Chie Emoto1, Trevor N Johnson2, Takaaki Yamada3, Hiroshi Yamazaki4, Tsuyoshi Fukuda4,5. 1. Laboratory of Drug Metabolism and Pharmacokinetics, Showa Pharmaceutical University, 3-3165, Machida, Tokyo, 194-8543, Japan. c-emoto@umin.ac.jp. 2. Certara UK Limited, Sheffield, UK. 3. Department of Pharmacy, Kyushu University Hospital, Fukuoka, Japan. 4. Laboratory of Drug Metabolism and Pharmacokinetics, Showa Pharmaceutical University, 3-3165, Machida, Tokyo, 194-8543, Japan. 5. National Center for Child Health and Development, Tokyo, Japan.
Abstract
PURPOSE: Variability in teicoplanin pharmacokinetics has been explained by multiple factors such as body weight, renal function, and serum albumin level. To improve mechanistic understanding of the causes of variability, a physiologically based pharmacokinetic (PBPK) model can be used as a systematic platform. In this study, a PBPK model of teicoplanin was developed to quantitatively assess the effects of physiological changes due to disease status using virtual populations. METHODS: Predictive performance of the models was evaluated by comparing simulated and observed concentration-time profiles of teicoplanin. Subsequently, sensitivity analyses were conducted to identify potential factors contributing to individual differences in teicoplanin PK. RESULTS: The developed PBPK model generated concentration-time profiles that were comparable to clinical observations in healthy adults, including Caucasians and Japanese, and after single-dose and multiple-dose administration. The predicted PK parameters (i.e., Cmax, AUC, clearance) were within a two-fold range of the observed data in patients with renal impairments as well as healthy adults. Changes in total and unbound teicoplanin concentrations at 72 h, after various dosing regimens (tested 4-14 mg/kg q12h for three doses as a loading dose and then 4-14 mg/kg daily as a maintenance dose), were sensitive to renal function and serum albumin concentrations. CONCLUSION: The PBPK model of teicoplanin provides mechanistic insight into the factors altering its disposition and allows assessments of the theoretical and quantitative impact of individual changes in physiological parameters on its PK even when an actual assessment with adequate sample sizes of patients is challenging.
PURPOSE: Variability in teicoplanin pharmacokinetics has been explained by multiple factors such as body weight, renal function, and serum albumin level. To improve mechanistic understanding of the causes of variability, a physiologically based pharmacokinetic (PBPK) model can be used as a systematic platform. In this study, a PBPK model of teicoplanin was developed to quantitatively assess the effects of physiological changes due to disease status using virtual populations. METHODS: Predictive performance of the models was evaluated by comparing simulated and observed concentration-time profiles of teicoplanin. Subsequently, sensitivity analyses were conducted to identify potential factors contributing to individual differences in teicoplanin PK. RESULTS: The developed PBPK model generated concentration-time profiles that were comparable to clinical observations in healthy adults, including Caucasians and Japanese, and after single-dose and multiple-dose administration. The predicted PK parameters (i.e., Cmax, AUC, clearance) were within a two-fold range of the observed data in patients with renal impairments as well as healthy adults. Changes in total and unbound teicoplanin concentrations at 72 h, after various dosing regimens (tested 4-14 mg/kg q12h for three doses as a loading dose and then 4-14 mg/kg daily as a maintenance dose), were sensitive to renal function and serum albumin concentrations. CONCLUSION: The PBPK model of teicoplanin provides mechanistic insight into the factors altering its disposition and allows assessments of the theoretical and quantitative impact of individual changes in physiological parameters on its PK even when an actual assessment with adequate sample sizes of patients is challenging.
Authors: Catherine J Byrne; Ted Parton; Brett McWhinney; Jerome P Fennell; Philomena O'Byrne; Evelyn Deasy; Sean Egan; Helen Enright; Ronan Desmond; Sheila A Ryder; Deirdre M D'Arcy; Johnny McHugh; Jason A Roberts Journal: J Antimicrob Chemother Date: 2018-04-01 Impact factor: 5.790
Authors: Mohd H Abdul-Aziz; Jan-Willem C Alffenaar; Matteo Bassetti; Hendrik Bracht; George Dimopoulos; Deborah Marriott; Michael N Neely; Jose-Artur Paiva; Federico Pea; Fredrik Sjovall; Jean F Timsit; Andrew A Udy; Sebastian G Wicha; Markus Zeitlinger; Jan J De Waele; Jason A Roberts Journal: Intensive Care Med Date: 2020-05-07 Impact factor: 17.440
Authors: V Ramos-Martín; A Johnson; L McEntee; N Farrington; K Padmore; P Cojutti; F Pea; M N Neely; W W Hope Journal: J Antimicrob Chemother Date: 2017-12-01 Impact factor: 5.790