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

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 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 synonymous with recommended doses. A recurring theme across cases was a greater emphasis on safety over efficacy in oncology drug dose determination.

Conclusion: 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 , and demonstrates its potential in guiding dose optimization by balancing therapeutic benefits against risk.