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

Abstract

What is known and objectives Tacrolimus (TAC) is a first‐line immunosuppressant which is used to prevent transplant rejection after solid organ transplantation (SOT). However, it has a narrow therapeutic index and high individual variability in pharmacokinetics (PK) and pharmacogenomics (PG). It has been reported that the metabolism of TAC can be affected by genetic factors, leading to different rates of metabolism in different subjects. Wuzhi Capsule (WZC) is a commonly used TAC‐sparing agent in Chinese SOT to reduce TAC dosing due to its inhibitory effect on TAC metabolism by enzymes of the CYP3A subfamily. The aims of this study were to assess the effect of TAC+WZC co‐administration and genetic polymorphism on the pharmacokinetics of TAC, by using a population pharmacokinetic (PPK) model. A dosing guideline for individualized TAC dosing is proposed based on the PPK study. Methods The medical records of 165 adult patients with kidney transplant and their 824 TAC concentrations from two kidney transplantation centres were reviewed. The genotypes of four single‐nucleotide polymorphisms (SNPs) in CYP3A5*3 and ABCB1 (rs1128503, rs2032582 and rs1045642) were tested by MASSARRAY. A PPK model was constructed by nonlinear mixed effect model (NONMEM®, Version 7.3). Finally, Monte Carlo simulations were employed to design initial dosing regimens based on the final model. Results and discussion The one‐compartmental PPK model with first‐order absorption and elimination of TAC was established in kidney transplant recipients (KTRs). CYP3A5*3 had significant impact on the PPK model. The haematocrit (HCT), postoperative time (POD) and CYP3A5*3 genotypes had a significant influence on TAC clearance when combined with WZC. The model was expressed as 23.4 × (HCT/0.3)−0.729 × 0.837 (combination with WZC) × e−0.0875(POD/12.6) ×1.18 (CYP3A5 expressors). For patients carrying the CYP3A5*3/*3 allele and with 30% HCT, the required TAC dose to achieve target trough concentrations of 10–15 ng/ml was 4 mg twice daily (q12h). For patients with the CYP3A5*3/*3 allele, the required dose was 3 mg TAC q12h when combined with WZC, and for patients with the CYP3A5*1/*1 or *1/*3 allele, the required dose was 4 mg of TAC q12h when co‐administered with WZC. What is new and conclusion Wuzhi Capsule co‐administration and CYP3A5 variants affect the PK of TAC Dosing guidelines are made based on the PPK model to allow individualized administration of TAC, especially when co‐administered with WZC.

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

“…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.…”

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

“…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 .…”

“…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.…”

“…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.…”

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

“…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.…”