FDA-AACR Strategies for Optimizing Dosages for Oncology Drug Products: Selecting Dosages for First-in-Human Trials
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Updated: Jun 2, 2026

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Published on: May 4, 2016
Hao Zhu1, Alex N Phipps2, Ying Yuan3
1U.S. Food and Drug Administration, Silver Spring, Maryland.
Abstract:
Dosage optimization has become a focus in oncology drug development, as highlighted by recent FDA initiatives, including Project Optimus. Traditionally, most oncology drug development programs identify a maximum tolerated dosage and advance this dose in subsequent clinical trials and premarket applications. This approach has been routinely applied for cytotoxic chemotherapeutics, in which higher dosages generally yield more efficacy as well as toxicity. However, it is less suited for the more targeted pharmacology of modern oncology drugs, in which excessive escalation may only add additional toxicity. Instead, paradigms that utilize the totality of data accumulated throughout drug development can better determine optimized dosages that minimize the risk of underdosing, leading to exposure to subtherapeutic dosages, and overdosing, leading to unnecessary toxicities. Appropriate selection of dosing in first-in-human (FIH) trials is crucial, as it facilitates the efficient identification of optimized doses for subsequent trials. Nonclinical research and clinical data from previous trials can inform both FIH dosage selection and trial design. When background data are lacking, modeling and simulation techniques have been developed to integrate information to determine a rational starting dose. Additionally, innovative model-informed clinical trial designs allow for statistically guided dose escalation and recommendations and can be updated in real time to maximize potential patient benefit within the FIH trial. Unfortunately, these techniques remain underutilized. In this article, in a series of three discussing innovative strategies for dosage optimization, we highlight expectations and provide suggestions for the future of dosage selection and optimization in FIH oncology trials.
Insights
Optimizing cancer drug dosage is crucial for efficacy and safety. Innovative methods, including modeling and simulation, can identify the best doses in early trials, avoiding under- or overdosing.
Area of Science:
- Oncology
- Pharmacology
- Drug Development
Background:
- Traditional oncology drug development relies on maximum tolerated dosage, suitable for cytotoxic drugs.
- This approach is less effective for targeted therapies where higher doses increase toxicity without improving efficacy.
- New strategies are needed to optimize dosing and minimize risks of subtherapeutic or excessive exposure.
Purpose of the Study:
- To highlight expectations and suggestions for dosage selection and optimization in first-in-human (FIH) oncology trials.
- To discuss innovative strategies beyond traditional methods for determining optimal drug doses.
- To emphasize the importance of data integration and model-informed designs in FIH trials.
Main Methods:
- Utilizing the totality of accumulated drug development data for dosage optimization.
- Employing modeling and simulation techniques to determine rational starting doses when background data is limited.
- Implementing innovative model-informed clinical trial designs for statistically guided dose escalation.
Main Results:
- Current techniques for dosage optimization in oncology are underutilized.
- Model-informed designs allow real-time updates to maximize patient benefit.
- Data integration and advanced modeling can lead to more rational and effective dose selection.
Conclusions:
- Dosage optimization is a key focus in modern oncology drug development, driven by initiatives like Project Optimus.
- Selecting appropriate doses in first-in-human trials is critical for efficient identification of optimized doses.
- Innovative, data-driven approaches are essential for minimizing risks and maximizing benefits in oncology drug development.
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