Predictive Performance of Published Tacrolimus Population Pharmacokinetic Models in Thai Kidney Transplant Patients

Methaneethorn, Janthima; Lohitnavy, Manupat; Onlamai, Kamonwan; Leelakanok, Nattawut

doi:10.1007/s13318-021-00735-8

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…In comparison to the recent study conducted by Methaneethorn et al on Thai patients, which showcased effective predictive performance using Bayesian methods and two-compartment models, it is crucial to highlight that their research did not take into account the influence of CYP3A5 genotype as a covariate. 10 Conversely, our study holds a distinct advantage over external investigations as we performed an external evaluation using both one- and two-compartment models, and notably, all of the models we assessed incorporated CYP3A5 as a covariate.…”

Section: Discussionmentioning

confidence: 99%

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Pharmacogenomic Analysis of CYP3A5*3 and Tacrolimus Trough Concentrations in Vietnamese Renal Transplant Outcomes

Nguyen,

Le,

Nguyen

et al. 2024

PGPM

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Purpose CYP3A5 polymorphisms have been associated with variations in the pharmacokinetics of tacrolimus (Tac) in kidney transplant patients. Our study aims to quantify how the CYP3A5 genotype influences tacrolimus trough concentrations (C 0 ) in a Vietnamese outpatient population by selecting an appropriate population pharmacokinetic model of Tac for our patients. Patients and Methods The external dataset was obtained prospectively from 54 data of adult kidney transplant recipients treated at the 103 Military Hospital. All published Tac population pharmacokinetic models were systematically screened from PubMed and Scopus databases and were selected based on our patient’s available characteristics. Mean absolute prediction error (MAPE), mean prediction error, and goodness-of-fit plots were used to identify the appropriate model for finding the formula that identifies the influence of CYP3A5 genotype on the pharmacokinetic data of Vietnamese patients. Results The model of Zhu et al had a good predictive ability with MAPE of 19.29%. The influence of CYP3A5 genotype on tacrolimus clearance was expressed by the following formulas: . The simulation result showed that Tac C 0 was significantly higher in patients not expressing CYP3A5 (p< 0.001). Conclusion The incorporation of the CYP3A5 phenotype into Zhu’s structural model has significantly enhanced our ability to predict Tacrolimus trough levels in the Vietnamese population. This study’s results underscore the valuable role of CYP3A5 phenotype in optimizing the forecast of Tac concentrations, offering a promising avenue to assist health-care practitioners in their clinical decision-making and ultimately advance patient care outcomes.

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Section: Discussionmentioning

confidence: 99%

“…Model performance based on the MAPE values was classified as acceptable prediction (MAPE < 50%), good prediction (10% < MAPE < 20%), and excellent prediction (MAPE < 10%). 10 …”

Section: Methodsmentioning

confidence: 99%

Pharmacogenomic Analysis of CYP3A5*3 and Tacrolimus Trough Concentrations in Vietnamese Renal Transplant Outcomes

Nguyen,

Le,

Nguyen

et al. 2024

PGPM

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“…9 To date, at least 70 tacrolimus population pharmacokinetic models have been developed from different solid organ transplant recipient (adult) populations, [10][11][12] among which 2 models were developed solely using data obtained from lung transplant recipients. 13,14 Although a number of studies have externally evaluated the robustness of some of the available models for renal, [15][16][17] liver, 18 and heart transplant recipients, 19,20 there has to date been no similar appraisal in the lung transplant recipient population.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Published Population Pharmacokinetic Models to Inform Tacrolimus Therapy in Adult Lung Transplant Recipients

Kirubakaran,

Singh,

Carland

et al. 2024

Therapeutic Drug Monitoring

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Background: The applicability of currently available tacrolimus population pharmacokinetic models in guiding dosing for lung transplant recipients is unclear. In this study, the predictive performance of relevant tacrolimus population pharmacokinetic models was evaluated for adult lung transplant recipients. Methods: Data from 43 lung transplant recipients (1021 tacrolimus concentrations) administered an immediate-release oral formulation of tacrolimus were used to evaluate the predictive performance of 17 published population pharmacokinetic models for tacrolimus. Data were collected from immediately after transplantation up to 90 days after transplantation. Model performance was evaluated using (1) prediction-based assessments (bias and imprecision) of individual predicted tacrolimus concentrations at the fourth dosing based on 1 to 3 previous dosings and (2) simulation-based assessment (prediction-corrected visual predictive check; pcVPC). Both assessments were stratified based on concomitant azole antifungal use. Model performance was clinically acceptable if the bias was within ±20%, imprecision was ≤20%, and the 95% confidence interval of bias crossed zero. Results: In the presence of concomitant antifungal therapy, no model showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33), and pcVPC plots displayed poor model fit to the data set. However, this fit slightly improved in the absence of azole antifungal use, where 4 models showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33). Conclusions: Although none of the evaluated models were appropriate in guiding tacrolimus dosing in lung transplant recipients receiving concomitant azole antifungal therapy, 4 of these models displayed potential applicability in guiding dosing in recipients not receiving concomitant azole antifungal therapy. However, further model refinement is required before the widespread implementation of such models in clinical practice.

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Development and Validation of a Nomogram for Predicting Subtherapeutic Tacrolimus Blood Levels in Renal Transplant Recipients: A Multivariate Logistic Regression Analysis

Duan,

Gao,

et al. 2024

Preprint

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Predictive Performance of Published Tacrolimus Population Pharmacokinetic Models in Thai Kidney Transplant Patients

Cited by 3 publications

References 39 publications

Pharmacogenomic Analysis of CYP3A5*3 and Tacrolimus Trough Concentrations in Vietnamese Renal Transplant Outcomes

Pharmacogenomic Analysis of CYP3A5*3 and Tacrolimus Trough Concentrations in Vietnamese Renal Transplant Outcomes

Evaluation of Published Population Pharmacokinetic Models to Inform Tacrolimus Therapy in Adult Lung Transplant Recipients

Development and Validation of a Nomogram for Predicting Subtherapeutic Tacrolimus Blood Levels in Renal Transplant Recipients: A Multivariate Logistic Regression Analysis

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