09/28/2023: 2:45 PM - 4:00 PM EDT
Parallel
Room: Salon A,Salon B,Salon C
For decades in industry practice, the oncology dose-finding study has been pursuing a single maximum tolerated dose (MTD), which typically becomes the de facto recommended phase 2 dose for further development. In light of recent development of innovative targeted agents and immunotherapies, this practice has been challenged, e.g., many agents failed to find their MTDs or their marketed dose levels are too high. FDA's Project Optimus initiated in 2021 marks a major milestone overdue in challenging the status quo of conventional dose-finding study design in oncology drug development. This initiative has sparked tremendous interest and questions on how to optimize the dose. Multiple doses and adequately sized cohorts are expected to select the optimal dose level. More efficient designs are needed especially in the presence of patient heterogeneity, which may not be addressed in a small dose-escalation study. While these changes are applauded to support more robust oncology dose-finding, practical considerations should also be acknowledged in specific settings, such as appropriate sample size and randomization given the development stage of an agent. Cross-functional perspectives should also be considered to facilitate adequate knowledge of exposure-response/toxicity curve and finally yield an informed decision for dose selection.
This session will feature regulatory agency, academia, and pharmaceutical industry speakers to share their perspectives and insight on the latest efforts and progress in response to the Project Optimus, including statistical methodology development, regulatory feedback from some case studies, and practical considerations in designing oncology dose optimization studies.
Dose optimization
Oncology
Project Optimus
Innovative design
Organizer
Freda Cooner, Eli Lilly and Company
Chair
Gu Mi, Sanofi
Discussant
Joyce Cheng, FDA
Co-Organizer(s)
Philip He, Daiichi-Sankyo
Ji Lin, Sanofi
Topic Description
Oncology
ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop 2023
Presentations
The Project Optimus initiative by the FDA's Oncology Center of Excellence is widely viewed as a groundbreaking effort to change the status quo of conventional dose-finding strategies in oncology. Unlike in other therapeutic areas where multiple doses are evaluated thoroughly in dose ranging studies, early-phase oncology dose-finding studies are characterized by the practice of identifying a single dose, such as the maximum tolerated dose (MTD) or the recommended phase 2 dose (RP2D). Following the spirit of Project Optimus, we propose an Multi-Arm Two-Stage (MATS) design for proof-of-concept (PoC) and dose optimization that allows the evaluation of two selected doses from a dose-escalation trial. The design assess the higher dose first across multiple indications in the first stage, and adaptively enters the second stage for an indication if the higher dose exhibits promising anti-tumor activities. In the second stage, a randomized comparison between the higher and lower doses is conducted to achieve proof-of-concept (PoC) and dose optimization. A Bayesian hierarchical model governs the statistical inference and decision making by borrowing information across doses, indications, and stages. Our simulation studies show that the proposed MATS design yield desirable performance. An R Shiny application has been developed and made available at https://matsdesign.shinyapps.io/mats/
Presenting Author
Yuan Ji, The University of Chicago
CoAuthor(s)
Zhenghao Jiang, The University of Chicago
Gu Mi, Sanofi
Ji Lin, Sanofi
Christelle Lorenzato, Sanofi
Dose optimization studies of new therapeutic agents aim to identify one or more promising doses for further evaluation in subsequent studies. Traditionally, dose optimization has focused on finding the maximum tolerated dose (MTD), assuming that drug activity and efficacy generally increase with increasing dose. For modern targeted agents, the dose-activity relationship is often non-monotone and such that activity starts to plateau or even decline before reaching the MTD. Finding the optimal biological dose (OBD) for a targeted agent requires considering both toxicity and activity in dose optimization. In this work, we propose a new design for finding the OBD that utilizes generalized likelihood ratios (GLRs) to measure statistical evidence regarding key scientific questions on toxicity and activity. This GLR-based design requires no parametric modeling assumptions and only assumes that the dose-toxicity relationship is monotone and that the dose-activity relationship follows a two-sided isotonic regression model. Compared with existing designs that operate under similar assumptions, the GLR-based design is more general and more flexible, and performs better in simulation experiments where drug activity starts to plateau or decline before reaching the MTD.
Presenting Author
Zhiwei Zhang, Gilead Sciences
CoAuthor
Yan Li, Gilead Sciences