Exposure-Response–Based Multiattribute Clinical Utility Score Framework to Facilitate Optimal Dose Selection for Oncology Drugs

作者：Yiming Cheng, Shuyu Chu, Jie Pu, Min Chen, Kevin Hong, Paulo Maciag, Ivan S. F. Chan, Li Zhu, Akintunde Bello, Yan Li · 发表于：Journal of Clinical Oncology · 年份：2024 · DOI：10.1200/jco.24.00349 · 被引用次数：9 · 研究领域：Health Systems, Economic Evaluations, Quality of Life、Statistical Methods in Clinical Trials、Economic and Financial Impacts of Cancer

PURPOSE The advent of new therapeutic modalities highlighted deficiencies in the traditional maximum tolerated dose approach for oncology drug dose selection and prompted the Food and Drug Administration (FDA)'s Project Optimus initiative, which suggests that sponsors take a holistic approach, including efficacy, safety, and pharmacokinetic (PK) and pharmacodynamic data, in conjunction with integrated exposure-response (ER) analyses. However, this method comes with an inherent challenge of the collation of the multisource data. To address this issue, an ER-based clinical utility score (CUS) framework, combining benefit and risk into a single measurement, was developed. METHODS Model-predicted outcomes for each clinically relevant end point, informed by ER modeling, are converted to a CUS using a user-defined utility function. Thereafter, individual CUS is integrated into a single score with user-defined weighting for each end point. The user-defined weighting feature allows the user to incorporate expert knowledge/understanding into weighing the product's benefit versus risk profile. RESULTS To validate the framework, data were leveraged from over 50 oncology programs from 2019 to 2023 on the basis of FDA new drug application/biologics license application review packages and/or related literature studies. Five representative cases were selected for in-depth evaluation. Results showed that the optimal benefit-risk ratio (highest CUS) was consistently observed at PK exposures s...