دورية أكاديمية
أعرض في EDS

A seamless phase II/III design with dose optimization for oncology drug development.

التفاصيل البيبلوغرافية
العنوان: A seamless phase II/III design with dose optimization for oncology drug development.
المؤلفون: Li Y; Department of Statistics, University of Illinois Urbana-Champaign, Champaign, Illinois, USA., Zhang Y; Department of Biostatistics and Programming, Sanofi US, Cambridge, Massachusetts, USA., Mi G; Department of Biostatistics and Programming, Sanofi US, Cambridge, Massachusetts, USA., Lin J; Department of Biostatistics and Programming, Sanofi US, Cambridge, Massachusetts, USA.
المصدر: Statistics in medicine [Stat Med] 2024 Aug 15; Vol. 43 (18), pp. 3383-3402. Date of Electronic Publication: 2024 Jun 06.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Chichester ; New York : Wiley, c1982-
مواضيع طبية MeSH: Clinical Trials, Phase II as Topic*/methods , Antineoplastic Agents*/administration & dosage , Antineoplastic Agents*/therapeutic use , Drug Development*/methods , Clinical Trials, Phase III as Topic*, Humans ; Sample Size ; Computer Simulation ; Dose-Response Relationship, Drug ; Research Design ; United States ; United States Food and Drug Administration ; Drug Approval ; Randomized Controlled Trials as Topic ; Neoplasms/drug therapy
مستخلص: The US FDA's Project Optimus initiative that emphasizes dose optimization prior to marketing approval represents a pivotal shift in oncology drug development. It has a ripple effect for rethinking what changes may be made to conventional pivotal trial designs to incorporate a dose optimization component. Aligned with this initiative, we propose a novel seamless phase II/III design with dose optimization (SDDO framework). The proposed design starts with dose optimization in a randomized setting, leading to an interim analysis focused on optimal dose selection, trial continuation decisions, and sample size re-estimation (SSR). Based on the decision at interim analysis, patient enrollment continues for both the selected dose arm and control arm, and the significance of treatment effects will be determined at final analysis. The SDDO framework offers increased flexibility and cost-efficiency through sample size adjustment, while stringently controlling the Type I error. This proposed design also facilitates both accelerated approval (AA) and regular approval in a "one-trial" approach. Extensive simulation studies confirm that our design reliably identifies the optimal dosage and makes preferable decisions with a reduced sample size while retaining statistical power.
(© 2024 John Wiley & Sons Ltd.)
References: Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2019;20(2):273‐286.
European Medicines Agency. Reflection Paper on Methodological Issues in Confirmatory Clinical Trials Planned with an Adaptive Design. London: EMEA; 2007.
Orloff J, Douglas F, Pinheiro J, et al. The future of drug development: advancing clinical trial design. Nat Rev Drug Discov. 2009;8(12):949‐957.
Jenkins M, Stone A, Jennison C. An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival endpoints. Pharm Stat. 2011;10(4):347‐356.
Chen C, Anderson K, Mehrotra DV, Rubin EH, Tse A. A 2‐in‐1 adaptive phase 2/3 design for expedited oncology drug development. Contemp Clin Trials. 2018;64:238‐242.
Li R, Wu L, Liu R, Lin J. Flexible Seamless 2‐in‐1 Design with Sample Size Adaptation. J Biopharm Stat. 2024;1‐9. .
US Food and Drug Administration. Project Optimus: Reforming the dose optimization and dose selection paradigm in oncology. https://www.fda.gov/about‐fda/oncology‐center‐excellence/project‐optimus. 2023.
Food US, Administration D. Optimizing the dosage of human prescription drugs and biological products for the treatment of oncologic diseases, Guidance for Industry (draft). https://www.fda.gov/media/164555/download. 2023.
Shah M, Rahman A, Theoret MR, Pazdur R. The drug‐dosing conundrum in oncology‐when less is more. N Engl J Med. 2021;385(16):1445‐1447.
Blumenthal G, Jain L, Loeser AL, et al. Optimizing dosing in oncology drug development. Friends Cancer Res. 2021;1‐14.
Fourie Zirkelbach J, Shah M, Vallejo J, et al. Improving dose‐optimization processes used in oncology drug development to minimize toxicity and maximize benefit to patients. J Clin Oncol. 2022;40(30):3489‐3500.
Thall PF, Cook JD. Dose‐finding based on efficacy–toxicity trade‐offs. Biometrics. 2004;60(3):684‐693.
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;4:1393‐1402.
Dehbi HM, O'Quigley J, Iasonos A. Controlled backfill in oncology dose‐finding trials. Contemp Clin Trials. 2021;111:106605.
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(4):673‐679.
Jiang Z, Mi G, Lin J, Lorenzato C, Ji Y. A multi‐arm two‐stage (MATS) design for proof‐of‐concept and dose optimization in early‐phase oncology trials. Contemp Clin Trials. 2023;132:107278.
Guo B, Yuan Y. DROID: dose‐ranging approach to optimizing dose in oncology drug development. Biometrics. 2023;79(4):2907‐2919.
Dehbi HM, O'Quigley J, Iasonos A. Controlled amplification in oncology dose‐finding trials. Contemp Clin Trials. 2023;125:107021.
Liu J, Yuan S, Bekele BN, Ji Y. The Backfill i3+ 3 Design for Dose‐Finding Trials in Oncology. The New England Journal of Statistics in Data Science. 2024;1‐13.
Zhang P, Li XN, Lu K, Wu C. A 2‐in‐1 adaptive design to seamlessly expand a selected dose from a phase 2 trial to a phase 3 trial for oncology drug development. Contemp Clin Trials. 2022;122:106931.
Jin M, Zhang P. A seamless adaptive 2‐in‐1 design expanding a phase 2 trial for treatment or dose selection into a phase 3 trial. Stat Biopharm Res. 2022;14(3):334‐341.
Zhang P, Li XN, Lu K, Wu C, Ge M. A variation of a 2‐in‐1 adaptive design to seamlessly expand a selected dose from a phase 2 trial to a phase 3 trial for oncology drug development. Contemp Clin Trials. 2023;127:107119.
Jiang L, Yuan Y. Seamless phase II/III design: a useful strategy to reduce the sample size for dose optimization. J Natl Cancer Inst. 2023; 115(9):1092‐1098.
Proschan MA. Sample size re‐estimation in clinical trials. Biometr J: J Math Methods Biosci. 2009;51(2):348‐357.
Mehta CR, Pocock SJ. Adaptive increase in sample size when interim results are promising: a practical guide with examples. Stat Med. 2011;30(28):3267‐3284.
Mehta CR. Adaptive clinical trial designs with pre‐specified rules for modifying the sample size: a different perspective. Stat Med. 2013;32(8):1276‐1279.
Pritchett YL, Menon S, Marchenko O, et al. Sample size re‐estimation designs in confirmatory clinical trials—current state, statistical considerations, and practical guidance. Stat Biopharm Res. 2015;7(4):309‐321.
US Food and Drug Administration. Adaptive Designs for Clinical Trials of Drugs and Biologics, Guidance for Industry. https://www.fda.gov/media/78495/download. 2019.
US Food and Drug Administration. Table of Surrogate Endpoints That Were the Basis of Drug Approval or Licensure. https://www.fda.gov/drugs/development‐resources/table‐surrogate‐endpoints‐were‐basis‐drug‐approval‐or‐licensure. 2022.
Driscoll JJ, Rixe O. Overall survival: still the gold standard: why overall survival remains the definitive end point in cancer clinical trials. Cancer J. 2009;15(5):401‐405.
Fayers PM, Ashby D, Parmar MK. Tutorial in biostatistics: Bayesian data monitoring in clinical trials. Stat Med. 1997;16(12):1413‐1430.
Louvet C, Gramont dA, Tournigand C, Artru P, Maindrault‐Goebel F, Krulik M. Correlation between progression free survival and response rate in patients with metastatic colorectal carcinoma. Cancer: Interdiscip Int J Am Cancer Soc. 2001;91(11):2033‐2038.
Ye J, Ji X, Dennis PA, Abdullah H, Mukhopadhyay P. Relationship between progression‐free survival, objective response rate, and overall survival in clinical trials of PD‐1/PD‐L1 immune checkpoint blockade: a meta‐analysis. Clin Pharmacol Therapeut. 2020;108(6):1274‐1288.
Fashoyin‐Aje LA, Mehta GU, Beaver JA, Pazdur R. The on‐and off‐ramps of oncology accelerated approval. N Engl J Med. 2022;387(16):1439‐1442.
US Food and Drug Administration. Accelerated Approval. https://wwwfdagov/patients/fast‐track‐breakthrough‐therapy‐accelerated‐approval‐priority‐review/accelerated‐approval. 2023.
US Food and Drug Administration. Clinical trial considerations to support accelerated approval of oncology therapeutics. https://www.fda.gov/media/166431/download. 2023.
Zhao Y, Li D, Liu R, Yuan Y. Bayesian optimal phase II designs with dual‐criterion decision making. Pharm Stat. 2023;22(4):605‐618.
Tang Z. Optimal futility interim design: a predictive probability of success approach with time‐to‐event endpoint. J Biopharm Stat. 2015;25(6):1312‐1319.
Liu Q, Hu G, Ye B, Wang S, Wu Y. Sample Size Re‐estimation Design in Phase II Dose Finding: Conditional Power versus Bayesian Predictive Power. Pharmaceutical Statistics. 2023;Mar 22(2):349‐64.
Kundu MG, Samanta S, Mondal S. Review of calculation of conditional power, predictive power and probability of success in clinical trials with continuous, binary and time‐to‐event endpoints. Health Serv Outcomes Res Methodol. 2023;24(1):14‐45.
Chen DT, Schell MJ, Fulp WJ, et al. Application of Bayesian predictive probability for interim futility analysis in single‐arm phase II trial. Transl Cancer Res. 2019;8(4):S404.
Miller K, Wang M, Gralow J, et al. Paclitaxel plus bevacizumab versus paclitaxel alone for metastatic breast cancer. New Engl J Med. 2007;357(26):2666‐2676.
Miles DW, Chan A, Dirix LY, et al. Phase III study of bevacizumab plus docetaxel compared with placebo plus docetaxel for the first‐line treatment of human epidermal growth factor receptor 2–negative metastatic breast cancer. J Clin Oncol. 2010;28(20):3239‐3247.
Robert NJ, Diéras V, Glaspy J, et al. RIBBON‐1: randomized, double‐blind, placebo‐controlled, phase III trial of chemotherapy with or without bevacizumab for first‐line treatment of human epidermal growth factor receptor 2–negative, locally recurrent or metastatic breast cancer. J Clin Oncol. 2011;29(10):1252‐1260.
Korn EL, Freidlin B. Surrogate and intermediate endpoints in randomized trials: what's the goal? Clin Cancer Res. 2018;24(10):2239‐2240.
Motzer RJ, Escudier B, McDermott DF, et al. Nivolumab versus everolimus in advanced renal‐cell carcinoma. New Engl J Med. 2015;373(19):1803‐1813.
Kantoff PW, Higano CS, Shore ND, et al. Sipuleucel‐T immunotherapy for castration‐resistant prostate cancer. New Engl J Med. 2010;363(5):411‐422.
Kok PS, Yoon WH, Lord S, Marschner I, Friedlander M, Lee CK. Tumor response end points as surrogates for overall survival in immune checkpoint inhibitor trials: a systematic review and meta‐analysis. JCO Precis Oncol. 2021;5:1151‐1159.
US Food and Drug Administration. Oncology Center of Excellence Research & Development Projects. https://www.fda.gov/about‐fda/fda‐organization/oncology‐center‐excellence. 2023.
US Food and Drug Administration. Project FrontRunner: Advancing Development of New Oncology Therapies to the Early Clinical Setting. https://www.fda.gov/about‐fda/oncology‐center‐excellence/project‐frontrunner. 2023.
Ahn MJ, Cho BC, Felip E, et al. Tarlatamab for patients with previously treated small‐cell lung cancer. New Engl J Med. 2023;389(22):2063‐2075.
Chen C, Sun L. Framing the statistical criteria for accelerated approval of oncology drugs: a pathway for front runners. Contemp Clin Trials. 2023;132:107295.
Haslam A, Gill J, Prasad V. The frequency of assessment of progression in randomized oncology clinical trials. Cancer Reports. 2022;5(7):e1527.
Cerqueira FP, Jesus AMC, Cotrim MD. Adaptive design: a review of the technical, statistical, and regulatory aspects of implementation in a clinical trial. Ther Innov Regul Sci. 2020;54:246‐258.
Dejardin D, Huang B, Yuan Y, Beyer U, Fridlyand J, Zhu J. Dose optimization for novel oncology agents: design options and strategies. Stat Biopharm Res. 2024;1‐20. https://www.tandfonline.com/doi/full/10.1080/19466315.2024.2308856.
Hansen AR, Stanton TS, Hong MH, et al. INDUCE‐3: A Randomized, Double‐Blind Study of GSK3359609 (GSK609), an Inducible T‐Cell co‐Stimulatory (ICOS) Agonist Antibody, plus Pembrolizumab (PE) Versus Placebo (PL) plus PE for First‐Line Treatment of PD‐L1‐Positive Recurrent/Metastatic Head and Neck Squamous Cell Carcinoma (R/M HNSCC). 2020.
Chen C, Rubin EH. Adaptive phase 2/3 designs for oncology drug development–time to hedge. Contemp Clin Trials. 2023;125:107047.
فهرسة مساهمة: Keywords: dose optimization; oncology; project optimus; sample‐size re‐estimation; seamless design
المشرفين على المادة: 0 (Antineoplastic Agents)
تواريخ الأحداث: Date Created: 20240607 Date Completed: 20240716 Latest Revision: 20240716
رمز التحديث: 20240716
DOI: 10.1002/sim.10129
PMID: 38845095
قاعدة البيانات: MEDLINE
الوصف
تدمد:1097-0258
DOI:10.1002/sim.10129