-II based on toxicity and efficacy. These designs rely on the implicit assumption that the dose of an experimental agent chosen using these short-term outcomes will maximize the agent's long-term therapeutic success rate. In many clinical settings, this assumption is not true. A dose selected in an early phase oncology trial may give suboptimal progression-free survival or overall survival time, often due to a high rate of relapse following response. To address this problem, a new family of Bayesian generalized phase I-II designs is proposed. First, a conventional phase I-II design based on short-term outcomes is used to identify a set of candidate doses, rather than selecting one dose. Additional patients then are randomized among the candidates, patients are followed for a predefined longer time period, and a final dose is selected to maximize the long-term therapeutic success rate, defined in terms of duration of response. Dose-specific sample sizes in the randomization are determined adaptively to obtain a desired level of selection reliability. The design was motivated by a phase I-II trial to find an optimal dose of natural killer cells as targeted immunotherapy for recurrent or treatment-resistant B-cell hematologic malignancies. A simulation study shows that, under a range of scenarios in the context of this trial, the proposed design has much better performance than two conventional phase I-II designs.
Collapse
Affiliation(s)
- Peter F. Thall
- Department of Biostatistics, M.D. Anderson Cancer Center
| | - Yong Zang
- Department of Biostatistics and Health Data Science; Center for Computational Biology and Bioinformatics, Indiana University
| | - Ying Yuan
- Department of Biostatistics, M.D. Anderson Cancer Center
| |
Collapse
|
7
|
A Bayesian design for finding optimal biological dose with mixed types of responses of toxicity and efficacy. Contemp Clin Trials 2023; 127:107113. [PMID: 36758934 DOI: 10.1016/j.cct.2023.107113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 02/10/2023]
Abstract
For molecularly targeted therapy and immunotherapy, the targeted dose in the early phase clinical trial has been shifted from the maximum tolerated dose for the cytotoxic drug to the optimal biological dose where both toxicity and efficacy are considered. In this paper, we consider the situation that the responses of toxicity and efficacy are mixed in binary and continuous types, respectively, where the continuous endpoint bears more magnitude information than the binary endpoint after dichotomization. We propose combining two model-based designs to sequentially identify the most efficacious and tolerably safe dose. The employed designs both take the dose level information into account to achieve high estimation efficiency. We demonstrate the superiority of the proposed method to some existing methods by simulation.
Collapse
|
8
|
Jiang M, Hu Y, Lin G, Chen C. Dosing Regimens of Immune Checkpoint Inhibitors: Attempts at Lower Dose, Less Frequency, Shorter Course. Front Oncol 2022; 12:906251. [PMID: 35795044 PMCID: PMC9251517 DOI: 10.3389/fonc.2022.906251] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 05/24/2022] [Indexed: 12/19/2022] Open
Abstract
Immune checkpoint inhibitors (ICIs) are a revolutionary breakthrough in the field of cancer by modulating patient's own immune system to exert anti-tumor effects. The clinical application of ICIs is still in its infancy, and their dosing regimens need to be continuously adjusted. Pharmacokinetic/pharmacodynamic studies showed a significant plateau in the exposure-response curve, with high receptor occupancy and plasma concentrations achieved at low dose levels. Coupled with concerns about drug toxicity and heavy economic costs, there has been an ongoing quest to reevaluate the current ICI dosing regimens while preserving maximum clinical efficacy. Many clinical data showed remarkable anticancer effects with ICIs at the doses far below the approved regimens, indicating the possibility of dose reduction. Our review attempts to summarize the clinical evidence for ICIs regimens with lower-dose, less-frequency, shorter-course, and provide clues for further ICIs regimen optimization.
Collapse
Affiliation(s)
| | | | | | - Chao Chen
- Department of Radiotherapy, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China
| |
Collapse
|
9
|
Takeda K, Morita S, Taguri M. gBOIN-ET: The generalized Bayesian optimal interval design for optimal dose-finding accounting for ordinal graded efficacy and toxicity in early clinical trials. Biom J 2022; 64:1178-1191. [PMID: 35561046 DOI: 10.1002/bimj.202100263] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 02/22/2022] [Accepted: 04/03/2022] [Indexed: 12/19/2022]
Abstract
One of the primary objectives of an oncology dose-finding trial for novel therapies, such as molecular targeted agents and immune-oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low- or moderate-grade toxicities than dose-limiting toxicities. Besides, efficacy should be evaluated as an overall response and stable disease in solid tumors and the difference between complete remission and partial remission in lymphoma. This paper proposes the generalized Bayesian optimal interval design for dose-finding accounting for efficacy and toxicity grades. The new design, named "gBOIN-ET" design, is model-assisted, simple, and straightforward to implement in actual oncology dose-finding trials than model-based approaches. These characteristics are quite valuable in practice. A simulation study shows that the gBOIN-ET design has advantages compared with the other model-assisted designs in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.
Collapse
Affiliation(s)
- Kentaro Takeda
- Data Science, Astellas Pharma Global Development, Inc., Northbrook, IL, USA
| | - Satoshi Morita
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masataka Taguri
- Department of Data Science, Yokohama City University, Yokohama, Japan
| |
Collapse
|
10
|
Guo B, Zang Y. A Bayesian phase I/II biomarker-based design for identifying subgroup-specific optimal dose for immunotherapy. Stat Methods Med Res 2022; 31:1104-1119. [PMID: 35191780 DOI: 10.1177/09622802221080753] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Immunotherapy is an innovative treatment that enlists the patient's immune system to battle tumors. The optimal dose for treating patients with an immunotherapeutic agent may differ according to their biomarker status. In this article, we propose a biomarker-based phase I/II dose-finding design for identifying subgroup-specific optimal dose for immunotherapy (BSOI) that jointly models the immune response, toxicity, and efficacy outcomes. We propose parsimonious yet flexible models to borrow information across different types of outcomes and subgroups. We quantify the desirability of the dose using a utility function and adopt a two-stage dose-finding algorithm to find the optimal dose for each subgroup. Simulation studies show that the BSOI design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.
Collapse
Affiliation(s)
- Beibei Guo
- Department of Experimental Statistics, 5779Louisiana State University, Baton Rouge, USA
| | - Yong Zang
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, USA.,Center for Computational Biology and Bioinformatics, Indiana University, Indianapolis, USA
| |
Collapse
|
11
|
TITE‐BOIN12: A Bayesian phase I/II trial design to find the optimal biological dose with late‐onset toxicity and efficacy. Stat Med 2022; 41:1918-1931. [DOI: 10.1002/sim.9337] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 12/19/2021] [Accepted: 01/09/2022] [Indexed: 12/17/2022]
|
12
|
Carmen Pardo M, Lu Y, Franco-Pereira AM. Extensions of empirical likelihood and chi-squared-based tests for ordered alternatives. J Appl Stat 2022; 49:24-43. [DOI: 10.1080/02664763.2020.1796944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- M. Carmen Pardo
- Department of Statistics and Operational Research, Universidad Complutense de Madrid, Madrid, Spain
| | - Ying Lu
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, USA
| | - Alba M. Franco-Pereira
- Department of Statistics and Operational Research, Universidad Complutense de Madrid, Madrid, Spain
- UC3M-BS Institute of Financial Big Data, Universidad Carlos III de Madrid, Getafe, Madrid, Spain
| |
Collapse
|
13
|
Zhang Y, Kutner M, Chen Z. Adaptive Bayesian phase I clinical trial designs for estimating the maximum tolerated doses for two drugs while fully utilizing all toxicity information. Biom J 2021; 63:1476-1492. [PMID: 33969525 PMCID: PMC10066875 DOI: 10.1002/bimj.202000142] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 03/02/2021] [Accepted: 03/22/2021] [Indexed: 01/24/2023]
Abstract
The combined treatments with multiple drugs are very common in the contemporary medicine, especially for medical oncology. Therefore, we developed a Bayesian adaptive Phase I clinical trial design entitled escalation with overdoing control using normalized equivalent toxicity score for estimating maximum tolerated dose (MTD) contour of two drug combination (EWOC-NETS-COM) used for oncology trials. The normalized equivalent toxicity score (NETS) as the primary endpoint of clinical trial is assumed to follow quasi-Bernoulli distribution and treated as quasi-continuous random variable in the logistic linear regression model which is used to describe the relationship between the doses of the two agents and the toxicity response. Four parameters in the dose-toxicity model were re-parameterized to parameters with explicit clinical meanings to describe the association between NETS and doses of two agents. Noninformative priors were used and Markov chain Monte Carlo was employed to update the posteriors of the four parameters in dose-toxicity model. Extensive simulations were conducted to evaluate the safety, trial efficiency, and MTD estimation accuracy of EWOC-NETS-COM under different scenarios, using the EWOC as reference. The results demonstrated that EWOC-NETS-COM not only efficiently estimates MTD contour of multiple drugs but also provides better trial efficiency by fully utilizing all toxicity information.
Collapse
Affiliation(s)
- Yuzi Zhang
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Michael Kutner
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Zhengjia Chen
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL, USA.,Biostatistics Shared Resource Core, University of Illinois Cancer Institute, Chicago, IL, USA
| |
Collapse
|
14
|
Wei W, Esserman D, Kane M, Zelterman D. Unified exact design with early stopping rules for single arm clinical trials with multiple endpoints. Stat Methods Med Res 2021; 30:1575-1588. [PMID: 34159859 PMCID: PMC8959087 DOI: 10.1177/09622802211013062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Adaptive designs are gaining popularity in early phase clinical trials because they enable investigators to change the course of a study in response to accumulating data. We propose a novel design to simultaneously monitor several endpoints. These include efficacy, futility, toxicity and other outcomes in early phase, single-arm studies. We construct a recursive relationship to compute the exact probabilities of stopping for any combination of endpoints without the need for simulation, given pre-specified decision rules. The proposed design is flexible in the number and timing of interim analyses. A R Shiny app with user-friendly web interface has been created to facilitate the implementation of the proposed design.
Collapse
Affiliation(s)
- Wei Wei
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Denise Esserman
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Michael Kane
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Daniel Zelterman
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| |
Collapse
|
15
|
Han Y, Liu H, Cao S, Zhang C, Zang Y. TSNP: A two-stage nonparametric phase I/II clinical trial design for immunotherapy. Pharm Stat 2020; 20:282-296. [PMID: 33025762 DOI: 10.1002/pst.2075] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 08/27/2020] [Accepted: 09/15/2020] [Indexed: 12/17/2022]
Abstract
We develop a transparent and efficient two-stage nonparametric (TSNP) phase I/II clinical trial design to identify the optimal biological dose (OBD) of immunotherapy. We propose a nonparametric approach to derive the closed-form estimates of the joint toxicity-efficacy response probabilities under the monotonic increasing constraint for the toxicity outcomes. These estimates are then used to measure the immunotherapy's toxicity-efficacy profiles at each dose and guide the dose finding. The first stage of the design aims to explore the toxicity profile. The second stage aims to find the OBD, which can achieve the optimal therapeutic effect by considering both the toxicity and efficacy outcomes through a utility function. The closed-form estimates and concise dose-finding algorithm make the TSNP design appealing in practice. The simulation results show that the TSNP design yields superior operating characteristics than the existing Bayesian parametric designs. User-friendly computational software is freely available to facilitate the application of the proposed design to real trials. We provide comprehensive illustrations and examples about implementing the proposed design with associated software.
Collapse
Affiliation(s)
- Yan Han
- Department of Biostatistics, Indiana University, Indianapolis, Indiana, USA
| | - Hao Liu
- Department of Biostatistics, Indiana University, Indianapolis, Indiana, USA
| | - Sha Cao
- Department of Biostatistics, Indiana University, Indianapolis, Indiana, USA
- Center for Computational Biology and Bioinformatics, Indiana University, Indianapolis, Indiana, USA
| | - Chi Zhang
- Center for Computational Biology and Bioinformatics, Indiana University, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, Indiana, USA
| | - Yong Zang
- Department of Biostatistics, Indiana University, Indianapolis, Indiana, USA
- Center for Computational Biology and Bioinformatics, Indiana University, Indianapolis, Indiana, USA
| |
Collapse
|
16
|
Li P, Liu R, Lin J, Ji Y. TEPI-2 and UBI: designs for optimal immuno-oncology and cell therapy dose finding with toxicity and efficacy. J Biopharm Stat 2020; 30:979-992. [PMID: 32951518 DOI: 10.1080/10543406.2020.1814802] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Conventional dose finding designs in oncology drug development target on the identification of the maximum tolerated dose (MTD), with the assumption that the MTD has the most potential of clinical activity among those identified tolerable dose levels. However, immuno-oncology (I-O) and cell therapy area, may lack dose-efficacy monotonicity, posing significant challenges in the statistical designs for dose finding trials. A desirable design should empower the trial to identify the right dose level with tolerable toxicity and acceptable efficacy. Such dose is called as optimal biological dose (OBD), which is more appropriate to be considered as the primary objective of the first-in-human trial in I-O and cell therapy than MTD. We propose two model-assisted designs in this setting: the toxicity and efficacy probability interval-2 (TEPI-2) design and the utility-based interval (UBI) design that incorporate the toxicity and efficacy outcomes simultaneously and identify a dose that has high probability of acceptable efficacy with manageable toxicity. The proposed designs can generate decision tables before trial starts to facilitate practical and easy-to-implement applications. Through simulation studies, our proposed novel designs demonstrate superior performance in accuracy, efficiency, and safety. Additionally, they can reduce the number of patients and shorten clinical development timeline. We also illustrate the advantages of proposed methods by redesigning a CAR T-cell therapy phase I clinical trial for multiple myeloma and summarize our recommendations in the discussion section.
Collapse
Affiliation(s)
- Pin Li
- Department of Public Health Sciences, Henry Ford Hospital Systems, Detroit, Michigan, USA
| | - Rachael Liu
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Jianchang Lin
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Yuan Ji
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| |
Collapse
|
17
|
Zhang Y, Cao S, Zhang C, Jin IH, Zang Y. A Bayesian adaptive phase I/II clinical trial design with late‐onset competing risk outcomes. Biometrics 2020; 77:796-808. [DOI: 10.1111/biom.13347] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/18/2020] [Accepted: 07/24/2020] [Indexed: 11/29/2022]
Affiliation(s)
- Yifei Zhang
- Department of BiostatisticsIndiana UniversityIndianapolis Indiana
| | - Sha Cao
- Department of BiostatisticsIndiana UniversityIndianapolis Indiana
- Center of Computational Biology and BioinformaticsIndiana UniversityIndianapolis Indiana
| | - Chi Zhang
- Center of Computational Biology and BioinformaticsIndiana UniversityIndianapolis Indiana
- Department of Medical and Molecular GeneticsIndiana UniversityIndianapolis Indiana
| | - Ick Hoon Jin
- Department of Applied StatisticsYonsei UniversitySeoul Republic of Korea
| | - Yong Zang
- Department of BiostatisticsIndiana UniversityIndianapolis Indiana
- Center of Computational Biology and BioinformaticsIndiana UniversityIndianapolis Indiana
| |
Collapse
|
18
|
Fan S, Lee BL, Lu Y. A curve free Bayesian decision-theoretic design for phase Ia/Ib trials considering both safety and efficacy outcomes. STATISTICS IN BIOSCIENCES 2020; 12:146-166. [PMID: 33815623 DOI: 10.1007/s12561-020-09272-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
A curve-free, Bayesian decision-theoretic two-stage design is proposed to select biological efficacious doses (BEDs) for phase Ia/Ib trials in which both toxicity and efficacy signals are observed. No parametric models are assumed to govern the dose-toxicity, dose-efficacy, and toxicity-efficacy relationships. We assume that the dose-toxicity curve is monotonic non-decreasing and the dose-efficacy curve is unimodal. In the phase Ia stage, a Bayesian model on the toxicity rates is used to locate the maximum tolerated dose. In the phase Ib stage, we model the dose-efficacy curve using a step function while continuing to monitor the toxicity rates. Furthermore, a measure of the goodness of fit of a candidate step function is proposed, and the interval of BEDs associated with the best fitting step function is recommended. At the end of phase Ib, if some doses are recommended as BEDs, a cohort of confirmation is recruited and assigned at these doses to improve the precision of estimates at these doses. Extensive simulation studies show that the proposed design has desirable operating characteristics across different shapes of the underlying true toxicity and efficacy curves.
Collapse
Affiliation(s)
- Shenghua Fan
- Department of Statistics and Biostatistics, California State University, East Bay, Hayward, 94542, CA, USA
| | - Bee Leng Lee
- Department of Mathematics and Statistics, San Jose State University, San Jose, 95192, CA, USA
| | - Ying Lu
- Department of Biomedical Data Science, Center for Innovative Study Designs and the Biostatistics Core, Stanford Cancer Institute, School of Medicine, Stanford University, Stanford, 94305, CA, USA
| |
Collapse
|
19
|
Mazzarella L, Morganti S, Marra A, Trapani D, Tini G, Pelicci P, Curigliano G. Master protocols in immuno-oncology: do novel drugs deserve novel designs? J Immunother Cancer 2020; 8:e000475. [PMID: 32238471 PMCID: PMC7174064 DOI: 10.1136/jitc-2019-000475] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/14/2020] [Indexed: 12/31/2022] Open
Abstract
The rapid rise to fame of immuno-oncology (IO) drugs has generated unprecedented interest in the industry, patients and doctors, and has had a major impact in the treatment of most cancers. An interesting aspect in the clinical development of many IO agents is the increasing reliance on nonconventional trial design, including the so-called 'master protocols' that incorporate various adaptive features and often heavily rely on biomarkers to select patient populations most likely to benefit. These novel designs promise to maximize the clinical benefit that can be reaped from clinical research, but are not without costs. Their acceptance as solid evidence basis for use outside of the research context requires profound cultural changes by multiple stakeholders, including regulatory bodies, decision-makers, statisticians, researchers, doctors and, most importantly, patients. Here we review characteristics of recent and ongoing trials testing IO drugs with unconventional design, and we highlight trends and critical aspects.
Collapse
Affiliation(s)
- Luca Mazzarella
- Division of Early Drug Development for Innovative Therapies, IEO European Institute of Oncology IRCCS, Milan, Italy
- Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Stefania Morganti
- Division of Early Drug Development for Innovative Therapies, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Antonio Marra
- Division of Early Drug Development for Innovative Therapies, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Dario Trapani
- Division of Early Drug Development for Innovative Therapies, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giulia Tini
- Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Piergiuseppe Pelicci
- Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hematology, Universita degli Studi di Milano, Milan, Italy
| | - Giuseppe Curigliano
- Division of Early Drug Development for Innovative Therapies, IEO European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hematology, Universita degli Studi di Milano, Milan, Italy
| |
Collapse
|
20
|
Domenicano I, Ventz S, Cellamare M, Mak RH, Trippa L. Bayesian uncertainty‐directed dose finding designs. J R Stat Soc Ser C Appl Stat 2019. [DOI: 10.1111/rssc.12355] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- I. Domenicano
- University of Rome “La Sapienza” Italy
- Dana–Farber Cancer Institute Boston USA
| | - S. Ventz
- Dana–Farber Cancer Institute Boston
- Harvard School of Public Health Boston USA
| | - M. Cellamare
- Dana–Farber Cancer Institute Boston
- Harvard School of Public Health Boston USA
| | - R. H. Mak
- Dana–Farber Cancer Institute Boston
- Harvard Medical School Boston USA
| | - L. Trippa
- Dana–Farber Cancer Institute Boston
- Harvard School of Public Health Boston USA
| |
Collapse
|
21
|
Altzerinakou MA, Paoletti X. An adaptive design for the identification of the optimal dose using joint modeling of continuous repeated biomarker measurements and time-to-toxicity in phase I/II clinical trials in oncology. Stat Methods Med Res 2019; 29:508-521. [DOI: 10.1177/0962280219837737] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
We present a new adaptive dose-finding method, based on a joint modeling of longitudinal continuous biomarker activity measurements and time to first dose limiting toxicity, with a shared random effect. Estimation relies on likelihood that does not require approximation, an important property in the context of small sample sizes, typical of phase I/II trials. We address the important case of missing at random data that stem from unacceptable toxicity, lack of activity and rapid deterioration of phase I patients. The objective is to determine the lowest dose within a range of highly active doses, under the constraint of not exceeding the maximum tolerated dose. The maximum tolerated dose is associated to some cumulative risk of dose limiting toxicity over a predefined number of treatment cycles. Operating characteristics are explored via simulations in various scenarios.
Collapse
Affiliation(s)
- Maria-Athina Altzerinakou
- CESP OncoStat, Inserm, Villejuif, France
- Université Paris-Saclay, Université Paris-Sud, UVSQ, Villejuif, France
- Gustave Roussy, Service de Biostatistique et d'Épidémiologie, Edouard Vaillant, Villejuif, France
| | - Xavier Paoletti
- CESP OncoStat, Inserm, Villejuif, France
- Université Paris-Saclay, Université Paris-Sud, UVSQ, Villejuif, France
- Gustave Roussy, Service de Biostatistique et d'Épidémiologie, Edouard Vaillant, Villejuif, France
| |
Collapse
|
22
|
Abstract
Australia is well positioned to conduct clinical trials in phage-based technology. Despite challenges with translating phage therapy to mainstream medicine, our regulations are designed for safe and innovative development. Recent success indicates that Australia is ideal for conducting further phage clinical trials. There are also expert clinical research organisations and generous tax incentives.
Collapse
|