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Adaptive Dosing Trial Simulation Framework to Inform Dose Optimization: Case Study Using Longitudinal PKPD Models
Jerry Li1, Li Zhou1, Abraham C F Leung2
1Clinical Pharmacology, Pfizer Inc., La Jolla, California, USA.
CPT: Pharmacometrics & Systems Pharmacology
|May 12, 2026
Summary
Developing an adaptive simulation framework helps optimize oncology drug dosing by accounting for real-time safety adjustments, improving benefit-risk assessment for novel therapeutics like PF-06804103.
Area of Science:
- Pharmacometrics and Computational Pharmacology
- Oncology Drug Development
- Clinical Trial Simulation
Background:
- Optimal dosing for oncology therapeutics requires integrating pharmacokinetics (PK), efficacy, and safety.
- Dose modifications due to toxicity are common for chronic treatments and impact outcomes.
- Existing simulation models often use static dose modification schedules, failing to capture dynamic adverse event impacts.
Purpose of the Study:
- To develop a quantitative modeling approach for optimizing the dosing regimen of PF-06804103, an anti-HER2 antibody-drug conjugate.
- To create an adaptive clinical trial simulation framework that incorporates adverse event-driven dose modifications.
- To better understand the exposure-response relationships for PF-06804103, considering its narrow therapeutic window.
Main Methods:
- Developed an adaptive clinical trial simulation framework.
- Integrated pharmacokinetic, tumor dynamic, and longitudinal models for peripheral neuropathy.
- Utilized a repeated time-to-event approach to model adverse events and dose modifications.
Main Results:
- The framework successfully accounts for real-time impacts of safety on PK and efficacy.
- Simulations enabled evaluation of different dosage regimens for PF-06804103.
- The approach facilitates identification of optimal dosing strategies balancing benefit and risk.
Conclusions:
- An adaptive simulation framework is crucial for optimizing oncology drug dosing, especially with narrow therapeutic windows.
- This model-based approach accurately captures the dynamic interplay between PK, safety, and efficacy.
- The framework can be adapted for other oncology therapeutics with complex exposure-related safety and efficacy profiles.
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