Dose optimisation with simultaneous pharmacokinetic estimation in adaptive clinical trials.

Area of Science:

• Pharmacokinetics and Pharmacodynamics
• Drug Development
• Biostatistics

Background:

• Determining optimal drug dosage is crucial for effective treatment and cost efficiency.
• Suboptimal dosing can lead to adverse patient outcomes and increased healthcare expenditure.
• Current methods may not efficiently integrate dose optimization with real-time parameter estimation.

Purpose of the Study:

• To present a model-based approach for simultaneous pharmacokinetic parameter estimation and dose regimen optimization.
• To develop an adaptive strategy for refining drug dosing based on collected patient data.
• To enhance the ethical and cost-effectiveness of clinical trials through efficient dose determination.

Main Methods:

• A model-based framework was employed for dose optimization.
• Pharmacokinetic (PK) model parameters were estimated using D-optimal sampling time points from collected blood samples.
• An adaptive approach was used, where estimated parameters informed the optimal dose regimen for subsequent patient cohorts.

Main Results:

• The methodology demonstrated simultaneous pharmacokinetic estimation and dose regimen optimization.
• Simulation studies and sensitivity analyses validated the adaptive approach.
• A two-stage design combining adaptive and non-adaptive elements was presented, showing improved performance.

Conclusions:

• The proposed method allows for simultaneous pharmacokinetic estimation and dose optimization in an adaptive clinical trial setting.
• This approach is ethical and cost-effective, protecting subjects from suboptimal doses and reducing the number of participants needed.
• The study provides a robust framework for optimizing drug development processes.