Population pharmacokinetic analysis and dosing guidelines for tacrolimus co‐administration with Wuzhi capsule in Chinese renal transplant recipients

Yan, Jing; Kong, Ying; Hou, Xiongjun; Liu, Hong; Fu, Qun; Jiao, Zheng; Peng, Hongwei; Wei, Xiaohua

doi:10.1111/jcpt.13407

Cited by 23 publications

(22 citation statements)

References 37 publications

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“…To the best of our knowledge, Wuzhi capsule is commonly used in Chinese solid organ transplantation to reduce tacrolimus dosing. There are two domestic PopPK studies 25 , 31 on the effects of co-administration of tacrolimus with Wuzhi capsule in Chinese renal transplant recipients. These two studies are limited to the effect of Wuzhi Capsule on tacrolimus clearance.…”

Section: Discussionmentioning

confidence: 99%

Wuzhi Capsule Dosage Affects Tacrolimus Elimination in Adult Kidney Transplant Recipients, as Determined by a Population Pharmacokinetics Analysis

Chen¹,

Yang²,

Wang³

et al. 2021

PGPM

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Purpose In this study, we aimed to establish a tacrolimus population pharmacokinetic model and better understand the drug-drug interaction between Wuzhi capsule and tacrolimus in Chinese renal transplant recipients. Patients and Methods We performed a population pharmacokinetic analysis using a non-linear mixed-effects model to determine the suitable Wuzhi capsule dose in combination with tacrolimus. Data on 1378 tacrolimus steady-state concentrations were obtained from 142 patients who received kidney transplant in Changhai Hospital and Huashan Hospital. Demographic characteristics, laboratory tests, genetic polymorphisms, and co-medications were evaluated. Results The one-compartment model best described data. Our final model identified creatinine clearance rate, hematocrit, Wuzhi capsule dose, CYP3A5*3 genetic polymorphisms, and tacrolimus daily dose as significant covariates for tacrolimus clearance, with the value of 14.4 L h −1 , and the between-subject variability (BSV) was 25.4%. The Wuzhi capsule showed a dose-dependent effect on tacrolimus pharmacokinetics, demonstrating a stronger inhibitory effect than inductive effect. Conclusion Our model can accurately describe population pharmacokinetics of tacrolimus when combined with different doses of Wuzhi capsule. Additionally, this model can be used for individualizing tacrolimus dose following kidney transplantation.

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Section: Discussionmentioning

confidence: 99%

Wuzhi Capsule Dosage Affects Tacrolimus Elimination in Adult Kidney Transplant Recipients, as Determined by a Population Pharmacokinetics Analysis

Chen¹,

Yang²,

Wang³

et al. 2021

PGPM

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“…The clinical application of TAC has been characterized by large inter‐individual variability and a narrow therapeutic window 2 . Previous research by our group has verified the main role of CYP3A4 and CYP3A5 in the metabolism of TAC in kidney transplant patients 3,4 . However, inter‐individual variability in drug efficacy is associated with genetic variations as well as individual endogenous and exogenous factors, which may significantly affect the in vivo exposure of patients to the drug 5 .…”

Section: What Is Known and Objectivementioning

confidence: 93%

“…2 Previous research by our group has verified the main role of CYP3A4 and CYP3A5 in the metabolism of TAC in kidney transplant patients. 3,4 However, inter-individual variability in drug efficacy is associated with genetic variations as well as individual endogenous and exogenous factors, which may significantly affect the in vivo exposure of patients to the drug. 5 Several factors can influence the pharmacokinetics of TAC; these include demographical characteristics (age, 6,7 height and weight 8,9 ), blood biochemical indicators (haematocrit 9,10 ), co-administration of drugs (Wuzhi capsules, [11][12][13] nifedipine), liver and kidney function and postoperative day.…”

Section: What Is K Nown and Objec Tivementioning

confidence: 99%

“…To improve the accuracy and precision of the prediction model, this study incorporated some factors that may affect the TAC whole blood trough concentrations when building the machine learning model. Here, we screened important features according to our previously established population pharmacokinetic model of TAC, 3,4 while other influencing factors were considered based on practical clinical applications. Meanwhile, considering the eventual impact of medication history on the model, the features were divided into six broad categories: (1) no consideration of medication history; (2) consideration of the previous drug administration and increase the dose of the previous drug administration and the number of days between them according to (1); (3) consideration of the situation of the previous two drug administrations, and increase the dose of the previous two drug administrations and the number of days between them according to (2); and (4) as described above, until consideration of the situation of the previous five drug administrations.…”

Section: Features Selectionmentioning

confidence: 99%

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Machine learning‐based method for tacrolimus dose predictions in Chinese kidney transplant perioperative patients

Yan

Mr³

et al. 2021

Clinical Pharmacy Therapeu

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What is Known and Objectives: Tacrolimus (TAC), a first-line immunosuppressant in solid-organ transplant, has a narrow therapeutic window and large inter-individual variability, which affects its use in clinical practice. Successful predictions using machine learning algorithms have been reported in several fields. However, a comparison of 10 machine learning model-based TAC pharmacogenetic and pharmacokinetic dosing algorithms for kidney transplant perioperative patients of Chinese descent has not been reported. The objective of this study was to screen and establish an appropriate machine learning method to predict the individualized dosages of TAC for perioperative kidney transplant patients. Methods:The records of 2551 patients were collected from three transplant centres, 80% of which were randomly selected as a 'derivation cohort' to develop the dose prediction algorithm, while the remaining 20% constituted a 'validation cohort' to validate the final algorithm selected. Important features were screened according to our previously established population pharmacokinetic model of tacrolimus. The performances of the algorithms were evaluated and compared using R-squared and the mean percentage in the remaining 20% of patients. Results and Discussion: This study identified several factors influencing TAC dosage, including CYP3A5 rs776746, CYP3A4 rs4646437, haematocrit, Wuzhi capsules, TAC daily dose, age, height, weight, post-operative time, nifedipine and the medication history of the patient. According to our results, among the 10 machine learning models, the extra trees regressor (ETR) algorithm showed the best performance in the training set (R-squared: 1, mean percentage within 20%: 100%) and test set (R-squared: 0.85, mean percentage within 20%: 92.77%) of the derivation cohort. The ETR model successfully predicted the ideal TAC dosage in 97.73% of patients, especially in the intermediate dosage range (>5 mg/day to <8 mg/day), whereby the ideal TAC dosage could be successfully predicted in 99% of the patients. What is New and Conclusion:The results indicated that the ETR algorithm, which was chosen to establish the dose prediction model, performed better than the other nine machine learning models. This study is the first to establish ETR algorithms to predict TAC dosage. This study will further promote the individualized medication of TAC in How to cite this article: Fu Q, Jing Y, Liu Mr G, et al. Machine learning-based method for tacrolimus dose predictions in Chinese kidney transplant perioperative patients. J Clin

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“…As an effective solution for dose prediction, PPK modeling uses software such as nonlinear mixedeffects modeling approach (NONMEM) to build a model based on drug's concentration data to estimate the typical values of its population, and to manage the intra-individual and inter-individual differences. Combining the model with Bayesian feedback method, PPK can also help guide dosage adjustment based on a limited number of drug concentrations, thereby optimizing the dosing regimen and realizing individualized medication (Jing et al, 2021;Kiang, Sherwin, Spigarelli & Ensom, 2012;Shi, Xiao, Mao, Wu & Lin, 2019). In the future, it is likely to be of interest and expectation to use NONMEM to characterize the relationship between the dose, concentration versus time in pediatric patients with atomoxetine treatment, and to examine whether inter-patient variability between children is related to influential covariates such as age, body weight, and CYP2D6 genotype.…”

Section: Conclusion and Future Perspectivementioning

confidence: 99%

Personalizing Atomoxetine Dosing in Children with ADHD: What Can We Learn from Current Supporting Evidence

Guo

et al. 2022

Preprint

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Atomoxetine is the first non-stimulant medication approved by the US Food and Drug Administration for the treatment of attention deficit/hyperactivity disorder (ADHD). It can significantly improve ADHD symptoms, with good efficacy and tolerability. However, its efficacy was not consistent among all patients, especially for pediatric population. Due to marked heterogeneity in treatment response, a precision therapy should be developed and evaluated to guide treatment planning at the individual level. We have gained a better understanding of the pharmacokinetic profile. This review summarized some factors affecting peak concentrations of atomoxetine, including food, CYP2D6 and CYP2C19 phenotypes, and drug-drug interactions. The association between response and genetic polymorphisms of genes encoding the pharmacological targets such as norepinephrine transporter (NET/SLC6A2) and dopamine β hydroxylase (DBH) was also discussed. Based on the well-developed and validated assays for monitoring plasma concentrations of atomoxetine, the therapeutic reference range in pediatric patients with ADHD proposed by several studies was summarized. However, supporting evidence on the relationship between systemic atomoxetine exposure levels and clinical response is far from sufficient. We have to create evidence to characterize clearly the dose-exposure relationship, to establish clinically relevant metric for systemic exposure, to define a therapeutic exposure range, and to provide a dose-adaptation strategy before implementing personalized dosing for atomoxetine in children with ADHD. Personalizing atomoxetine dosage may be even more complex than we anticipated, but we can be optimistic about the future based on the remarkable advances in understanding the nature and causes of ADHD, as well as environmental stressors.

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Population pharmacokinetic analysis and dosing guidelines for tacrolimus co‐administration with Wuzhi capsule in Chinese renal transplant recipients

Cited by 23 publications

References 37 publications

Wuzhi Capsule Dosage Affects Tacrolimus Elimination in Adult Kidney Transplant Recipients, as Determined by a Population Pharmacokinetics Analysis

Wuzhi Capsule Dosage Affects Tacrolimus Elimination in Adult Kidney Transplant Recipients, as Determined by a Population Pharmacokinetics Analysis

Machine learning‐based method for tacrolimus dose predictions in Chinese kidney transplant perioperative patients

Personalizing Atomoxetine Dosing in Children with ADHD: What Can We Learn from Current Supporting Evidence

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