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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.
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 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 Project Optimus, and demonstrates its potential in guiding dose optimization by balancing therapeutic benefits against risk.
Insights
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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