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Karas S, Etheridge AS, Tsakalozou E, Ramírez J, Cecchin E, van Schaik RHN, Toffoli G, Ratain MJ, Mathijssen RHJ, Forrest A, Bies RR, Innocenti F. Optimal Sampling Strategies for Irinotecan (CPT-11) and its Active Metabolite (SN-38) in Cancer Patients. AAPS JOURNAL 2020; 22:59. [PMID: 32185579 DOI: 10.1208/s12248-020-0429-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 02/03/2020] [Indexed: 01/02/2023]
Abstract
Irinotecan (CPT-11) is an anticancer agent widely used in the treatment of a variety of adult solid tumors. The objective of this study was to develop an optimal sampling strategy model that accurately estimates pharmacokinetic parameters of CPT-11 and its active metabolite, SN-38. This study included 221 patients with advanced solid tumors or lymphoma receiving CPT-11 single or combination therapy with 5-fluorouracil (5-FU)/leucovorin (LV) (FOLFIRI) plus bevacizumab from 4 separate clinical trials. Population pharmacokinetic analysis of CPT-11 and SN-38 was performed by non-linear mixed effects modeling. The optimal sampling strategy model was developed using D-optimality with expected distribution approach. The pharmacokinetic profiles of CPT-11 and SN-38 were best described by a 3- and 2-compartment model, respectively, with first-order elimination. Body surface area and co-administration with 5-FU/LV plus bevacizumab were significant covariates (p < 0.01) for volumes of the central compartment of CPT-11 and SN-38, and clearance of CPT-11. Pre-treatment total bilirubin and co-administration with 5-FU/LV and bevacizumab were significant covariates (p < 0.01) for clearance of SN-38. Accurate and precise predictive performance (r2 > 0.99, -2 < bias (%ME) < 0, precision (% RMSE) < 12) of both CPT-11 and SN-38 was achieved using: (i) 6 fixed sampling times collected at 1.5, 3.5, 4, 5.75, 22, 23.5 hours post-infusion; or (ii) 1 fixed time and 2 sampling windows collected at 1.5, [3-5.75], [22-23.5] hours post-infusion. The present study demonstrates that an optimal sampling design with three blood samples achieves accurate and precise pharmacokinetic parameter estimates for both CPT-11 and SN-38.
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Affiliation(s)
- Spinel Karas
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Amy S Etheridge
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Eleftheria Tsakalozou
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Erika Cecchin
- Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, Italy
| | | | - Giuseppe Toffoli
- Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, Italy
| | - Mark J Ratain
- Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Ron H J Mathijssen
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Alan Forrest
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Robert R Bies
- Department of Pharmaceutical Sciences, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY, USA. .,Computational and Data Enabled Sciences and Engineering Program, University at Buffalo, State University of New York at Buffalo, Buffalo, NY, USA.
| | - Federico Innocenti
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Mizuno K, Dong M, Fukuda T, Chandra S, Mehta PA, McConnell S, Anaissie EJ, Vinks AA. Population Pharmacokinetics and Optimal Sampling Strategy for Model-Based Precision Dosing of Melphalan in Patients Undergoing Hematopoietic Stem Cell Transplantation. Clin Pharmacokinet 2017; 57:625-636. [DOI: 10.1007/s40262-017-0581-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Nguyen TT, Bénech H, Delaforge M, Lenuzza N. Design optimisation for pharmacokinetic modeling of a cocktail of phenotyping drugs. Pharm Stat 2015; 15:165-77. [DOI: 10.1002/pst.1731] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Indexed: 12/24/2022]
Affiliation(s)
- Thu Thuy Nguyen
- CEA, LIST; Data Analysis and Systems Intelligence Laboratory; Gif-sur-Yvette France
| | | | | | - Natacha Lenuzza
- CEA, LIST; Data Analysis and Systems Intelligence Laboratory; Gif-sur-Yvette France
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McGree JM, Drovandi CC, Pettitt AN. A sequential Monte Carlo approach to derive sampling times and windows for population pharmacokinetic studies. J Pharmacokinet Pharmacodyn 2012; 39:519-26. [DOI: 10.1007/s10928-012-9265-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2011] [Accepted: 07/09/2012] [Indexed: 10/28/2022]
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Foo LK, McGree J, Duffull S. A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies. Pharm Stat 2012; 11:325-33. [PMID: 22411749 DOI: 10.1002/pst.1509] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2011] [Revised: 12/07/2011] [Accepted: 02/09/2012] [Indexed: 11/09/2022]
Abstract
Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.
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Affiliation(s)
- Lee Kien Foo
- School of Pharmacy, University of Otago, Frederick St, Dunedin, Otago 9001, New Zealand.
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Ogungbenro K, Matthews I, Looby M, Kaiser G, Graham G, Aarons L. Population pharmacokinetics and optimal design of paediatric studies for famciclovir. Br J Clin Pharmacol 2010; 68:546-60. [PMID: 19843058 DOI: 10.1111/j.1365-2125.2009.03479.x] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
AIMS To develop a population pharmacokinetic model for penciclovir (famciclovir is a prodrug of penciclovir) in adults and children and suggest an appropriate dose for children. Furthermore, to develop a limited sampling design based on sampling windows for three different paediatric age groups (1-2, 2-5 and 5-12 years) using an adequate number of subjects for future pharmacokinetic studies. METHODS Penciclovir plasma data from six different adult and paediatric studies were supplied by Novartis. Population pharmacokinetic modelling was undertaken in NONMEM version VI. Simulations in MATLAB were used to select an oral paediatric dose that gives similar exposure to 500 mg in adults. Optimal sampling times and sampling windows were obtained in MATLAB and simulations in NONMEM were used to select adequate sample sizes for three paediatric age groups. RESULTS A two-compartment, first-order absorption model with an absorption lag time, allometric weight models on V(1), V(2) and Q, and an allometric weight model, age and creatinine clearance as covariates on CL adequately describe the pharmacokinetics of penciclovir in adults and children. Estimated CL (l h(-1) 70 kg(-1)) and V(ss) (l.70 kg(-1)) were 31.2 and 83.1, respectively. An oral dose of 10 mg kg(-1) body weight in children was predicted to give similar exposure as 500 mg in adults. A single sampling windows design (0.25-0.4, 0.5-1, 1.25-1.75, 2.75-3.5 and 7.25-8 h) for five samples per subject and 10 subjects in each of the paediatric age groups is recommended for future studies. CONCLUSIONS A population pharmacokinetic model of penciclovir in adults and children has been developed. A prospective study design, including dose adjustment, cohort size and blood sampling design has been recommended.
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Affiliation(s)
- Kayode Ogungbenro
- Centre for Applied Pharmacokinetics Research, School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester M13 9, UK.
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