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Li N, Zhou X, Yan D. Phase I clinical trial designs in oncology: A systematic literature review from 2020 to 2022. J Clin Transl Sci 2024; 8:e134. [PMID: 39345694 PMCID: PMC11428115 DOI: 10.1017/cts.2024.599] [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: 03/27/2024] [Revised: 06/10/2024] [Accepted: 07/19/2024] [Indexed: 10/01/2024] Open
Abstract
Background Phase I clinical trials aim to find the highest dose of a novel drug that may be administrated safely without having serious adverse effects. Model-based designs have recently become popular in dose-finding procedures. Our objective is to provide an overview of phase I clinical trials in oncology. Methods A retrospective analysis of phase I clinical trials in oncology was performed by using the PubMed database between January 1, 2020, and December 31, 2022. We extracted all papers with the inclusion of trials in oncology and kept only those in which dose escalation or/ and dose expansion were conducted. We also compared the study parameters, design parameters, and patient parameters between industry-sponsored studies and academia-sponsored research. Result Among the 1450 papers retrieved, 256 trials described phase I clinical trials in oncology. Overall, 71.1% of trials were done with a single study cohort, 56.64% of trials collected a group of at least 20 study volunteers, 55.1% were sponsored by industry, and 99.2% of trials had less than 10 patients who experienced DLTs.The traditional 3 + 3 (73.85%) was still the most prevailing method for the dose-escalation approach. More than 50% of the trials did not reach MTDs. Industry-sponsored study enrolled more patients in dose-escalation trials with benefits of continental cooperation. Compared to previous findings, the usage of model-based design increased to about 10%, and the percentage of traditional 3 + 3 design decreased to 74%. Conclusions Phase I traditional 3 + 3 designs perform well, but there is still room for development in novel model-based dose-escalation designs in clinical practice.
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Affiliation(s)
- Ning Li
- Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY, USA
- Markey Cancer Center, University of Kentucky, Lexington, KY, USA
| | - Xitong Zhou
- Dr. Bing Zhang Department of Statistics, University of Kentucky, Lexington, KY, USA
| | - Donglin Yan
- Markey Cancer Center, University of Kentucky, Lexington, KY, USA
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Jiang L, Yin Z, Yan F, Yuan Y. MC-Keyboard: A Practical Phase I Trial Design for Targeted Therapies and Immunotherapies Integrating Multiple-Grade Toxicities. JOURNAL OF IMMUNOTHERAPY AND PRECISION ONCOLOGY 2024; 7:159-167. [PMID: 39219992 PMCID: PMC11361345 DOI: 10.36401/jipo-23-35] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 01/25/2024] [Accepted: 01/25/2024] [Indexed: 09/04/2024]
Abstract
In targeted therapies and immunotherapies, the occurrence of low-grade (e.g., grade 1-2) toxicities (LGT) is common, while dose-limiting toxicities (DLT) are relatively rare. As a result, conventional phase I trial designs, solely based on DLTs and disregarding milder toxicities, are problematic when evaluating these novel therapies. Methods: To address this issue, we propose a novel phase I design called a multiple-constraint keyboard (MC-Keyboard) that integrates multiple toxicity constraints, accounting for both DLT and LGT, for precise dose escalation and de-escalation, and identification of the maximum tolerated dose (MTD). As a model-assisted design, an important feature of MC-Keyboard is that its dose-escalation or de-escalation rule can be pretabulated and incorporated into the trial protocol before the initiation of the trial, greatly simplifying its implementation. Results: The simulation study showed that the MC-Keyboard had high accuracy in identifying the MTD and is safer than some existing designs. Conclusion: The MC-Keyboard provides a novel, simple, and safe approach to assessing safety and identifying the MTD for targeted therapies and immunotherapies.
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Affiliation(s)
- Liyun Jiang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Zhulin Yin
- Clinical Trials and Statistics Unit, Institute of Cancer Research, London, UK
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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3
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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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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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Zhao Y, Yuan Y, Korn EL, Freidlin B. Backfilling Patients in Phase I Dose-Escalation Trials Using Bayesian Optimal Interval Design (BOIN). Clin Cancer Res 2024; 30:673-679. [PMID: 38048044 DOI: 10.1158/1078-0432.ccr-23-2585] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/24/2023] [Accepted: 11/29/2023] [Indexed: 12/05/2023]
Abstract
In recent years, there has been increased interest in incorporation of backfilling into dose-escalation clinical trials, which involves concurrently assigning patients to doses that have been previously cleared for safety by the dose-escalation design. Backfilling generates additional information on safety, tolerability, and preliminary activity on a range of doses below the maximum tolerated dose (MTD), which is relevant for selection of the recommended phase II dose and dose optimization. However, in practice, backfilling may not be rigorously defined in trial protocols and implemented consistently. Furthermore, backfilling designs require careful planning to minimize the probability of treating additional patients with potentially inactive agents (and/or subtherapeutic doses). In this paper, we propose a simple and principled approach to incorporate backfilling into the Bayesian optimal interval design (BOIN). The design integrates data from the dose-escalation and backfilling components of the design and ensures that the additional patients are treated at doses where some activity has been seen. Simulation studies demonstrated that the proposed backfilling BOIN design (BF-BOIN) generates additional data for future dose optimization, maintains the accuracy of the MTD identification, and improves patient safety without prolonging the trial duration.
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Affiliation(s)
- Yixuan Zhao
- Department of Biostatistics, University of Texas Health Science Center, Houston, Texas
| | - Ying Yuan
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Texas
| | - Edward L Korn
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, Maryland
| | - Boris Freidlin
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, Maryland
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6
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Guo B, Yuan Y. DROID: dose-ranging approach to optimizing dose in oncology drug development. Biometrics 2023; 79:2907-2919. [PMID: 36807110 DOI: 10.1111/biom.13840] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 01/26/2023] [Indexed: 02/22/2023]
Abstract
In the era of targeted therapy, there has been increasing concern about the development of oncology drugs based on the "more is better" paradigm, developed decades ago for chemotherapy. Recently, the US Food and Drug Administration (FDA) initiated Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development. To accommodate this paradigm shifting, we propose a dose-ranging approach to optimizing dose (DROID) for oncology trials with targeted drugs. DROID leverages the well-established dose-ranging study framework, which has been routinely used to develop non-oncology drugs for decades, and bridges it with established oncology dose-finding designs to optimize the dose of oncology drugs. DROID consists of two seamlessly connected stages. In the first stage, patients are sequentially enrolled and adaptively assigned to investigational doses to establish the therapeutic dose range (TDR), defined as the range of doses with acceptable toxicity and efficacy profiles, and the recommended phase 2 dose set (RP2S). In the second stage, patients are randomized to the doses in RP2S to assess the dose-response relationship and identify the optimal dose. The simulation study shows that DROID substantially outperforms the conventional approach, providing a new paradigm to efficiently optimize the dose of targeted oncology drugs. DROID aligns with the approach of a randomized, parallel dose-response trial design recommended by the FDA in the Guidance on Optimizing the Dosage of Human Prescription Drugs and Biological Products for the Treatment of Oncologic Diseases.
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Affiliation(s)
- Beibei Guo
- Department of Experimental Statistics, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Yap C, Solovyeva O, de Bono J, Rekowski J, Patel D, Jaki T, Mander A, Evans TRJ, Peck R, Hayward KS, Hopewell S, Ursino M, Rantell KR, Calvert M, Lee S, Kightley A, Ashby D, Chan AW, Garrett-Mayer E, Isaacs JD, Golub R, Kholmanskikh O, Richards D, Boix O, Matcham J, Seymour L, Ivy SP, Marshall LV, Hommais A, Liu R, Tanaka Y, Berlin J, Espinasse A, Dimairo M, Weir CJ. Enhancing reporting quality and impact of early phase dose-finding clinical trials: CONSORT Dose-finding Extension (CONSORT-DEFINE) guidance. BMJ 2023; 383:e076387. [PMID: 37863501 PMCID: PMC10583500 DOI: 10.1136/bmj-2023-076387] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/05/2023] [Indexed: 10/22/2023]
Affiliation(s)
| | | | - Johann de Bono
- Institute of Cancer Research, London SM2 5NG, UK
- Royal Marsden NHS Foundation Trust, London, UK
| | - Jan Rekowski
- Institute of Cancer Research, London SM2 5NG, UK
| | | | - Thomas Jaki
- MRC Biostatistics Unit, Cambridge University, Cambridge, UK
- Computational Statistics Group, University of Regensburg, Regensburg, Germany
| | - Adrian Mander
- Centre For Trials Research, Cardiff University, Heath Park, Cardiff, UK
| | - Thomas R Jeffry Evans
- Institute of Cancer Sciences, CR-UK Beatson Institute, University of Glasgow, Glasgow, UK
| | - Richard Peck
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- Hoffmann-La Roche, Basel, Switzerland
| | - Kathryn S Hayward
- Departments of Physiotherapy, and Medicine (Royal Melbourne Hospital), University of Melbourne, VIC, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Sally Hopewell
- Oxford Clinical Research Unit, NDORMS, University of Oxford, Oxford, UK
| | - Moreno Ursino
- ReCAP/F CRIN, INSERM, Paris, France
- Unit of Clinical Epidemiology, CHU Robert Debré, APHP, URC, INSERM CIC-EC 1426, Reims, France
- INSERM Centre de Recherche des Cordeliers, Sorbonne University, Paris Cité University, Paris, France
- Health data and model driven approaches for Knowledge Acquisition team, Centre Inria, Paris, France
| | | | - Melanie Calvert
- Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Applied Research Collaboration West Midlands, University of Birmingham, Birmingham, UK
- NIHR Research Blood and Transplant Research Unit in Precision Transplant and Cellular Therapeutics, University of Birmingham, Edgbaston, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, Institute of Translational Medicine, University Hospital NHS Foundation Trust, Birmingham, UK
| | - Shing Lee
- Columbia University Mailman School of Public Health, New York, NY, USA
| | | | - Deborah Ashby
- School of Public Health, Imperial College London, London, UK
| | - An-Wen Chan
- Department of Medicine, Women's College Research Institute, University of Toronto, Toronto, ON, Canada
| | - Elizabeth Garrett-Mayer
- Center for Research and Analytics, American Society of Clinical Oncology, Alexandria, VA, USA
| | - John D Isaacs
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Musculoskeletal Unit, Newcastle upon Tyne Hospitals NHS Foundation Trust, Freeman Hospital, Newcastle upon Tyne, UK
| | - Robert Golub
- Department of Medicine, Northwestern University Feinberg School of Medicine, 633 Clark Street, Evanston, IL, USA
| | - Olga Kholmanskikh
- Federal Agency for Medicines and Health Products, Brussels, Belgium
- European Medicines Agency, Amsterdam, Netherlands
| | - Dawn Richards
- Clinical Trials Ontario, MaRS Centre, Toronto, ON, Canada
| | | | - James Matcham
- Strategic Consulting, Cytel (Australia), Perth, WA, Australia
| | - Lesley Seymour
- Investigational New Drug Programme, Canadian Cancer Trials Group, Cancer Research Institute, Queen's University, Kingston, ON, Canada
| | - S Percy Ivy
- Investigational Drug Branch, Cancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Institute of Health, Bethesda, MD, USA
| | - Lynley V Marshall
- Institute of Cancer Research, London SM2 5NG, UK
- Royal Marsden NHS Foundation Trust, London, UK
| | - Antoine Hommais
- Department of Clinical Research, National Cancer Institute, Boulogne-Billancourt, France
| | - Rong Liu
- Bristol Myers Squibb, New York, NY, USA
| | - Yoshiya Tanaka
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan
| | | | | | - Munyaradzi Dimairo
- Division of Population Health, Sheffield Centre for Health and Related Research, University of Sheffield, Sheffield, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
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8
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Yap C, Rekowski J, Ursino M, Solovyeva O, Patel D, Dimairo M, Weir CJ, Chan AW, Jaki T, Mander A, Evans TRJ, Peck R, Hayward KS, Calvert M, Rantell KR, Lee S, Kightley A, Hopewell S, Ashby D, Garrett-Mayer E, Isaacs J, Golub R, Kholmanskikh O, Richards DP, Boix O, Matcham J, Seymour L, Ivy SP, Marshall LV, Hommais A, Liu R, Tanaka Y, Berlin J, Espinasse A, de Bono J. Enhancing quality and impact of early phase dose-finding clinical trial protocols: SPIRIT Dose-finding Extension (SPIRIT-DEFINE) guidance. BMJ 2023; 383:e076386. [PMID: 37863491 DOI: 10.1136/bmj-2023-076386] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Affiliation(s)
| | - Jan Rekowski
- Institute of Cancer Research, London SM2 5NG, UK
| | - Moreno Ursino
- ReCAP/F CRIN, INSERM, Paris, France
- Unit of Clinical Epidemiology, University Hospital Centre Robert Debré, Reims, France
- INSERM Centre de Recherche des Cordeliers, Sorbonne University, Paris, France
- Health data and model driven approaches for Knowledge Acquisition team, Centre Inria, Paris, France
| | | | | | - Munyaradzi Dimairo
- Division of Population Health, Sheffield Centre for Health and Related Research, University of Sheffield, Sheffield, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - An-Wen Chan
- Department of Medicine, Women's College Research Institute, University of Toronto, Toronto, Canada
| | - Thomas Jaki
- MRC Biostatistics Unit, Cambridge University, Cambridge, UK
- Computational Statistics Group, University of Regensburg, Regensburg, Germany
| | - Adrian Mander
- Centre For Trials Research, Cardiff University, Cardiff, UK
| | - Thomas R Jeffry Evans
- Institute of Cancer Sciences, CR-UK Beatson Institute, University of Glasgow, Glasgow, UK
| | - Richard Peck
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- Hoffmann-La Roche, Basel, Switzerland
| | - Kathryn S Hayward
- Departments of Physiotherapy, and Medicine (Royal Melbourne Hospital), University of Melbourne, Parkville, VIC, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC, Australia
| | - Melanie Calvert
- Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research Applied Research Collaboration West Midlands, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Precision Transplant and Cellular Therapeutics, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research Birmingham Biomedical Research Centre, NIHR Birmingham Biomedical Research Centre, Institute of Translational Medicine, University Hospital NHS Foundation Trust, Birmingham, UK
| | | | - Shing Lee
- Columbia University Mailman School of Public Health, New York, NY, USA
| | | | - Sally Hopewell
- Oxford Clinical Research Unit, NDORMS, University of Oxford, Oxford, UK
| | - Deborah Ashby
- School of Public Health, Imperial College London, St Mary's Hospital, London, UK
| | - Elizabeth Garrett-Mayer
- Center for Research and Analytics, American Society of Clinical Oncology, Alexandria, VA, USA
| | - John Isaacs
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Musculoskeletal Unit, Newcastle upon Tyne Hospitals NHS Foundation Trust, Freeman Hospital, Newcastle upon Tyne, UK
| | - Robert Golub
- Department of Medicine, Northwestern University Feinberg School of Medicine, Evanston, IL, USA
| | | | | | | | - James Matcham
- Strategic Consulting, Cytel (Australia), Perth, WA, Australia
| | - Lesley Seymour
- Investigational New Drug Programme, Canadian Cancer Trials Group, Cancer Research Institute, Queen's University, Kingston, ON, Canada
| | - S Percy Ivy
- Investigational Drug Branch, Cancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Institute of Health, Bethesda, MD, USA
| | - Lynley V Marshall
- Institute of Cancer Research, London SM2 5NG, UK
- Royal Marsden NHS Foundation Trust, London, UK
| | - Antoine Hommais
- Department of Clinical Research, National Cancer Institute, Boulogne-Billancourt, France
| | - Rong Liu
- Bristol Myers Squibb, New York, NY, USA
| | - Yoshiya Tanaka
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan
| | | | | | - Johann de Bono
- Institute of Cancer Research, London SM2 5NG, UK
- Royal Marsden NHS Foundation Trust, London, UK
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Korn EL, Moscow JA, Freidlin B. Dose optimization during drug development: whether and when to optimize. J Natl Cancer Inst 2023; 115:492-497. [PMID: 36534891 PMCID: PMC10165487 DOI: 10.1093/jnci/djac232] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 11/10/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
The goal of dose optimization during drug development is to identify a dose that preserves clinical benefit with optimal tolerability. Traditionally, the maximum tolerated dose in a small phase I dose escalation study is used in the phase II trial assessing clinical activity of the agent. Although it is possible that this dose level could be altered in the phase II trial if an unexpected level of toxicity is seen, no formal dose optimization has routinely been incorporated into later stages of drug development. Recently it has been suggested that formal dose optimization (involving randomly assigning patients between 2 or more dose levels) be routinely performed early in drug development, even before it is known that the experimental therapy has any clinical activity at any dose level. We consider the relative merits of performing dose optimization earlier vs later in the drug development process and demonstrate that a considerable number of patients may be exposed to ineffective therapies unless dose optimization is delayed until after clinical activity or benefit of the new agent has been established. We conclude that patient and public health interests may be better served by conducting dose optimization after (or during) phase III evaluation, with some exceptions when dose optimization should be performed after activity shown in phase II evaluation.
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Affiliation(s)
- Edward L Korn
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA
| | - Jeffrey A Moscow
- Cancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA
| | - Boris Freidlin
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA
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Bulsara S, Wu M, Wang T. Phase I CAR-T Clinical Trials Review. Anticancer Res 2022; 42:5673-5684. [PMID: 36456127 PMCID: PMC10132085 DOI: 10.21873/anticanres.16076] [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: 09/21/2022] [Revised: 10/14/2022] [Accepted: 10/24/2022] [Indexed: 12/05/2022]
Abstract
BACKGROUND/AIM Chimeric antigen receptor (CAR) T cells with tumor specificity are being increasingly investigated. Phase I trials are the first step of testing for safety of novel CAR-T therapy to determine the maximum tolerated dose (MTD). Several dose escalation methods have been developed over time including rule-based, model-based, and model-assisted designs. The goal of this project is to overview the phase I designs used in current CAR-T trials. MATERIALS AND METHODS We searched PubMed for peer-reviewed literature published between January 1, 2015 and December 31, 2021. The search was limited to human studies in the English language using the keywords "CAR-T phase I", "clinical trials", and "full text". RESULTS One hundred nine papers with at least partial phase I components were included for analysis. 31.2% of the trials used the traditional 3+3 or a variation of said design, and 60.6% did not mention the dose escalation design. The majority of the manuscripts (59.6%) did not report cohort size while 19.3% did not specify the timing of evaluation. Although most of the studies were registered with CT.gov, only 33.9% had any results submitted or posted to CT.gov These trends persisted even in manuscripts published in journals with high impact factors. CONCLUSION Standardizing the publication criteria and providing basic elements of phase I clinical trials are critical to ensure high quality of manuscripts. With the quick development and high costs of CAR-T cell therapy, adoption of advanced designs such as model-based and model-assisted should increase to improve efficiency of clinical trials.
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Affiliation(s)
- Shaun Bulsara
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, U.S.A
| | - Mengfen Wu
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, U.S.A
| | - Tao Wang
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, U.S.A.
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11
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[Progress and Application of Bayesian Approach in the Early Research and Development of New Anticancer Drugs]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2022; 25:730-734. [PMID: 36285392 PMCID: PMC9619348 DOI: 10.3779/j.issn.1009-3419.2022.102.43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Bayesian statistics is an approach for learning from evidences as it accumulates, combining prior distribution with current information on a quantity of interest, in which posterior distribution and inferences are being updated each time new data become available using Bayes' Theorem. Though frequentist approach has dominated medical studies, Bayesian approach has been more and more widely recognized by its flexibility and efficiency. Research and development (R&D) on anti-cancer new drugs have been so hot globally in recent years in spite of relatively high failure rate. It is the common demand of pharmaceutical enterprises and researchers to identify the optimal dose, regime and right population in the early-phase R&D stage more accurately and efficiently, especially when the following three major changes have been observed. The R&D on anticancer drugs have transformed from chemical drugs to biological products, from monotherapy to combination therapy, and the study design has also gradually changed from traditional way to innovative and adaptive mode. This also raises a number of subsequent challenges on decision-making of early R&D, such as inability to determine MTD, flexibility to deal with delayed toxicity, delayed response and dose-response changing relationships. It is because of the above emerging changes and challenges that the Bayesian approach is getting more and more attention from the industry. At least, Bayesian approach has more information for decision-making, which could potentially help enterprises achieve higher efficiency, shorter period and lower investment. This study also expounds the application of Bayesian statistics in the early R&D on anticancer new drugs, and compares and analyzes its idea and application scenarios with frequentist statistics, aiming to provide macroscopic and systematic reference for all related stakeholders.
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12
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Brown SR, Hinsley S, Hall E, Hurt C, Baird RD, Forster M, Scarsbrook AF, Adams RA. A Road Map for Designing Phase I Clinical Trials of Radiotherapy-Novel Agent Combinations. Clin Cancer Res 2022; 28:3639-3651. [PMID: 35552622 PMCID: PMC9433953 DOI: 10.1158/1078-0432.ccr-21-4087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/26/2022] [Accepted: 04/28/2022] [Indexed: 01/07/2023]
Abstract
Radiotherapy has proven efficacy in a wide range of cancers. There is growing interest in evaluating radiotherapy-novel agent combinations and a drive to initiate this earlier in the clinical development of the novel agent, where the scientific rationale and preclinical evidence for a radiotherapy combination approach are high. Optimal design, delivery, and interpretation of studies are essential. In particular, the design of phase I studies to determine safety and dosing is critical to an efficient development strategy. There is significant interest in early-phase research among scientific and clinical communities over recent years, at a time when the scrutiny of the trial methodology has significantly increased. To enhance trial design, optimize safety, and promote efficient trial conduct, this position paper reviews the current phase I trial design landscape. Key design characteristics extracted from 37 methodology papers were used to define a road map and a design selection process for phase I radiotherapy-novel agent trials. Design selection is based on single- or dual-therapy dose escalation, dose-limiting toxicity categorization, maximum tolerated dose determination, subgroup evaluation, software availability, and design performance. Fifteen of the 37 designs were identified as being immediately accessible and relevant to radiotherapy-novel agent phase I trials. Applied examples of using the road map are presented. Developing these studies is intensive, highlighting the need for funding and statistical input early in the trial development to ensure appropriate design and implementation from the outset. The application of this road map will improve the design of phase I radiotherapy-novel agent combination trials, enabling a more efficient development pathway.
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Affiliation(s)
- Sarah R. Brown
- Leeds Cancer Research UK Clinical Trials Unit, Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, United Kingdom
| | - Samantha Hinsley
- Clinical Trials Unit Glasgow, University of Glasgow, Glasgow, United Kingdom
| | - Emma Hall
- Clinical Trials and Statistics Unit, The Institute of Cancer Research, London, United Kingdom
| | - Chris Hurt
- Centre for Trials Research, Cardiff University, Cardiff, United Kingdom
| | | | | | - Andrew F. Scarsbrook
- Radiotherapy Research Group, Leeds Institute of Medical Research at St James's, Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom
| | - Richard A. Adams
- Centre for Trials Research, Cardiff University and Velindre Cancer Centre, Cardiff, United Kingdom
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13
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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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14
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Zhao Y, Yang B, Lee JJ, Wang L, Yuan Y. Bayesian Optimal Phase II Design for Randomized Clinical Trials. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2050290] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Yujie Zhao
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Bo Yang
- Vertex Pharmaceuticals, Boston, MA
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
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15
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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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16
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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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17
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Park M, Liu S, Yap TA, Yuan Y. Evaluation of Deviation From Planned Cohort Size and Operating Characteristics of Phase 1 Trials. JAMA Netw Open 2021; 4:e2037563. [PMID: 33595664 PMCID: PMC7890531 DOI: 10.1001/jamanetworkopen.2020.37563] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE The cohort size of phase 1 clinical trials and thus the timing of the interim decisions are typically prespecified in the trial protocol. During trial implementation, however, the cohort size often deviates from the planned one, which shifts the schedule of the interims. Despite its pervasiveness in phase 1 trials, the association of cohort size deviation with the operating characteristics of these trials is not clear. OBJECTIVE To explore the association between cohort size deviation and the operating characteristics of phase 1 clinical trials. DESIGN, SETTING, AND PARTICIPANTS In this cross-sectional simulation study, a review was conducted of 102 phase 1 dose-escalation trials published between January 2017 and May 2018 in 3 peer-reviewed journals (Journal of Clinical Oncology, Clinical Cancer Research, and Cancer). After exclusion of studies that did not report the cohort size, 45 trials remained for analysis. Based on the analysis results, a simulation study was performed to systematically investigate the association of cohort size deviation with the operating characteristics of the trials. MAIN OUTCOMES AND MEASURES The prevalence of cohort size deviation and the percentage of correct selection of the maximum tolerated dose. RESULTS Of the 45 reviewed trials, 10 (22.2%) adhered strictly to the planned cohort size. The simulation study showed that when cohort size deviation was random, it had little association with the performance of novel model-based and model-assisted designs (mean reduction in the percentage of correct selection of the maximum tolerated dose was 0.87 percentage point for the continual reassessment method and 0.84 percentage point for the bayesian optimal interval design). When the cohort size deviation was informative and made based on the observed data on toxicity (eg, if dose-limiting toxicity was observed, the size of the next or current cohort was reduced or expanded), the variation of the design performance increased. The range of the change in the percentage of correct selection was -3.7 to 1.3 percentage points for the continual reassessment method and -4.5 to 0 percentage points for the bayesian optimal interval design. CONCLUSIONS AND RELEVANCE The findings suggest that when novel phase 1 clinical trial designs are used, some cohort size deviation is acceptable and has little association with the performance of the designs. These deviations may be used by expert investigators to properly interpret the data, ensure safety, and leverage flexibility in the protocol.
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Affiliation(s)
- Minjeong Park
- Division of Biometrics 1, Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research Food and Drug Administration, Silver Spring, Maryland
| | - Suyu Liu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston
| | - Timothy Anthony Yap
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston
- Khalifa Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston
- The Institute for Applied Cancer Science, The University of Texas MD Anderson Cancer Center, Houston
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston
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18
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Zhou Y, Lin R, Kuo YW, Lee JJ, Yuan Y. BOIN Suite: A Software Platform to Design and Implement Novel Early-Phase Clinical Trials. JCO Clin Cancer Inform 2021; 5:91-101. [PMID: 33439726 PMCID: PMC8462603 DOI: 10.1200/cci.20.00122] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 10/27/2020] [Accepted: 11/16/2020] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Using novel Bayesian adaptive designs has great potential to improve the efficiency of early-phase clinical trials. A major barrier for clinical researchers to adopt novel designs is the lack of easy-to-use software. Our purpose is to develop a user-friendly software platform to implement novel clinical trial designs that address various challenges in early-phase dose-finding trials. METHODS We used R Shiny to develop a web-based software platform to facilitate the use of recent novel adaptive designs. RESULTS We developed a web-based software suite, called Bayesian optimal interval (BOIN) suite, which includes R Shiny applications to handle various clinical settings, including single-agent phase I trials with and without prior information, trials with late-onset toxicity, trials to find the optimal biological dose based on risk-benefit trade-off, and drug combination trials to find a single maximum tolerated dose (MTD) or the MTD contour. The applications are built using the same software architecture to ensure the best and a uniform user experience, and they are developed using a proven software development standard operating procedure to ensure accuracy, robustness, and reproducibility. The suite is freely available with internet access and a web browser without the need of installing any other software. CONCLUSION The BOIN suite allows clinical researchers to design various types of early-phase clinical trials under a unified framework. This work is extremely important because it not only advances the clinical research and drug development by facilitating the use of novel trial designs with optimal performance but also enhances collaborations between biostatisticians and clinicians by disseminating novel statistical methodology to broader scientific communities through user-friendly software. The BOIN suite establishes a KISS principle: keep it simple, but smart.
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Affiliation(s)
- Yanhong Zhou
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ying-Wei Kuo
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
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19
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Lin R, Zhou Y, Yan F, Li D, Yuan Y. BOIN12: Bayesian Optimal Interval Phase I/II Trial Design for Utility-Based Dose Finding in Immunotherapy and Targeted Therapies. JCO Precis Oncol 2020; 4:2000257. [PMID: 33283133 DOI: 10.1200/po.20.00257] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/02/2020] [Indexed: 12/15/2022] Open
Abstract
PURPOSE For immunotherapy, such as checkpoint inhibitors and chimeric antigen receptor T-cell therapy, where the efficacy does not necessarily increase with the dose, the maximum tolerated dose may not be the optimal dose for treating patients. For these novel therapies, the objective of dose-finding trials is to identify the optimal biologic dose (OBD) that optimizes patients' risk-benefit trade-off. METHODS We propose a simple and flexible Bayesian optimal interval phase I/II (BOIN12) trial design to find the OBD that optimizes the risk-benefit trade-off. The BOIN12 design makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity and adaptively allocates patients to the dose that optimizes the toxicity-efficacy trade-off. We performed simulation studies to evaluate the performance of the BOIN12 design. RESULTS Compared with existing phase I/II dose-finding designs, the BOIN12 design is simpler to implement, has higher accuracy to identify the OBD, and allocates more patients to the OBD. One of the most appealing features of the BOIN12 design is that its adaptation rule can be pretabulated and included in the protocol. During the trial conduct, clinicians can simply look up the decision table to allocate patients to a dose without complicated computation. CONCLUSION The BOIN12 design is simple to implement and yields desirable operating characteristics. It overcomes the computational and implementation complexity that plagues existing Bayesian phase I/II dose-finding designs and provides a useful design to optimize the dose of immunotherapy and targeted therapy. User-friendly software is freely available to facilitate the application of the BOIN12 design.
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Affiliation(s)
- Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Yanhong Zhou
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Fangrong Yan
- China Pharmaceutical University, Nanjing, People's Republic of China
| | - Daniel Li
- Juno Therapeutics, a Bristol Myers Squibb Company, Seattle, WA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
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20
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Yin J, Yuan Y. Checkerboard: a Bayesian efficacy and toxicity interval design for phase I/II dose-finding trials. J Biopharm Stat 2020; 30:1006-1025. [DOI: 10.1080/10543406.2020.1815033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Jun Yin
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Ying Yuan
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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21
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Zhou Y, Li R, Yan F, Lee JJ, Yuan Y. A comparative study of Bayesian optimal interval (BOIN) design with interval 3+3 (i3+3) design for phase I oncology dose-finding trials. Stat Biopharm Res 2020; 13:147-155. [PMID: 34249223 PMCID: PMC8261789 DOI: 10.1080/19466315.2020.1811147] [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: 02/11/2020] [Revised: 06/16/2020] [Accepted: 08/02/2020] [Indexed: 10/23/2022]
Abstract
Bayesian optimal interval (BOIN) design is a model-assisted phase I dose-finding design to find the maximum tolerated dose (MTD). The hallmark of the BOIN design is its concise decision rule - making the decision of dose escalation and de-escalation by simply comparing the observed dose-limiting toxicity (DLT) rate at the current dose with a pair of optimal dose escalation and de-escalation boundaries. The interval 3+3 (i3+3) design is a recently proposed algorithm-based dose-finding design based on a similar decision rule with some modifications. The similarity in the appearance of the two designs has caused confusions among practitioners. In this article, we demystify the i3+3 design by elucidating its links with the BOIN design and compare their similarities and differences, as well as pros and cons. We perform comprehensive simulation studies to compare the operating characteristics of the two designs. Our results show that, compared to the algorithm-based i3+3 design, which are characterized by ad hoc and often scientifically and logically incoherent decision rules, the mode-assisted BOIN design is not only simpler, but also statistically more rigorous with better operating characteristics, thus providing a better design choice for phase I oncology trials.
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Affiliation(s)
- Yanhong Zhou
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ruobing Li
- The Center for Drug Evaluation, Beijing, China
| | | | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Zhou Y, Lee JJ, Yuan Y. A utility-based Bayesian optimal interval (U-BOIN) phase I/II design to identify the optimal biological dose for targeted and immune therapies. Stat Med 2019; 38:5299-5316. [PMID: 31621952 DOI: 10.1002/sim.8361] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 05/15/2019] [Accepted: 08/12/2019] [Indexed: 11/08/2022]
Abstract
In the era of targeted therapy and immunotherapy, the objective of dose finding is often to identify the optimal biological dose (OBD), rather than the maximum tolerated dose. We develop a utility-based Bayesian optimal interval (U-BOIN) phase I/II design to find the OBD. We jointly model toxicity and efficacy using a multinomial-Dirichlet model, and employ a utility function to measure dose risk-benefit trade-off. The U-BOIN design consists of two seamless stages. In stage I, the Bayesian optimal interval design is used to quickly explore the dose space and collect preliminary toxicity and efficacy data. In stage II, we continuously update the posterior estimate of the utility for each dose after each cohort, using accumulating efficacy and toxicity from both stages I and II, and then use the posterior estimate to direct the dose assignment and selection. Compared to existing phase I/II designs, one prominent advantage of the U-BOIN design is its simplicity for implementation. Once the trial is designed, it can be easily applied using predetermined decision tables, without complex model fitting and estimation. Our simulation study shows that, despite its simplicity, the U-BOIN design is robust and has high accuracy to identify the OBD. We extend the design to accommodate delayed efficacy by leveraging the short-term endpoint (eg, immune activity or other biological activity of targeted agents), and using it to predict the delayed efficacy outcome to facilitate real-time decision making. A user-friendly software to implement the U-BOIN is freely available at www.trialdesign.org.
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Affiliation(s)
- Yanhong Zhou
- Quantitative Science, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas.,Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - J Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
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