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

Riff, Camille; Debord, Jean; Monchaud, Caroline; Marquet, Pierre; Woillard, Jean‐Baptiste

doi:10.1111/bcp.13960

Cited by 16 publications

(18 citation statements)

References 31 publications

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“…(0, 0.33, 0.66, 1, 2, 3, 4, 6, 8, 12, 13, 15, and 24 hours postdosing) collected at 7 days and 3 months postrenal transplantation. In liver transplant recipients, 14 our database included 68 PK profiles of 9 samples for TAC b.i.d. (0, 0.5, 1, 2, 3, 4, 6, 8, and 12 hours postdosing) and 91 PK profiles of 17 samples for TAC q.d.…”

Section: Methodsmentioning

confidence: 99%

“…For that, two datasets of TAC b.i.d. in renal transplant recipients, 7,11 one in liver transplant recipients, 14 and one in heart transplant recipients, 15,16 as well as a dataset of TAC q.d. in renal transplant recipients 7 and another one in liver transplant recipients 14 were used.…”

Section: Plan Of the Studymentioning

confidence: 99%

“…in renal transplant recipients, 7,11 one in liver transplant recipients, 14 and one in heart transplant recipients, 15,16 as well as a dataset of TAC q.d. in renal transplant recipients 7 and another one in liver transplant recipients 14 were used. The performance of the Xgboost models in these confirmation datasets was also compared with that of the BE based on the three sample limited sampling strategies available on ISBA.…”

Section: Plan Of the Studymentioning

confidence: 99%

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Tacrolimus Exposure Prediction Using Machine Learning

Woillard

Labriffe

Debord

et al. 2021

Clin Pharma and Therapeutics

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Plan Of the Studymentioning

confidence: 99%

Section: Plan Of the Studymentioning

confidence: 99%

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Tacrolimus Exposure Prediction Using Machine Learning

Woillard

Labriffe

Debord

et al. 2021

Clin Pharma and Therapeutics

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“…Research has shown that using a population pharmacokinetics-based Bayesian model (a statistical pharmacology model using large volumes of clinical data) with sparse sampling improves target achievement over traditional therapeutic drug monitoring in adults, and has been applied to pediatrics clinically. 13,14 The incorporation of pharmacogenetics factors, such as CYP3A5*3 and CYP3A4*22 genotypes, has further reduced variability and improves prediction of drug exposure. 9 A randomized clinical trial in pediatrics showed that CYP3A5 genotypeguided dosing based on age resulted in earlier attainment of target concentrations.…”

Section: Researchmentioning

confidence: 99%

Partnering with Clinical Pharmacologists to Improve Medication Use in Children

Miyagi

Lam

Girdwood

2020

The Journal of Pediatrics

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A merican children receive on average 3.5 prescriptions annually, with 62%-85% used off-label. 1,2 Lack of formulations suitable for children is a major reason for off-label and unlicensed use of medications. 3 Although this practice does not imply improper, illegal, contraindicated, or investigational use, it often lacks substantial evidence of effectiveness and/or safety from adequate and well-controlled studies. The passage of legislation (Best Pharmaceuticals for Children Act in 2002, the Pediatric Research Equity Act in 2003, the Food and Drug Administration Safety and Innovation Act in 2012; and the Food and Drug Administration Reauthorization Act in 2017) has helped to change >500 labels to include pediatric indications, yet 59% of drug labels still do not contain pediatric information. 2 Furthermore, clinical data for preterm and full-term neonates, infants, children <2 years of age, in addition to children with chronic or rare diseases, are lacking. 2,4 The lack of pediatric clinical data can lead to subtherapeutic effects owing to underdosing or toxicity related to overexposure. Furthermore, children are also inherently predisposed to adverse drug events owing to developmental pharmacology, the age-dependent changes that affect response to and elimination of drugs. 1,2,4-6 It is also unethical to deny children the benefit of medications that are otherwise approved for adults just because they are a vulnerable population and the conduct of pediatric trials is difficult. Thus, it is necessary to conduct pediatric drug trials under conditions that optimize protection for children as participants in research.

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“…Fortunately, population pharmacokinetic models and Monte Carlo simulation can be used to assist in optimizing the initial dosage (15,16). Since the initial clinical use of tacrolimus, several population pharmacokinetics models based on patients receiving a liver transplant have been built (17)(18)(19)(20)(21)(22). However, the precise treatment of tacrolimus and a model for the recommended initial dose in Chinese patients undergoing pediatric liver transplants has not been determined.…”

Section: Introductionmentioning

confidence: 99%

Population pharmacokinetics and pharmacogenomics of tacrolimus in Chinese children receiving a liver transplant: initial dose recommendation

Chen

Wang

et al. 2020

Transl Pediatr

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Background: In order to improve the precision of treatment with tacrolimus in Chinese patients undergoing pediatric liver transplantation, the optimum initial dose of tacrolimus was determined based on population pharmacokinetics and pharmacogenomics.Methods: Demographic data, clinical parameters, drug combinations and pharmacogenomics were integrated to build a population pharmacokinetic model using NONMEM. Additionally, Monte Carlo simulations were used to optimize the recommended initial dose.Results: Weight, patient cytochrome 450 3A (CYP3A)5 genotype, and co-administration with wuzhicapsule (WZ) were incorporated into the final model. For children with a CYP3A5*3/*3 genotype not co-administered WZ, 0.10 mg/kg/day split into two doses was recommended for patients weighing 5-17 kg, and 0.05 mg/kg/day split into two doses was recommended for patients weighing 17-60 kg. For children with a CYP3A5*1 allele not co-administered WZ, 0.25 mg/kg/day for patients weighing 5-10 kg, 0.20 mg/kg/day for patients weighing 10-17 kg, 0.15 mg/kg/day for patients weighing 17-36 kg, and 0.10 mg/kg/day for patients weighing 36-60 kg; all split into two doses was recommended. For children with a CYP3A5*3/*3 genotype co-administered WZ, 0.10 mg/kg/day for patients weighing 5-11 kg, and 0.05 mg/kg/day for patients weighing 11-60 kg; both split into two doses was recommended. For children with a CYP3A5*1 allele who were co-administered WZ, 0.20 mg/kg/day for patients weighing 5-10 kg, 0.15 mg/kg/day for patients weighing 10-22 kg, and 0.10 mg/kg/day for patients weighing 22-60 kg all split into two doses was recommended. Conclusions:The optimal initial dose of tacrolimus was determined based on population pharmacokinetics and pharmacogenomics in Chinese patients undergoing pediatric liver transplantation.

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

Cited by 16 publications

References 31 publications

Tacrolimus Exposure Prediction Using Machine Learning

Tacrolimus Exposure Prediction Using Machine Learning

Partnering with Clinical Pharmacologists to Improve Medication Use in Children

Population pharmacokinetics and pharmacogenomics of tacrolimus in Chinese children receiving a liver transplant: initial dose recommendation

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