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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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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3
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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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4
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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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5
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Yuan Y, Zhou H, Liu S. Statistical and practical considerations in planning and conduct of dose-optimization trials. Clin Trials 2024; 21:273-286. [PMID: 38243399 PMCID: PMC11134987 DOI: 10.1177/17407745231207085] [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: 01/21/2024]
Abstract
The U.S. Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection toward identifying the optimal biological dose that offers the best balance between benefit and risk, rather than the maximum tolerated dose. However, achieving dose optimization is a challenging task that involves a variety of factors and is considerably more complicated than identifying the maximum tolerated dose, both in terms of design and implementation. This article provides a comprehensive review of various design strategies for dose-optimization trials, including phase 1/2 and 2/3 designs, and highlights their respective advantages and disadvantages. In addition, practical considerations for selecting an appropriate design and planning and executing the trial are discussed. The article also presents freely available software tools that can be utilized for designing and implementing dose-optimization trials. The approaches and their implementation are illustrated through real-world examples.
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Affiliation(s)
- Ying Yuan
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Heng Zhou
- Biostatistics and Research Decision Sciences, Merck and Co., Inc, Rahway, NJ, USA
| | - Suyu Liu
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Biard L, Andrillon A, Silva RB, Lee SM. Dose optimization for cancer treatments with considerations for late-onset toxicities. Clin Trials 2024; 21:322-330. [PMID: 38591582 PMCID: PMC11132952 DOI: 10.1177/17407745231221152] [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: 04/10/2024]
Abstract
Given that novel anticancer therapies have different toxicity profiles and mechanisms of action, it is important to reconsider the current approaches for dose selection. In an effort to move away from considering the maximum tolerated dose as the optimal dose, the Food and Drug Administration Project Optimus points to the need of incorporating long-term toxicity evaluation, given that many of these novel agents lead to late-onset or cumulative toxicities and there are no guidelines on how to handle them. Numerous methods have been proposed to handle late-onset toxicities in dose-finding clinical trials. A summary and comparison of these methods are provided. Moreover, using PI3K inhibitors as a case study, we show how late-onset toxicity can be integrated into the dose-optimization strategy using current available approaches. We illustrate a re-design of this trial to compare the approach to those that only consider early toxicity outcomes and disregard late-onset toxicities. We also provide proposals going forward for dose optimization in early development of novel anticancer agents with considerations for late-onset toxicities.
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Affiliation(s)
- Lucie Biard
- INSERM U1153 Team ECSTRRA, Université Paris Cité, Paris, France
| | - Anaïs Andrillon
- INSERM U1153 Team ECSTRRA, Université Paris Cité, Paris, France
- Department of Statistical Methodology, Saryga, Tournus, France
| | - Rebecca B Silva
- Columbia University, Mailman School of Public Health, Department of Biostatistics, New York, USA
| | - Shing M Lee
- Columbia University, Mailman School of Public Health, Department of Biostatistics, New York, USA
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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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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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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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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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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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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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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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Lancman G, Moshier E, Cho HJ, Parekh S, Richard S, Richter J, Rodriguez C, Rossi A, Sanchez L, Jagannath S, Chari A. Trial designs and endpoints for immune therapies in multiple myeloma. Am J Hematol 2023; 98 Suppl 2:S35-S45. [PMID: 36200130 DOI: 10.1002/ajh.26753] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/18/2022] [Accepted: 09/29/2022] [Indexed: 11/08/2022]
Abstract
Immune therapies, including CAR-T cells, bispecific antibodies, and antibody-drug conjugates, are revolutionizing the treatment of multiple myeloma. In this review, we discuss clinical trial design considerations relevant to immune therapies. We first examine issues pertinent to specific populations, including elderly, patients with renal impairment, high-risk/extramedullary disease, and prior immune therapies. We then highlight trial designs to optimize the selection of dose and schedule, explore rational combination therapies based on preclinical data, and evaluate the nuances of commonly used endpoints. By exploiting their pharmacokinetic/pharmacodynamic profiles and utilizing novel translational insights, we can optimize the use of immune therapies in multiple myeloma.
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Affiliation(s)
- Guido Lancman
- Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Erin Moshier
- Department of Population Health Science and Policy, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Hearn Jay Cho
- Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Samir Parekh
- Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Shambavi Richard
- Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Joshua Richter
- Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Cesar Rodriguez
- Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Adriana Rossi
- Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Larysa Sanchez
- Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Sundar Jagannath
- Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Ajai Chari
- Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
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15
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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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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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17
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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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18
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Phillips A, Mondal S. Improving early phase oncology clinical trial design: An opportunity for statisticians. Pharm Stat 2022; 22:577-580. [PMID: 36573346 DOI: 10.1002/pst.2287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 12/14/2022] [Indexed: 12/28/2022]
Abstract
This short communication supports that rule-based study designs such as the '3 + 3' study design are still being used in early phase oncology development programs despite their inferior performance to model-based and model-assisted designs. Statisticians have an opportunity to shape and improve early phase oncology drug development programs by introducing newer, more efficient study designs that estimate the Optimal Biological dose to their oncology trialist colleges.
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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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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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Jiang M, Hu Y, Lin G, Chen C. Dosing Regimens of Immune Checkpoint Inhibitors: Attempts at Lower Dose, Less Frequency, Shorter Course. Front Oncol 2022; 12:906251. [PMID: 35795044 PMCID: PMC9251517 DOI: 10.3389/fonc.2022.906251] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 05/24/2022] [Indexed: 12/19/2022] Open
Abstract
Immune checkpoint inhibitors (ICIs) are a revolutionary breakthrough in the field of cancer by modulating patient's own immune system to exert anti-tumor effects. The clinical application of ICIs is still in its infancy, and their dosing regimens need to be continuously adjusted. Pharmacokinetic/pharmacodynamic studies showed a significant plateau in the exposure-response curve, with high receptor occupancy and plasma concentrations achieved at low dose levels. Coupled with concerns about drug toxicity and heavy economic costs, there has been an ongoing quest to reevaluate the current ICI dosing regimens while preserving maximum clinical efficacy. Many clinical data showed remarkable anticancer effects with ICIs at the doses far below the approved regimens, indicating the possibility of dose reduction. Our review attempts to summarize the clinical evidence for ICIs regimens with lower-dose, less-frequency, shorter-course, and provide clues for further ICIs regimen optimization.
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Affiliation(s)
| | | | | | - Chao Chen
- Department of Radiotherapy, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China
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