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

CYP3A5 Genotype-Dependent Drug-Drug Interaction Between Tacrolimus and Voriconazole in Chinese Kidney Transplant Patients

BACKGROUND

The effect of drug-drug interaction (DDI) between tacrolimus and voriconazole on the pharmacokinetics of tacrolimus in different CYP3A5 genotypes has not been reported in previous studies.

OBJECTIVE

The objective of this study was to investigate whether CYP3A5 genotype could influence tacrolimus-voriconazole DDI in Chinese kidney transplant patients.

METHODS

All kidney transplant patients were divided into combination and non-combination groups based on whether tacrolimus was combined with or without voriconazole. Each group was subdivided into CYP3A5 expresser (CYP3A5*1/*1 or CYP3A5*1/*3) and CYP3A5 nonexpresser (CYP3A5*3/*3). A retrospective analysis compared tacrolimus dose (D)-corrected trough concentrations (C0) (C0/D) between combination and non-combination groups, respectively. Tacrolimus C0/D was also compared between CYP3A5 expresser and nonexpresser in both groups.

RESULTS

The C0/D values of tacrolimus were significantly different between CYP3A5 expresser and nonexpresser in combination group (378.20 [219.38, 633.48] ng/mL/[mg/kg/d] vs 720.00 [595.35, 1681.50] ng/mL/[mg/kg/d], P = 0.0010). Either in CYP3A5 expresser or nonexpresser, we found a statistically significant difference in tacrolimus C0/D between combination and non-combination group (P < 0.0001). The increase in CYP3A5 nonexpresser was 1.38 times higher than that in CYP3A5 expresser (320.93% vs 232.19%).

CONCLUSION AND RELEVANCE

The median C0/D values were 90.38% higher in kidney transplant recipients with CYP3A5*3/*3 genotype than in those with CYP3A5*1/*1 or CYP3A5*1/*3 genotype when treated with both tacrolimus and voriconazole. A CYP3A5 genotype-dependent DDI was found between tacrolimus and voriconazole. Therefore, personalized therapy accounting for CYP3A5 genotype detection and therapeutic drug monitoring is necessary for kidney transplant patients when treating with tacrolimus and voriconazole.

Pharmacokinetic assessment of tacrolimus in combination with deoxyschizandrin in rats

Background

Tacrolimus (Tac) is commonly used for postoperative immunosuppressive therapy in transplant patients. However, problems, for example, low bioavailability and unstable plasma concentration, persist for a long time, Studies have reported that the deoxyschizandrin could effectively improve these problems, but the pharmacokinetic parameters (PKs) of Tac combined with deoxyschizandrin are still unknown.

Method

In this study, an UHPLC-MS/MS method has been established for simultaneous quantitation of Tac and deoxyschizandrin. The PKs of Tac influenced by different doses of deoxyschizandrin after single and multiple administrations were analyzed, and the different impact of deoxyschizandrin and Wuzhi capsule on PKs of Tac were compared.

Result

The modified UHPLC-MS/MS method could rapid quantification of Tac and deoxyschizandrin within 2 min using bifendatatum as the internal standard (IS). All items were successfully validated. The C max of deoxyschizandrin increased from 148.27 ± 23.20 to 229.13 ± 54.77 ng/mL in rats after multiple administrations for 12 days. After co-administration of 150 mg/mL deoxyschizandrin, Tac had an earlier T max and greater C max and AUC0-t, and the C max and AUC0-t of Tac increased from 14.26 ± 4.73 to 54.48 ± 14.37 ng/mL and from 95.10 ± 32.61 to 315.23 ± 92.22 h/ng/mL, respectively; this relationship was positively proportional to the dosage of deoxyschizandrin. In addition, compared with Wuzhi capsule, the same dose of deoxyschizandrin has a better effective on Tac along with more stable overall PKs.

Conclusion

An UHPLC-MS/MS method was established and validated for simultaneous detection of deoxyschizandrin and Tac. Deoxyschizandrin could improve the in vivo exposure level and stability of Tac, besides, this effect is better than Wuzhi capsule in same dose.

Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients

Aims

The population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients.

Methods

Conventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group.

Results

The final PPK model, incorporating hematocrit and CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance.

Conclusion

The PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.

Artificial Neural Network Analysis of Determinants of Tacrolimus Pharmacokinetics in Liver Transplant Recipients

BACKGROUND

The efficacy and toxicity of tacrolimus are closely related to its trough blood concentrations. Identifying the influencing factors of pharmacokinetics of tacrolimus in the early postoperative period is conducive to the optimization of the individualized tacrolimus administration protocol and to help liver transplant (LT) recipients achieve the target blood concentrations.

OBJECTIVE

This study aimed to develop an artificial neural network (ANN) for predicting the blood concentration of tacrolimus soon after liver transplantation and for identifying determinants of the concentration based on Shapley additive explanation (SHAP).

METHODS

In this retrospective study, we enrolled 31 recipients who were first treated with liver transplantation from the Department of Liver Transplantation and Hepatic Surgery, the First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital) from November 2020 to May 2021. The basic information, biochemical indexes, use of concomitant drugs, and genetic factors of organ donors and recipients were used for the ANN model inputs, and the output was the steady-state trough concentration (C0) of tacrolimus after oral administration in LT recipients. The ANN model was established to predict C0 of tacrolimus, SHAP was applied to the trained model, and the SHAP value of each input was calculated to analyze quantitatively the influencing factors for the output C0.

RESULTS

A back-propagation ANN model with 3 hidden layers was established using deep learning. The mean prediction error was 0.27 ± 0.75 ng/mL; mean absolute error, 0.60 ± 0.52 ng/mL; correlation coefficient between predicted and actual C0 values, 0.9677; and absolute prediction error of all blood concentrations obtained by the ANN model, ≤3.0 ng/mL. The results indicated that the following factors had the most significant effect on C0: age, daily drug dose, genotype at CYP3A5 polymorphism rs776746 in both recipient and donor, and concomitant use of caspofungin. The predicted C0 value of tacrolimus in LT recipients increased in a dose-dependent manner when the daily dose exceeded 3 mg, whereas it decreased with age when LT recipients were older than 48 years. The predicted C0 was higher when recipients and donors had the genotype CYP3A5*3*3 than when they had the genotype CYP3A5*1. The predicted C0 value also increased with the use of caspofungin or Wuzhi capsule.

CONCLUSION AND RELEVANCE

The established ANN model can be used to predict the C0 value of tacrolimus in LT recipients with high accuracy and good predictive ability, serving as a reference for personalized treatment in the early stage after liver transplantation.

Exploring the effect of Wuzhi capsule on the pharmacokinetics of regorafenib and its main metabolites in rat plasma using liquid chromatography-tandem mass spectrometry

Regorafenib is a small-molecule tyrosine kinase inhibitor with severe hepatotoxicity. It undergoes metabolism mainly by CYP3A4 to generate active metabolites regorafenib-N-oxide (M2) and N-desmethyl-regorafenib-N-oxide (M5). Wuzhi capsule (WZC) is an herbal preparation derived from Schisandra sphenanthera and is potentially used to prevent regorafenib-induced hepatotoxicity. This study aims to explore the effect of WZC on the pharmacokinetics of regorafenib in rats. An efficient and sensitive liquid chromatography-tandem mass spectrometry method was developed to quantitatively determine regorafenib and its main metabolites in rat plasma. The proposed method was applied to the pharmacokinetic study of regorafenib in rats, with or without WZC. Coadministration of regorafenib with WZC resulted in a prolonged mean residence time (MRT) of the parent drug but had no statistically significant difference in other pharmacokinetic parameters. While for the main metabolites of regorafenib, WZC decreased the area under the curve and maximum concentration (Cmax ), delayed the time to reach Cmax , and prolonged the MRT of M2 and M5. These results indicate that WZC delayed and inhibited the metabolism of regorafenib to M2 and M5 by suppressing CYP3A4. Our study provides implications for the rational use of the WZC-regorafenib combination in clinical practice.

Optimizing the initial tacrolimus dosage in Chinese children with lung transplantation within normal hematocrit levels

Background

The appropriate initial dosage of tacrolimus is undefined in Chinese pediatric lung transplant patients with normal hematocrit values. The purpose of this study is to optimize the initial dose of tacrolimus in Chinese children who are undergoing lung transplantation and have normal hematocrit levels.

Methods

The present study is based on a published population pharmacokinetic model of tacrolimus in lung transplant patients and uses the Monte Carlo simulation to optimize the initial tacrolimus dosage in Chinese children with lung transplantation within normal hematocrit levels.

Results

Within normal hematocrit levels, for children with lung transplantation who do not carry the CYP3A5*1 gene and have no coadministration with voriconazole, it is recommended to administer tacrolimus at a dosage of 0.02 mg/kg/day, divided into two doses, for children weighing 10-32 kg, and a dosage of 0.03 mg/kg/day, also divided into two doses, for children weighing 32-40 kg. For children with lung transplantation who carry the CYP3A5*1 gene and have no coadministration with voriconazole, tacrolimus dosages of 0.02, 0.03, and 0.04 mg/kg/day split into two doses are recommended for children weighing 10-15, 15-32, and 32-40 kg, respectively. For children with lung transplantation who do not carry the CYP3A5*1 gene and have coadministration with voriconazole, tacrolimus dosages of 0.01 and 0.02 mg/kg/day split into two doses are recommended for children weighing 10-17 and 17-40 kg, respectively. For children with lung transplantation who carry the CYP3A5*1 gene and have coadministration with voriconazole, a tacrolimus dosage of 0.02 mg/kg/day split into two doses is recommended for children weighing 10-40 kg.

Conclusions

It is the first time to optimize the initial dosage of tacrolimus in Chinese children undergoing lung transplantation within normal hematocrit.

Dose-sparing effect of lapatinib co-administered with a high-fat enteral nutrition emulsion: preclinical pharmacokinetic study

Background

Lapatinib is an oral small-molecule tyrosine kinase inhibitor indicated for advanced or metastatic HER2-positive breast cancer. In order to reduce the treatment cost, a high-fat enteral nutrition emulsion TPF-T was selected as a dose-sparing agent for lapatinib-based therapies. This study aimed to investigate the effect of TPF-T on lapatinib pharmacokinetics.

Methods

First, a simple and rapid liquid chromatography tandem mass spectrometry (LC-MS/MS) method was developed to quantitatively evaluate lapatinib in rabbit plasma. The method was fully validated according to the China Pharmacopoeia 2020 guidance. Rabbits and rats were chosen as the animal models due to their low and high bile flows, respectively. The proposed LC-MS/MS method was applied to pharmacokinetic studies of lapatinib, with or without TPF-T, in rabbit and rat plasma.

Results

The LC-MS/MS method revealed high sensitivity and excellent efficiency. In the rabbit model, co-administration with TPF-T resulted in a 32.2% increase in lapatinib exposure. In the rat model, TPF-T had minimal influence on the lapatinib exposure. In both models, TPF-T was observed to significantly elevate lapatinib concentration in the absorption phase.

Conclusion

Co-administration with TPF-T had a moderate effect on increasing exposure to lapatinib. Dose sparing using a high-fat liquid diet is potentially feasible for lapatinib-based therapies.

Population pharmacokinetics of mycophenolic acid and dose optimisation in adult Chinese kidney transplant recipients

1. This study aimed to establish a population pharmacokinetic (PPK) model of mycophenolic acid (MPA), quantify the effect of clinical factors and pharmacogenomics of MPA, and optimise the dosage for adult kidney transplant recipients.2. One-hundred and four adult renal transplant patients were enrolled. The PPK model was established using the Phoenix® NMLE software and the stepwise methods were filtered for significant covariates. Monte Carlo simulations were performed to optimise the dosage regimen.3. A two-compartment model with first-order absorption and elimination (including lag time) provided a more accurate description of MPA pharmacokinetics. Serum albumin (ALB) significantly affected the central apparent clearance (CL/F), whereas post-transplant time and creatinine clearance were associated with a central apparent volume of distribution (V/F). The estimated population values obtained by the final model were 17.5 L/h and 93.97 L for CL/F and V/F, respectively. Simulation results revealed that larger mycophenolate mofetil doses are required as the ALB concentration decreases. This study established a PPK model of MPA and validated it using various methods. ALB significantly affected CL/F and recommended optimal dose strategies were given based on the final model. These results provide a reference for the personalised therapy of MPA for kidney transplant patients.

Individualized dosing algorithms for tacrolimus in kidney transplant recipients: current status and unmet needs

INTRODUCTION

Tacrolimus is a potent immunosuppressive drug with many side effects including nephrotoxicity and post-transplant diabetes mellitus. To limit its toxicity, therapeutic drug monitoring (TDM) is performed. However, tacrolimus' pharmacokinetics are highly variable within and between individuals, which complicates their clinical management. Despite TDM, many kidney transplant recipients will experience under- or overexposure to tacrolimus. Therefore, dosing algorithms have been developed to limit the time a patient is exposed to off-target concentrations.

AREAS COVERED

Tacrolimus starting dose algorithms and models for follow-up doses developed and/or tested since 2015, encompassing both adult and pediatric populations. Literature was searched in different databases, i.e. Embase, PubMed, Web of Science, Cochrane Register, and Google Scholar, from inception to February 2023.

EXPERT OPINION

Many algorithms have been developed, but few have been prospectively evaluated. These performed better than bodyweight-based starting doses, regarding the time a patient is exposed to off-target tacrolimus concentrations. No benefit in reduced tacrolimus toxicity has yet been observed. Most algorithms were developed from small datasets, contained only a few tacrolimus concentrations per person, and were not externally validated. Moreover, other matrices should be considered which might better correlate with tacrolimus toxicity than the whole-blood concentration, e.g. unbound plasma or intra-lymphocytic tacrolimus concentrations.

Personalizing atomoxetine dosing in children with ADHD: what can we learn from current supporting evidence

PURPOSE

There is marked heterogeneity in treatment response of atomoxetine in patients with attention deficit/hyperactivity disorder (ADHD), especially for the pediatric population. This review aims to evaluate current evidence to characterize the dose-exposure relationship, establish clinically relevant metrics for systemic exposure to atomoxetine, define a therapeutic exposure range, and to provide a dose-adaptation strategy before implementing personalized dosing for atomoxetine in children with ADHD.

METHODS

A comprehensive search was performed across electronic databases (PubMed and Embase) covering the period of January 1, 1985 to July 10, 2022, to summarize recent advances in the pharmacokinetics, pharmacogenomics/pharmacogenetics (PGx), therapeutic drug monitoring (TDM), physiologically based pharmacokinetics (PBPK), and population pharmacokinetics (PPK) of atomoxetine in children with ADHD.

RESULTS

Some factors affecting the pharmacokinetics of atomoxetine were summarized, including food, CYP2D6 and CYP2C19 phenotypes, and drug‒drug interactions (DDIs). The association between treatment response and genetic polymorphisms of genes encoding pharmacological targets, such as norepinephrine transporter (NET/SLC6A2) and dopamine β hydroxylase (DBH), was also discussed. Based on 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 was far from sufficient.

CONCLUSION

Personalizing atomoxetine dosage may be even more complex than anticipated thus far, but elucidating the best way to tailor the non-stimulant to a patient's individual need will be achieved by combining two strategies: detailed research in linking the pharmacokinetics and pharmacodynamics in pediatric patients, and better understanding in nature and causes of ADHD, as well as environmental stressors.

[A precision medication study of atomoxetine in children with attention deficit hyperactivity disorder: CYP2D6 genetic testing and therapeutic drug monitoring]

Atomoxetine is the first non-stimulant drug for the treatment of children and adults with attention deficit hyperactivity disorder (ADHD), and its safety and efficacy show significant differences in the pediatric population. This article reviews the genetic factors influencing the pharmacokinetic differences of atomoxetine from the aspect of the gene polymorphisms of the major metabolizing enzyme CYP2D6 of atomoxetine, and then from the perspective of therapeutic drug monitoring, this article summarizes the reference ranges of the effective concentration of atomoxetine in children with ADHD proposed by several studies. In general, there is an association between the peak plasma concentration of atomoxetine and clinical efficacy, but with a lack of data from the Chinese pediatric population. Therefore, it is necessary to establish related clinical indicators for atomoxetine exposure, define the therapeutic exposure range of children with ADHD in China, and combine CYP2D6 genotyping to provide support for the precision medication of atomoxetine.

The Dosage Recommendation of Cyclosporin in Children with Hemophagocytic Lymphohistiocytosis based on Population Pharmacokinetic Model

OBJECTIVES

Cyclosporin is one of the therapeutic regimens for hemophagocytic lymphohistiocytosis (HLH); however, the optimal dosage of cyclosporine in children with HLH is unknown. It has been found that piperacillin-tazobactam affects the cyclosporine pharmacokinetic process in pediatric HLH patients. Thus, the purpose of the present study was to recommend cyclosporin dosage for pediatric HLH with and without piperacillin- tazobactam.

METHODS

A previously established cyclosporine population pharmacokinetic model for pediatric HLH patients has been used in this study to recommend optimal dosage based on Monte Carlo simulation. The pediatric HLH patients have been included in eight weight groups (5, 10, 20, 30, 40, 50, 60, 70 kg) for sixteen dosages (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 mg/kg), split into one dose or two doses.

RESULTS

The optimal cyclosporin dosages for children having HLH without piperacillin-tazobactam have been found to be 15, 13, 12, 11, 10, and 9 mg/kg, split into two doses for weights of 5-7, 7-10, 10-20, 20-28, 28-45, and 45-70 kg, respectively. For children with HLH, optimal cyclosporin dosages with piperacillin-tazobactam have been found to be 8 and 7 mg/kg, split into two doses for weights of 5-20 and 20-70 kg, respectively.

CONCLUSION

It is the first time that the cyclosporin dosage regimens for HLH in children have been developed based on Monte Carlo simulation, and the initial dosage optimizations of cyclosporine in pediatric HLH patients have been recommended.

Initial sirolimus dosage recommendations for pediatric patients with PIK3CD mutation-related immunodeficiency disease

Sirolimus is used to treat pediatric patients with PIK3CD mutation-related immunodeficiency disease. However, the initial dosages of sirolimus remain undecided. The present study aims to explore initial dosages in pediatric patients with PIK3CD mutation-related immunodeficiency disease. Pediatric patients with this disease were analyzed using the population pharmacokinetic (PPK) model and the Monte Carlo simulation. Body weight and concomitant use of posaconazole were included in the final PPK model, where, under the same weight, clearances of sirolimus were 1 : 0.238 between children without and children with posaconazole. Without posaconazole, the initial dosages of sirolimus were 0.07, 0.06, 0.05, and 0.04 mg/kg/day for body weights of 10–14, 14–25, 25–50, and 50–60 kg, respectively. With posaconazole, the initial dosages of sirolimus were 0.02 mg/kg/day for body weights of 10–60 kg. This is the first attempt to build a sirolimus PPK model for recommending initial dosages in children with PIK3CD mutation-related immunodeficiency disease, thereby providing a reference for individualized clinical drug administration.

Optimization of initial dose regimen of tacrolimus in paediatric lung transplant recipients based on Monte Carlo simulation

WHAT IS KNOWN AND OBJECTIVES

The initial tacrolimus dose regimen in paediatric lung transplant recipients is unknown. The present study optimized the initial tacrolimus dose regimen for paediatric lung transplant recipients.

METHODS

This study was based on a published population pharmacokinetic model of tacrolimus in lung transplant recipients and used Monte Carlo simulations to recommend an initial dose regimen of tacrolimus in paediatric lung transplant recipients.

RESULTS

Without voriconazole, the tacrolimus doses recommended for paediatric lung transplant recipients who were not CYP3A5*1 carriers were 0.02, 0.03, and 0.04 mg/kg/day, split into two doses, for weights of 10-16, 16-30, and 30-40 kg, respectively. For paediatric lung transplant recipients who were CYP3A5*1 carriers, the tacrolimus doses of 0.03, 0.04, 0.05, and 0.06 mg/kg/day, split into two doses, were recommended for weights of 10-16, 16-25, 25-30, and 30-40 kg, respectively. With voriconazole, the tacrolimus dose recommended for paediatric lung transplant recipients who were not CYP3A5*1 carriers was 0.02 mg/kg/day, split into two doses, for weights of 10-40 kg. For paediatric lung transplant recipients who were CYP3A5*1 carriers, tacrolimus doses of 0.02 and 0.03 mg/kg/day, split and two doses, were recommended for weights of 10-24 and 24-40 kg, respectively.

WHAT IS NEW AND CONCLUSIONS

This study developed tacrolimus dose regimens for the first time for paediatric lung transplant recipients using Monte Carlo simulation and optimized initial dosage in paediatric lung transplant recipients.

Population pharmacokinetics and dosage optimization of tacrolimus coadministration with Wuzhi capsule in adult liver transplant patients

1. This study aimed to establish a population pharmacokinetic model of tacrolimus coadministration with Wuzhi capsule and optimize the dosage regimen in adult liver transplant patients.2. Totally 1327 tacrolimus trough concentrations from 116 adult liver transplant patients were obtained for model development. A one-compartment model with first-order absorption and elimination was used to analyse the data, and the final model was internally verified using a goodness-of-fit diagnostic plot, bootstrap methods, and visual prediction test. A total of 29 patients with 250 tacrolimus trough concentrations was used for external validation via prediction-based diagnostics. Additionally, the simulation was used to optimize the recommended dose of tacrolimus and Wuzhi capsules.3. The estimated apparent clearance and volume of the distribution of tacrolimus were 15.4 L/h and 1210 L, respectively. The tacrolimus daily dose, Wuzhi capsule daily dose, postoperative time, alanine transaminase, haemoglobin, total bilirubin, direct bilirubin, estimated glomerular filtration rate, and urea, concomitant with voriconazole and fluconazole, were identified as significant covariates affecting the pharmacokinetic parameters. Internal and external validation showed that the final model was stable and reliable for predicting performance.4. The final model could provide guidance for dosage optimization of tacrolimus coadministered with Wuzhi capsules in adult liver transplantation patients.

Effect of drug combination on tacrolimus target dose in renal transplant patients with different CYP3A5 genotypes

Various factors, including genetic polymorphisms, drug-drug interactions, and patient characteristics influence the blood concentrations of tacrolimus in renal transplant patients. In the present study, we established a population pharmacokinetic model to explore the effect of combined use of Wuzhi capsules/echinocandins and the patients' biochemical parameters such as hematocrit on blood concentrations and target doses of tacrolimus in renal transplant patients with different CYP3A5 genotypes. The aim of the study was to propose an individualized tacrolimus administration regimen for early renal transplant recipients.In this retrospective cohort study, we included 240 renal transplant recipients within 21 days of surgery (174 males and 66 females, mean age 39.4 years), who received tacrolimus alone (n = 54), in combination with Wuzhi capsules (99) or caspofungin (57) or micafungin (30). We collected demographic characteristics, clinical indicators, CYP3A5 genotypes, and 1950 steady-state trough concentrations of tacrolimus and included them in population pharmacokinetic model. An additional 110 renal transplant recipients and 625 steady-state trough concentrations of tacrolimus were included for external validation of the model. The population pharmacokinetic model was established and Monte Carlo was used to simulate probabilities for achieving the target concentration for individual tacrolimus administration.A two-compartment model of first-order absorption and elimination was developed to describe the population pharmacokinetics of tacrolimus. CYP3A5 genotypes and co-administration of Wuzhi capsules, as well as time after renal transplantation and hematocrit, were important factors affecting the clearance of tacrolimus. We found no obvious change in trend in the scatter plot of tacrolimus clearance rate vs. hematocrit. The Monte Carlo simulation indicated the following recommended doses of tacrolimus alone: 0.14 mg·kg^-1·d^-1 for genotype CYP3A5*1*1, 0.12 mg·kg^-1·d^-1 for CYP3A5*1*3, and 0.10 mg·kg^-1·d^-1 for CYP3A5*3*3. For patients receiving the combination with Wuzhi capsules, the recommended doses of tacrolimus were 0.10 mg·kg^-1·d^-1 for CYP3A5*1*1, 0.08 mg·kg^-1·d^-1 for CYP3A5*1*3, and 0.06 mg·kg^-1·d^-1 for CYP3A5*3*3 genotypes. Caspofungin or micafungin had no effect on the clearance of tacrolimus in renal transplant recipients.The population pharmacokinetics of tacrolimus in renal transplant patients was evaluated and the individual administration regimen of tacrolimus was simulated. For early kidney transplant recipients receiving tacrolimus treatment, not only body weight, but also CYP3A5 genotypes and drugs used in combination should be considered when determining the target dose of tacrolimus.

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

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 kidney transplant patients in the future, which has great significance in ensuring the safety and effectiveness of drug use.

The Influence of Wuzhi Capsule on the Pharmacokinetics of Cyclophosphamide

BACKGROUND

Cyclophosphamide is approved for the treatment of a variety of tumors, yet the use of cyclophosphamide is limited by kidney and liver toxicity. In the clinic, the Wuzhi capsule is approved to attenuate cyclophosphamide toxicity in the kidney and liver.

OBJECTIVE

We aimed to investigate the effects of the principal ingredients of Wuzhi capsule, schisandrin A (SIA) and schisantherin A (STA), on the pharmacokinetics of cyclophosphamide.

METHODS

The essential pharmacokinetic data and physicochemical parameters of SIA, STA, and cyclophosphamide were collected. Physiologically based pharmacokinetic (PBPK) models of SIA, STA, and cyclophosphamide were built in Simcyp Simulator and verified using published clinical pharmacokinetic data. The verified PBPK models were used to predict potential herb-drug interactions (HDIs) between cyclophosphamide and SIA and STA in cancer patients.

RESULTS

The area under the plasma concentration-time curve (AUC) of cyclophosphamide was increased by 18% and 1% when co-administered with STA and SIA at a single dose, respectively, and increased by 301% and 29% when co-administered with STA and SIA at multiple doses, respectively. The maximum concentration (Cmax) of cyclophosphamide was increased by 75% and 7% when co-administered with STA and SIA at multiple doses, respectively.

CONCLUSION

The AUC and Cmax of cyclophosphamide were increased when cyclophosphamide was combined with the Wuzhi capsule, compared to cyclophosphamide alone. Our study shows that the adverse drug reactions and toxicity of cyclophosphamide should be closely monitored and an effective dosage adjustment of cyclophosphamide may need to be considered when co-administered with the Wuzhi capsule.

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

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.