Model based dose personalization in clinical trials
Kabir Soeny1, Barbara Bogacka1, Byron Jones2
1School of Mathematical Sciences, Queen Mary University of London, UK.
This study introduces a new method for optimizing drug doses during clinical trials, ensuring patients receive the intended drug exposure while accurately estimating pharmacokinetic profiles. The algorithm achieves high efficiency, minimizing over- and under-dosing for personalized medicine development.
Area of Science:
- Pharmacokinetics and Drug Development
- Personalized Medicine
- Clinical Trial Optimization
Background:
- Personalized medicine aims to tailor therapies to individual patients.
- Therapeutic drug monitoring is crucial for drugs with narrow therapeutic indices but is not feasible for drugs in early development.
- Limited pharmacokinetic data hinders dose optimization for investigational drugs.
Purpose of the Study:
- To present a novel methodology for optimizing drug dose regimens during pharmacokinetic studies.
- To ensure drug blood concentrations remain within a target range for each subject.
- To facilitate individualized dosing and accurate pharmacokinetic estimation.
Main Methods:
- Development of an algorithm for explicit dose regimen optimization.
- Integration of dose individualization with pharmacokinetic estimation.
- Application during the course of pharmacokinetic studies.
Main Results:
- The algorithm successfully estimates pharmacokinetic profiles while individualizing doses.
- Achieved an average relative efficiency of 97% with a standard deviation under 5%.
- Demonstrated ability to minimize subject over- and under-exposure to the therapy.
Conclusions:
- The methodology ensures correct dosing and intended drug exposure for clinical trial subjects.
- Facilitates simultaneous estimation of drug pharmacokinetic profiles.
- Applicable to randomized concentration-controlled trials requiring target concentration maintenance.
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