Effects and safety evaluation of Wuzhi Capsules combined with tacrolimus for the treatment of kidney transplantation recipients

Cheng, Fang; Li, Qiang; Wang, Jinglin; Zeng, Fang

doi:10.1111/jcpt.13493

Cited by 15 publications

(14 citation statements)

References 46 publications

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“…For example, Fang Cheng et al evaluated the efficacy and safety of Wuzhi combined with tacrolimus in the treatment of kidney transplant recipients, and 77 out of 194 recipients received Wuzhi. 14 A machine-learning study for dose optimization of tacrolimus enrolled 2551 renal transplant recipients, with 776 recipients receiving Wuzhi. 15 Therefore, it was estimated that approximately 35%-40% of transplant recipients may receive Wuzhi in China.…”

Section: Introductionmentioning

confidence: 99%

Prediction of tacrolimus and Wuzhi tablet pharmacokinetic interaction magnitude in renal transplant recipients

Chen

Dai

She

et al. 2022

Clinical Transplantation

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Aim: Wuzhi tablets are a dose-sparing agent for tacrolimus (TAC) in China and increase the bioavailability of TAC. The current study aimed to evaluate the pharmacokinetic interaction magnitude of Wuzhi and TAC and explore the potential determinants of this interaction.Methods: This study performed a retrospective, self-controlled study of 138 renal transplant recipients who were co-administered TAC and Wuzhi. The trough concentration (C0) of TAC at baseline and 3, 7, 14 and 21 days after Wuzhi co-therapy initiation was measured, and the CYP3A5 polymorphism was genotyped. The corresponding clinical factors were recorded. The ratio of dose-adjusted C0 (C0/D) post-and pre-combination therapy (ΔC0/D) indicates the interaction magnitude. Univariate and multivariate analyses were used to identify determinants and establish the prediction model.Results: ΔC0/D reached a steady state within 14 days. The geometrical mean ΔC0/D was 2.91 (range 1.02-9.49, IQR 2.13-3.80). ΔC0/D was blunted in CYP3A5 expressers (estimated effect: -39.8%, P = .001) and affected by hematocrit (Hct) (+24.0% per 10% increase, P = .005) and baseline C0/D (-31.9% per 1 ng⋅ml -1 ⋅mg -1 increase, P < .001). The prediction model was ΔC0/D = .319baseline C0/D × 1.398CYP3A5 (expressers = 0/non-expressers = 1) × 1.024Hct × 1.744, and it explained 28.1% of the variability. Conclusion:Our study is the first attempt to date to give an assessment of the magnitude of pharmacokinetic interaction between TAC and Wuzhi in a cohort of renal transplant recipients, and CYP3A5 genotypes, baseline C0/D and Hct were identified as determinants of this interaction.

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Section: Introductionmentioning

confidence: 99%

Prediction of tacrolimus and Wuzhi tablet pharmacokinetic interaction magnitude in renal transplant recipients

Chen

Dai

She

et al. 2022

Clinical Transplantation

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“…What is new in our study is that we first studied the pharmacokinetics change of tacrolimus when combined with WZC among different CYP3A5 genotype donors and recipients. Cheng et al’s results also supported that CYP3A5 genotype had significant influence in tacrolimus, whereas this was not observed in patients with other genotypes ( Cheng et al, 2021 ). During our study, we divided the donors and recipients into two groups, respectively according to their genotypes.…”

Section: Discussionmentioning

confidence: 87%

The pharmacokinetic study of tacrolimus and Wuzhi capsule in Chinese liver transplant patients

Bian²,

Liu

et al. 2022

Front. Pharmacol.

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Objectives: Wuzhi Capsule (WZC) is often administrated with tacrolimus in liver transplant patients to reduce the toxicity of tacrolimus and relieve the financial burden of patients. We aimed to investigate the interaction between Wuzhi Capsule (WZC) and tacrolimus in liver transplant patients.Methods: We applied the LC-MS/MS analytical method previously established to study the pharmacokinetic characteristics of the analytes in 15 liver transplant patients. CYP3A5 genotypes were determined in 15 donors and recipients, and they were categorized into CYP3A5 expressers and non-expressers respectively.Results: The influences of CYP3A5 in donors and recipients on the pharmacokinetics of tacrolimus with or without WZC were also studied. We found that 1) WZC could influence the metabolism of tacrolimus, which shortened the Tmax of tacrolimus and decreased V/F and CL/F. 2) Moreover, our results showed that, in donors, the CL/F of tacrolimus were significantly lower in CYP3A5 (CYP3A5*1) expressers (decreased from 24.421 to 12.864) and non-expressers (decreased from 23.532 to 11.822) when co-administration with WZC. For recipients, the decreased trend of CL/F of tacrolimus was seen when co-administrated with WZC by 15.376 and 12.243 in CYP3A5 expressers and non-expressers, respectively.Conclusion: In this study, the pharmacokinetics effects of WZC on tacrolimus were identified. The co-administration of WZC can increase the tacrolimus blood concentration in Chinese liver transplant patients in clinical practice.

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“…In addition, the individual clearance of TAC can be decreased by the combination of Wuzhi capsules. Studies have shown that the main active ingredients in Wuzhi capsules (pentosidine and pentosanol) can significantly increase TAC concentrations by inhibiting CYP3A and P-glycoprotein (P-gp)-mediated metabolism and transport ( Qin et al, 2010 ; Wei et al, 2013 ; Qin et al, 2014 ; Cheng et al, 2021 ). In addition to age and combined use of Wuzhi capsules, CYP3A5 genotype is an important influencing factor on the clearance of TAC.…”

Section: Discussionmentioning

confidence: 99%

Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

et al. 2022

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Background and Aim: Tacrolimus (TAC) is a first-line immunosuppressant for the treatment of refractory nephrotic syndrome (RNS), but the pharmacokinetics of TAC varies widely among individuals, and there is still no accurate model to predict the pharmacokinetics of TAC in RNS. Therefore, this study aimed to combine population pharmacokinetic (PPK) model and machine learning algorithms to develop a simple and accurate prediction model for TAC.Methods: 139 children with RNS from August 2013 to December 2018 were included, and blood samples of TAC trough and partial peak concentrations were collected. The blood concentration of TAC was determined by enzyme immunoassay; CYP3A5 was genotyped by polymerase chain reaction-restriction fragment length polymorphism method; MYH9, LAMB2, ACTN4 and other genotypes were determined by MALDI-TOF MS method; PPK model was established by nonlinear mixed-effects method. Based on this, six machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Extra-Trees, Gradient Boosting Decision Tree (GBDT), Adaptive boosting (AdaBoost) and Lasso, were used to establish the machine learning model of TAC clearance.Results: A one-compartment model of first-order absorption and elimination adequately described the pharmacokinetics of TAC. Age, co-administration of Wuzhi capsules, CYP3A5 *3/*3 genotype and CTLA4 rs4553808 genotype were significantly affecting the clearance of TAC. Among the six machine learning models, the Lasso algorithm model performed the best (R2 = 0.42).Conclusion: For the first time, a clearance prediction model of TAC in pediatric patients with RNS was established using PPK combined with machine learning, by which the individual clearance of TAC can be predicted more accurately, and the initial dose of administration can be optimized to achieve the goal of individualized treatment.

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Effects and safety evaluation of Wuzhi Capsules combined with tacrolimus for the treatment of kidney transplantation recipients

Cited by 15 publications

References 46 publications

Prediction of tacrolimus and Wuzhi tablet pharmacokinetic interaction magnitude in renal transplant recipients

Prediction of tacrolimus and Wuzhi tablet pharmacokinetic interaction magnitude in renal transplant recipients

The pharmacokinetic study of tacrolimus and Wuzhi capsule in Chinese liver transplant patients

Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

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