Journal
AAPS JOURNAL
Volume 7, Issue 4, Pages E759-E785Publisher
SPRINGER
DOI: 10.1208/aapsj070476
Keywords
pharmacokinetics; pharmacodynamics; D-optimality; estimator; bias; precision; experiment design
Categories
Funding
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [P41EB001975] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM060021] Funding Source: NIH RePORTER
- NIBIB NIH HHS [P41 EB-001975] Funding Source: Medline
- NIGMS NIH HHS [R01 GM-60021] Funding Source: Medline
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Multiple outputs or measurement types are commonly gathered in biological experiments. Often, these experiments are expensive ( such as clinical drug trials) or require careful design to achieve the desired information content. Optimal experimental design protocols could help alleviate the cost and increase the accuracy of these experiments. In general, optimal design techniques ignore between-individual variability, but even work that incorporates it ( population optimal design) has treated simultaneous multiple output experiments separately by computing the optimal design sequentially, first finding the optimal design for one output ( eg, a pharmacokinetic [ PK] measurement) and then determining the design for the second output ( eg, a pharmacodynamic [ PD] measurement). Theoretically, this procedure can lead to biased and imprecise results when the second model parameters are also included in the first model ( as in PK-PD models). We present methods and tools for simultaneous population D-optimal experimental designs, which simultaneously compute the design of multiple output experiments, allowing for correlation between model parameters. We then apply these methods to simulated PKPD experiments. We compare the new simultaneous designs to sequential designs that first compute the PK design, fix the PK parameters, and then compute the PD design in an experiment. We find that both population designs yield similar results in designs for low sample number experiments, with simultaneous designs being possibly superior in situations in which the number of samples is unevenly distributed between outputs. Simultaneous population D-optimality is a potentially useful tool in the emerging field of experimental design.
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