Bootstrap method-based estimation of the minimum sample number for obtaining pharmacokinetic parameters in preclinical experiments

Empirically, 3-6 samples at each sampling time point have been used for most preclinical one-point sampling experiments without any theoretical justification. The purpose of the present study is to propose a practical approach to determine the minimum sample number (N(min)) based on Monte Carlo simulation and a bootstrap resampling. A computer program MOMENT(BS), in which a bootstrap resampling algorithm is used to estimate mean and standard deviations of pharmacokinetic parameters, such as area under the curve and mean residence time, was applied to estimate N(min). A new simulation program, MONTE1, was developed to generate simulated data for bootstrap resampling using the model parameters including inter- and/or intra-individual variations. Then, an index, S(2)CV calculated as the sum of the squared coefficient of variation is proposed to determine the N(min). The proposed approach was applied to the actual data in preclinical experiments, and the usefulness of the approach was suggested. An issue that one-point sampling data cannot separately assess inter- and intra-individual variability is discussed.