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Exposure-Response-Based Multiattribute Clinical Utility Score Framework to Facilitate Optimal Dose Selection for
Yiming Cheng1, Shuyu Chu2, Jie Pu1
1Clinical Pharmacology, Pharmacometrics & Bioanalysis, Bristol Myers Squibb, Princeton, NJ.
Summary
A new clinical utility score (CUS) framework integrates oncology drug efficacy and safety data. This tool aids dose selection by balancing therapeutic benefits against risks, aligning with FDA's Project Optimus initiative.
Area of Science:
- Pharmacometrics and Drug Development
- Oncology Therapeutics
- Regulatory Science
Background:
- Traditional oncology drug dose selection relies on maximum tolerated dose, which has limitations with novel therapeutics.
- The FDA's Project Optimus advocates a holistic approach using efficacy, safety, pharmacokinetic (PK), and pharmacodynamic data with exposure-response (ER) analyses.
- Integrating multisource data for ER analyses presents significant collation challenges.
Purpose of the Study:
- To develop and validate an Exposure-Response (ER)-based Clinical Utility Score (CUS) framework for oncology drug dose selection.
- To combine drug benefit and risk into a single, integrated measurement for strategic decision-making.
- To address the data collation challenges in multisource ER analyses.
Main Methods:
- Developed a CUS framework converting model-predicted outcomes from ER modeling into a CUS using a user-defined utility function.
- Integrated individual CUS scores with user-defined weighting for each endpoint to reflect expert knowledge on benefit-risk profiles.
- Validated the framework using data from over 50 oncology programs (2019-2023) based on FDA new drug/biologics license application reviews and literature.
Main Results:
- The ER-based CUS framework consistently identified optimal benefit-risk ratios (highest CUS) at PK exposures aligned with recommended doses.
- In-depth evaluation of five representative oncology cases demonstrated the framework's utility.
- A consistent finding across evaluated cases was the prioritization of safety over efficacy in oncology drug dose determination.
Conclusions:
- The ER-based CUS framework provides a strategic approach to navigating complex oncology drug dose selection.
- This framework supports the integration of diverse data types, aligning with the goals of FDA's Project Optimus.
- The CUS framework demonstrates potential for guiding dose optimization by effectively balancing therapeutic benefits against risks.
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