Population Pharmacokinetics and Bayesian Estimators for Refined Dose Adjustment of a New Tacrolimus Formulation in Kidney and Liver Transplant Patients

Woillard, Jean‐Baptiste; Debord, Jean; Monchaud, Caroline; Saint-Marcoux, Franck; Marquet, Pierre

doi:10.1007/s40262-017-0533-5

Cited by 45 publications

(64 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This underlines the usefulness of AUC estimation based on 3 observed concentrations and individual estimation of parameters using the Bayesian method (of note, VPC are based on population parameters and not individual parameters) in comparison to the C 0 . 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 .…”

Section: Discussionmentioning

confidence: 96%

“…For TAC in liver transplant recipients, the LSSs described in the literature include sampling times up to a maximum of 4 h post dose, probably or explicitly because it is difficult to prolong patient stay at the hospital further for sample collection. However, this restriction could theoretically increase the bias and decrease the precision of AUC estimation for OD formulations.…”

Section: Discussionmentioning

confidence: 99%

“…The PK parameters were determined using an iterative 2‐stage Bayesian method implemented in our own computer program following a procedure described in previous papers . Briefly, the ITSIM program uses an expectation/maximization algorithm: the maximization step computes the kinetic curve of each patient by Bayesian estimation; the expectation step computes the population parameters from a weighted average of the individual parameters; the 2 steps are iterated until a good fit is obtained between the observed and computed concentrations.…”

Section: Methodsmentioning

confidence: 99%

“…The PK of TAC is influenced by several factors including TAC formulation, the type of organ transplanted, the time period post‐transplantation, haematocrit, albumin and cytochrome P450 3A5 genotypes . However, previous studies in kidney transplant patients showed that a 1‐compartment model describing the absorption process with 2 γ‐distributions fitted TAC PK with excellent predictive performances (bias, precision and number of predictions out of the ±20% relative error interval were respectively: 0.89 ± 7.32%, 7.38% and 0/28 using iterative two‐stage Bayesian modelling in ITSIM; and − 2.62 ± 8.65%, 8.69% and 2/28 using Pmetrics), without including any covariate in the model.…”

Section: Introductionmentioning

confidence: 96%

“…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%

See 4 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

Self Cite

View full text Add to dashboard Cite

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).

show abstract

Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

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

Self Cite

View full text Add to dashboard Cite

show abstract

Tacrolimus Exposure Prediction Using Machine Learning

Woillard

Labriffe

Debord

et al. 2021

Clin Pharma and Therapeutics

Self Cite

View full text Add to dashboard Cite

The aim of this work is to estimate the area‐under the blood concentration curve of tacrolimus (TAC) following b.i.d. or q.d. dosing in organ transplant patients, using Xgboost machine learning (ML) models. A total of 4,997 and 1,452 TAC interdose area under the curves (AUCs) from patients on b.i.d. and q.d. TAC, sent to our Immunosuppressant Bayesian Dose Adjustment expert system (http://www.pharmaco.chu-limoges.fr/) for AUC estimation and dose recommendation based on TAC concentrations measured at least at 3 sampling times (predose, ~ 1 and 3 hours after dosing) were used to develop 4 ML models based on 2 or 3 concentrations. For each model, data splitting was performed to obtain a training set (75%) and a test set (25%). The Xgboost models in the training set with the lowest root mean square error (RMSE) in a 10‐fold cross‐validation experiment were evaluated in the test set and in 6 independent full‐pharmacokinetic (PK) datasets from renal, liver, and heart transplant patients. ML models based on two or three concentrations, differences between these concentrations, relative deviations from theoretical times of sampling, and four covariates (dose, type of transplantation, age, and time between transplantation and sampling) yielded excellent AUC estimation performance in the test datasets (relative bias < 5% and relative RMSE < 10%) and better performance than maximum a posteriori Bayesian estimation in the six independent full‐PK datasets. The Xgboost ML models described allow accurate estimation of TAC interdose AUC and can be used for routine TAC exposure estimation and dose adjustment. They will soon be implemented in a dedicated web interface.

show abstract

Understanding the Role of Pharmacometrics‐Based Clinical Decision Support Systems in Pediatric Patient Management: A Case Study Using Lyv Software

Jarugula

Ivaturi

Noack

et al. 2021

The Journal of Clinical Pharma

View full text Add to dashboard Cite

Pharmacometrics could play a key role in shifting pediatric pharmacotherapy from dosing for an average patient to individualizing dosing. Clinicians can have these quantitative tools at their disposal without requiring significant training through the development of clinical decision support systems with easy‐to‐use interfaces that have a back‐end analysis engine or pharmacometric model that uses extensive electronic health record data to predict an individualized dose for each patient. There has been increased development of these clinical decision support systems recently, and for these tools to make the proper breakthrough into clinical practice, it is of utmost importance to perform rigorous testing to ensure adequate predictive performance. In this article, we walk through the components of a decision support tool and the testing required to determine its robustness using an example of a decision support tool we developed for vancomycin dosing in pediatrics.

show abstract

Population Pharmacokinetics and Bayesian Estimators for Refined Dose Adjustment of a New Tacrolimus Formulation in Kidney and Liver Transplant Patients

Abstract: Population pharmacokinetic models and Bayesian estimators for Envarsus in kidney and liver transplantation were developed and are now available online for area under the curve-based tacrolimus dose adjustment.

Cited by 45 publications

References 21 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

Tacrolimus Exposure Prediction Using Machine Learning

Understanding the Role of Pharmacometrics‐Based Clinical Decision Support Systems in Pediatric Patient Management: A Case Study Using Lyv Software

Contact Info

Product

Resources

About