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Shifting a paradigm: highlights of model informed drug development in dosage selection and optimization for oncology
Ye Xiong1, Robyn Konicki1, Jeanne Fourie Zirkelbach2
1Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, United States.
Abstract:
Applying model-based approaches to leverage clinical and non-clinical data can support the identification of optimized dosage(s) for oncology products and allow for a better benefit-risk balance. In oncology, conventional dose-finding studies may lead to the selection of a poorly optimized dosage into subsequent studies, including those designed to demonstrate safety and efficacy in support of a marketing application. As a result, the clinical data obtained may be insufficient to optimize the benefit-risk profile of the drug without postmarketing studies. Model-informed drug development (MIDD), which includes modeling and simulation (eg, quantitative system pharmacology, empirical or mechanistic pharmacokinetic/pharmacodynamic models), and integrative exposure-response analysis, can significantly enhance dosage selection and optimization. This review provides an update on the recent utilization and unique challenges of MIDD for dosage optimization in oncology drug development.
Insights
Model-informed drug development (MIDD) optimizes oncology drug dosage by integrating diverse data, improving benefit-risk balance and potentially reducing the need for postmarketing studies. This approach enhances early dosage selection for better clinical outcomes.
Area of Science:
- Oncology
- Pharmacometrics
- Drug Development
Background:
- Conventional oncology dose-finding studies often result in suboptimal dosage selection.
- This can lead to insufficient clinical data for optimal benefit-risk assessment, necessitating postmarketing studies.
Purpose of the Study:
- To review the utilization of model-informed drug development (MIDD) for optimizing oncology drug dosage.
- To highlight the unique challenges associated with applying MIDD in this field.
Main Methods:
- Leveraging model-based approaches with clinical and non-clinical data.
- Utilizing modeling and simulation techniques, including quantitative systems pharmacology and PK/PD models.
- Employing integrative exposure-response analysis.
Main Results:
- MIDD significantly enhances dosage selection and optimization in oncology.
- Model-based approaches can improve the benefit-risk balance of oncology products.
- Early identification of optimized dosages can streamline drug development.
Conclusions:
- MIDD is a powerful tool for optimizing oncology drug dosage and improving benefit-risk profiles.
- Addressing the challenges of MIDD is crucial for its effective implementation in oncology drug development.
- This approach supports more efficient and effective drug development pathways.
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