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

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  • 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
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    References listed on IDEAS

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    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. David B. Dunson & Brian Neelon, 2003. "Bayesian Inference on Order-Constrained Parameters in Generalized Linear Models," Biometrics, The International Biometric Society, vol. 59(2), pages 286-295, June.
    4. 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.
    5. 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.
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