Development of a Population Pharmacokinetic Model for Cyclosporine from Therapeutic Drug Monitoring Data.
Aim. To develop a population pharmacokinetic model for Uruguayan patients under treatment with cyclosporine (CsA) that can be applied to TDM. Patients and Methods. A total of 53 patients under treatment with CsA were included. 37 patients with at least one pharmacokinetic profile described with four...
| Publicado en: | BioMed Research International pp. 1 - 10 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/9/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149733440&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149733440 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/9/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 149733440 149733440 149733440 10.1155/2021/3108749 149733440 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Development of a Population Pharmacokinetic Model for Cyclosporine from Therapeutic Drug Monitoring Data. aug: au: Umpiérrez, Martín Guevara, Natalia Ibarra, Manuel Fagiolino, Pietro Vázquez, Marta Maldonado, Cecilia affil: Pharmaceutical Sciences Department, Faculty of Chemistry, Universidad de la República, Uruguay, General Flores, 2124 Montevideo, Uruguay sug: subj: Cyclosporine Pharmacokinetics Drug Monitoring Methods Prediction Models Population Surveillance Human Female Male Simulations Data Analysis Software Models, Structural Time Factors Creatinine Descriptive Statistics Female Male ab: Aim. To develop a population pharmacokinetic model for Uruguayan patients under treatment with cyclosporine (CsA) that can be applied to TDM. Patients and Methods. A total of 53 patients under treatment with CsA were included. 37 patients with at least one pharmacokinetic profile described with four samples were considered for model building, while the remaining 16 were considered for the assessments of predictive performances. Pharmacokinetic parameter estimation was performed using a nonlinear mixed effect modelling implemented in the Monolix® software (version 2019R1, Lixoft, France); meanwhile, simulations were performed in R v.3.6.0 with the mlxR package. Results. A two-compartment model with a first-order disposition model including lag time was used as a structural model. The final model was internally validated using prediction corrected visual predictive check (pcVPC) and other graphical diagnostics. A total of 621 CsA steady-state concentrations were analyzed for model development. Population estimates for the absorption constant (ka) and lag time were 0.523 h-1 and 0.512 h, respectively; apparent clearance (CL/F) was 30.3 L/h (relative standard error RSE ± 8.25 %) with an interindividual variability of 39.8% and interoccasion variability of 38.0%; meanwhile, apparent clearance of distribution (Q/F) was 17.0 L/h (RSE ± 12.1 %) with and interindividual variability of 53.2%. The covariate analysis identified creatinine clearance (ClCrea) as an individual factor influencing the Cl of CsA. The predictive capacity of the population model was demonstrated to be effective since predictions made for new patients were accurate for C1 and C2 (MPPEs below 50%). Bayesian forecasting improved significantly in the second and third occasions. Conclusion. A population pharmacokinetic model was developed to reasonably estimate the individual cyclosporine clearance for patients. Hence, it can be utilized to individualize CsA doses for prompt and adequate achievement of target blood concentrations of CsA. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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