新治疗方式的出现凸显了传统最大耐受剂量方法在肿瘤药物剂量选择方面的缺陷，并促使美国食品和药物管理局 (FDA) 发起了 Optimus 计划，该计划建议申办者采取整体方法，包括疗效、安全性、药代动力学 (PK) 和药效学数据，以及综合暴露反应 (ER) 分析。然而，这种方法带来了多源数据整理的固有挑战。为了解决这个问题，开发了一个基于 ER 的临床效用评分 (CUS) 框架，将效益和风险合并到一个测量中。根据 ER 模型的信息，每个临床相关终点的模型预测结果将转换为 CUS使用用户定义的实用函数。此后，各个 CUS 被集成为单个分数，并为每个终点定义用户定义的权重。用户定义的权重功能允许用户将专业知识/理解融入到权衡产品的收益与风险状况中。为了验证该框架，根据 FDA 新药申请/，利用了 2019 年至 2023 年 50 多个肿瘤学项目的数据生物制品许可证申请审查包和/或相关文献研究。选取5个具有代表性的案例进行深入评价。结果表明，在与推荐剂量同义的 PK 暴露下，始终观察到最佳获益风险比（最高 CUS）。各个案例中反复出现的一个主题是，在肿瘤药物剂量确定中，更加强调安全性而不是疗效。基于 ER 的 CUS 框架提供了一个战略工具，可以解决肿瘤项目中剂量选择的复杂性。它是综合数据分析重要性的支柱，与 Optimus 项目的愿景相一致，并展示了其通过平衡治疗效益与风险来指导剂量优化的潜力。

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.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.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 synonymous with recommended doses. A recurring theme across cases was a greater emphasis on safety over efficacy in oncology drug dose determination.The ER-based CUS framework offers a strategic tool to navigate the complexities of dose selection in oncology programs. It serves as a pillar to the importance of integrative data analysis, aligning with the vision of Project Optimus, and demonstrates its potential in guiding dose optimization by balancing therapeutic benefits against risk.