IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v79y2023i4p2907-2919.html
   My bibliography  Save this article

DROID: dose‐ranging approach to optimizing dose in oncology drug development

Author

Listed:
  • Beibei Guo
  • Ying Yuan

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.

Suggested Citation

  • Beibei Guo & Ying Yuan, 2023. "DROID: dose‐ranging approach to optimizing dose in oncology drug development," Biometrics, The International Biometric Society, vol. 79(4), pages 2907-2919, December.
  • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:2907-2919
    DOI: 10.1111/biom.13840
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13840
    Download Restriction: no

    File URL: https://libkey.io/10.1111/biom.13840?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Beibei Guo & Ying Yuan, 2017. "Bayesian Phase I/II Biomarker-Based Dose Finding for Precision Medicine With Molecularly Targeted Agents," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 508-520, April.
    2. Ying Yuan & Guosheng Yin, 2009. "Bayesian dose finding by jointly modelling toxicity and efficacy as time‐to‐event outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 719-736, December.
    3. F. Bretz & J. C. Pinheiro & M. Branson, 2005. "Combining Multiple Comparisons and Modeling Techniques in Dose-Response Studies," Biometrics, The International Biometric Society, vol. 61(3), pages 738-748, September.
    4. Ick Hoon Jin & Suyu Liu & Peter F. Thall & Ying Yuan, 2014. "Using Data Augmentation to Facilitate Conduct of Phase I-II Clinical Trials With Delayed Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 525-536, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Beibei Guo & Ying Yuan, 2017. "Bayesian Phase I/II Biomarker-Based Dose Finding for Precision Medicine With Molecularly Targeted Agents," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 508-520, April.
    2. Yifei Zhang & Sha Cao & Chi Zhang & Ick Hoon Jin & Yong Zang, 2021. "A Bayesian adaptive phase I/II clinical trial design with late‐onset competing risk outcomes," Biometrics, The International Biometric Society, vol. 77(3), pages 796-808, September.
    3. Kathrin Möllenhoff & Frank Bretz & Holger Dette, 2020. "Equivalence of regression curves sharing common parameters," Biometrics, The International Biometric Society, vol. 76(2), pages 518-529, June.
    4. Francesco De Pretis & Barbara Osimani, 2019. "New Insights in Computational Methods for Pharmacovigilance: E-Synthesis , a Bayesian Framework for Causal Assessment," IJERPH, MDPI, vol. 16(12), pages 1-19, June.
    5. Johan Verbeeck & Martin Geroldinger & Konstantin Thiel & Andrew Craig Hooker & Sebastian Ueckert & Mats Karlsson & Arne Cornelius Bathke & Johann Wolfgang Bauer & Geert Molenberghs & Georg Zimmermann, 2023. "How to analyze continuous and discrete repeated measures in small‐sample cross‐over trials?," Biometrics, The International Biometric Society, vol. 79(4), pages 3998-4011, December.
    6. Thomas A. Murray & Peter F. Thall & Ying Yuan & Sarah McAvoy & Daniel R. Gomez, 2017. "Robust Treatment Comparison Based on Utilities of Semi-Competing Risks in Non-Small-Cell Lung Cancer," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 11-23, January.
    7. Qiqi Deng & Kun Wang & Xiaofei Bai & Naitee Ting, 2019. "A Cautionary Note When a Dose-Ranging Study is Used for Proving the Concept," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 127-140, April.
    8. Beibei Guo & Rui Zhang, 2018. "Photographic Capture-Recapture for Free-Roaming Dog Population Estimation: Is It Possible to Optimize the Dog Photo-Identification?," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 5(3), pages 88-90, February.
    9. Liu, W. & Ah-Kine, P. & Bretz, F. & Hayter, A.J., 2013. "Exact simultaneous confidence intervals for a finite set of contrasts of three, four or five generally correlated normal means," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 141-148.
    10. Peter F. Thall & Aniko Szabo & Hoang Q. Nguyen & Catherine M. Amlie-Lefond & Osama O. Zaidat, 2011. "Optimizing the Concentration and Bolus of a Drug Delivered by Continuous Infusion," Biometrics, The International Biometric Society, vol. 67(4), pages 1638-1646, December.
    11. Kathrin Möllenhoff & Kirsten Schorning & Franziska Kappenberg, 2023. "Identifying alert concentrations using a model‐based bootstrap approach," Biometrics, The International Biometric Society, vol. 79(3), pages 2076-2088, September.
    12. Miller, Frank & Dette, Holger & Guilbaud, Olivier, 2007. "Optimal designs for estimating the interesting part of a dose-effect curve," Technical Reports 2007,21, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    13. Laura Deldossi & Silvia Angela Osmetti & Chiara Tommasi, 2019. "Optimal design to discriminate between rival copula models for a bivariate binary response," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 147-165, March.
    14. Dette, Holger & Scheder, Regine, 2008. "A finite sample comparison of nonparametric estimates of the effective dose in quantal bioassay," Technical Reports 2008,05, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    15. Jianan Peng & Chu‐In Charles Lee & Karelyn A. Davis & Weizhen Wang, 2008. "Stepwise Confidence Intervals for Monotone Dose–Response Studies," Biometrics, The International Biometric Society, vol. 64(3), pages 877-885, September.
    16. Nairanjana Dasgupta & Monte J. Shaffer, 2012. "Many-to-one comparison of nonlinear growth curves for Washington's Red Delicious apple," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(8), pages 1781-1795, April.
    17. José L. Jiménez & Mourad Tighiouart, 2022. "Combining cytotoxic agents with continuous dose levels in seamless phase I‐II clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1996-2013, November.
    18. Qiqi Deng & Xiaofei Bai & Dacheng Liu & Dooti Roy & Zhiliang Ying & Dan‐Yu Lin, 2019. "Power and sample size for dose‐finding studies with survival endpoints under model uncertainty," Biometrics, The International Biometric Society, vol. 75(1), pages 308-314, March.
    19. Yu, Jun & Kong, Xiangshun & Ai, Mingyao & Tsui, Kwok Leung, 2018. "Optimal designs for dose–response models with linear effects of covariates," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 217-228.
    20. Dette, Holger & Bretz, Frank, 2007. "Optimal designs for dose finding studies," Technical Reports 2007,01, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:2907-2919. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.