1
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Li D, Xu Z, Wen S, Ananthakrishnan R, Kim Y, Rantell KR, Anderson P, Whitmore J, Chiang A. Challenges and Lessons Learned in Autologous Chimeric Antigen Receptor T-Cell Therapy Development from a Statistical Perspective. Ther Innov Regul Sci 2024; 58:817-830. [PMID: 38704515 DOI: 10.1007/s43441-024-00652-3] [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: 01/23/2024] [Accepted: 03/29/2024] [Indexed: 05/06/2024]
Abstract
Chimeric antigen receptor (CAR) T-cell therapy is a human gene therapy product where T cells from a patient are genetically modified to enable them to recognize desired target antigen(s) more effectively. In recent years, promising antitumor activity has been seen with autologous CAR T cells. Since 2017, six CAR T-cell therapies for the treatment of hematological malignancies have been approved by the Food and Drug Administration (FDA). Despite the rapid progress of CAR T-cell therapies, considerable statistical challenges still exist for this category of products across all phases of clinical development that need to be addressed. These include (but not limited to) dose finding strategy, implementation of the estimand framework, use of real-world data in contextualizing single-arm CAR T trials, analysis of safety data and long-term follow-up studies. This paper is the first step in summarizing and addressing these statistical hurdles based on the development of the six approved CAR T-cell products.
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Affiliation(s)
- Daniel Li
- Bristol Myers Squibb, Seattle, WA, USA.
| | - Zhenzhen Xu
- US Food and Drug Administration, Silver Spring, MD, USA
| | - Shihua Wen
- Novartis Pharmaceuticals, East Hanover, NJ, USA
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2
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Yang CH, Kwiatkowski E, Lee JJ, Lin R. REDOMA: Bayesian random-effects dose-optimization meta-analysis using spike-and-slab priors. Stat Med 2024; 43:3484-3502. [PMID: 38857904 DOI: 10.1002/sim.10107] [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: 01/08/2024] [Revised: 04/15/2024] [Accepted: 04/29/2024] [Indexed: 06/12/2024]
Abstract
The rise of cutting-edge precision cancer treatments has led to a growing significance of the optimal biological dose (OBD) in modern oncology trials. These trials now prioritize the consideration of both toxicity and efficacy simultaneously when determining the most desirable dosage for treatment. Traditional approaches in early-phase oncology trials have conventionally relied on the assumption of a monotone relationship between treatment efficacy and dosage. However, this assumption may not hold valid for novel oncology therapies. In reality, the dose-efficacy curve of such treatments may reach a plateau at a specific dose, posing challenges for conventional methods in accurately identifying the OBD. Furthermore, achieving reliable identification of the OBD is typically not possible based on a single small-sample trial. With data from multiple phase I and phase I/II trials, we propose a novel Bayesian random-effects dose-optimization meta-analysis (REDOMA) approach to identify the OBD by synthesizing toxicity and efficacy data from each trial. The REDOMA method can address trials with heterogeneous characteristics. We adopt a curve-free approach based on a Gamma process prior to model the average dose-toxicity relationship. In addition, we utilize a Bayesian model selection framework that uses the spike-and-slab prior as an automatic variable selection technique to eliminate monotonic constraints on the dose-efficacy curve. The good performance of the REDOMA method is confirmed by extensive simulation studies.
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Affiliation(s)
- Cheng-Han Yang
- Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Evan Kwiatkowski
- Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - J Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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3
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Li Y, Zhang Y, Mi G, Lin J. A seamless phase II/III design with dose optimization for oncology drug development. Stat Med 2024; 43:3383-3402. [PMID: 38845095 DOI: 10.1002/sim.10129] [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: 11/17/2023] [Revised: 03/19/2024] [Accepted: 05/20/2024] [Indexed: 07/17/2024]
Abstract
The US FDA's Project Optimus initiative that emphasizes dose optimization prior to marketing approval represents a pivotal shift in oncology drug development. It has a ripple effect for rethinking what changes may be made to conventional pivotal trial designs to incorporate a dose optimization component. Aligned with this initiative, we propose a novel seamless phase II/III design with dose optimization (SDDO framework). The proposed design starts with dose optimization in a randomized setting, leading to an interim analysis focused on optimal dose selection, trial continuation decisions, and sample size re-estimation (SSR). Based on the decision at interim analysis, patient enrollment continues for both the selected dose arm and control arm, and the significance of treatment effects will be determined at final analysis. The SDDO framework offers increased flexibility and cost-efficiency through sample size adjustment, while stringently controlling the Type I error. This proposed design also facilitates both accelerated approval (AA) and regular approval in a "one-trial" approach. Extensive simulation studies confirm that our design reliably identifies the optimal dosage and makes preferable decisions with a reduced sample size while retaining statistical power.
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Affiliation(s)
- Yuhan Li
- Department of Statistics, University of Illinois Urbana-Champaign, Champaign, Illinois, USA
| | - Yiding Zhang
- Department of Biostatistics and Programming, Sanofi US, Cambridge, Massachusetts, USA
| | - Gu Mi
- Department of Biostatistics and Programming, Sanofi US, Cambridge, Massachusetts, USA
| | - Ji Lin
- Department of Biostatistics and Programming, Sanofi US, Cambridge, Massachusetts, USA
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4
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Deng Q, Zhu L, Weiss B, Aanur P, Gao L. Strategies for successful dose optimization in oncology drug development: a practical guide. J Biopharm Stat 2024:1-15. [PMID: 39127994 DOI: 10.1080/10543406.2024.2387364] [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: 09/28/2023] [Accepted: 07/27/2024] [Indexed: 08/12/2024]
Abstract
Dose optimization is a critical challenge in drug development. Historically, dose determination in oncology has followed a divergent path from other non-oncology therapeutic areas due to the unique characteristics and requirements in Oncology. However, with the emergence of new drug modalities and mechanisms of drugs in oncology, such as immune therapies, radiopharmaceuticals, targeted therapies, cytostatic agents, and others, the dose-response relationship for efficacy and toxicity could be vastly varied compared to the cytotoxic chemotherapies. The doses below the MTD may demonstrate similar efficacy to the MTD with an improved tolerability profile, resembling what is commonly observed in non-oncology treatments. Hence, alternate strategies for dose optimization are required for new modalities in oncology drug development. This paper delves into the historical evolution of dose finding methods from non-oncology to oncology, highlighting examples and summarizing the underlying drivers of change. Subsequently, a practical framework and guidance are provided to illustrate how dose optimization can be incorporated into various stages of the development program. We provide the following general recommendations: 1) The objective for phase I is to identify a dose range rather than a single MTD dose for subsequent development to better characterize the safety and tolerability profile within the dose range. 2) At least two doses separable by PK are recommended for dose optimization in phase II. 3) Ideally, dose optimization should be performed before launching the confirmatory study. Nevertheless, innovative designs such as seamless II/III design can be implemented for dose selection and may accelerate the drug development program.
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Affiliation(s)
- Qiqi Deng
- Biostatistics and Programming, Moderna Inc., Cambridge, MA, USA
| | - Lili Zhu
- Biostatistics and Programming, Moderna Inc., Cambridge, MA, USA
| | - Brendan Weiss
- Clinical Development Oncology, Moderna Inc., Cambridge, MA, USA
| | - Praveen Aanur
- Clinical Development Oncology, Moderna Inc., Cambridge, MA, USA
| | - Lei Gao
- Biostatistics and Programming, Moderna Inc., Cambridge, MA, USA
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5
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Park Y, Chang W. A Personalized Dose-Finding Algorithm Based on Adaptive Gaussian Process Regression. Pharm Stat 2024. [PMID: 39119879 DOI: 10.1002/pst.2417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 04/22/2024] [Accepted: 06/18/2024] [Indexed: 08/10/2024]
Abstract
Dose-finding studies play a crucial role in drug development by identifying the optimal dose(s) for later studies while considering tolerability. This not only saves time and effort in proceeding with Phase III trials but also improves efficacy. In an era of precision medicine, it is not ideal to assume patient homogeneity in dose-finding studies as patients may respond differently to the drug. To address this, we propose a personalized dose-finding algorithm that assigns patients to individualized optimal biological doses. Our design follows a two-stage approach. Initially, patients are enrolled under broad eligibility criteria. Based on the Stage 1 data, we fit a regression model of toxicity and efficacy outcomes on dose and biomarkers to characterize treatment-sensitive patients. In the second stage, we restrict the trial population to sensitive patients, apply a personalized dose allocation algorithm, and choose the recommended dose at the end of the trial. Simulation study shows that the proposed design reliably enriches the trial population, minimizes the number of failures, and yields superior operating characteristics compared to several existing dose-finding designs in terms of both the percentage of correct selection and the number of patients treated at target dose(s).
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Affiliation(s)
- Yeonhee Park
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Won Chang
- Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio, USA
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6
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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.
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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
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7
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Yang CH, Cheng G, Lin R. On the relative conservativeness of Bayesian logistic regression method in oncology dose-finding studies. Pharm Stat 2024; 23:585-594. [PMID: 38317370 DOI: 10.1002/pst.2364] [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: 05/11/2023] [Revised: 10/26/2023] [Accepted: 01/08/2024] [Indexed: 02/07/2024]
Abstract
The Bayesian logistic regression method (BLRM) is a widely adopted and flexible design for finding the maximum tolerated dose in oncology phase I studies. However, the BLRM design has been criticized in the literature for being overly conservative due to the use of the overdose control rule. Recently, a discussion paper titled "Improving the performance of Bayesian logistic regression model with overall control in oncology dose-finding studies" in Statistics in Medicine has proposed an overall control rule to address the "excessive conservativeness" of the standard BLRM design. In this short communication, we discuss the relative conservativeness of the standard BLRM design and also suggest a dose-switching rule to further enhance its performance.
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Affiliation(s)
- Cheng-Han Yang
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Guanghui Cheng
- Guangzhou Institute of International Finance, Guangzhou University, Guangzhou, Guangdong, China
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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8
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Mu R, Zhan X, Tang RS, Yuan Y. A Bayesian latent-subgroup platform design for dose optimization. Biometrics 2024; 80:ujae093. [PMID: 39253988 PMCID: PMC11385043 DOI: 10.1093/biomtc/ujae093] [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: 06/20/2023] [Revised: 07/28/2024] [Accepted: 08/22/2024] [Indexed: 09/11/2024]
Abstract
The US Food and Drug Administration launched Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development, calling for the paradigm shift from finding the maximum tolerated dose to the identification of optimal biological dose (OBD). Motivated by a real-world drug development program, we propose a master-protocol-based platform trial design to simultaneously identify OBDs of a new drug, combined with standards of care or other novel agents, in multiple indications. We propose a Bayesian latent subgroup model to accommodate the treatment heterogeneity across indications, and employ Bayesian hierarchical models to borrow information within subgroups. At each interim analysis, we update the subgroup membership and dose-toxicity and -efficacy estimates, as well as the estimate of the utility for risk-benefit tradeoff, based on the observed data across treatment arms to inform the arm-specific decision of dose escalation and de-escalation and identify the OBD for each arm of a combination partner and an indication. The simulation study shows that the proposed design has desirable operating characteristics, providing a highly flexible and efficient way for dose optimization. The design has great potential to shorten the drug development timeline, save costs by reducing overlapping infrastructure, and speed up regulatory approval.
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Affiliation(s)
- Rongji Mu
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaojiang Zhan
- Global Biometrics, Servier, Boston, MA 02210, United States
| | - Rui Sammi Tang
- Global Biometrics, Servier, Boston, MA 02210, United States
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
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9
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Yuan S, Huang Z, Liu J, Ji Y. Pharmacometrics-Enabled DOse OPtimization (PEDOOP) for seamless phase I-II trials in oncology. J Biopharm Stat 2024:1-20. [PMID: 38888933 DOI: 10.1080/10543406.2024.2364716] [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: 10/01/2023] [Accepted: 05/31/2024] [Indexed: 06/20/2024]
Abstract
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.
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Affiliation(s)
- Shijie Yuan
- Department of Statistics and Data Science, The University of Texas at Austin, Austin, USA
| | - Zhanbo Huang
- School of Data Science, Fudan University, Shanghai, China
| | | | - Yuan Ji
- Department of Public Health Sciences, The University of Chicago, Chicago, USA
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10
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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.
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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
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11
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Zhang J, Lin R, Chen X, Yan F. Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses. Clin Trials 2024; 21:308-321. [PMID: 38243401 PMCID: PMC11132956 DOI: 10.1177/17407745231212193] [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: 01/21/2024]
Abstract
In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.
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Affiliation(s)
- Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xin Chen
- 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
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12
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Tian F, Lin R, Wang L, Yuan Y. A Bayesian quasi-likelihood design for identifying the minimum effective dose and maximum utility dose in dose-ranging studies. Stat Methods Med Res 2024; 33:931-944. [PMID: 38573788 PMCID: PMC11162096 DOI: 10.1177/09622802241239268] [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: 04/06/2024]
Abstract
Most existing dose-ranging study designs focus on assessing the dose-efficacy relationship and identifying the minimum effective dose. There is an increasing interest in optimizing the dose based on the benefit-risk tradeoff. We propose a Bayesian quasi-likelihood dose-ranging design that jointly considers safety and efficacy to simultaneously identify the minimum effective dose and the maximum utility dose to optimize the benefit-risk tradeoff. The binary toxicity endpoint is modeled using a beta-binomial model. The efficacy endpoint is modeled using the quasi-likelihood approach to accommodate various types of data (e.g. binary, ordinal or continuous) without imposing any parametric assumptions on the dose-response curve. Our design utilizes a utility function as a measure of benefit-risk tradeoff and adaptively assign patients to doses based on the doses' likelihood of being the minimum effective dose and maximum utility dose. The design takes a group-sequential approach. At each interim, the doses that are deemed overly toxic or futile are dropped. At the end of the trial, we use posterior probability criteria to assess the strength of the dose-response relationship for establishing the proof-of-concept. If the proof-of-concept is established, we identify the minimum effective dose and maximum utility dose. Our simulation study shows that compared with some existing designs, the Bayesian quasi-likelihood dose-ranging design is robust and yields competitive performance in establishing proof-of-concept and selecting the minimum effective dose. Moreover, it includes an additional feature for further maximum utility dose selection.
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Affiliation(s)
- Feng Tian
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Li Wang
- Department of Statistics, AbbVie Inc., North Chicago, IL, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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13
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Shi H, Lin R, Lin X. Comparative review of novel model-assisted designs for phase I/II clinical trials. Biom J 2024; 66:e2300398. [PMID: 38738318 DOI: 10.1002/bimj.202300398] [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: 12/21/2023] [Revised: 12/21/2023] [Accepted: 03/07/2024] [Indexed: 05/14/2024]
Abstract
In recent years, both model-based and model-assisted designs have emerged to efficiently determine the optimal biological dose (OBD) in phase I/II trials for immunotherapy and targeted cellular agents. Model-based designs necessitate repeated model fitting and computationally intensive posterior sampling for each dose-assignment decision, limiting their practical application in real trials. On the other hand, model-assisted designs employ simple statistical models and facilitate the precalculation of a decision table for use throughout the trial, eliminating the need for repeated model fitting. Due to their simplicity and transparency, model-assisted designs are often preferred in phase I/II trials. In this paper, we systematically evaluate and compare the operating characteristics of several recent model-assisted phase I/II designs, including TEPI, PRINTE, Joint i3+3, BOIN-ET, STEIN, uTPI, and BOIN12, in addition to the well-known model-based EffTox design, using comprehensive numerical simulations. To ensure an unbiased comparison, we generated 10,000 dosing scenarios using a random scenario generation algorithm for each predetermined OBD location. We thoroughly assess various performance metrics, such as the selection percentages, average patient allocation to OBD, and overdose percentages across the eight designs. Based on these assessments, we offer design recommendations tailored to different objectives, sample sizes, and starting dose locations.
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Affiliation(s)
- Haolun Shi
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Xiaolei Lin
- School of Data Science, Fudan University, Shanghai, China
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14
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Yamaguchi Y, Takeda K, Yoshida S, Maruo K. Optimal biological dose selection in dose-finding trials with model-assisted designs based on efficacy and toxicity: a simulation study. J Biopharm Stat 2024; 34:379-393. [PMID: 37114985 DOI: 10.1080/10543406.2023.2202259] [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: 11/18/2022] [Accepted: 04/06/2023] [Indexed: 04/29/2023]
Abstract
With the emergence of molecular targeted agents and immunotherapies in anti-cancer treatment, a concept of optimal biological dose (OBD), accounting for efficacy and toxicity in the framework of dose-finding, has been widely introduced into phase I oncology clinical trials. Various model-assisted designs with dose-escalation rules based jointly on toxicity and efficacy are now available to establish the OBD, where the OBD is generally selected at the end of the trial using all toxicity and efficacy data obtained from the entire cohort. Several measures to select the OBD and multiple methods to estimate the efficacy probability have been developed for the OBD selection, leading to many options in practice; however, their comparative performance is still uncertain, and practitioners need to take special care of which approaches would be the best for their applications. Therefore, we conducted a comprehensive simulation study to demonstrate the operating characteristics of the OBD selection approaches. The simulation study revealed key features of utility functions measuring the toxicity-efficacy trade-off and suggested that the measure used to select the OBD could vary depending on the choice of the dose-escalation procedure. Modelling the efficacy probability might lead to limited gains in OBD selection.
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Affiliation(s)
- Yusuke Yamaguchi
- Astellas Pharma Global Development, Inc, Northbrook, Illinois, USA
| | - Kentaro Takeda
- Astellas Pharma Global Development, Inc, Northbrook, Illinois, USA
| | | | - Kazushi Maruo
- Department of Biostatistics, University of Tsukuba, Tsukuba, Japan
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15
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Viraswami-Appanna K, Buenconsejo J, Baidoo C, Chan I, Li D, Micsinai-Balan M, Tiwari R, Yang L, Sethuraman V. Accelerating drug development at Bristol Myers Squibb through innovation. Drug Discov Today 2024; 29:103952. [PMID: 38508230 DOI: 10.1016/j.drudis.2024.103952] [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: 12/30/2023] [Revised: 03/07/2024] [Accepted: 03/14/2024] [Indexed: 03/22/2024]
Abstract
This paper focuses on the use of novel technologies and innovative trial designs to accelerate evidence generation and increase pharmaceutical Research and Development (R&D) productivity, at Bristol Myers Squibb. We summarize learnings with case examples, on how we prepared and continuously evolved to address the increasing cost, complexities, and external pressures in drug development, to bring innovative medicines to patients much faster. These learnings were based on review of internal efforts toward accelerating R&D focusing on four key areas: adopting innovative trial designs, optimizing trial designs, leveraging external control data, and implementing novel methods using artificial intelligence and machine learning.
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Affiliation(s)
| | - Joan Buenconsejo
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Charlotte Baidoo
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Ivan Chan
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Daniel Li
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | | | - Ram Tiwari
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Ling Yang
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Venkat Sethuraman
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
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16
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Kakizume T, Takeda K, Taguri M, Morita S. BOIN-ETC: A Bayesian optimal interval design considering efficacy and toxicity to identify the optimal dose combinations. Stat Methods Med Res 2024; 33:716-727. [PMID: 38444354 DOI: 10.1177/09622802241236936] [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] [Indexed: 03/07/2024]
Abstract
One of the primary objectives of a dose-finding trial for novel anti-cancer agent combination therapies, such as molecular targeted agents and immune-oncology therapies, is to identify optimal dose combinations that are tolerable and therapeutically beneficial for subjects in subsequent clinical trials. The goal differs from that of a dose-finding trial for traditional cytotoxic agents, in which the goal is to determine the maximum tolerated dose combinations. This paper proposes the new design, named 'BOIN-ETC' design, to identify optimal dose combinations based on both efficacy and toxicity outcomes using the waterfall approach. The BOIN-ETC design is model-assisted, so it is expected to be robust, and straightforward to implement in actual oncology dose-finding trials. These characteristics are quite valuable from a practical perspective. Simulation studies show that the BOIN-ETC design has advantages compared with the other approaches in the percentage of correct optimal dose combination selection and the average number of patients allocated to the optimal dose combinations across various realistic settings.
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17
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Zimmerman DH, Szekanecz Z, Markovics A, Rosenthal KS, Carambula RE, Mikecz K. Current status of immunological therapies for rheumatoid arthritis with a focus on antigen-specific therapeutic vaccines. Front Immunol 2024; 15:1334281. [PMID: 38510240 PMCID: PMC10951376 DOI: 10.3389/fimmu.2024.1334281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 02/08/2024] [Indexed: 03/22/2024] Open
Abstract
Rheumatoid arthritis (RA) is recognized as an autoimmune joint disease driven by T cell responses to self (or modified self or microbial mimic) antigens that trigger and aggravate the inflammatory condition. Newer treatments of RA employ monoclonal antibodies or recombinant receptors against cytokines or immune cell receptors as well as small-molecule Janus kinase (JAK) inhibitors to systemically ablate the cytokine or cellular responses that fuel inflammation. Unlike these treatments, a therapeutic vaccine, such as CEL-4000, helps balance adaptive immune homeostasis by promoting antigen-specific regulatory rather than inflammatory responses, and hence modulates the immunopathological course of RA. In this review, we discuss the current and proposed therapeutic products for RA, with an emphasis on antigen-specific therapeutic vaccine approaches to the treatment of the disease. As an example, we describe published results of the beneficial effects of CEL-4000 vaccine on animal models of RA. We also make a recommendation for the design of appropriate clinical studies for these newest therapeutic approaches, using the CEL-4000 vaccine as an example. Unlike vaccines that create or boost a new immune response, the clinical success of an immunomodulatory therapeutic vaccine for RA lies in its ability to redirect autoreactive pro-inflammatory memory T cells towards rebalancing the "runaway" immune/inflammatory responses that characterize the disease. Human trials of such a therapy will require alternative approaches in clinical trial design and implementation for determining safety, toxicity, and efficacy. These approaches include adaptive design (such as the Bayesian optimal design (BOIN), currently employed in oncological clinical studies), and the use of disease-related biomarkers as indicators of treatment success.
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Affiliation(s)
| | - Zoltan Szekanecz
- Department of Rheumatology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Adrienn Markovics
- Department of Orthopedic Surgery and Department of Internal Medicine, Division of Rheumatology, Rush University Medical Center, Chicago, IL, United States
| | - Kenneth S Rosenthal
- Department of Basic Sciences, Augusta University/University of Georgia Medical Partnership, Athens, GA, United States
| | | | - Katalin Mikecz
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, United States
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18
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Pourmir I, Van Halteren HK, Elaidi R, Trapani D, Strasser F, Vreugdenhil G, Clarke M. A conceptual framework for cautious escalation of anticancer treatment: How to optimize overall benefit and obviate the need for de-escalation trials. Cancer Treat Rev 2024; 124:102693. [PMID: 38330752 DOI: 10.1016/j.ctrv.2024.102693] [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: 05/29/2023] [Revised: 01/24/2024] [Accepted: 01/29/2024] [Indexed: 02/10/2024]
Abstract
BACKGROUND The developmental workflow of the currently performed phase 1, 2 and 3 cancer trial stages lacks essential information required for the determination of the optimal efficacy threshold of new anticancer regimens. Due to this there is a serious risk of overdosing and/or treating for an unnecessary long time, leading to excess toxicity and a higher financial burden for society. But often post-approval de-escalation trials for dose-optimization and treatment de-intensification are not performed due to failing resources and time. Therefore, the developmental workflow needs to be restructured toward cautious systemic cancer treatment escalation, in order to guarantee optimal efficacy and sustainability. METHODS In this manuscript we discuss opportunities to produce the information needed for cautious escalation, based on models of cancer growth and cancer kill kinetics as well as exploratory biomarkers, for the purpose of designing the optimal phase 3 superiority trial. Subsequently, we compare the sample size needed for a phase 3 superiority trial, followed by a necessary de-escalation trial with the sample size needed for a multi-arm phase 3 trial with intervention arms of differing intensity. All essential items are structured within a Framework for Cautious Escalation (FCE). The discussion uses illustrations from the breast cancer setting, but aims to be applicable for all cancers. RESULTS The FCE is a promising model of clinical development in oncology to prevent overtreatment and associated issues, especially with regard to the number of repetitive treatment cycles. It will hopefully increase the relevance and success rate of clinical trials, to deliver improved patient-centric outcomes.
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Affiliation(s)
- I Pourmir
- Department of Thoracic Oncology, European Hospital Georges Pompidou, Paris, France; INSERM U970, Paris Research Cardiovascular Center, Paris, France
| | - H K Van Halteren
- Department of Medical Oncology, Adrz Hospital, Goes, the Netherlands.
| | - R Elaidi
- Consultant/advisor in Clinical Trials Methodology and Biostatistic, Paris, France
| | - D Trapani
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, Milan, Italy; Department of Oncology and Haematology, University of Milan, Milan, Italy
| | - F Strasser
- Center for Integrative Medicine, Cantonal Hospital Gallen, St. Gallen University of Bern, Switzerland
| | - G Vreugdenhil
- Department of Medical Oncology, Maxima Medical Center, Veldhoven, the Netherlands
| | - M Clarke
- Professor and Director of Northern Ireland Methodology Hub, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
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19
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Zang Y, Thall PF, Yuan Y. A generalized phase 1-2-3 design integrating dose optimization with confirmatory treatment comparison. Biometrics 2024; 80:ujad022. [PMID: 38364811 PMCID: PMC10873567 DOI: 10.1093/biomtc/ujad022] [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: 05/09/2023] [Revised: 10/10/2023] [Accepted: 12/06/2023] [Indexed: 02/18/2024]
Abstract
A generalized phase 1-2-3 design, Gen 1-2-3, that includes all phases of clinical treatment evaluation is proposed. The design extends and modifies the design of Chapple and Thall (2019), denoted by CT. Both designs begin with a phase 1-2 trial including dose acceptability and optimality criteria, and both select an optimal dose for phase 3. The Gen 1-2-3 design has the following key differences. In stage 1, it uses phase 1-2 criteria to identify a set of candidate doses rather than 1 dose. In stage 2, which is intermediate between phase 1-2 and phase 3, it randomizes additional patients fairly among the candidate doses and an active control treatment arm and uses survival time data from both stage 1 and stage 2 patients to select an optimal dose. It then makes a Go/No Go decision of whether or not to conduct phase 3 based on the predictive probability that the selected optimal dose will provide a specified substantive improvement in survival time over the control. A simulation study shows that the Gen 1-2-3 design has desirable operating characteristics compared to the CT design and 2 conventional designs.
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Affiliation(s)
- Yong Zang
- Department of Biostatistics and Health Data Science; Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN 46202, United States
| | - Peter F Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
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20
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Tu J, Chen Z. Bayesian dose escalation with overdose and underdose control utilizing all toxicities in Phase I/II clinical trials. Biom J 2024; 66:e2200189. [PMID: 38047521 DOI: 10.1002/bimj.202200189] [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: 06/30/2022] [Revised: 07/06/2023] [Accepted: 07/23/2023] [Indexed: 12/05/2023]
Abstract
Escalation with overdose control (EWOC) is a commonly used Bayesian adaptive design, which controls overdosing risk while estimating maximum tolerated dose (MTD) in cancer Phase I clinical trials. In 2010, Chen and his colleagues proposed a novel toxicity scoring system to fully utilize patients' toxicity information by using a normalized equivalent toxicity score (NETS) in the range 0 to 1 instead of a binary indicator of dose limiting toxicity (DLT). Later in 2015, by adding underdosing control into EWOC, escalation with overdose and underdose control (EWOUC) design was proposed to guarantee patients the minimum therapeutic effect of drug in Phase I/II clinical trials. In this paper, the EWOUC-NETS design is developed by integrating the advantages of EWOUC and NETS in a Bayesian context. Moreover, both toxicity response and efficacy are treated as continuous variables to maximize trial efficiency. The dose escalation decision is based on the posterior distribution of both toxicity and efficacy outcomes, which are recursively updated with accumulated data. We compare the operation characteristics of EWOUC-NETS and existing methods through simulation studies under five scenarios. The study results show that EWOUC-NETS design treating toxicity and efficacy outcomes as continuous variables can increase accuracy in identifying the optimized utility dose (OUD) and provide better therapeutic effects.
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Affiliation(s)
- Jieqi Tu
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, USA
- Biostatistics Shared Resource, University of Illinois Cancer Center, Chicago, Illinois, USA
| | - Zhengjia Chen
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, USA
- Biostatistics Shared Resource, University of Illinois Cancer Center, Chicago, Illinois, USA
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21
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Halabi S, Chiuzan C. Randomized Phase I Trials - One Size Fits All? NEJM EVIDENCE 2023; 2:EVIDe2300282. [PMID: 38320508 DOI: 10.1056/evide2300282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
In this issue of NEJM Evidence, Gaudet et al. present the safety profile and pharmacodynamics of ARO-APOC3, a small interfering RNA therapeutic that inhibits apolipoprotein C-III (APOC3) mRNA expression in a phase I trial.1 Assignment to treatment was based on fasting levels of triglycerides. The trial included two double-blinded cohorts with 52 randomly assigned healthy participants and 40 patients with hypertriglyceridemia assigned to escalating doses of ARO-APOC3 at 10, 25, 50, or 100 mg or placebo in a single- and/or repeat-dose (days 1 and 29) regimen. An open-label cohort of patients with chylomicronemia was treated with ARO-APOC3 at 50 mg.
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Affiliation(s)
- Susan Halabi
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC
| | - Codruta Chiuzan
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, NY
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22
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Kojima M. Application of multi-armed bandits to dose-finding clinical designs. Artif Intell Med 2023; 146:102713. [PMID: 38042600 DOI: 10.1016/j.artmed.2023.102713] [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: 06/07/2022] [Revised: 10/29/2023] [Accepted: 10/30/2023] [Indexed: 12/04/2023]
Abstract
Multi-armed bandits are very simple and powerful methods to determine actions to maximize a reward in a limited number of trials. An early phase in dose-finding clinical trials needs to identify the maximum tolerated dose among multiple doses by repeating the dose-assignment. We consider applying the superior selection performance of multi-armed bandits to dose-finding clinical designs. Among the multi-armed bandits, we first consider the use of Thompson sampling which determines actions based on random samples from a posterior distribution. In the small sample size, as shown in dose-finding trials, because the tails of posterior distribution are heavier and random samples are too much variability, we also consider an application of regularized Thompson sampling and greedy algorithm. The greedy algorithm determines a dose based on a posterior mean. In addition, we also propose a method to determine a dose based on a posterior mode. We evaluate the performance of our proposed designs for nine scenarios via simulation studies.
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Affiliation(s)
- Masahiro Kojima
- Kyowa Kirin Co., Ltd, Japan; The Institute of Statistical Mathematics, Japan.
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23
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Takeda K, Zhu J, Li R, Yamaguchi Y. A Bayesian optimal interval design for dose optimization with a randomization scheme based on pharmacokinetics outcomes in oncology. Pharm Stat 2023; 22:1104-1115. [PMID: 37545018 DOI: 10.1002/pst.2332] [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: 02/04/2023] [Revised: 05/15/2023] [Accepted: 07/26/2023] [Indexed: 08/08/2023]
Abstract
The primary objective of an oncology dose-finding trial for novel therapies, such as molecularly targeted agents and immune-oncology therapies, is to identify the optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. Pharmacokinetic (PK) information is considered an appropriate indicator for evaluating the level of drug intervention in humans from a pharmacological perspective. Several novel anticancer agents have been shown to have significant exposure-efficacy relationships, and some PK information has been considered an important predictor of efficacy. This paper proposes a Bayesian optimal interval design for dose optimization with a randomization scheme based on PK outcomes in oncology. A simulation study shows that the proposed design has advantages compared to the other designs in the percentage of correct OD selection and the average number of patients allocated to OD in various realistic settings.
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Affiliation(s)
- Kentaro Takeda
- Data Science, Astellas Pharma Global Development, Inc., Northbrook, Illinois, USA
| | - Jing Zhu
- Data Science, Astellas Pharma China, Beijing, China
| | - Ran Li
- Data Science, Astellas Pharma Global Development, Inc., Northbrook, Illinois, USA
| | - Yusuke Yamaguchi
- Data Science, Astellas Pharma Global Development, Inc., Northbrook, Illinois, USA
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24
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Takeda K, Yamaguchi Y, Taguri M, Morita S. TITE-gBOIN-ET: Time-to-event generalized Bayesian optimal interval design to accelerate dose-finding accounting for ordinal graded efficacy and toxicity outcomes. Biom J 2023; 65:e2200265. [PMID: 37309248 DOI: 10.1002/bimj.202200265] [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: 09/23/2022] [Revised: 03/17/2023] [Accepted: 05/08/2023] [Indexed: 06/14/2023]
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, for efficacy, evaluating the overall response and long-term stable disease in solid tumors and considering the difference between complete remission and partial remission in lymphoma are preferable. It is also essential to accelerate early-stage trials to shorten the entire period of drug development. However, it is often challenging to make real-time adaptive decisions due to late-onset outcomes, fast accrual rates, and differences in outcome evaluation periods for efficacy and toxicity. To solve the issues, we propose a time-to-event generalized Bayesian optimal interval design to accelerate dose finding, accounting for efficacy and toxicity grades. The new design named "TITE-gBOIN-ET" design is model-assisted and straightforward to implement in actual oncology dose-finding trials. Simulation studies show that the TITE-gBOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment while having comparable or higher performance in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.
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Affiliation(s)
- Kentaro Takeda
- Data Science, Astellas Pharma Global Development, Inc., Northbrook, Illinois, USA
| | - Yusuke Yamaguchi
- Data Science, Astellas Pharma Global Development, Inc., Northbrook, Illinois, USA
| | - Masataka Taguri
- Department of Health Data Science, Tokyo Medical University, Tokyo, Japan
| | - Satoshi Morita
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
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25
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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.
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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
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26
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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.
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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
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27
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Li R, Takeda K, Rong A. Comparison Between Simultaneous and Sequential Utilization of Safety and Efficacy for Optimal Dose Determination in Bayesian Model-Assisted Designs. Ther Innov Regul Sci 2023; 57:728-736. [PMID: 37087525 DOI: 10.1007/s43441-023-00517-1] [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: 11/21/2022] [Accepted: 03/21/2023] [Indexed: 04/24/2023]
Abstract
It has become quite common in recent early oncology trials to include both the dose-finding and the dose-expansion parts within the same study. This shift can be viewed as a seamless way of conducting the trials to obtain information on safety and efficacy hence identifying an optimal dose (OD) rather than just the maximum tolerated dose (MTD). One approach is to conduct a dose-finding part based solely on toxicity outcomes, followed by a dose expansion part to evaluate efficacy outcomes. Another approach employs only the dose-finding part, where the dose-finding decisions are made utilizing both the efficacy and toxicity outcomes of those enrolled patients. In this paper, we compared the two approaches through simulation studies under various realistic settings. The percentage of correct ODs selection, the average number of patients allocated to the ODs, and the average trial duration are reported in choosing the appropriate designs for their early-stage dose-finding trials, including expansion cohorts.
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Affiliation(s)
- Ran Li
- Biostatistics, Data Science, Astellas Pharma Inc, 1 Astellas Way, N3.272.A, Northbrook, IL, 60062, USA.
| | - Kentaro Takeda
- Biostatistics, Data Science, Astellas Pharma Inc, 1 Astellas Way, N3.272.A, Northbrook, IL, 60062, USA
| | - Alan Rong
- Biostatistics, Data Science, Astellas Pharma Inc, 1 Astellas Way, N3.272.A, Northbrook, IL, 60062, USA
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28
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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.
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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
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29
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Kojima M. Adaptive Cohort Size Determination Method for Bayesian Optimal Interval Phase I/II Design to Shorten Clinical Trial Duration. JCO Precis Oncol 2023; 7:e2300087. [PMID: 37487148 DOI: 10.1200/po.23.00087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 06/10/2023] [Accepted: 06/20/2023] [Indexed: 07/26/2023] Open
Abstract
PURPOSE Recently, the strategy for dose optimization in oncology has shifted toward conducting phase II randomized controlled trials with multiple doses. Optimal biologic dose (OBD) selection from phase I trial data to determine candidate doses for phase II trials has been gaining attention. Trials to identify the OBD have a fixed cohort size, which increases the trial duration. We propose a method to increase the cohort size using trial data and shorten the trial duration while maintaining accuracy. METHODS We propose a novel adaptive cohort size determination method in which the increase of cohort size is determined using desirability probability on the basis of toxicity and efficacy data. The desirability probability is a measure of how desirable a dose is and thus how close it is to the OBD. However, during the trial, the desirability probability does not need to be calculated. Instead, the cohort size expansion can be determined by a simple table generated in advance from toxicity and efficacy data. An illustrated example is provided and the performance was evaluated in a simulation study with 16 scenarios. RESULTS In the simulation study, the trial duration was reduced by an average of 20% compared with the conventional design. The percentages of correct OBD selection are almost the same as those with the conventional design. CONCLUSION The proposed adaptive cohort size determination method described in this study reduces trial duration while maintaining accuracy.
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Affiliation(s)
- Masahiro Kojima
- Biometrics Department, R&D Division, Kyowa Kirin Co, Ltd, Tokyo, Japan
- Research Center for Medical and Health Data Science, The Institute of Statistical Mathematics, Tokyo, Japan
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30
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Vanderbeek AM, Redd RA, Ventz S, Trippa L. Looking ahead in early-phase trial design to improve the drug development process: examples in oncology. BMC Med Res Methodol 2023; 23:151. [PMID: 37386450 PMCID: PMC10308797 DOI: 10.1186/s12874-023-01979-5] [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: 12/05/2022] [Accepted: 06/16/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND Clinical trial design must consider the specific resource constraints and overall goals of the drug development process (DDP); for example, in designing a phase I trial to evaluate the safety of a drug and recommend a dose for a subsequent phase II trial. Here, we focus on design considerations that involve the sequence of clinical trials, from early phase I to late phase III, that constitute the DDP. METHODS We discuss how stylized simulation models of clinical trials in an oncology DDP can quantify important relationships between early-phase trial designs and their consequences for the remaining phases of development. Simulations for three illustrative settings are presented, using stylized models of the DDP that mimic trial designs and decisions, such as the potential discontinuation of the DDP. RESULTS We describe: (1) the relationship between a phase II single-arm trial sample size and the likelihood of a positive result in a subsequent phase III confirmatory trial; (2) the impact of a phase I dose-finding design on the likelihood that the DDP will produce evidence of a safe and effective therapy; and (3) the impact of a phase II enrichment trial design on the operating characteristics of a subsequent phase III confirmatory trial. CONCLUSIONS Stylized models of the DDP can support key decisions, such as the sample size, in the design of early-phase trials. Simulation models can be used to estimate performance metrics of the DDP under realistic scenarios; for example, the duration and the total number of patients enrolled. These estimates complement the evaluation of the operating characteristics of early-phase trial design, such as power or accuracy in selecting safe and effective dose levels.
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Affiliation(s)
- Alyssa M Vanderbeek
- Department of Data Science, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02115, USA
- Unlearn.AI, San Francisco, CA, USA
| | - Robert A Redd
- Department of Data Science, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02115, USA
| | - Steffen Ventz
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Lorenzo Trippa
- Department of Data Science, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02115, USA.
- Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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31
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Zhou Y, Zhao Y, Cicconetti G, Mu Y, Yuan Y, Wang L, Penugonda S, Salman Z. AIDE: Adaptive intrapatient dose escalation designs to accelerate Phase I clinical trials. Pharm Stat 2023; 22:300-311. [PMID: 36333972 DOI: 10.1002/pst.2272] [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: 12/14/2021] [Revised: 09/30/2022] [Accepted: 10/14/2022] [Indexed: 11/08/2022]
Abstract
Designing Phase I clinical trials is challenging when accrual is slow or sample size is limited. The corresponding key question is: how to efficiently and reliably identify the maximum tolerated dose (MTD) using a sample size as small as possible? We propose model-assisted and model-based designs with adaptive intrapatient dose escalation (AIDE) to address this challenge. AIDE is adaptive in that the decision of conducting intrapatient dose escalation depends on both the patient's individual safety data, as well as other enrolled patient's safety data. When both data indicate reasonable safety, a patient may perform intrapatient dose escalation, generating toxicity data at more than one dose. This strategy not only provides patients the opportunity to receive higher potentially more effective doses, but also enables efficient statistical learning of the dose-toxicity profile of the treatment, which dramatically reduces the required sample size. Simulation studies show that the proposed designs are safe, robust, and efficient to identify the MTD with a sample size that is substantially smaller than conventional interpatient dose escalation designs. Practical considerations are provided and R code for implementing AIDE is available upon request.
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Affiliation(s)
- Yanhong Zhou
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Illinois, USA
| | - Yujie Zhao
- AbbVie Inc., North Chicago, Illinois, USA
| | | | - Yunming Mu
- AbbVie Inc., North Chicago, Illinois, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Li Wang
- AbbVie Inc., North Chicago, Illinois, USA
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32
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Chen X, Zhang J, Jiang L, Yan F. Shotgun-2: A Bayesian phase I/II basket trial design to identify indication-specific optimal biological doses. Stat Methods Med Res 2023; 32:443-464. [PMID: 36217826 DOI: 10.1177/09622802221129049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
For novel molecularly targeted agents and immunotherapies, the objective of dose-finding is often to identify the optimal biological dose, rather than the maximum tolerated dose. However, optimal biological doses may not be the same for different indications, challenging the traditional dose-finding framework. Therefore, we proposed a Bayesian phase I/II basket trial design, named "shotgun-2," to identify indication-specific optimal biological doses. A dose-escalation part is conducted in stage I to identify the maximum tolerated dose and admissible dose sets. In stage II, dose optimization is performed incorporating both toxicity and efficacy for each indication. Simulation studies under both fixed and random scenarios show that, compared with the traditional "phase I + cohort expansion" design, the shotgun-2 design is robust and can improve the probability of correctly selecting the optimal biological doses. Furthermore, this study provides a useful tool for identifying indication-specific optimal biological doses and accelerating drug development.
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Affiliation(s)
- Xin Chen
- Research Center of Biostatistics and Computational Pharmacy, 56651China Pharmaceutical University, Nanjing, China
| | - Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, 56651China Pharmaceutical University, Nanjing, China
| | - Liyun Jiang
- Research Center of Biostatistics and Computational Pharmacy, 56651China Pharmaceutical University, Nanjing, China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, 56651China Pharmaceutical University, Nanjing, China
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33
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Kojima M. Data-dependent early completion of dose-finding trials for drug-combination. Stat Methods Med Res 2023; 32:820-828. [PMID: 36775992 DOI: 10.1177/09622802231155094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
PURPOSE Model-assisted designs for drug combination trials have been proposed as novel designs with simple and superior performance. However, model-assisted designs have the disadvantage that the sample size must be set in advance, and trials cannot be completed until the number of patients treated reaches the pre-set sample size. Model-assisted designs have a stopping rule that can be used to terminate the trial if the number of patients treated exceeds the predetermined number, there is no statistical basis for the predetermined number. Here, I propose two methods for data-dependent early completion of dose-finding trials for drug combination: (1) an early completion method based on dose retainment probability, and (2) an early completion method in which the dose retainment probability is adjusted by a bivariate isotonic regression. METHODS Early completion is determined when the dose retainment probability using both trial data and the number of remaining patients is high. Early completion of a virtual trial was demonstrated. The performances of the early completion methods were evaluated by simulation studies with 12 scenarios. RESULTS The simulation studies showed that the percentage of early completion was an average of approximately 70%, and the number of patients treated was 25% less than the planned sample size. The percentage of correct maximum tolerated dose combination selection for the early completion methods was similar to that of non-early completion methods with an average difference of approximately 3%. CONCLUSION The performance of the proposed early completion methods was similar to that of the non-early completion methods. Furthermore, the number of patients for determining early completion before the trial starts was determined and a program code for calculating the dose retainment probability was proposed.
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Affiliation(s)
- Masahiro Kojima
- Biometrics Department, R&D Division, 13486Kyowa Kirin Co., Ltd, Tokyo, Japan.,Research Center for Medical and Health Data Science, The Institute of Statistical Mathematics, Tokyo, Japan
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34
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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.
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35
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Phillips A, Mondal S. Improving early phase oncology clinical trial design: The case for finding the optimal biological dose. Pharm Stat 2023. [PMID: 36669771 DOI: 10.1002/pst.2291] [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: 09/19/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 01/22/2023]
Abstract
Historically early phase oncology drug development programmes have been based on the belief that "more is better". Furthermore, rule-based study designs such as the "3 + 3" design are still often used to identify the MTD. Phillips and Clark argue that newer Bayesian model-assisted designs such as the BOIN design should become the go to designs for statisticians for MTD finding. This short communication goes one stage further and argues that Bayesian model-assisted designs such as the BOIN12 which balances risk-benefit should be included as one of the go to designs for early phase oncology trials, depending on the study objectives. Identifying the optimal biological dose for future research for many modern targeted drugs, immunotherapies, cell therapies and vaccine therapies can save significant time and resources.
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Affiliation(s)
- Alan Phillips
- Centre for Pharmaceutical Medicine Research, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Shalini Mondal
- Centre for Pharmaceutical Medicine Research, Faculty of Life Sciences and Medicine, King's College London, London, UK
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36
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Marchenko O, Sridhara R, Jiang Q, Barksdale E, Ando Y, Alwis DD, Brown K, Fernandes L, van Bussel MT, Choo Q, Coory M, Garrett-Mayer E, Gwise T, Hess L, Liu R, Mandrekar S, Ouellet D, Pinheiro J, Posch M, Rahman NA, Rantell KR, Raven A, Sarem S, Sen S, Shah M, Shen YL, Simon R, Theoret M, Yuan Y, Pazdur R. Designing Dose-Optimization Studies in Cancer Drug Development: Discussions with Regulators. Stat Biopharm Res 2023. [DOI: 10.1080/19466315.2023.2166099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Thomas Gwise
- Office of Biostatistics, CDER US FDA, Silver Spring, MD
| | | | - Rong Liu
- Bristol Myers Squibb, Berkeley Heights, NJ
| | | | | | | | - Martin Posch
- Institute for Medical Statistics at the Medical University of Vienna, Vienna, Austria
| | | | | | | | | | | | - Mirat Shah
- Office of Oncologic Diseases, CDER, US FDA, Silver Spring, MD
| | - Yuan Li Shen
- Office of Biostatistics, CDER US FDA, Silver Spring, MD
| | | | - Marc Theoret
- Oncology Center of Excellence, US FDA, Silver Spring, MD
| | - Ying Yuan
- MD Anderson Cancer Center, Houston, TX
| | - Richard Pazdur
- Oncology Center of Excellence, US FDA, Silver Spring, MD
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37
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Andrillon A, Chevret S, Lee SM, Biard L. Surv-CRM-12: A Bayesian phase I/II survival CRM for right-censored toxicity endpoints with competing disease progression. Stat Med 2022; 41:5753-5766. [PMID: 36259523 PMCID: PMC9691552 DOI: 10.1002/sim.9591] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 09/15/2022] [Accepted: 09/23/2022] [Indexed: 01/12/2023]
Abstract
The growing interest in new classes of anti-cancer agents, such as molecularly-targeted therapies and immunotherapies with modes of action different from those of cytotoxic chemotherapies, has changed the dose-finding paradigm. In this setting, the observation of late-onset toxicity endpoints may be precluded by treatment and trial discontinuation due to disease progression, defining a competing event to toxicity. Trial designs where dose-finding is modeled in the framework of a survival competing risks model appear particularly well-suited. We aim to provide a phase I/II dose-finding design that allows dose-limiting toxicity (DLT) outcomes to be delayed or unobserved due to competing progression within the possibly long observation window. The proposed design named the Survival-continual reassessment method-12, uses survival models for right-censored DLT and progression endpoints. In this competing risks framework, cause-specific hazards for DLT and progression-free of DLT were considered, with model parameters estimated using Bayesian inference. It aims to identify the optimal dose (OD), by minimizing the cumulative incidence of disease progression, given an acceptable toxicity threshold. In a simulation study, design operating characteristics were evaluated and compared to the TITE-BOIN-ET design and a nonparametric benchmark approach. The performance of the proposed method was consistent with the complexity of scenarios as assessed by the nonparametric benchmark. We found that the proposed design presents satisfying operating characteristics in selecting the OD and safety.
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Affiliation(s)
- Anaïs Andrillon
- ECSTRRA Team, UMR‐1153Université de Paris, INSERM, AP‐HP, Hôpital Saint LouisParisFrance,Department of BiostatisticsMailman School of Public Health, Columbia UniversityNew YorkNew YorkUSA
| | - Sylvie Chevret
- ECSTRRA Team, UMR‐1153Université de Paris, INSERM, AP‐HP, Hôpital Saint LouisParisFrance
| | - Shing M. Lee
- Department of BiostatisticsMailman School of Public Health, Columbia UniversityNew YorkNew YorkUSA
| | - Lucie Biard
- ECSTRRA Team, UMR‐1153Université de Paris, INSERM, AP‐HP, Hôpital Saint LouisParisFrance
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38
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Zhao Y, Liu R, Takeda K. Incorporating historical information to improve dose optimization design with toxicity and efficacy endpoints: iBOIN-ET. Pharm Stat 2022; 22:440-460. [PMID: 36514849 DOI: 10.1002/pst.2281] [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: 05/04/2022] [Revised: 10/13/2022] [Accepted: 11/28/2022] [Indexed: 12/15/2022]
Abstract
In modern oncology drug development, adaptive designs have been proposed to identify the recommended phase 2 dose. The conventional dose finding designs focus on the identification of maximum tolerated dose (MTD). However, designs ignoring efficacy could put patients under risk by pushing to the MTD. Especially in immuno-oncology and cell therapy, the complex dose-toxicity and dose-efficacy relationships make such MTD driven designs more questionable. Additionally, it is not uncommon to have data available from other studies that target on similar mechanism of action and patient population. Due to the high variability from phase I trial, it is beneficial to borrow historical study information into the design when available. This will help to increase the model efficiency and accuracy and provide dose specific recommendation rules to avoid toxic dose level and increase the chance of patient allocation at potential efficacious dose levels. In this paper, we propose iBOIN-ET design that uses prior distribution extracted from historical studies to minimize the probability of decision error. The proposed design utilizes the concept of skeleton from both toxicity and efficacy data, coupled with prior effective sample size to control the amount of historical information to be incorporated. Extensive simulation studies across a variety of realistic settings are reported including a comparison of iBOIN-ET design to other model based and assisted approaches. The proposed novel design demonstrates the superior performances in percentage of selecting the correct optimal dose (OD), average number of patients allocated to the correct OD, and overdosing control during dose escalation process.
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Affiliation(s)
- Yunqi Zhao
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Rachael Liu
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Kentaro Takeda
- Data Science, Astellas Pharma Global Development, Inc., Northbrook, Illinois, USA
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39
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Li C, Sun H, Cheng C, Tang L, Pan H. A software tool for both the maximum tolerated dose and the optimal biological dose finding trials in early phase designs. Contemp Clin Trials Commun 2022; 30:100990. [PMID: 36203850 PMCID: PMC9529556 DOI: 10.1016/j.conctc.2022.100990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 08/14/2022] [Accepted: 08/29/2022] [Indexed: 10/25/2022] Open
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40
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Yuan Y, Zhao Y. Commentary on “Improving the performance of Bayesian logistic regression model with overdose control in oncology dose‐finding studies”. Stat Med 2022; 41:5484-5490. [DOI: 10.1002/sim.9496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 05/26/2022] [Indexed: 11/18/2022]
Affiliation(s)
- Ying Yuan
- Department of Biostatistics The University of Texas MD Anderson Cancer Center Houston Texas USA
| | - Yixuan Zhao
- Department of Biostatistics and Data Science, School of Public Health The University of Texas Health Science Center at Houston Houston Texas USA
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41
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Zhang H, Chiang AY, Wang J. Rejoinder: Improving the performance of Bayesian logistic regression model with overdose control in oncology dose‐finding studies. Stat Med 2022; 41:5497-5500. [DOI: 10.1002/sim.9561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 08/11/2022] [Indexed: 11/18/2022]
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42
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An overview of the BOIN design and its current extensions for novel early-phase oncology trials. Contemp Clin Trials Commun 2022; 28:100943. [PMID: 35812822 PMCID: PMC9260438 DOI: 10.1016/j.conctc.2022.100943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 05/02/2022] [Accepted: 06/07/2022] [Indexed: 11/24/2022] Open
Abstract
Bayesian Optimal Interval (BOIN) designs are a class of model-assisted dose-finding designs that can be used in oncology trials to determine the maximum tolerated dose (MTD) of a study drug based on safety or the optimal biological dose (OBD) based on safety and efficacy. BOIN designs provide a complete suite for dose finding in early phase trials, as well as a consistent way to explore different scenarios such as toxicity, efficacy, continuous outcomes, delayed toxicity or efficacy and drug combinations in a unified manner with easy access to software to implement most of these designs. Although built upon Bayesian probability models, BOIN designs are operationally simple in general and have good statistical operating characteristics compared to other dose-finding designs. This review paper describes the original BOIN design and its many extensions, their advantages and limitations, the software used to implement them, and the most suitable situation for use of each of these designs. Published examples of the implementation of BOIN designs are provided in the Appendix.
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43
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Zhou Y, Lin R, Lee JJ, Li D, Wang L, Li R, Yuan Y. 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. [PMID: 35098585 PMCID: PMC9199061 DOI: 10.1002/sim.9337] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 12/19/2021] [Accepted: 01/09/2022] [Indexed: 12/17/2022]
Abstract
In the era of immunotherapies and targeted therapies, the focus of early phase clinical trials has shifted from finding the maximum tolerated dose to identifying the optimal biological dose (OBD), which maximizes the toxicity-efficacy trade-off. One major impediment to using adaptive designs to find OBD is that efficacy or/and toxicity are often late-onset, hampering the designs' real-time decision rules for treating new patients. To address this issue, we propose the model-assisted TITE-BOIN12 design to find OBD with late-onset toxicity and efficacy. As an extension of the BOIN12 design, the TITE-BOIN12 design also uses utility to quantify the toxicity-efficacy trade-off. We consider two approaches, Bayesian data augmentation and an approximated likelihood method, to enable real-time decision making when some patients' toxicity and efficacy outcomes are pending. Extensive simulations show that, compared to some existing designs, TITE-BOIN12 significantly shortens the trial duration while having comparable or higher accuracy to identify OBD and a lower risk of overdosing patients. To facilitate the use of the TITE-BOIN12 design, we develop a user-friendly software freely available at http://www.trialdesign.org.
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Affiliation(s)
- Yanhong Zhou
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, TX, USA
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, TX, USA
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, TX, USA
| | - Daniel Li
- Juno Therapeutics, a Bristol-Myers Squibb Company, WA, USA
| | - Li Wang
- Org Division, AbbVie Inc., IL, USA
| | - Ruobing Li
- The Center for Drug Evaluation, The National Medical Products Administration, Beijing, China
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, TX, USA
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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.
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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
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45
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Tidwell RSS, Thall PF, Yuan Y. Lessons Learned From Implementing a Novel Bayesian Adaptive Dose-Finding Design in Advanced Pancreatic Cancer. JCO Precis Oncol 2021; 5:PO.21.00212. [PMID: 34805718 PMCID: PMC8594665 DOI: 10.1200/po.21.00212] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 08/03/2021] [Accepted: 10/04/2021] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Novel Bayesian adaptive designs provide an effective way to improve clinical trial efficiency. These designs are superior to conventional methods, but implementing them can be challenging. The aim of this article was to describe what we learned while applying a novel Bayesian phase I-II design in a recent trial. METHODS The primary goal of the trial was to optimize radiation therapy (RT) dose among three levels (low, standard, and high), given either with placebo (P) or an investigational agent (A), for treating locally advanced, radiation-naive pancreatic cancer, deemed appropriate for RT rather than surgery. Up to 48 patients were randomly assigned fairly between RT plus P and RT plus A, with RT dose-finding done within each arm using the late-onset efficacy-toxicity design on the basis of two coprimary end points, tumor response and dose-limiting toxicity, both evaluated at up to 90 days. The random assignment was blinded, but within each arm, unblinded RT doses were chosen adaptively using software developed within the institution. RESULTS Implementing the design involved double-blind balance-restricted random assignment, real-time assessment of patient outcomes to evaluate the efficacy-toxicity trade-off for each RT dose in each arm to optimize each patient's RT dose adaptively, and transition from a single-center trial to a multicenter trial. We present lessons learned and illustrative documentation. CONCLUSION Implementing novel Bayesian adaptive trial designs requires close collaborations between physicians, pharmacists, statisticians, data managers, and sponsors. The process is difficult but manageable and essential for efficient trial conduct. Close collaboration during trial conduct is a key component of any trial that includes real-time adaptive decision rules.
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Affiliation(s)
- Rebecca S S Tidwell
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Peter F Thall
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ying Yuan
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX
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46
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Yuan Y, Wu J, Gilbert MR. BOIN: a novel Bayesian design platform to accelerate early phase brain tumor clinical trials. Neurooncol Pract 2021; 8:627-638. [PMID: 34777832 DOI: 10.1093/nop/npab035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Despite decades of extensive research, the progress in developing effective treatments for primary brain tumors lags behind that of other cancers, largely due to the unique challenges of brain tumors (eg, the blood-brain barrier and high heterogeneity) that limit the delivery and efficacy of many therapeutic agents. One way to address this issue is to employ novel trial designs to better optimize the treatment regimen (eg, dose and schedule) in early phase trials to improve the success rate of subsequent phase III trials. The objective of this article is to introduce Bayesian optimal interval (BOIN) designs as a novel platform to design various types of early phase brain tumor trials, including single-agent and combination regimen trials, trials with late-onset toxicities, and trials aiming to find the optimal biological dose (OBD) based on both toxicity and efficacy. Unlike many novel Bayesian adaptive designs, which are difficult to understand and complicated to implement by clinical investigators, the BOIN designs are self-explanatory and user friendly, yet yield more robust and powerful operating characteristics than conventional designs. We illustrate the BOIN designs using a phase I clinical trial of brain tumor and provide software (freely available at www.trialdesign.org) to facilitate the application of the BOIN design.
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Affiliation(s)
- Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jing Wu
- Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Mark R Gilbert
- Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
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Lin R, Yin G, Shi H. Bayesian adaptive model selection design for optimal biological dose finding in phase I/II clinical trials. Biostatistics 2021; 24:277-294. [PMID: 34296266 PMCID: PMC10102885 DOI: 10.1093/biostatistics/kxab028] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 05/26/2021] [Accepted: 06/06/2021] [Indexed: 11/13/2022] Open
Abstract
Identification of the optimal dose presents a major challenge in drug development with molecularly targeted agents, immunotherapy, as well as chimeric antigen receptor T-cell treatments. By casting dose finding as a Bayesian model selection problem, we propose an adaptive design by simultaneously incorporating the toxicity and efficacy outcomes to select the optimal biological dose (OBD) in phase I/II clinical trials. Without imposing any parametric assumption or shape constraint on the underlying dose-response curves, we specify curve-free models for both the toxicity and efficacy endpoints to determine the OBD. By integrating the observed data across all dose levels, the proposed design is coherent in dose assignment and thus greatly enhances efficiency and accuracy in pinning down the right dose. Not only does our design possess a completely new yet flexible dose-finding framework, but it also has satisfactory and robust performance as demonstrated by extensive simulation studies. In addition, we show that our design enjoys desirable coherence properties, while most of existing phase I/II designs do not. We further extend the design to accommodate late-onset outcomes which are common in immunotherapy. The proposed design is exemplified with a phase I/II clinical trial in chronic lymphocytic leukemia.
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Affiliation(s)
- Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Guosheng Yin
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Haolun Shi
- Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Dr, Burnaby, BC V5A 1S6, Canada
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Kim H, Kim YJ, Park D, Park WY, Choi DH, Park W, Cho WK, Kim N. Dynamics of circulating tumor DNA during postoperative radiotherapy in patients with residual triple-negative breast cancer following neoadjuvant chemotherapy: a prospective observational study. Breast Cancer Res Treat 2021; 189:167-175. [PMID: 34152505 DOI: 10.1007/s10549-021-06296-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 06/12/2021] [Indexed: 01/13/2023]
Abstract
BACKGROUND This study was performed to evaluate circulating tumor DNA (ctDNA) kinetics during postoperative radiotherapy (PORT) in patients with residual triple-negative breast cancer (TNBC) at surgery following neoadjuvant chemotherapy (NAC). METHODS Stage II/III patients with post-NAC residual TNBC who required PORT were prospectively included in this study between March 2019 and July 2020. For 11 TNBC patients, next-generation sequencing targeting 38 genes was conducted in 55 samples, including tumor tissue, three plasma samples, and leukocytes from each patient. The plasma samples were collected at three-time points; pre-PORT (T0), after 3 weeks of PORT (T1), and 1 month after PORT (T2). Serial changes in ctDNA variant allele frequency (VAF) were analyzed. RESULTS Somatic variants were found in the tumor specimens in 9 out of 11 (81.8%) patients. Mutated genes included TP53 (n = 7); PIK3CA (n = 2); and AKT1, APC, CSMD3, MYC, PTEN, and RB1 (n = 1). These tumor mutations were not found in plasma samples. Plasma ctDNA variants were detected in three (27.3%) patients at T0. Mutations in EGFR (n = 1), CTNNB1 (n = 1), and MAP2K (n = 1) was identified with ctDNA analysis. In two (18.2%) patients, the ctDNA VAF decreased through T1 and T2 while increasing at T2 in one (9.1%) patient. After a median follow-up of 22 months, no patient showed cancer recurrence. CONCLUSION Among patients with post-NAC residual TNBC, more than a quarter exhibited a detectable amount of ctDNA after curative surgery. The ctDNA VAF changed variably during the course of PORT. Therefore, ctDNA kinetics can serve as a biomarker for optimizing adjuvant treatment.
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Affiliation(s)
- Haeyoung Kim
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Yeon Jeong Kim
- Samsung Genome Institute, Samsung Medical Center, Seoul, South Korea
| | - Donghyun Park
- Samsung Genome Institute, Samsung Medical Center, Seoul, South Korea.,GENINUS Inc, Seoul, South Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Seoul, South Korea.,Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon, South Korea
| | - Doo Ho Choi
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Won Park
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Won Kyung Cho
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Nalee Kim
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, Republic of Korea
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How I treat pediatric acute myeloid leukemia. Blood 2021; 138:1009-1018. [PMID: 34115839 DOI: 10.1182/blood.2021011694] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 06/07/2021] [Indexed: 11/20/2022] Open
Abstract
Treatment outcomes for pediatric patients with acute myeloid leukemia (AML) have continued to lag behind outcomes reported for children with acute lymphoblastic leukemia (ALL), in part because of the heterogeneity of the disease, a paucity of targeted therapies, and the relatively slow development of immunotherapy compared to ALL. In addition, we have reached the limits of treatment intensity and, even with outstanding supportive care, it is highly unlikely that further intensification of conventional chemotherapy alone will impact relapse rates. However, comprehensive genomic analyses and a more thorough characterization of the leukemic stem cell have provided insights that should lead to tailored and more effective therapies in the near future. In addition, new therapies are finally emerging, including the BCL-2 inhibitor venetoclax, CD33 and CD123-directed chimeric antigen receptor T cell therapy, CD123-directed antibody therapy, and menin inhibitors. Here we present four cases to illustrate some of the controversies regarding the optimal treatment of children with newly diagnosed or relapsed AML.
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Zhou Y, Lin R, Lee JJ. The use of local and nonlocal priors in Bayesian test-based monitoring for single-arm phase II clinical trials. Pharm Stat 2021; 20:1183-1199. [PMID: 34008317 DOI: 10.1002/pst.2139] [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: 05/21/2020] [Revised: 03/24/2021] [Accepted: 05/01/2021] [Indexed: 11/10/2022]
Abstract
Bayesian sequential monitoring is widely used in adaptive phase II studies where the objective is to examine whether an experimental drug is efficacious. Common approaches for Bayesian sequential monitoring are based on posterior or predictive probabilities and Bayesian hypothesis testing procedures using Bayes factors. In the first part of the paper, we briefly show the connections between test-based (TB) and posterior probability-based (PB) sequential monitoring approaches. Next, we extensively investigate the choice of local and nonlocal priors for the TB monitoring procedure. We describe the pros and cons of different priors in terms of operating characteristics. We also develop a user-friendly Shiny application to implement the TB design.
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Affiliation(s)
- Yanhong Zhou
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Houstan, Texas, USA
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Houstan, Texas, USA
| | - J Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Houstan, Texas, USA
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