A constrained optimum adaptive design for dose finding in early phase clinical trials

Area of Science:

• Pharmacometrics
• Clinical Trial Design
• Drug Development

Background:

• Growing interest in dose-finding methods considering both toxicity and efficacy.
• Pharmacokinetic (PK) data integration improves patient safety and trial efficiency.

Purpose of the Study:

• To develop an adaptive trial design incorporating PK measures (maximum concentration, Cmax) and efficacy probability for dose selection.
• To optimize dose selection criteria under toxicity constraints.
• To address inter-patient variability in PK models.

Main Methods:

• Utilized maximum concentration (Cmax) as the PK measure for dose guidance.
• Employed probability of efficacy as the dose optimization criterion.
• Incorporated constraints on Cmax and toxicity probability.
• Considered inter-patient variability in PK parameters.
• Calculated population optimal sampling times for drug concentration measurement.
• Modeled dose-response using a Cox model for bivariate binary responses.
• Illustrated with a one-compartment PK model with random parameters.

Main Results:

• Demonstrated significant gains in design efficiency through simulation studies.
• Showed a reduction in the proportion of toxic responses.
• Validated the method's effectiveness across various dose-response scenarios.

Conclusions:

• The proposed adaptive trial design effectively integrates PK data and efficacy probability for optimized dose finding.
• The method enhances clinical trial efficiency and patient safety by minimizing toxicity.
• This approach offers a robust framework for future drug development studies.