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The U.S. Food and Drug Administration (FDA) launched Project Optimus to shift the objective of dose selection from the maximum tolerated dose to the optimal biological dose (OBD), optimizing the benefit-risk tradeoff. One approach…

统计方法学 · 统计学 2025-08-22 Shuqi Wang , Ying Yuan , Suyu Liu

The conventional more-is-better dose selection paradigm, which targets the maximum tolerated dose (MTD), is not suitable for the development of targeted therapies and immunotherapies as the efficacy of these novel therapies may not increase…

统计方法学 · 统计学 2023-08-31 Peng Yang , Daniel Li , Ruitao Lin , Bo Huang , Ying Yuan

One common approach for dose optimization is a two-stage design, which initially conducts dose escalation to identify the maximum tolerated dose (MTD), followed by a randomization stage where patients are assigned to two or more doses to…

统计方法学 · 统计学 2024-11-11 Yixuan Zhao , Rachael Liu , Jianchang Lin , Ying Yuan

The primary goal of a two-stage Phase I/II trial is to identify the optimal dose for the following large-scale Phase III trial. Recently, Phase I dose-finding designs have shifted from identifying the maximum tolerated dose (MTD) to the…

统计方法学 · 统计学 2025-01-16 Hao Sun , Jerry Li

The Project Optimus initiative by the FDA's Oncology Center of Excellence is widely viewed as a groundbreaking effort to change the $\textit{status quo}$ of conventional dose-finding strategies in oncology. Unlike in other therapeutic areas…

应用统计 · 统计学 2023-04-14 Zhenghao Jiang , Gu Mi , Ji Lin , Christelle Lorenzato , Yuan Ji

The initiation of dose optimization has driven a paradigm shift in oncology clinical trials to determine the optimal biological dose (OBD). Early-phase trials with randomized doses can facilitate additional investigation of the identified…

统计方法学 · 统计学 2025-02-27 Gina DAngelo , Guannan Chen , Di Ran

Traditional dose selection for oncology registration trials typically employs a one- or two-step single maximum tolerated dose (MTD) approach. However, this approach may not be appropriate for molecularly targeted therapy that tends to have…

统计方法学 · 统计学 2023-09-28 Jason J. Z. Liao , Ekaterine Asatiani , Qingyang Liu , Kevin Hou

The US Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection towards identifying the optimal biological dose that offers the best balance between benefit and risk, rather…

统计方法学 · 统计学 2023-09-13 Ying Yuan , Heng Zhou , Suyu Liu

Dose optimization in oncology clinical trials has shifted from seeking the maximum tolerated dose to identifying the Optimal Biological Dose (OBD) that balances therapeutic benefits and risks across multiple clinical attributes. Existing…

统计方法学 · 统计学 2025-05-07 Fanni Zhang , Kristine Broglio , Michael Sweeting , Gina D'Angelo

Project Optimus, an initiative by the FDA's Oncology Center of Excellence, seeks to reform the dose-optimization and dose-selection paradigm in oncology. We propose a dose-optimization design that considers plateau efficacy profiles,…

应用统计 · 统计学 2025-04-22 Rebecca B. Silva , Bin Cheng , Shing M. Lee

Early phase, personalized dose-finding trials for combination therapies seek to identify patient-specific optimal biological dose (OBD) combinations, which are defined as safe dose combinations which maximize therapeutic benefit for a…

统计方法学 · 统计学 2024-04-18 James Willard , Shirin Golchi , Erica EM Moodie

The traditional more-is-better dose selection paradigm, developed based on cytotoxic chemotherapeutics, is often problematic When applied to the development of novel molecularly targeted agents (e.g., kinase inhibitors, monoclonal…

统计方法学 · 统计学 2022-11-04 Liyun Jiang , Ying Yuan

The US Food and Drug Administration (FDA) launched Project Optimus and issued guidance to reform dose-finding and selection trials, shifting the paradigm from identifying the maximum tolerable dose (MTD) to determining the optimal…

统计方法学 · 统计学 2025-09-16 Kai Chen , Yixuan Zhao , Kentaro Takeda , Ying Yuan

Identification of optimal dose combinations in early phase dose-finding trials is challenging, due to the trade-off between precisely estimating the many parameters required to flexibly model the possibly non-monotonic dose-response…

统计方法学 · 统计学 2024-02-13 James Willard , Shirin Golchi , Erica E. M. Moodie , Bruno Boulanger , Bradley P. Carlin

We consider a dose-optimization design for first-in-human oncology trial that aims to identify a suitable dose for late-phase drug development. The proposed approach, called the Pharmacometrics-Enabled DOse OPtimization (PEDOOP) design,…

应用统计 · 统计学 2024-06-19 Shijie Yuan , Zhanbo Huang , Jiaxin Liu , Yuan Ji

Dose optimization is a hallmark of Project Optimus for oncology drug development. The number of doses to include in a dose optimization study depends on the totality of evidence, which is often unclear in early-phase development. With equal…

统计方法学 · 统计学 2026-01-29 Linda Sun , Yixin Ren , Cong Chen

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)…

统计方法学 · 统计学 2022-09-07 Beibei Guo , Ying Yuan

The use of drug combinations in clinical trials is increasingly common during the last years since a more favorable therapeutic response may be obtained by combining drugs. In phase I clinical trials, most of the existing methodology…

统计方法学 · 统计学 2020-02-17 José L. Jiménez , Sungjin Kim , Mourad Tighiouart

We propose a new integrated phase I/II trial design to identify the most efficacious dose combination that also satisfies certain safety requirements for drug-combination trials. We first take a Bayesian copula-type model for dose finding…

应用统计 · 统计学 2011-08-09 Ying Yuan , Guosheng Yin

Phase I dose escalation trials in oncology generally aim to find the maximum tolerated dose (MTD). However, with the advent of molecular targeted therapies and antibody drug conjugates, dose limiting toxicities are less frequently observed,…

统计方法学 · 统计学 2025-08-19 Ayon Mukherjee , Jonathan L. Moscovici , Zheng Liu
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