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Article

Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data

1
College of Pharmacy, Chungnam National University, Deajeon 34134, Korea
2
Division of Convergence Technology New Drug Development Center, Osong Medical Innovation Foundation, Cheongju 28160, Korea
3
Laboratory Animal Resource Center, Korea Research Institute of Bioscience and Biotechnology, Ochang 28116, Korea
4
College of Pharmacy, Kyungsung University, Busan 48434, Korea
5
College of Pharmacy, Pusan National University, Busan 46241, Korea
6
Department, Pharmaceutics, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, New Brunswick, NJ 08854, USA
7
Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Korea
8
Department of Bioengineering and Therapeutic Sciences, School of Pharmacy, University of California, San Francisco, CA 94158, USA
*
Authors to whom correspondence should be addressed.
Those of authors were contributed equally for this work as first author.
Academic Editor: Félix Carvalho
Pharmaceuticals 2021, 14(2), 114; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14020114
Received: 11 January 2021 / Revised: 26 January 2021 / Accepted: 29 January 2021 / Published: 3 February 2021
Sample sizes for single-period clinical trials, including pharmacokinetic studies, are statistically determined by within-subject variability (WSV). However, it is difficult to determine WSV without replicate-designed clinical trial data, and statisticians typically estimate optimal sample sizes using total variability, not WSV. We have developed an efficient population-based method to predict WSV accurately with single-period clinical trial data and demonstrate method performance with eperisone. We simulated 1000 virtual pharmacokinetic clinical trial datasets based on single-period and dense sampling studies, with various study sizes and levels of WSV and interindividual variabilities (IIVs). The estimated residual variability (RV) resulting from population pharmacokinetic methods were compared with WSV values. In addition, 3 × 3 bioequivalence results of eperisone were used to evaluate method performance with a real clinical dataset. With WSV of 40% or less, regardless of IIV magnitude, RV was well approximated by WSV for sample sizes greater than 18 subjects. RV was underestimated at WSV of 50% or greater, even with datasets having low IIV and numerous subjects. Using the eperisone dataset, RV was 44% to 48%, close to the true value of 50%. In conclusion, the estimated RV accurately predicted WSV in single-period studies, validating this method for sample size estimation in clinical trials. View Full-Text
Keywords: interindividual variability; residual variability; pharmacokinetics; statistical modeling interindividual variability; residual variability; pharmacokinetics; statistical modeling
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    Doi: 10.5281/zenodo.4431011
    Link: https://10.5281/zenodo.4431011
    Description: Figure S1. The proportion of predictive success for the first experiment. Figure S2. The proportion of predictive success for the second experiment. Table S1. Tabulated summary for results of comparison with and without covariance between omegas. Text S1. Example as a R code for generating simulation dataset. Text S2. C++ and R script code when used mrgsolve R package.
MDPI and ACS Style

Kang, W.-h.; Lee, J.-y.; Chae, J.-w.; Lee, K.-R.; Baek, I.-h.; Kim, M.-S.; Back, H.-m.; Jung, S.; Shaffer, C.; Savic, R.; Yun, H.-y. Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data. Pharmaceuticals 2021, 14, 114. https://0-doi-org.brum.beds.ac.uk/10.3390/ph14020114

AMA Style

Kang W-h, Lee J-y, Chae J-w, Lee K-R, Baek I-h, Kim M-S, Back H-m, Jung S, Shaffer C, Savic R, Yun H-y. Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data. Pharmaceuticals. 2021; 14(2):114. https://0-doi-org.brum.beds.ac.uk/10.3390/ph14020114

Chicago/Turabian Style

Kang, Won-ho, Jae-yeon Lee, Jung-woo Chae, Kyeong-Ryoon Lee, In-hwan Baek, Min-Soo Kim, Hyun-moon Back, Sangkeun Jung, Craig Shaffer, Rada Savic, and Hwi-yeol Yun. 2021. "Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data" Pharmaceuticals 14, no. 2: 114. https://0-doi-org.brum.beds.ac.uk/10.3390/ph14020114

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