Cargando…

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

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 usi...

Descripción completa

Detalles Bibliográficos
Autores principales: Kang, Won-ho, Lee, Jae-yeon, Chae, Jung-woo, Lee, Kyeong-Ryoon, Baek, In-hwan, Kim, Min-Soo, Back, Hyun-moon, Jung, Sangkeun, Shaffer, Craig, Savic, Rada, Yun, Hwi-yeol
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913178/
https://www.ncbi.nlm.nih.gov/pubmed/33546114
http://dx.doi.org/10.3390/ph14020114
_version_ 1783656745474719744
author Kang, Won-ho
Lee, Jae-yeon
Chae, Jung-woo
Lee, Kyeong-Ryoon
Baek, In-hwan
Kim, Min-Soo
Back, Hyun-moon
Jung, Sangkeun
Shaffer, Craig
Savic, Rada
Yun, Hwi-yeol
author_facet Kang, Won-ho
Lee, Jae-yeon
Chae, Jung-woo
Lee, Kyeong-Ryoon
Baek, In-hwan
Kim, Min-Soo
Back, Hyun-moon
Jung, Sangkeun
Shaffer, Craig
Savic, Rada
Yun, Hwi-yeol
author_sort Kang, Won-ho
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7913178
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-79131782021-02-28 Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data Kang, Won-ho Lee, Jae-yeon Chae, Jung-woo Lee, Kyeong-Ryoon Baek, In-hwan Kim, Min-Soo Back, Hyun-moon Jung, Sangkeun Shaffer, Craig Savic, Rada Yun, Hwi-yeol Pharmaceuticals (Basel) Article 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. MDPI 2021-02-03 /pmc/articles/PMC7913178/ /pubmed/33546114 http://dx.doi.org/10.3390/ph14020114 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kang, Won-ho
Lee, Jae-yeon
Chae, Jung-woo
Lee, Kyeong-Ryoon
Baek, In-hwan
Kim, Min-Soo
Back, Hyun-moon
Jung, Sangkeun
Shaffer, Craig
Savic, Rada
Yun, Hwi-yeol
Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data
title Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data
title_full Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data
title_fullStr Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data
title_full_unstemmed Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data
title_short Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data
title_sort population pharmacokinetic method to predict within-subject variability using single-period clinical data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913178/
https://www.ncbi.nlm.nih.gov/pubmed/33546114
http://dx.doi.org/10.3390/ph14020114
work_keys_str_mv AT kangwonho populationpharmacokineticmethodtopredictwithinsubjectvariabilityusingsingleperiodclinicaldata
AT leejaeyeon populationpharmacokineticmethodtopredictwithinsubjectvariabilityusingsingleperiodclinicaldata
AT chaejungwoo populationpharmacokineticmethodtopredictwithinsubjectvariabilityusingsingleperiodclinicaldata
AT leekyeongryoon populationpharmacokineticmethodtopredictwithinsubjectvariabilityusingsingleperiodclinicaldata
AT baekinhwan populationpharmacokineticmethodtopredictwithinsubjectvariabilityusingsingleperiodclinicaldata
AT kimminsoo populationpharmacokineticmethodtopredictwithinsubjectvariabilityusingsingleperiodclinicaldata
AT backhyunmoon populationpharmacokineticmethodtopredictwithinsubjectvariabilityusingsingleperiodclinicaldata
AT jungsangkeun populationpharmacokineticmethodtopredictwithinsubjectvariabilityusingsingleperiodclinicaldata
AT shaffercraig populationpharmacokineticmethodtopredictwithinsubjectvariabilityusingsingleperiodclinicaldata
AT savicrada populationpharmacokineticmethodtopredictwithinsubjectvariabilityusingsingleperiodclinicaldata
AT yunhwiyeol populationpharmacokineticmethodtopredictwithinsubjectvariabilityusingsingleperiodclinicaldata