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Araujo D, Greystoke A, Bates S, Bayle A, Calvo E, Castelo-Branco L, de Bono J, Drilon A, Garralda E, Ivy P, Kholmanskikh O, Melero I, Pentheroudakis G, Petrie J, Plummer R, Ponce S, Postel-Vinay S, Siu L, Spreafico A, Stathis A, Steeghs N, Yap C, Yap TA, Ratain M, Seymour L. Oncology phase I trial design and conduct: time for a change - MDICT Guidelines 2022. Ann Oncol 2023; 34:48-60. [PMID: 36182023 DOI: 10.1016/j.annonc.2022.09.158] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 09/18/2022] [Indexed: 02/03/2023] Open
Abstract
In 2021, the Food and Drug Administration Oncology Center of Excellence announced Project Optimus focusing on dose optimization for oncology drugs. The Methodology for the Development of Innovative Cancer Therapies (MDICT) Taskforce met to review and discuss the optimization of dosage for oncology trials and to develop a practical guide for oncology phase I trials. Defining a single recommended phase II dose based on toxicity may define doses that are neither the most effective nor the best tolerated. MDICT recommendations address the need for robust non-clinical data which are needed to inform trial design, as well as an expert team including statisticians and pharmacologists. The protocol must be flexible and adaptive, with clear definition of all endpoints. Health authorities should be consulted early and regularly. Strategies such as randomization, intrapatient dose escalation, and real-world eligibility criteria are encouraged whereas serial tumor sampling is discouraged in the absence of a strong rationale and appropriately validated assay. Endpoints should include consideration of all longitudinal toxicity. The phase I dose escalation trial should define the recommended dose range for later testing in randomized phase II trials, rather than a single recommended phase II dose, and consider scenarios where different populations may require different dosages. The adoption of these recommendations will improve dosage selection in early clinical trials of new anticancer treatments and ultimately, outcomes for patients.
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Affiliation(s)
- D Araujo
- Hospital de Base, Sao Jose do Rio Preto, Brazil
| | - A Greystoke
- Northern Centre for Cancer Care, Newcastle, UK
| | - S Bates
- Division of Hematology and Oncology, Department of Medicine, Columbia University, New York, USA
| | - A Bayle
- Institut Gustave Roussy, Paris, France
| | - E Calvo
- START Madrid-CIOCC, Centro Integral Oncológico Clara Campal, Madrid, Spain
| | - L Castelo-Branco
- European Society for Medical Oncology (ESMO), Lugano, Switzerland
| | - J de Bono
- Institute of Cancer Research, University of London, London; The Royal Marsden Hospital, London, UK
| | - A Drilon
- Memorial Sloan Kettering Cancer Center and Weill Cornell Medical College, New York, USA
| | - E Garralda
- Vall d'Hebron Institute of Oncology, Barcelona, Spain
| | - P Ivy
- National Cancer Institute, USA Cancer Therapy Evaluation Program Investigational Drug Branch (NCI/CTEP/IDB), Bethesda, USA
| | - O Kholmanskikh
- European Medicines Agency, Amsterdam, Netherlands; Federal Agency for Medicines and Health Products, Brussels, Belgium
| | - I Melero
- CUN and CIMA, University of Navarra, Pamplona, Spain
| | - G Pentheroudakis
- European Society for Medical Oncology (ESMO), Lugano, Switzerland
| | - J Petrie
- Canadian Cancer Trials Group, Queen's University, Kingston
| | - R Plummer
- Northern Centre for Cancer Care, Newcastle, UK
| | - S Ponce
- Institut Gustave Roussy, Paris, France
| | | | - L Siu
- Princess Margaret Cancer Centre, Toronto, Canada
| | - A Spreafico
- Princess Margaret Cancer Centre, Toronto, Canada
| | - A Stathis
- Oncology Institute of Southern Switzerland, EOC, Bellinzona, Switzerland
| | - N Steeghs
- The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - C Yap
- Institute of Cancer Research, University of London, London
| | - T A Yap
- Department of Investigational Cancer Therapeutics, University of Texas, MD Anderson Cancer Center, Houston
| | - M Ratain
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - L Seymour
- Canadian Cancer Trials Group, Queen's University, Kingston.
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Bittlinger M, Bicer S, Peppercorn J, Kimmelman J. Ethical Considerations for Phase I Trials in Oncology. J Clin Oncol 2022; 40:3474-3488. [PMID: 35275736 DOI: 10.1200/jco.21.02125] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Phase I trials often represent the first occasion where new cancer strategies are tested in patients. Various developments in cancer biology, methodology, regulation, and medical ethics have altered the ethical landscape of such trials. We provide a narrative review of contemporary ethical challenges in design, conduct, and reporting of phase I cancer trials and outline recommendations for addressing each. We organized our review around four topics, supplementing the first three with scoping reviews: (1) benefit/risk, (2) research biopsies, (3) therapeutic misconception and misestimation, and (4) reporting. The main ethical challenges of conducting phase I trials stem from three issues. First, phase I trials often involve higher research burden and scientific uncertainty compared with other cancer trials. Second, many patients arrive at phase I trials at a transitional point in their illness trajectory where they have exhausted standard survival-extending options. Third, phase I trial results play a major role in informing downstream drug development and regulatory decisions. Together, these issues create distinct pressures for study design, ethical review, informed consent, and reporting. Developments in methodology, regulation, cancer biology, and ethical awareness have helped mitigate some of these challenges, while introducing others. We conclude our review with a series of recommendations regarding trial design, ethical review, consent, and reporting. We also outline several unresolved questions that, if addressed, would strengthen the ethical foundation of phase I cancer trials.
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Affiliation(s)
- Merlin Bittlinger
- Studies of Translation, Ethics and Medicine (STREAM), Department of Equity, Ethics and Policy, McGill University, Montreal, Quebec, Canada
| | - Selin Bicer
- Studies of Translation, Ethics and Medicine (STREAM), Department of Equity, Ethics and Policy, McGill University, Montreal, Quebec, Canada
| | | | - Jonathan Kimmelman
- Studies of Translation, Ethics and Medicine (STREAM), Department of Equity, Ethics and Policy, McGill University, Montreal, Quebec, Canada
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Zhou H, Chen C, Sun L, Zeng Z. A novel framework of Bayesian optimal interval design for phase I trials with late-onset toxicities. Contemp Clin Trials 2021; 105:106404. [PMID: 33862287 DOI: 10.1016/j.cct.2021.106404] [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: 03/29/2021] [Accepted: 04/10/2021] [Indexed: 11/25/2022]
Abstract
As molecularly targeted agents (MTAs) and immunotherapies have widely demonstrated delayed toxicity profile after multiple treatment cycles, the traditional phase I dose-finding designs may not be appropriate anymore because they just account for the acute toxicities occurring in the early period of treatment. When the dose-limiting toxicity (DLT) assessment window is prolonged to account for late-onset DLTs, it will cause logistic issues if the enrollment is suspended until all the DLT information is collected. We propose a novel framework to estimate the toxicity probability in the scenarios where some patients' DLT information are not complete and then implement the Bayesian optimal interval (BOIN) design to make decisions on dose escalation/de-escalation. Our proposed approach maintains BOIN's transparency by simply comparing the estimated toxicity probability with the escalation/de-escalation boundaries to decide the next dose level. The numerical studies show that our proposed framework can achieve comparable operating characteristics as other dose-finding designs considering late-onset DLTs, thus providing an attractive option of phase I dose-finding clinical trials for MTAs and immunotherapies.
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Affiliation(s)
- Heng Zhou
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07033, USA
| | - Cong Chen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07033, USA
| | - Linda Sun
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07033, USA
| | - Zhen Zeng
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07033, USA.
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Altzerinakou MA, Paoletti X. Change-point joint model for identification of plateau of activity in early phase trials. Stat Med 2021; 40:2113-2138. [PMID: 33561898 DOI: 10.1002/sim.8889] [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: 01/25/2020] [Revised: 12/19/2020] [Accepted: 01/06/2021] [Indexed: 11/10/2022]
Abstract
This article presents a phase I/II trial design for targeted therapies and immunotherapies, with the objective of identifying the optimal dose (OD). We employ a joint modeling technique for discrete time-to-event toxicity data and repeated and continuous biomarker measurements. For the biomarker measurements, we implement a change point linear mixed effects skeleton model. This model can accommodate both plateauing and nonplateauing dose-activity relationships. For each new cohort of patients, we estimate the maximum tolerated dose (MTD) taking toxicity as a cumulative endpoint, over six treatment cycles. Then, we select the OD using two different criteria. The OD is a dose that is equally active to the MTD or a dose located on the beginning of the plateau of the dose-activity relationship. Joint modeling allows us to take into account informative censoring due to toxicities or lack of activity and we also consider consent withdrawal and intermittent missing responses. Model estimation relies on likelihood inference.
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Affiliation(s)
| | - Xavier Paoletti
- Université Versailles St Quentin, Université Paris Saclay, INSERM U900 STAMPM, Saint-Cloud, France.,Institut Curie, Paris, France
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