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Wages NA, Saleh RR, Braun TM. Concurrent dose-finding of a novel cancer drug with and without a second agent. J Clin Transl Sci 2023; 7:e126. [PMID: 37313388 PMCID: PMC10260343 DOI: 10.1017/cts.2023.542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 04/13/2023] [Accepted: 05/01/2023] [Indexed: 06/15/2023] Open
Abstract
Introduction More complex research questions are being posed in early-phase oncology clinical trials, necessitating design strategies tailored to contemporary study objectives. This paper describes the proposed design of a Phase I trial concurrently evaluating the safety of a hematopoietic progenitor kinase-1 inhibitor (Agent A) as a single agent and in combination with an anti-PD-1 agent in patients with advanced malignancies. The study's primary objective was to concurrently determine the maximum tolerated dose (MTD) of Agent A with and without anti-PD-1 therapy among seven possible study dose levels. Methods Our solution to this challenge was to apply a continual reassessment method shift model to meet the research objectives of the study. Results The application of this method is described herein, and a simulation study of the design's operating characteristics is conducted. This work was developed through collaboration and mentoring between the authors at the American Association for Cancer Research (AACR) and the American Society of Clinical Oncology (ASCO) annual AACR/ASCO Methods in Clinical Cancer Research Workshop. Conclusions The aim of this manuscript is to highlight examples of novel design applications as a means of augmenting the implementation of innovative designs in the future and to demonstrate the flexibility of adaptive designs in satisfying modern design conditions. Although the design is presented using an investigation of Agent A with and without anti-PD-1 therapy as an illustrative example, the approach described is not specific to these agents and could be applied to other concurrent monotherapy and combination therapy studies with well-defined binary safety endpoints.
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Affiliation(s)
- Nolan A. Wages
- Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
- Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA
| | - Ramy R. Saleh
- Division of Medical Oncology & Hematology, Department of Medicine, Princess Margaret Cancer Centre, and the University of Toronto, Toronto, ON, Canada
| | - Thomas M. Braun
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
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Liu R, Yuan Y, Sen S, Yang X, Jiang Q, Li X(N, Lu C(C, Göneng M, Tian H, Zhou H, Lin R, Marchenko O. Accuracy and Safety of Novel Designs for Phase I Drug-Combination Oncology Trials*. Stat Biopharm Res 2022. [PMID: 37275462 PMCID: PMC10237505 DOI: 10.1080/19466315.2022.2081602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Despite numerous innovative designs having been published for phase I drug-combination dose finding trials, their use in real applications is rather limited. As a working group under the American Statistical Association Biopharmaceutical Section, our goal is to identify the unique challenges associated with drug combination, share industry's experiences with combination trials, and investigate the pros and cons of the existing designs. Toward this goal, we review seven existing designs and distinguish them based on the criterion of whether their primary objectives are to find a single maximum tolerated dose (MTD) or the MTD contour (i.e., multiple MTDs). Numerical studies, based on either industry-specified fixed scenarios or randomly generated scenarios, are performed to assess their relative accuracy, safety, and ease of implementation. We show that the algorithm-based 3+3 design has poor performance and often fails to find the MTD. The performance of model-based combination trial designs is mixed: some demonstrate high accuracy of finding the MTD but poor safety, while others are safe but with compromised identification accuracy. In comparison, the model-assisted designs, such as BOIN and waterfall designs, have competitive and balanced performance in the accuracy of MTD identification and patient safety, and are also simple to implement, thus offering an attractive approach to designing phase I drug-combination trials. By taking into consideration the design's operating characteristics, ease of implementation and regulation, the need for advanced infrastructures, as well as the risk of regulatory acceptance, our paper offers practical guidance on the selection of a suitable dose-finding approach for designing future combination trials.
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Affiliation(s)
- Rong Liu
- Bristol-Myers Squibb, Berkeley Heights, NJ 07922
| | - Ying Yuan
- The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | | | - Xin Yang
- Novartis, East Hanover, NJ 07936
| | - Qi Jiang
- Seattle Genetics, Bothell, WA 98021
| | | | | | - Mithat Göneng
- Memorial Sloan Kettering Cancer Center, New York, NY 10022
| | | | - Heng Zhou
- Merck & Co., Inc., Kenilworth, NJ 07033
| | - Ruitao Lin
- The University of Texas MD Anderson Cancer Center, Houston, TX 77030
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Takahashi A, Suzuki T. Bayesian optimization design for finding a maximum tolerated dose combination in phase I clinical trials. Int J Biostat 2021; 18:39-56. [PMID: 33818029 DOI: 10.1515/ijb-2020-0147] [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: 06/17/2020] [Accepted: 03/17/2021] [Indexed: 11/15/2022]
Abstract
The development of combination therapies has become commonplace because potential synergistic benefits are expected for resistant patients of single-agent treatment. In phase I clinical trials, the underlying premise is toxicity increases monotonically with increasing dose levels. This assumption cannot be applied in drug combination trials, however, as there are complex drug-drug interactions. Although many parametric model-based designs have been developed, strong assumptions may be inappropriate owing to little information available about dose-toxicity relationships. No standard solution for finding a maximum tolerated dose combination has been established. With these considerations, we propose a Bayesian optimization design for identifying a single maximum tolerated dose combination. Our proposed design utilizing Bayesian optimization guides the next dose by a balance of information between exploration and exploitation on the nonparametrically estimated dose-toxicity function, thereby allowing us to reach a global optimum with fewer evaluations. We evaluate the proposed design by comparing it with a Bayesian optimal interval design and with the partial-ordering continual reassessment method. The simulation results suggest that the proposed design works well in terms of correct selection probabilities and dose allocations. The proposed design has high potential as a powerful tool for use in finding a maximum tolerated dose combination.
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Affiliation(s)
- Ami Takahashi
- Tokyo Institute of Technology, School of Computing, Meguro-ku, Tokyo, Japan
| | - Taiji Suzuki
- The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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Lyu J, Ji Y, Zhao N, Catenacci DVT. AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual-agent dose finding trials. J R Stat Soc Ser C Appl Stat 2019; 68:385-410. [PMID: 31190687 DOI: 10.1111/rssc.12291] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
We propose a flexible design for the identification of optimal dose combinations in dual-agent dose finding clinical trials. The design is called AAA, standing for three adaptations: adaptive model selection, adaptive dose insertion and adaptive cohort division. The adaptations highlight the need and opportunity for innovation for dual-agent dose finding and are supported by the numerical results presented in the proposed simulation studies. To our knowledge, this is the first design that allows for all three adaptations at the same time. We find that AAA enhances the chance of finding the optimal dose combinations and shortens the trial duration. A clinical trial is being planned to apply the AAA design and a Web tool is being developed for both statisticians and non-statisticians.
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Affiliation(s)
- Jiaying Lyu
- Fudan University, Shanghai, People's Republic of China
| | - Yuan Ji
- NorthShore University HealthSystem, Evanston, and University of Chicago, USA
| | - Naiqing Zhao
- Fudan University, Shanghai, People's Republic of China
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Tighiouart M. Two-stage design for phase I-II cancer clinical trials using continuous dose combinations of cytotoxic agents. J R Stat Soc Ser C Appl Stat 2019; 68:235-250. [PMID: 30745708 DOI: 10.1111/rssc.12294] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
We present a two-stage phase I/II design of a combination of two drugs in cancer clinical trials. The goal is to estimate safe dose combination regions with a desired level of efficacy. In stage I, conditional escalation with overdose control is used to allocate dose combinations to successive cohorts of patients and the maximum tolerated dose curve is estimated as a function of Bayes estimates of the model parameters. In stage II, we propose a Bayesian adaptive design for conducting the phase II trial to determine dose combination regions along the MTD curve with a desired level of efficacy. The methodology is evaluated by extensive simulations and application to a real trial.
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Stathis A, Iasonos A, Seymour JF, Thieblemont C, Ribrag V, Zucca E, Younes A. Report of the 14th International Conference on Malignant Lymphoma (ICML) Closed Workshop on Future Design of Clinical Trials in Lymphomas. Clin Cancer Res 2018. [PMID: 29535129 DOI: 10.1158/1078-0432.ccr-17-3021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The 14th ICML held in Lugano in June 2017 was preceded by a closed workshop (organized in collaboration with the American Association for Cancer Research and the European School of Oncology) where experts in preclinical and clinical research in lymphomas met to discuss the current drug development landscape focusing on critical open questions that need to be addressed in the future to permit a more efficient drug development paradigm in lymphoma. Topics discussed included both preclinical models that can be used to test new drugs and drug combinations, as well as the optimal design of clinical trials and the endpoints that should be used to facilitate accelerated progress. This report represents a summary of the workshop. Clin Cancer Res; 24(13); 2993-8. ©2018 AACR.
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Affiliation(s)
| | - Alexia Iasonos
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - John F Seymour
- Department of Hematology, Peter MacCallum Cancer Center and Royal Melbourne Hospital, and University of Melbourne, Victoria, Australia
| | - Catherine Thieblemont
- Hemato-oncology Department, Assistance Publique-Hôpitaux de Paris (APHP), Hôpital Saint-Louis, Paris, France
| | - Vincent Ribrag
- DITEP, Gustave Roussy Comprehensive Cancer Center, Villejuif, France
| | - Emanuele Zucca
- Oncology Institute of Southern Switzerland, Bellinzona, Switzerland.,Institute of Oncology Research, Bellinzona, Switzerland.,Medical Oncology, University of Bern, Switzerland
| | - Anas Younes
- Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Iasonos A, O'Quigley J. Phase I Designs that Allow for Uncertainty in the Attribution of Adverse Events. J R Stat Soc Ser C Appl Stat 2017; 66:1015-1030. [PMID: 29085158 DOI: 10.1111/rssc.12195] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
In determining dose limiting toxicities in Phase I studies, it is necessary to attribute adverse events (AE) to being drug related or not. Such determination is subjective and may introduce bias. In this paper, we develop methods for removing or at least diminishing the impact of this bias on the estimation of the maximum tolerated dose (MTD). The approach we suggest takes into account the subjectivity in the attribution of AE by using model-based dose escalation designs. The results show that gains can be achieved in terms of accuracy by recovering information lost to biases. These biases are a result of ignoring the errors in toxicity attribution.
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Affiliation(s)
- Alexia Iasonos
- Department of Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - John O'Quigley
- Université Pierre et Marie Curie-Paris VI, Paris, France
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Conaway MR. A design for phase I trials in completely or partially ordered groups. Stat Med 2017; 36:2323-2332. [PMID: 28384843 DOI: 10.1002/sim.7295] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 03/03/2017] [Accepted: 03/03/2017] [Indexed: 11/10/2022]
Abstract
We propose a design for dose finding for cytotoxic agents in completely or partially ordered groups of patients. By completely ordered groups, we mean that prior to the study, there is clinical information that would indicate that for a given dose, the groups can be ordered with respect to the probability of toxicity at that dose. With partially ordered groups, at a given dose, only some of the groups can be ordered with respect to the probability of toxicity at that dose. The method we propose includes elements of the parametric model used in the continual reassessment method combined with the Hwang-Peddada order-restricted estimation procedure. We evaluate the operating characteristics of these designs in a family of dose-toxicity curves representing complete and partial orders. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- Mark R Conaway
- Department of Public Health Sciences, University of Virginia Health System, P.O. Box 800717, Charlottesville, 22908, VA, U.S.A
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9
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Iasonos A, O'Quigley J. Early phase dose finding methodology. Stat Med 2017; 36:201-203. [PMID: 27921353 DOI: 10.1002/sim.7155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Accepted: 10/03/2016] [Indexed: 11/11/2022]
Affiliation(s)
- Alexia Iasonos
- Memorial Sloan Kettering Cancer Center, New York, NY, U.S.A
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Diniz MA, Quanlin-Li, Tighiouart M. Dose Finding for Drug Combination in Early Cancer Phase I Trials using Conditional Continual Reassessment Method. ACTA ACUST UNITED AC 2017; 8. [PMID: 29552377 DOI: 10.4172/2155-6180.1000381] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We describe a dose escalation algorithm for drug combinations in cancer phase I clinical trials. Parametric models for describing the association between the doses and the probability of dose limiting toxicity are used assuming univariate monotonicity of the dose-toxicity relationship. Trial design proceeds using the continual reassessment method, where at each stage of the trial, we seek the dose of one agent with estimated probability of toxicity closest to a target probability of toxicity given the current dose of the other agent. A Bayes estimate of the maximum tolerated dose (MTD) curve is proposed at the conclusion of the trial for continuous doses or a set of MTDs is determined in the case of discrete dose levels. We evaluate design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD under various model generated scenarios and misspecification. The method is further assessed for varying algorithms enrolling cohorts of two and three patients receiving different doses and compared to previous approaches such as escalation with overdose control and two-dimensional design.
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Affiliation(s)
- Márcio Augusto Diniz
- Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center 8700 Beverly Blvd, Los Angeles, CA 90048
| | - Quanlin-Li
- Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center 8700 Beverly Blvd, Los Angeles, CA 90048
| | - Mourad Tighiouart
- Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center 8700 Beverly Blvd, Los Angeles, CA 90048
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