Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in Chinese liver transplant patients

Chen, B.; Shi, Hao; Liu, X.‐X.; Zhang, W.‐X.; Lu, Jun Q.; Xu, Bei-Ming; Chen, H.

doi:10.1111/jcpt.12599

Cited by 30 publications

(27 citation statements)

References 47 publications

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“…The best LSS (0, 2 h, 4 h) was selected using the D‐optimal design but was only evaluated in patients from the development cohort (and not an external validation cohort, which is known to highly underestimate the bias and precision) showing bias of 3.5 ± 13.8% and precision of 12.6 ± 13.9% in the estimation of AUC 0–12h. In the present study, the results of the Bayesian estimation of TAC AUC and of the dose required in the independent validation group showed that both ITSIM and Pmetrics have good predictive performance in term of bias and RMSE (<10% and <20% respectively is consensually accepted for MAP Bayesian estimation). The imprecision was overall lower in stable than in immediate post‐transplantation period for both TAC OD and TD.…”

Section: Discussionmentioning

confidence: 48%

“…In a previous study, we found that the same structural model also accurately described the PK of Envarsus (another once‐daily formulation of TAC) in liver transplant recipients . Several other structural models were proposed to describe the PK of TAC OD and TD in liver transplant patients . For example, Moes et al previously described the PK of TAC OD in stable liver transplant recipients using nonlinear mixed effects modelling (NONMEM).…”

Section: Discussionmentioning

confidence: 90%

“…To date, 2 LSSs have been proposed to estimate TAC AUC 0–12h or AUC 0–24h after the administration of the OD or TD formulations in adult liver transplant recipients . Moes et al investigated an LSS based on a POPPK model developed using NONMEM (version 7.2.1), in 49 stable liver transplant recipients given OD TAC.…”

Section: Discussionmentioning

confidence: 99%

“…POPPK models and BEs are most often built using a parametric POPPK modelling approach . However, this approach assumes a specific and simple distribution of the PK parameters (gaussian, log‐normal etc.…”

Section: Introductionmentioning

confidence: 99%

“…Several POPPK models have been developed for TAC in kidney transplantation . In contrast, not much modelling work has focused on TAC for adult liver transplant recipients, whether in the stable or early post‐transplant periods . In addition, only 2 studies used rich PK samples (with at least 9 samples) and only 1 was developed for the Advagraf once daily (OD) formulation…”

Section: Introductionmentioning

confidence: 99%

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Population pharmacokinetic model and Bayesian estimator for 2 tacrolimus formulations in adult liver transplant patients

Riff

Debord

Monchaud

et al. 2019

Brit J Clinical Pharma

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Aims: Tacrolimus is a narrow therapeutic range drug that requires fine dose adjustment, for which pharmacokinetic (PK) models have been amply proposed in renal, but not in liver, transplant recipients. This study aimed to build population PK models and Bayesian estimators (BEs) in adult de novo liver transplant patients receiving either the immediate-release (Prograf, twice daily, TD) or prolonged-release (Advagraf, once daily, OD) forms to help PK-guided dose individualization.Methods: In total, 160 tacrolimus concentration-time profiles (1654 samples) were collected from 80 patients, at day 7 (D7) and week 6 (W6) post-transplant. Four population PK models were developed using in-parallel parametric and nonparametric approaches for each formulation and period post-transplant. The best limited sampling strategies for estimating the area-under-the-curve (AUC) were selected by comparing predicted values to an independent dataset. Finally, the doses required to reach AUC targets were estimated using each BE and compared to the doses obtained using the trapezoidal AUC.Results: Tacrolimus PK was best described using a 1-compartmental model with first-order elimination and 2 γ-distributions to describe the absorption. In the validation datasets, Bayesian AUC estimates yielded mean bias/root mean squared prediction error of −5.06%/13.43% (OD D7), 2.25%/8.51% (OD W6), −2.36%/ 7.27% (TD D7) and 0.87%/9.07% (TD W6) for the in-parallel parametric approach; and 8.95%/17.84% (OD D7), −0.11%/10.13% (OD W6), 3.57%/18.40% (TD D7) and 4.48%/12.59% (TD W6) for the nonparametric approach.Conclusion: The BEs and limited sampling strategies proposed here are able to predict accurately and precisely tacrolimus AUC in liver patients using only 3 plasma concentrations. The dosing methods are available on our ImmunoSuppressive Bayesian dose Adjustment website (www.pharmaco.chu-limoges.fr).

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Section: Discussionmentioning

confidence: 48%

Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Population pharmacokinetic model and Bayesian estimator for 2 tacrolimus formulations in adult liver transplant patients

Riff

Debord

Monchaud

et al. 2019

Brit J Clinical Pharma

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Bacterial Influence on Pharmacokinetics of Tacrolimus and Sulfasalazine through Regulation of Host Metabolism

Cooper,

Bhushan

2024

Advanced Therapeutics

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The unpredictable oral bioavailability of established drugs like tacrolimus and sulfasalazine presents a significant clinical challenge. This variability can lead to either toxicity or insufficient therapeutic effect. It is known that alterations in drug transporters and metabolic enzymes influence drug bioavailability. Recent evidence suggests that the indirect metabolism by gut microbes could influence transporters and enzymes which can affect the bioavailability of oral drugs. In this study, the pharmacokinetics of tacrolimus and sulfasalazine are modeled under varying host colonic conditions induced by the bacteria E. coli Nissle 1917 and Bifidobacterium adolescentis. Insight is provided into the sensitivity of pharmacokinetics to the bacterial influence on expression of intestinal drug transporters and cytochrome p450 enzymes. Our findings demonstrate that bacterial modulation reduces tacrolimus peak blood concentration compared to healthy renal transplant patients. Conversely, bacterial presence leads to a two‐fold increase in sulfasalazine's peak plasma concentration compared to healthy subjects. These results suggest that incorporating the gut bacteria's influence on colonic transporters and enzymes can improve the explanation of pharmacokinetic variability. This approach has the potential to refine pharmacokinetic models and ultimately address the challenge of variable oral drug bioavailability.

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Mass Spectrometry‐based Personalized Drug Therapy

Cui

Wang

Tan

et al. 2020

Mass Spectrometry Reviews

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Personalized drug therapy aims to provide tailored treatment for individual patient. Mass spectrometry (MS) is revolutionarily involved in this area because MS is a rapid, customizable, cost‐effective, and easy to be used high‐throughput method with high sensitivity, specificity, and accuracy. It is driving the formation of a new field, MS‐based personalized drug therapy, which currently mainly includes five subfields: therapeutic drug monitoring (TDM), pharmacogenomics (PGx), pharmacomicrobiomics, pharmacoepigenomics, and immunopeptidomics. Gas chromatography‐MS (GC‐MS) and liquid chromatography‐MS (LC‐MS) are considered as the gold standard for TDM, which can be used to optimize drug dosage. Matrix‐assisted laser desorption ionization‐time of flight‐MS (MALDI‐TOF‐MS) significantly improves the capability of detecting biomacromolecule, and largely promotes the application of MS in PGx. It is becoming an indispensable tool for genotyping, which is used to discover and validate genetic biomarkers. In addition, MALDI‐TOF‐MS also plays important roles in identity of human microbiome whose diversity can explain interindividual differences of drug response. Pharmacoepigenetics is to study the role of epigenetic factors in individualized drug treatment. MS can be used to discover and validate pharmacoepigenetic markers (DNA methylation, histone modification, and noncoding RNA). For the emerging cancer immunotherapy, personalized cancer vaccine has effective immunotherapeutic activity in the clinic. MS‐based immunopeptidomics can effectively discover and screen neoantigens. This article systematically reviewed MS‐based personalized drug therapy in the above mentioned five subfields. © 2020 John Wiley & Sons Ltd. Mass Spec Rev

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Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in Chinese liver transplant patients

Cited by 30 publications

References 47 publications

Population pharmacokinetic model and Bayesian estimator for 2 tacrolimus formulations in adult liver transplant patients

Population pharmacokinetic model and Bayesian estimator for 2 tacrolimus formulations in adult liver transplant patients

Bacterial Influence on Pharmacokinetics of Tacrolimus and Sulfasalazine through Regulation of Host Metabolism

Mass Spectrometry‐based Personalized Drug Therapy

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