Population pharmacokinetics and bioavailability of tacrolimus in kidney transplant patients

Pharmacogenetic analysis of interleukin-10 variants and tacrolimus metabolism in kidney transplant patients from Pakistani population

BACKGROUND

End stage renal disease (ESRD) occurs when the kidneys are unable to filter the waste products and excessive fluids from the blood that results into the accumulation of toxins and fluid in the body. Tacrolimus is commonly used immunosuppressant while sirolimus and cyclosporin are rarely used drugs to stop solid organ transplant rejection. The host's immunological response following transplantation produces interleukin-10 (IL-10), which influences the varied CYP3A-dependent drug disposition of tacrolimus. The aim of this study was to determine the genetic polymorphisms of IL-10 (rs1800871, rs1800872 and rs1800896) gene associated with tacrolimus metabolism in kidney transplant patients from Lahore Punjab, Pakistan.

METHODS

The study collected blood samples of 103 healthy individuals and 137 kidney transplant patients as control and treatment groups, respectively. We employed Tetra ARMS PCR for the genotype analysis of extracted DNA. The alleles were called on 2% agarose gel. Moreover, the study utilized SPSS software to analyze statistical significance of polymorphism.

RESULTS

It was found that genotypic frequencies of IL-10 (rs1800871), IL-10 (rs1800872), and IL-10 (rs1800896) were (TT: 66.4%; TC: 31.4%; CC: 2.2%), (AA: 27.7%; AC: 54%; CC: 18.2%), (AA: 64.2%; GA: 17.5%; GG: 18.3%), respectively among kidney transplant patients. All parameters show significant association at different points after transplantation. Genetic analysis showed that TC and CC genotypes in rs1800871 (OR (95%CI) = 5.721 (3.231-10.131), P < 0.001; OR (95%CI) = 3.370 (0.642-17.672), P = 0.150), AC and CC genotypes in rs1800872 (OR (95%CI) = 1.294 (0.695-2.410), P = 0.415; OR (95%CI) = 1.453 (0.671-3.147), P = 0.342), GA and GG genotypes in rs1800896 (OR (95%CI) = 42.952 (17.566-105.021), P = 0.001; OR (95%CI) = 7.040 (2.563-19.333), P = 0.342) was associated with risk of renal rejection in kidney transplant patients. Besides, genetic models showed that TT in rs1800871, AA genotypes in rs1800872 and rs1800892 were associated with risk of renal rejection under dominant model when compared to controls (OR (95%CI) = 5.721 (3.231-10.131), P < 0.001; OR (95%CI) = 1.335 (0.735-9.290), P < 0.341; OR (95%CI) = 24.629 (10.599-57.230), P < 0.001), respectively.

CONCLUSION

From the results, it is concluded that genetic polymorphism of IL-10 (rs1800871, rs1800872 and rs1800896) has a highly significant association with risk of renal rejection in Pakistani kidney transplant patients.

The effects of the CYP3A5*3 variant on tacrolimus pharmacokinetics and outcomes in Tunisian kidney transplant recipients

INTRODUCTION

Tacrolimus, exhibits interindividual pharmacokinetic variability and a narrow therapeutic index. The influence of the CYP3A5 6986A>G single nucleotide polymorphism (SNP) on this variability remains a topic of debate.

AIM

To assess the impact of the aforementioned SNP on tacrolimus area under curve (AUC0-12h), adverse drug reactions (ADRs), and kidney graft outcomes.

METHODS

Blood samples were collected from Tunisian kidney transplants over a five-year period during either the early (<3 months) or late (>3 months) post-transplant phases. Through blood concentration (C0) and AUC0-12h of tacrolimus were measured. Patients were prospectively followed to assess graft outcomes. Polymerase chain reaction of restriction fragment length polymorphism was used for CYP3A5 6986A>G genotyping.

RESULTS

Fifty Tunisian kidney recipients receiving tacrolimus were enrolled in the study. Acute and chronic graft rejections were observed in eight and three patients, respectively. Twenty-one patients (42%) reported ADRs. C0 and AUC0-12h, showed a significant difference between CYP3A5*1 carriers (mean C0=4 ng.mL-1 and AUC0-12h=94.37 ng.h.mL-1) and CYP3A5*3/3 or poor metabolizers carriers (mean C0=7.45 ng.mL-1; AUC0-12h=151.27 ng.h.mL-1) (p=0.0001; p=0.003, respectively). Supratherapeutic tacrolimus levels were significantly more common in poor metabolizers (p=0.046; Odds-ratio =1.3; confidence interval 95% [1.12-1.66]). The impact of SNP was significant on C0, AUC0-12h, C0/Dose and AUC0-12h/Dose, only in the late phase (p=0.01, 0.002, 0.012, 0.003 respectively).

CONCLUSION

CYP3A5*3 variant was significantly associated with tacrolimus pharmacokinetics but had no impact on graft outcomes.

Gut microbiome modulates tacrolimus pharmacokinetics through the transcriptional regulation of ABCB1

BACKGROUND

Following solid organ transplantation, tacrolimus (TAC) is an essential drug in the immunosuppressive strategy. Its use constitutes a challenge due to its narrow therapeutic index and its high inter- and intra-pharmacokinetic (PK) variability. As the contribution of the gut microbiota to drug metabolism is now emerging, it might be explored as one of the factors explaining TAC PK variability. Herein, we explored the consequences of TAC administration on the gut microbiota composition. Reciprocally, we studied the contribution of the gut microbiota to TAC PK, using a combination of in vivo and in vitro models.

RESULTS

TAC oral administration in mice resulted in compositional alterations of the gut microbiota, namely lower evenness and disturbance in the relative abundance of specific bacterial taxa. Compared to controls, mice with a lower intestinal microbial load due to antibiotics administration exhibit a 33% reduction in TAC whole blood exposure and a lower inter-individual variability. This reduction in TAC levels was strongly correlated with higher expression of the efflux transporter ABCB1 (also known as the p-glycoprotein (P-gp) or the multidrug resistance protein 1 (MDR1)) in the small intestine. Conventionalization of germ-free mice confirmed the ability of the gut microbiota to downregulate ABCB1 expression in a site-specific fashion. The functional inhibition of ABCB1 in vivo by zosuquidar formally established the implication of this efflux transporter in the modulation of TAC PK by the gut microbiota. Furthermore, we showed that polar bacterial metabolites could recapitulate the transcriptional regulation of ABCB1 by the gut microbiota, without affecting its functionality. Finally, whole transcriptome analyses pinpointed, among others, the Constitutive Androstane Receptor (CAR) as a transcription factor likely to mediate the impact of the gut microbiota on ABCB1 transcriptional regulation.

CONCLUSIONS

We highlight for the first time how the modulation of ABCB1 expression by bacterial metabolites results in changes in TAC PK, affecting not only blood levels but also the inter-individual variability. More broadly, considering the high number of drugs with unexplained PK variability transported by ABCB1, our work is of clinical importance and paves the way for incorporating the gut microbiota in prediction algorithms for dosage of such drugs. Video Abstract.

Population pharmacokinetic analyses of tacrolimus in non-transplant patients: a systematic review

BACKGROUND AND OBJECTIVES

Tacrolimus (TAC) has been increasingly used in patients with non-transplant settings. Because of its large between-subject variability, several population pharmacokinetic (PPK) studies have been performed to facilitate individualized therapy. This review summarized published PPK models of TAC in non-transplant patients, aiming to clarify factors affecting PKs of TAC and identify the knowledge gap that may require further research.

METHODS

The PubMed, Embase databases, and Cochrane Library, as well as related references, were searched from the time of inception of the databases to February 2023, to identify TAC population pharmacokinetic studies modeled in non-transplant patients using a non-linear mixed-effects modeling approach.

RESULTS

Sixteen studies, all from Asian countries (China and Korea), were included in this study. Of these studies, eleven and four were carried out in pediatric and adult patients, respectively. One-compartment models were the commonly used structural models for TAC. The apparent clearance (CL/F) of TAC ranged from 2.05 to 30.9 L·h^-1 (median of 14.9 L·h^-1). Coadministered medication, genetic factors, and weight were the most common covariates affecting TAC-CL/F, and variability in the apparent volume of distribution (V/F) was largely explained by weight. Coadministration with Wuzhi capsules reduced CL/F by about 19 to 43%. For patients with CYP3A5*1*1 and *1*3 genotypes, the CL/F was 39-149% higher CL/F than patients with CYP3A5*1*1.

CONCLUSION

The optimal TAC dosage should be adjusted based on the patient's co-administration, body weight, and genetic information (especially CYP3A5 genotype). Further studies are needed to assess the generalizability of the published models to other ethnic groups. Moreover, external validation should be frequently performed to improve the clinical practicality of the models.

Prospective population pharmacokinetic study of tacrolimus in adult recipients early after liver transplantation: A comparison of Michaelis-Menten and theory-based pharmacokinetic models

Background and Objective: Tacrolimus, a calcineurin inhibitor widely used as a potent immunosuppressant to prevent graft rejection, exhibits nonlinear kinetics in patients with kidney transplantation and nephrotic syndrome. However, whether nonlinear drug metabolism occurs in adult patients undergoing liver transplantation remains unclear, as do the main underlying mechanisms. Therefore, here we aimed to further confirm the characteristics of nonlinearity through a large sample size, and determine the potential influence of nonlinearity and its possible mechanisms.

Methods: In total, 906 trough concentrations from 176 adult patients (150 men/26 women; average age: 50.68 ± 9.71 years, average weight: 64.54 ± 11.85 kg after first liver transplantation) were included in this study. Population pharmacokinetic analysis was performed using NONMEM®. Two modeling strategies, theory-based linear compartmental and nonlinear Michaelis–Menten (MM) models, were evaluated and compared. Potential covariates were screened using a stepwise approach. Bootstrap, prediction-, and simulation-based diagnostics (prediction-corrected visual predictive checks) were performed to determine model stability and predictive performance. Finally, Monte Carlo simulations based on the superior model were conducted to design dosing regimens.

Results: Postoperative days (POD), Aspartate aminotransferase (AST), daily tacrolimus dose, triazole antifungal agent (TAF) co-therapy, and recipient CYP3A5*3 genotype constituted the main factors in the theory-based compartmental final model, whereas POD, Total serum bilirubin (TBIL), Haematocrit (HCT), TAF co-therapy, and recipient CYP3A5*3 genotype were important in the nonlinear MM model. The theory-based final model exhibited 234 L h−1 apparent plasma clearance and 11,000 L plasma distribution volume. The maximum dose rate (Vmax) of the nonlinear MM model was 6.62 mg day−1; the average concentration at steady state at half-Vmax (Km) was 6.46 ng ml−1. The nonlinear MM final model was superior to the theory-based final model and used to propose dosing regimens based on simulations.

Conclusion: Our findings demonstrate that saturated tacrolimus concentration-dependent binding to erythrocytes and the influence of daily tacrolimus dose on metabolism may partly contribute to nonlinearity. Further investigation is needed is need to explore the causes of nonlinear pharmacokinetic of tacrolimus. The nonlinear MM model can provide reliable support for tacrolimus dosing optimization and adjustment in adult patients undergoing liver transplantation.

Drug-induced Fanconi syndrome in patients with kidney allograft transplantation

Background

Patients after kidney transplantation need to take long-term immunosuppressive and other drugs. Some of these drug side effects are easily confused with the symptoms of Fanconi syndrome, resulting in misdiagnosis and missed diagnosis, and causing serious consequences to patients. Therefore, improving awareness, early diagnosis and treatment of Fanconi syndrome after kidney transplantation is critical.

Methods

This retrospective study analyzed 1728 cases of allogeneic kidney transplant patients admitted to the Second Xiangya Hospital of Central South University from July 2016 to January 2021. Two patients with Fanconi syndrome secondary to drugs, adefovir dipivoxil (ADV) and tacrolimus, were screened. We summarized the diagnostic process, clinical data, and prognosis.

Results

The onset of Fanconi syndrome secondary to ADV after renal transplantation was insidious, and the condition developed after long-term medication (&gt;10 years). It mainly manifested as bone pain, osteomalacia, and scoliosis in the late stage and was accompanied by obvious proximal renal tubular damage (severe hypophosphatemia, hypokalemia, hypocalcemia, hypouricemia, glycosuria, protein urine, acidosis, etc.) and renal function damage (increased creatinine and azotemia). The pathological findings included mitochondrial swelling and deformity in renal tubular epithelial cells. The above symptoms and signs were relieved after drug withdrawal, but the scoliosis was difficult to rectify. Fanconi syndrome secondary to tacrolimus has a single manifestation, increased creatinine, which can be easily confused with tacrolimus nephrotoxicity. However, it is often ineffective to reduce the dose of tacrolomus, and proximal renal failure can be found in the later stage of disease development. There was no abnormality in the bone metabolism index and imageological examination findings. The creatinine level decreased rapidly, the proximal renal tubule function returned to normal, and no severe electrolyte imbalance or urinary component loss occurred when the immunosuppression was changed from tacrolimus to cyclosporine A.

Conclusions

For the first time, drug-induced Fanconi syndrome after kidney transplantation was reported. These results confirmed that the long-term use of ADV or tacrolimus after kidney transplantation may have serious consequences, some of which are irreversible. Greater understanding of Fanconi syndrome after kidney transplantation is necessary in order to avoid incorrect and missed diagnosis.

Predictive Performance of Published Tacrolimus Population Pharmacokinetic Models in Thai Kidney Transplant Patients

BACKGROUND AND OBJECTIVE

Tacrolimus is a narrow therapeutic index drug with high pharmacokinetic variability, and several tacrolimus population pharmacokinetic (PopPK) models were developed to guide individualized drug dosing. These models, however, may not perform well in other clinical settings. Therefore, we aimed to assess the predictive ability of published tacrolimus PopPK models using a dataset of Thai kidney transplant patients.

METHODS

The external dataset was retrospectively collected from medical records of Bhumibol Adulyadej Hospital, Thailand. Published tacrolimus PopPK models were systematically searched from PubMed, Science Direct, CINAHL Complete, and Scopus databases. Models conducted using a nonlinear mixed-effects approach with covariate resemblance to our external dataset were selected. The external dataset consisted of Thai kidney transplant patients receiving oral immediate- or extended-release tacrolimus formulations twice or once daily, respectively. Accuracy and precision of predicted concentrations were evaluated using mean absolute prediction error (MAPE), root mean square error (RMSE), and goodness of fit plots.

RESULTS

Only three models produced acceptable population predictions with the MAPE of < 50%. By using the Bayesian posthoc estimate of individual pharmacokinetic parameters, all models well performed with the MAPE and RMSE of < 30% and 40%, respectively, except two models; one could not successfully converge and the other substantially underpredicted tacrolimus concentrations.

CONCLUSION

We evaluated ten tacrolimus PopPK models, and eight models resulted in satisfactorily individual predicted tacrolimus concentrations in Thai kidney transplant patients and may be used to aid tacrolimus dose adjustment along with a clinical judgment.

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.

Clinical Experience with Extended-Release Tacrolimus in Older Adult Kidney Transplant Recipients: A Retrospective Cohort Study

BACKGROUND

Extended-release tacrolimus (LCP-Tac) prescribing information states that there is insufficient data in older adult patients from which to make recommendations on use in this population. This study sought to provide information on de novo use of LCP-Tac in the older adult kidney transplant population.

METHODS

This single-center retrospective study had two distinct objectives; to determine if weight-based doses of LCP-Tac differ based on recipient age and to compare safety and efficacy between LCP-Tac and immediate-release tacrolimus (IR-Tac) in older adult transplant recipients. Data was obtained through electronic chart review up to 2 years after transplant with censoring for graft loss and death.

RESULTS

Weight-based doses were compared between patients aged ≥ 65 years (n = 84), 36-64 years (n = 64), and ≤35 years (n = 44). LCP-Tac weight-based doses were lower at all time points in patients ≥ 65 years of age. Both age and race significantly impacted required dose on linear regression. The doses required to achieve therapeutic tacrolimus troughs were significantly lower in all age groups compared with the current FDA de novo dosing recommendation. In the older adult population, graft outcomes and infectious and metabolic complications were compared between recipients of LCP-Tac (n = 84) and IR-Tac (n = 42). Within this cohort, there were no differences between LCP-Tac and IR-Tac on graft function, rejection, graft loss, death, cytomegalovirus viremia, BK viremia, hypertension, diabetes, alopecia, or tremor up to 2 years after transplant.

CONCLUSIONS

Older adult recipients required significantly lower LCP-Tac doses compared with younger recipients and with the FDA-labeled starting dose. There were no differences in graft outcomes or adverse effects in older adult patients who received LCP-Tac versus IR-Tac.

Avoiding Tacrolimus Underexposure and Overexposure with a Dosing Algorithm for Renal Transplant Recipients: A Single Arm Prospective Intervention Trial

Bodyweight‐based tacrolimus dosing followed by therapeutic drug monitoring is standard clinical care after renal transplantation. However, after transplantation, a meager 38% of patients are on target at first steady‐state and it can take up to 3 weeks to reach the target tacrolimus predose concentration (C₀). Tacrolimus underexposure and overexposure is associated with an increased risk of rejection and drug‐related toxicity, respectively. To minimize subtherapeutic and supratherapeutic tacrolimus exposure in the immediate post‐transplant phase, a previously developed dosing algorithm to predict an individual’s tacrolimus starting dose was tested prospectively. In this single‐arm, prospective, therapeutic intervention trial, 60 de novo kidney transplant recipients received a tacrolimus starting dose based on a dosing algorithm instead of a standard, bodyweight‐based dose. The algorithm included cytochrome P450 (CYP)3A4 and CYP3A5 genotype, body surface area, and age as covariates. The target tacrolimus C₀, measured for the first time at day 3, was 7.5–12.5 ng/mL. Between February 23, 2019, and July 7, 2020, 60 patients were included. One patient was excluded because of a protocol violation. On day 3 post‐transplantation, 34 of 59 patients (58%, 90% CI 47–68%) had a tacrolimus C₀ within the therapeutic range. Markedly subtherapeutic (< 5.0 ng/mL) and supratherapeutic (> 20 ng/mL) tacrolimus concentrations were observed in 7% and 3% of the patients, respectively. Biopsy‐proven acute rejection occurred in three patients (5%). In conclusion, algorithm‐based tacrolimus dosing leads to the achievement of the tacrolimus target C₀ in as many as 58% of the patients on day 3 after kidney transplantation.

Evaluation of the performance of a prior tacrolimus population pharmacokinetic kidney transplant model among adult allogeneic hematopoietic stem cell transplant patients

Abstract

Tacrolimus is a calcineurin inhibitor used to prevent acute graft versus host disease in adult patients receiving allogeneic hematopoietic stem cell transplantation (HCT). Previous population pharmacokinetic (PK) models have been developed in solid organ transplant, yet none exists for patients receiving HCT. The primary objectives of this study were to (1) use a previously published population PK model in adult patients who underwent kidney transplant and apply it to allogeneic HCT; (2) evaluate model‐predicted tacrolimus steady‐state trough concentrations and simulations in patients receiving HCT; and (3) evaluate covariates that affect tacrolimus PK in allogeneic HCT. A total of 252 adult patients receiving allogeneic HCT were included in the study. They received oral tacrolimus twice daily (0.03 mg/kg) starting 3 days prior to transplant. Data for these analyses included baseline clinical and demographic data, genotype data for single nucleotide polymorphisms in CYP3A4/5 and ABCB1, and the first tacrolimus steady‐state trough concentration. A dosing simulation strategy based on observed trough concentrations (rather than model‐based predictions) resulted in 12% more patients successfully achieving tacrolimus trough concentrations within the institutional target range (5–10 ng/ml). Stepwise covariate analyses identified HLA match and conditioning regimen (myeloablative vs. reduced intensity) as significant covariates. Ultimately, a previously published tacrolimus population PK model in kidney transplant provided a platform to help establish a model‐based dose adjustment strategy in patients receiving allogenic HCT, and identified HCT‐specific covariates to be considered for future prospective studies.

Tacrolimus variability is associated with de novo donor-specific antibody development in pediatric renal transplant recipients

BACKGROUND

Donor-specific antibody (DSA) is a risk factor for antibody-mediated rejection and shortened graft survival. We investigated the role of intrapatient variability in tacrolimus trough levels on graft outcomes (i.e., de novo DSA, rejection, graft loss) in pediatric renal transplant recipients.

METHODS

This was a single-center retrospective study which included 38 pediatric renal transplant recipients. Intrapatient tacrolimus variability was defined using the coefficient of variation (CV; SD/Mean × 100) for all levels obtained after 3 months post-transplant. CV cut-points of 30%, 40%, and 50% were used in the analyses.

RESULTS

The median CV 43.1% (35.0%, 58.6%). Out of 38 patients, 19 (50%) developed de novo DSA. In the logistic regression model, after adjusting for age, rejection history, maintenance immunosuppression, and CV, for every 10% increase in tacrolimus variability, the odds of developing de novo DSA increased by 53% (p = 0.048, CI 1.0005, 1.11). Age at transplant was also an independent risk factor for DSA development; every 1 year increase in age was associated with a 31% increase in the odds of developing DSA (p = 0.03, CI 1.03, 1.67). At a CV cut-point ≥ 30%, higher tacrolimus variability was associated with an increased incidence of allograft rejection (0% vs 42%, < 30 and ≥ 30% respectively, p = 0.07). As there were few graft loss events (n = 4) in our study population, an association could not be determined between tacrolimus variability and graft loss.

CONCLUSION

Tacrolimus variability and age at transplant were identified as independent risk factors for de novo DSA development. There was an association between tacrolimus variability and rejection in pediatric renal transplant recipients. Adding the assessment of tacrolimus variability to current monitoring methods may be an important step towards improving graft outcomes.

Off-label use of tacrolimus in children with glomerular disease: Effectiveness, safety and pharmacokinetics

Glomerular diseases are leading causes of end-stage renal disease in children. Tacrolimus is frequently used off-label in the treatment of glomerular diseases. The effectiveness, safety and pharmacokinetic data of tacrolimus in the treatment of glomerular diseases in children are reviewed in this paper to provide evidence to support its rational use in clinical practice. The remission rates in previously published studies were different. In 19 clinical trials on children with nephrotic syndrome, the overall remission rate was 52.6-97.6%. In four clinical trials on children with lupus nephritis, the overall remission rate was 81.8-89.5%. In a pilot study with paediatric Henoch-Schönlein purpura nephritis patients, the overall remission rate was 100.0%. Infection, nephrotoxicity, gastrointestinal symptoms and hypertension are the most common adverse events. Body weight, age, CYP3A5 genotype, cystatin-C and daily dose of tacrolimus may have significant effects on the pharmacokinetics of tacrolimus in children with glomerular disease. More prospective controlled trials with long follow-up are needed to demonstrate definitely the effectiveness, safety and pharmacokinetics of tacrolimus in children with glomerular diseases.

Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach

AIMS

The objective of this study is to develop a generic model for tacrolimus pharmacokinetics modelling using a meta-analysis approach, that could serve as a first step towards a prediction tool to inform pharmacokinetics-based optimal dosing of tacrolimus in different populations and indications.

METHODS

A systematic literature review was performed and a meta-model developed with NONMEM software using a top-down approach. Historical (previously published) data were used for model development and qualification. In-house individual rich and sparse tacrolimus blood concentration profiles from adult and paediatric kidney, liver, lung and heart transplant patients were used for model validation. Model validation was based on successful numerical convergence, adequate precision in parameter estimation, acceptable goodness of fit with respect to measured blood concentrations with no indication of bias, and acceptable performance of visual predictive checks. External validation was performed by fitting the model to independent data from 3 external cohorts and remaining previously published studies.

RESULTS

A total of 76 models were found relevant for meta-model building from the literature and the related parameters recorded. The meta-model developed using patient level data was structurally a 2-compartment model with first-order absorption, absorption lag time and first-time varying elimination. Population values for clearance, intercompartmental clearance, central and peripheral volume were 22.5 L/h, 24.2 L/h, 246.2 L and 109.9 L, respectively. The absorption first-order rate and the lag time were fixed to 3.37/h and 0.33 hours, respectively. Transplanted organ and time after transplantation were found to influence drug apparent clearance whereas body weight influenced both the apparent volume of distribution and the apparent clearance. The model displayed good results as regards the internal and external validation.

CONCLUSION

A meta-model was successfully developed for tacrolimus in solid organ transplantation that can be used as a basis for the prediction of concentrations in different groups of patients, and eventually for effective dose individualization in different subgroups of the population.

Population Pharmacokinetic Analysis of Immediate-Release Oral Tacrolimus Co-administered with Mycophenolate Mofetil in Corticosteroid-Free Adult Kidney Transplant Recipients

BACKGROUND AND OBJECTIVE

Tacrolimus is the mainstay calcineurin inhibitor frequently administered with mycophenolic acid with or without corticosteroids to prevent graft rejection in adult kidney transplant recipients. The primary objective of this study was to develop and evaluate a population pharmacokinetic model characterizing immediate-release oral tacrolimus co-administered with mycophenolate mofetil (a pro-drug of mycophenolic acid) in adult kidney transplant recipients on corticosteroid-free regimens. The secondary objective was to investigate the effects of clinical covariates on the pharmacokinetics of tacrolimus, emphasizing the interacting effects of mycophenolic acid.

METHODS

Population modeling and evaluation were conducted with Monolix (Suite-2018R1) using the stochastic approximation expectation-maximization algorithm in 49 adult subjects (a total of 320 tacrolimus whole-blood concentrations). Effects of clinical variables on tacrolimus pharmacokinetics were determined by population covariate modeling, regression modeling, and categorical analyses.

RESULTS

A two-compartment, first-order absorption with a lag-time, linear elimination, and constant error model best represented the population pharmacokinetics of tacrolimus. The apparent clearance value for tacrolimus was 17.9 l/h (6.95% relative standard error) in our model, which is lower compared with similar subjects on corticosteroid-based therapy. The glomerular filtration rate had significant effects on the apparent clearance and central compartment volume of distribution. Conversely, mycophenolic acid did not affect the apparent clearance of tacrolimus.

CONCLUSION

We have developed and internally evaluated a novel population pharmacokinetic model for tacrolimus co-administered with mycophenolate mofetil in corticosteroid-free adult kidney transplant patients. These findings are clinically important and provide further reasons for conducting therapeutic drug monitoring in this specific population.

Model based development of tacrolimus dosing algorithm considering CYP3A5 genotypes and mycophenolate mofetil drug interaction in stable kidney transplant recipients

This study quantifies the interaction between tacrolimus (TAC) and mycophenolate mofetil (MMF) in kidney transplant recipients. Concentrations of TAC, mycophenolic acid (MPA), and metabolites were analyzed and relevant genotypes were determined from 32 patients. A population model was developed to estimate the effect of interaction. Concentrations of TAC were simulated in clinical scenarios and dose-adjusted trough concentrations per dose (C/D) were compared. Effect of interaction was described as the inverse exponential relationship. Major determinants of trough levels of TAC were CYP3A5 genotype and interaction with MPA. The absolute difference in C/D of TAC according to co-administered MMF was higher in CYP3A5 non-expressers (0.55 ng/mL) than in CYP3A5 expressers (0.35 ng/mL). The effect of MMF in determining the TAC exposure is more pronounced in CYP3A5 non-expressers. Based on population pharmacokinetic model, we suggest the TAC dosing algorithm considering the effects of CYP3A5 and MMF drug interaction in stable kidney transplant recipients.

Effects of an Ex Vivo Pediatric Extracorporeal Membrane Oxygenation Circuit on the Sequestration of Mycophenolate Mofetil, Tacrolimus, Hydromorphone, and Fentanyl

OBJECTIVES

With the expanding use of extracorporeal membrane oxygenation (ECMO), understanding drug pharmacokinetics has become increasingly important, particularly in pediatric patients. This ex vivo study examines the effect of a pediatric Quadrox-iD ECMO circuit on the sequestration and binding of mycophenolate mofetil (MMF), tacrolimus, and hydromorphone hydrochloride, which have not been extensively studied to date in pediatric ECMO circuits. Fentanyl, which has been well studied, was used as a comparator.

METHODS

ECMO circuits were set up using Quadrox-iD pediatric oxygenators and centrifugal pumps. The circuit was primed with whole blood and a reservoir was attached to represent a 5-kg patient. Fourteen French venous and 12 French arterial ECMO cannulas were inserted into the sealed reservoir. Temperature, pH, PO₂, and PCO₂ were monitored and corrected. MMF, tacrolimus, hydromorphone, and fentanyl were injected into the ECMO circuit. Serial blood samples were taken from a postoxygenator site at intervals over 12 hours, and levels were measured.

RESULTS

Hydromorphone hydrochloride was not as significantly sequestered by the ex vivo pediatric ECMO circuit when compared with fentanyl. Both mycophenolic acid and tacrolimus serum concentrations were stable in the circuit over 12 hours.

CONCLUSIONS

Hydromorphone may represent a useful medication for pain control for pediatric patients on ECMO due to its minimal sequestration. Mycophenolic acid and tacrolimus also did not show significant sequestration in the circuit, which was unexpected given their lipophilicity and protein-binding characteristics, but may provide insight into unexplored pharmacokinetics of particular medications in ECMO circuits.

A Minimal Physiologically-Based Pharmacokinetic Model for Tacrolimus in Living-Donor Liver Transplantation: Perspectives Related to Liver Regeneration and the cytochrome P450 3A5 (CYP3A5) Genotype

In adult patients after living‐donor liver transplantation, postoperative days and the cytochrome P450 3A5 (CYP3A5) genotype are known to affect tacrolimus pharmacokinetics. In this study, we constructed a physiologically‐based pharmacokinetic model adapted to the clinical data and evaluated the contribution of liver regeneration as well as hepatic and intestine CYP3A5 genotypes on tacrolimus pharmacokinetics. As a result, liver function recovered immediately and affected the total body clearance of tacrolimus only during a limited period after living‐donor liver transplantation. The clearance was about 1.35‐fold higher in the recipients who had a liver with the CYP3A5*1 allele than in those with the CYP3A5*3/*3 genotype, whereas bioavailability was ~0.7‐fold higher in the recipients who had intestines with the CYP3A5*1 allele than those with CYP3A5*3/*3. In conclusion, the constructed physiologically‐based pharmacokinetic model clarified that the oral clearance of tacrolimus was affected by the CYP3A5 genotypes in both the liver and intestine to the same extent.

A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients

Aims

The aims of this study were to describe the pharmacokinetics of tacrolimus immediately after kidney transplantation, and to develop a clinical tool for selecting the best starting dose for each patient.

Methods

Data on tacrolimus exposure were collected for the first 3 months following renal transplantation. A population pharmacokinetic analysis was conducted using nonlinear mixed‐effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates.

Results

A total of 4527 tacrolimus blood samples collected from 337 kidney transplant recipients were available. Data were best described using a two‐compartment model. The mean absorption rate was 3.6 h⁻¹, clearance was 23.0 l h^–1 (39% interindividual variability, IIV), central volume of distribution was 692 l (49% IIV) and the peripheral volume of distribution 5340 l (53% IIV). Interoccasion variability was added to clearance (14%). Higher body surface area (BSA), lower serum creatinine, younger age, higher albumin and lower haematocrit levels were identified as covariates enhancing tacrolimus clearance. Cytochrome P450 (CYP) 3A5 expressers had a significantly higher tacrolimus clearance (160%), whereas CYP3A4*22 carriers had a significantly lower clearance (80%). From these significant covariates, age, BSA, CYP3A4 and CYP3A5 genotype were incorporated in a second model to individualize the tacrolimus starting dose: Dosemg=222nghml–1*22.5lh–1*1.0ifCYP3A5*3/*3or1.62ifCYP3A5*1/*3orCYP3A5*1/*1*1.0ifCYP3A4*1or unknownor0.814ifCYP3A4*22*Age56−0.50*BSA1.930.72/1000Both models were successfully internally and externally validated. A clinical trial was simulated to demonstrate the added value of the starting dose model.

Conclusions

For a good prediction of tacrolimus pharmacokinetics, age, BSA, CYP3A4 and CYP3A5 genotype are important covariates. These covariates explained 30% of the variability in CL/F. The model proved effective in calculating the optimal tacrolimus dose based on these parameters and can be used to individualize the tacrolimus dose in the early period after transplantation.

Population Pharmacokinetics of Tacrolimus in Transplant Recipients: What Did We Learn About Sources of Interindividual Variabilities?

Tacrolimus, a calcineurin inhibitor, is a common immunosuppressant prescribed after organ transplantation and has notable inter- and intrapatient pharmacokinetic variability. The sources of variability have been investigated using population pharmacokinetic modeling over the last 2 decades. This article provides an updated synopsis on published nonlinear mixed-effects analyses developed for tacrolimus in transplant recipients. The objectives were to establish a detailed overview of the current data and to investigate covariate relationships determined by the models. Sixty-three published analyses were reviewed, and data regarding the study design, modeling approach, and resulting findings were extracted and summarized. Most of the studies investigated tacrolimus pharmacokinetics in adult and pediatric renal and liver transplants after administration of the immediate-release formulation. Model structures largely depended on the study sampling strategy, with ∼50% of studies developing a 1-compartment model using trough concentrations and a 2-compartment model with delayed absorption from intensive sampling. The CYP3A5 genotype, as a covariate, consistently impacted tacrolimus clearance, and dosing adjustments were required to achieve similar drug exposure among patients. Numerous covariates were identified as sources of interindividual variability on tacrolimus pharmacokinetics with limited consistency across these studies, which may be the result of the study designs. Additional analyses are required to further evaluate the potential impact of these covariates and the clinical implementation of these models to guide tacrolimus dosing recommendations. This article may be useful for guiding the design of future population pharmacokinetic studies and provides recommendations for the selection of an existing optimal model to individualize tacrolimus therapy.

Population pharmacokinetics of tacrolimus in children with nephrotic syndrome

AIMS

Nephrotic syndrome (NS) is the most common clinical manifestation of glomerular disease in children. Currently, tacrolimus (TAC) is widely used in children with NS. However, pharmacokinetic data in children with nephrotic syndrome is limited. This study was intended to evaluate the population pharmacokinetics (PPK) of TAC in paediatric NS and to optimize dosing regimen.

METHODS

Blood samples from NS children treated with TAC were collected and the blood concentrations of TAC were detected using HPLC-MS/MS. A PPK model was developed using NONMEM software. Pharmacogenetic analysis was carried out in the CYP3A5 gene.

RESULTS

The data from 28 children were used for PPK analysis. A one-compartment model and first-order elimination were accorded with the TAC data in paediatric NS. A covariate analysis showed that body weight and CYP3A5 genotype significantly affected TAC pharmacokinetics. Monte Carlo simulation indicated that NS children with CYP3A5*3/*3 receiving 0.10 mg kg-1  dose-1 twice daily and NS children with CYP3A5*1 receiving 0.25 mg kg-1  dose-1 twice daily TAC could achieve the target concentrations of 5-10 ng ml-1 .

CONCLUSION

The PPK of TAC was estimated in children with NS and a CYP3A5 genotype-based dosing regimen was set up based on simulations.

Evaluating tacrolimus pharmacokinetic models in adult renal transplant recipients with different CYP3A5 genotypes

PURPOSE

Numerous studies have been conducted on the population pharmacokinetics of tacrolimus in adult renal transplant recipients. It has been reported that the cytochrome P450 (CYP) 3A5 genotype is an important cause of variability in tacrolimus pharmacokinetics. However, the predictive performance of population pharmacokinetic (PK) models of tacrolimus should be evaluated prior to their implementation in clinical practice. The aim of the study reported here was to test the predictive performance of these published PK models of tacrolimus.

METHODS

A literature search of the PubMed and Web of Science databases ultimately led to the inclusion of eight one-compartment models in our analysis. We collected a total of 1715 trough concentrations from 174 patients. Predictive performance was assessed based on visual and numerical comparison bias and imprecision and by the use of simulation-based diagnostics and Bayesian forecasting.

RESULTS

Of the eight one-compartment models assessed, seven showed better predictive performance in CYP3A5 extensive metabolizers in terms of bias and imprecision. Results of the simulation-based diagnostics also supported the findings. The model based on a Chinese population in 2013 (model 3) showed the best and most stable predictive performance in all the tests and was more informative in CYP3A5 extensive metabolizers. As expected, Bayesian forecasting improved model predictability. Diversity among models and between different CYP3A5 genotypes of the same model was also narrowed by Bayesian forecasting.

CONCLUSIONS

Based on our results, we recommend using model 3 in CYP3A5 extensive metabolizers in clinical practice. All models had a poor predictive performance in CYP3A5 poor metabolizers, and they should be used with caution in this patient population. However, Bayesian forecasting improved the predictability and reduced differences, and thus the models could be applied in this latter patient population for the design of maintenance dose.

Tacrolimus Population Pharmacokinetics and Multiple CYP3A5 Genotypes in Black and White Renal Transplant Recipients

Tacrolimus exhibits inter-patient pharmacokinetic variability attributed to CYP3A5 isoenzymes and the efflux transporter, P-glycoprotein. Most black renal transplant recipients require higher tacrolimus doses compared to whites to achieve similar troughs when race-adjusted recommendations are used. An established guideline provides tacrolimus genotype dosing recommendations based on CYP3A5*1(W/T) and loss of protein function variants: CYP3A5*3 (rs776746), CYP3A5*6 (rs10264272), CYP3A5*7 (rs41303343) and may provide more comprehensive race-adjusted dosing recommendations. Our objective was to develop a tacrolimus population pharmacokinetic model evaluating demographic, clinical, and genomic factors in stable black and white renal transplant recipients. A secondary objective investigated race-based tacrolimus regimens and genotype-specific dosing. Sixty-seven recipients receiving oral tacrolimus and mycophenolic acid ≥6 months completed a 12-hour pharmacokinetic study. CYP3A5*3,*6,*7 and ABCB1 1236C>T, 2677G>T/A, 3435C>T polymorphisms were characterized. Patients were classified as extensive, intermediate, and poor metabolizers using a novel CYP3A5*3*6*7 metabolic composite. Modeling and simulation was performed with computer software (NONMEM 7.3, ICON Development Solutions; Ellicott City, Maryland). A 2-compartment model with first-order elimination and absorption with lag time best described the data. The CYP3A5*3*6*7 metabolic composite was significantly associated with tacrolimus clearance (P value < .05), which was faster in extensive (mean: 45.0 L/hr) and intermediate (29.5 L/hr) metabolizers than poor metabolizers (19.8 L/hr). Simulations support CYP3A5*3*6*7 genotype-based tacrolimus dosing to enhance general race-adjusted regimens, with dose increases of 1.5-fold and 2-fold, respectively, in intermediate and extensive metabolizers for comparable exposures to poor metabolizers. This model offers a novel approach to determine tacrolimus dosing adjustments that maintain comparable therapeutic exposure between black and white recipients with different CYP3A5 genotypes.

The Effect of Weight and CYP3A5 Genotype on the Population Pharmacokinetics of Tacrolimus in Stable Paediatric Renal Transplant Recipients

BACKGROUND

The aim of this study was to develop a population pharmacokinetic model of tacrolimus in paediatric patients at least 1 year after renal transplantation and simulate individualised dosage regimens.

PATIENTS AND METHODS

We included 54 children with median age of 11.1 years (range 3.8-18.4 years) with 120 pharmacokinetic profiles performed over 2 to 4 h. The pharmacokinetic analysis was performed using the non-linear mixed-effects modelling software (NONMEM(®)). The impact of covariates including concomitant medications, age, the cytochrome P450 (CYP) CYP3A5*3 gene and the adenosine triphosphate binding cassette protein B1 (ABCB1) 3435 C→T gene polymorphism on tacrolimus pharmacokinetics was analysed. The final model was externally validated on an independent dataset and dosing regimens were simulated.

RESULTS

A two-compartment model adequately described tacrolimus pharmacokinetics. Apparent oral clearance (CL/F) was associated with weight (allometric scaling) but not age. Children with lower weight and CYP3A5 expressers required higher weight-normalised tacrolimus doses. CL/F was inversely associated with haematocrit (P < 0.05) and γ-glutamyl transpeptidase (γGT) (P < 0.001) and was increased by 45 % in carriers of the CYP3A5*1 allele (P < 0.001). CL/F was not associated with concomitant medications. Dose simulations show that a daily tacrolimus dose of 0.2 mg/kg generates a pre-dose concentration (C 0) ranging from 5 to 10 µg/L depending on the weight and CYP3A5 polymorphism. The median area under the plasma concentration-time curve (AUC) corresponding with a tacrolimus C 0 of 4-8 µg/L was 97 h·µg/L (interquartile range 80-120).

CONCLUSIONS

In patients beyond the first year after transplantation, there is a cumulative effect of CYP3A5*1 polymorphism and weight on the tacrolimus C 0. Children with lower weight and carriers of the CYP3A5*1 allele have higher weight-normalised tacrolimus dose requirements.

Impact of intrapatient variability (IPV) in tacrolimus trough levels on long-term renal transplant function: multicentre collaborative retrospective cohort study protocol

INTRODUCTION

High intrapatient variability (IPV) in tacrolimus trough levels has been shown to be associated with higher rates of renal transplant failure. There is no consensus on what level of IPV constitutes a risk of graft loss. The establishment of such a threshold could help to guide clinicians in identifying at-risk patients to receive targeted interventions to improve IPV and thus outcomes.

METHODS AND ANALYSIS

A multicentre Transplant Audit Collaborative has been established to conduct a retrospective study examining tacrolimus IPV and renal transplant outcomes. Patients in receipt of a renal transplant at participating centres between 2009 and 2014 and fulfilling the inclusion criteria will be included in the study. The aim is to recruit a minimum of 1600 patients with follow-up spanning at least 2 years in order to determine a threshold IPV above which a renal transplant recipient would be considered at increased risk of graft loss. The study also aims to determine any national or regional trends in IPV and any demographic associations.

ETHICS AND DISSEMINATION

Consent will not be sought from patients whose data are used in this study as no additional procedures or information will be required from participants beyond that which would normally take place as part of clinical care. The study will be registered locally in each participating centre in line with local research and development protocols. It is anticipated that the results of this audit will be disseminated locally, in participating NHS Trusts, through national and international meetings and publications in peer-reviewed journals.

External evaluation of published population pharmacokinetic models of tacrolimus in adult renal transplant recipients

AIM

Several tacrolimus population pharmacokinetic models in adult renal transplant recipients have been established to facilitate dose individualization. However, their applicability when extrapolated to other clinical centres is not clear. This study aimed to (1) evaluate model external predictability and (2) analyze potential influencing factors.

METHODS

Published models were screened from the literature and were evaluated using an external dataset with 52 patients (609 trough samples) collected by postoperative day 90 via methods that included (1) prediction-based prediction error (PE%), (2) simulation-based prediction- and variability-corrected visual predictive check (pvcVPC) and normalized prediction distribution error (NPDE) tests and (3) Bayesian forecasting to assess the influence of prior observations on model predictability. The factors influencing model predictability, particularly the impact of structural models, were evaluated.

RESULTS

Sixteen published models were evaluated. In prediction-based diagnostics, the PE% within ±30% was less than 50% in all models, indicating unsatisfactory predictability. In simulation-based diagnostics, both the pvcVPC and the NPDE indicated model misspecification. Bayesian forecasting improved model predictability significantly with prior 2-3 observations. The various factors influencing model extrapolation included bioassays, the covariates involved (CYP3A5*3 polymorphism, postoperative time and haematocrit) and whether non-linear kinetics were used.

CONCLUSIONS

The published models were unsatisfactory in prediction- and simulation-based diagnostics, thus inappropriate for direct extrapolation correspondingly. However Bayesian forecasting could improve the predictability considerably with priors. The incorporation of non-linear pharmacokinetics in modelling might be a promising approach to improving model predictability.

Population pharmacokinetic analysis of tacrolimus in Mexican paediatric renal transplant patients: role of CYP3A5 genotype and formulation

AIMS

The aims of this study were (i) to develop a population pharmacokinetic (PK) model of tacrolimus in a Mexican renal transplant paediatric population (n = 53) and (ii) to test the influence of different covariates on its PK properties to facilitate dose individualization.

METHODS

Population PK and variability parameters were estimated from whole blood drug concentration profiles obtained at steady-state using the non-linear mixed effect modelling software NONMEM® Version 7.2.

RESULTS

Tacrolimus PK profiles exhibited high inter-patient variability (IPV). A two compartment model with first order input and elimination described the tacrolimus PK profiles in the studied population. The relationship between CYP3A5 genotype and tacrolimus CL/F was included in the final model. CL/F in CYP3A5*1/*1 and *1/*3 carriers was approximately 2- and 1.5-fold higher than in CYP3A5*3/*3 carriers (non-expressers), respectively, and explained almost the entire IPV in CL/F. Other covariates retained in the final model were the tacrolimus dose and formulation type. Limustin® showed markedly lower concentrations than the rest of the formulations.

CONCLUSIONS

Population PK modelling of tacrolimus in paediatric renal transplant recipients identified the tacrolimus formulation type as a significant covariate affecting the blood concentrations and confirmed the previously reported significant effect of CYP3A5 genotype on CL/F. It allowed the design of a proposed dosage based on the final model that is expected to help to improve tacrolimus dosing.

Improved prediction of tacrolimus concentrations early after kidney transplantation using theory-based pharmacokinetic modelling

Aims

The aim was to develop a theory-based population pharmacokinetic model of tacrolimus in adult kidney transplant recipients and to externally evaluate this model and two previous empirical models.

Methods

Data were obtained from 242 patients with 3100 tacrolimus whole blood concentrations. External evaluation was performed by examining model predictive performance using Bayesian forecasting.

Results

Pharmacokinetic disposition parameters were estimated based on tacrolimus plasma concentrations, predicted from whole blood concentrations, haematocrit and literature values for tacrolimus binding to red blood cells. Disposition parameters were allometrically scaled to fat free mass. Tacrolimus whole blood clearance/bioavailability standardized to haematocrit of 45% and fat free mass of 60 kg was estimated to be 16.1 l h⁻¹ [95% CI 12.6, 18.0 l h⁻¹]. Tacrolimus clearance was 30% higher (95% CI 13, 46%) and bioavailability 18% lower (95% CI 2, 29%) in CYP3A5 expressers compared with non-expressers. An E_max model described decreasing tacrolimus bioavailability with increasing prednisolone dose. The theory-based model was superior to the empirical models during external evaluation displaying a median prediction error of −1.2% (95% CI −3.0, 0.1%). Based on simulation, Bayesian forecasting led to 65% (95% CI 62, 68%) of patients achieving a tacrolimus average steady-state concentration within a suggested acceptable range.

Conclusion

A theory-based population pharmacokinetic model was superior to two empirical models for prediction of tacrolimus concentrations and seemed suitable for Bayesian prediction of tacrolimus doses early after kidney transplantation.

A population pharmacokinetic study of tacrolimus in healthy Chinese volunteers and liver transplant patients

AIM

To develop a population pharmacokinetic (PopPK) model of tacrolimus in healthy Chinese volunteers and liver transplant recipients for investigating the difference between the populations, and for potential individualized medication.

METHODS

A set of 1100 sparse trough concentration data points from 112 orthotopic liver transplant recipients, as well as 851 dense data points from 40 healthy volunteers receiving a single dose of tacrolimus (2 mg, p.o.) were collected. PopPK model of tacrolimus was constructed using the program NONMEM. Related covariates such as age, hepatic and renal functions that were potentially associated with tacrolimus disposition were evaluated. The final model was validated using bootstrapping and a visual predictive check.

RESULTS

A two-compartment model of tacrolimus could best describe the data from the two populations. The final model including two covariates, population (liver transplant recipients or volunteers) and serum ALT (alanine aminotransferase) level, was verified and adequately described the pharmacokinetic characteristics of tacrolimus. The estimates of V2/F, Q/F and V3/F were 22.7 L, 76.3 L/h and 916 L, respectively. The estimated CL/F in the volunteers and liver transplant recipients was 32.8 and 18.4 L/h, respectively. Serum ALT level was inversely related to CL/F, whereas age did not influence CL/F. Thus, the elderly (≥65 years) and adult (<65 years) groups in the liver transplant recipients showed no significant difference in the clearance of tacrolimus.

CONCLUSION

Compared with using the sparse data only, the integrating modeling technique combining sparse data from the patients and dense data from the healthy volunteers improved the PopPK analysis of tacrolimus.

Effect of CYP3A4*22, CYP3A5*3, and CYP3A Combined Genotypes on Cyclosporine, Everolimus, and Tacrolimus Pharmacokinetics in Renal Transplantation

Cyclosporine, everolimus, and tacrolimus are the cornerstone of immunosuppressive therapy in renal transplantation. These drugs are characterized by narrow therapeutic windows, highly variable pharmacokinetics (PK), and metabolism by CYP3A enzymes. Recently, the decreased activity allele, CYP3A4*22, was described as a potential predictive marker for CYP3A4 activity. This study investigated the effect of CYP3A4*22, CYP3A5*3, and CYP3A combined genotypes on cyclosporine, everolimus, and tacrolimus PK in renal transplant patients. CYP3A4*22 carriers showed a significant lower clearance for cyclosporine (-15%), and a trend was observed for everolimus (-7%) and tacrolimus (-16%). Patients carrying at least one CYP3A5*1 allele had 1.5-fold higher tacrolimus clearance compared with noncarriers; however, CYP3A5*3 appeared to be nonpredictive for everolimus and cyclosporine. CYP3A combined genotype did not significantly improve prediction of clearance compared with CYP3A5*3 or CYP3A4*22 alone. These data suggest that dose individualization of cyclosporine, everolimus, or tacrolimus therapy based on CYP3A4*22 is not indicated.CPT: Pharmacometrics Systems Pharmacology (2014); 3, e100; doi:10.1038/psp.2013.78; published online 12 February 2014.

Clinical pharmacokinetics of tacrolimus in Iranian liver transplant recipients

Tacrolimus, a cornerstone of immunosuppressive therapy in solid organ transplantation, has a narrow therapeutic range with considerable inter-individual and intra-individual pharmacokinetic variability. To date, there is no information on the pharmacokinetics of tacrolimus in Iranian liver transplant recipients. This study was designed to determine pharmacokinetic properties of orally administered tacrolimus in Iranian adult liver transplant recipients. Tacrolimus doses and steady state whole blood trough concentrations as well as patient demographic and clinical data were obtained retrospectively using the 30 included patients' medical records. Pharmacokinetic parameters were estimated by using a nonlinear mixed effect model program (Monolix version 3.1). Absorption rate constant was fixed at two hours(-1). Drug apparent clearance (CL/F), apparent volume of distribution (Vd/F), and elimination half life (t½β) were calculated. The administered dose of tacrolimus to the patients ranged from 0.02 to 0.14 mg/Kg/day. Tacrolimus blood trough concentrations varied widely within the range of 1.8 to 30 ng/mL. The mean values of CL/F, Vd/F, and t½β were found to be 9.3 ± 0.96 L/h, 101 ± 29 L, and 7.5 hours, respectively. The pharmacokinetics of tacrolimus was highly variable among our patients. CL/F, Vd/F, and t½β of tacrolimus in this study were comparable to reported values from Italian heart transplant patients but somewhat different from reported ones from other solid organ transplant populations.

Population pharmacokinetic and pharmacogenetic analysis of tacrolimus in paediatric liver transplant patients

AIMS

To build a population pharmacokinetic model that describes the apparent clearance of tacrolimus and the potential demographic, clinical and genetically controlled factors that could lead to inter-patient pharmacokinetic variability within children following liver transplantation.

METHODS

The present study retrospectively examined tacrolimus whole blood pre-dose concentrations (n = 628) of 43 children during their first year post-liver transplantation. Population pharmacokinetic analysis was performed using the non-linear mixed effects modelling program (nonmem) to determine the population mean parameter estimate of clearance and influential covariates.

RESULTS

The final model identified time post-transplantation and CYP3A5*1 allele as influential covariates on tacrolimus apparent clearance according to the following equation: TVCL = 12.9 x (Weight/13.2)(0.75) x EXP(-0.00158 x TPT) x EXP(0.428 x CYP3A5) where TVCL is the typical value for apparent clearance, TPT is time post-transplantation in days and the CYP3A5 is 1 where *1 allele is present and 0 otherwise. The population estimate and inter-individual variability (%CV) of tacrolimus apparent clearance were found to be 0.977 l  h(-1)  kg(-1) (95% CI 0.958, 0.996) and 40.0%, respectively, while the residual variability between the observed and predicted concentrations was 35.4%.

CONCLUSION

Tacrolimus apparent clearance was influenced by time post-transplantation and CYP3A5 genotypes. The results of this study, once confirmed by a large scale prospective study, can be used in conjunction with therapeutic drug monitoring to recommend tacrolimus dose adjustments that take into account not only body weight but also genetic and time-related changes in tacrolimus clearance.

Inclusion of CYP3A5 genotyping in a nonparametric population model improves dosing of tacrolimus early after transplantation

Following organ engraftment, initial dosing of tacrolimus is based on recipient weight and adjusted by measured C₀ concentrations. The bioavailability and elimination of tacrolimus are affected by the patients CYP3A5 genotype. Prospective data of the clinical advantage of knowing patient's CYP3A5 genotype prior to transplantation are lacking. A nonparametric population model was developed for tacrolimus in renal transplant recipients. Data from 99 patients were used for model development and validation. A three-compartment model with first-order absorption and lag time from the dosing compartment described the data well. Clearances and volumes of distribution were allometrically scaled to body size. The final model included fat-free mass, body mass index, hematocrit, time after transplantation, and CYP3A5 genotype as covariates. The bias and imprecision were 0.35 and 1.38, respectively, in the external data set. Patients with functional CYP3A5 had 26% higher clearance and 37% lower bioavailability. Knowledge of CYP3A5 genotype provided an initial advantage, but only until 3-4 tacrolimus concentrations were known. After this, a model without CYP3A5 genotype predicted just as well. The present models seem applicable for clinical individual dose predictions but need a prospective evaluation.

Statistical tools for dose individualization of mycophenolic acid and tacrolimus co-administered during the first month after renal transplantation

AIM

To predict simultaneously the area under the concentration-time curve during one dosing interval [AUC(0,12 h)] for mycophenolic acid (MPA) and tacrolimus (TAC), when concomitantly used during the first month after transplantation, based on common blood samples.

METHODS

Data were from two different sources, real patient pharmacokinetic (PK) profiles from 65 renal transplant recipients and 9000 PK profiles simulated from previously published models on MPA or TAC in the first month after transplantation. Multiple linear regression (MLR) and Bayesian estimation using optimal samples were performed to predict MPA and TAC AUC(0,12 h) based on two concentrations.

RESULTS

The following models were retained: AUC(0,12 h) = 16.5 + 4.9 × C1.5 + 6.7 × C3.5 (r(2) = 0.82, rRMSE = 9%, with simulations and r(2) = 0.66, rRMSE = 24%, with observed data) and AUC(0,12 h) = 24.3 + 5.9 × C1.5 + 12.2 × C3.5 (r(2) = 0.94, rRMSE = 12.3%, with simulations r(2) = 0.74, rRMSE = 15%, with observed data) for MPA and TAC, respectively. In addition, bayesian estimators were developed including parameter values from final models and values of concentrations at 1.5 and 3.5 h after dose. Good agreement was found between predicted and reference AUC(0,12 h) values: r(2) = 0.90, rRMSE = 13% and r(2) = 0.97, rRMSE = 5% with simulations for MPA and TAC, respectively and r(2) = 0.75, rRMSE = 11% and r(2) = 0.83, rRMSE = 7% with observed data for MPA and TAC, respectively.

CONCLUSION

Statistical tools were developed for simultaneous MPA and TAC therapeutic drug monitoring. They can be incorporated in computer programs for patient dose individualization.

Importance of hematocrit for a tacrolimus target concentration strategy

Purpose

To identify patient characteristics that influence tacrolimus individual dose requirement in kidney transplant recipients.

Methods

Data on forty-four 12-h pharmacokinetic profiles from 29 patients and trough concentrations in 44 patients measured during the first 70 days after transplantation (1,546 tacrolimus whole blood concentrations) were analyzed. Population pharmacokinetic modeling was performed using NONMEM 7.2®.

Results

Standardization of tacrolimus whole blood concentrations to a hematocrit value of 45 % improved the model fit significantly (p < 0.001). Fat-free mass was the best body size metric to predict tacrolimus clearance and volume of distribution. Bioavailability was 49 % lower in expressers of cytochrome P450 3A5 (CYP3A5) than in CYP3A5 nonexpressers. Younger females (<40 years) showed a 35 % lower bioavailability than younger males. Bioavailability increased with age for both males and females towards a common value at age >55 years that was 47 % higher than the male value at age <40 years. Bioavailability was highest immediately after transplantation, decreasing steeply thereafter to reach its nadir at day 5, following which it increased during the next 55 days towards an asymptotic value that was 28 % higher than that on day 5.

Conclusions

Hematocrit predicts variability in tacrolimus whole blood concentrations but is not expected to influence unbound (therapeutically active) concentrations. Fat-free mass, CYP3A5 genotype, sex, age and time after transplant influence the tacrolimus individual dose requirement. Because hematocrit is highly variable in kidney transplant patients and increases substantially after kidney transplantation, hematocrit is a key factor in the interpretation of tacrolimus whole blood concentrations.

Electronic supplementary material

The online version of this article (doi:10.1007/s00228-013-1584-7) contains supplementary material, which is available to authorized users.

Dosing equation for tacrolimus using genetic variants and clinical factors

AIM

To develop a dosing equation for tacrolimus, using genetic and clinical factors from a large cohort of kidney transplant recipients. Clinical factors and six genetic variants were screened for importance towards tacrolimus clearance (CL/F).

METHODS

Clinical data, tacrolimus troughs and corresponding doses were collected from 681 kidney transplant recipients in a multicentre observational study in the USA and Canada for the first 6 months post transplant. The patients were genotyped for 2,724 single nucleotide polymorphisms using a customized Affymetrix SNP chip. Clinical factors and the most important SNPs (rs776746, rs12114000, rs3734354, rs4926, rs3135506 and rs2608555) were analysed for their influence on tacrolimus CL/F.

RESULTS

The CYP3A5*1 genotype, days post transplant, age, transplant at a steroid sparing centre and calcium channel blocker (CCB) use significantly influenced tacrolimus CL/F. The final model describing CL/F (l h(-1)) was: 38.4 ×[(0.86, if days 6-10) or (0.71, if days 11-180)]×[(1.69, if CYP3A5*1/*3 genotype) or (2.00, if CYP3A5*1/*1 genotype)]× (0.70, if receiving a transplant at a steroid sparing centre) × ([age in years/50](-0.4)) × (0.94, if CCB is present). The dose to achieve the desired trough is then prospectively determined using the individuals CL/F estimate.

CONCLUSIONS

The CYP3A5*1 genotype and four clinical factors were important for tacrolimus CL/F. An individualized dose is easily determined from the predicted CL/F. This study is important towards individualization of dosing in the clinical setting and may increase the number of patients achieving the target concentration. This equation requires validation in an independent cohort of kidney transplant recipients.

Population pharmacokinetic model and Bayesian estimator for two tacrolimus formulations--twice daily Prograf and once daily Advagraf

AIM

To investigate the differences in the pharmacokinetics of Prograf and the prolonged release formulation Advagraf and to develop a Bayesian estimator to estimate tacrolimus inter-dose area under the curve (AUC) in renal transplant patients receiving either Prograf or Advagraf.

METHODS

Tacrolimus concentration-time profiles were collected, in adult renal transplant recipients, at weeks 1 and 2, and at months 1, 3 and 6 post-transplantation from 32 Prograf treated patients, and one profile was collected from 41 Advagraf patients more than 12 months post-transplantation. Population pharmacokinetic (popPK) parameters were estimated using nonmem. In a second step, the popPK model was used to develop a single Bayesian estimator for the two tacrolimus formulations.

RESULTS

A two-compartment model with Erlang absorption (n= 3) and first-order elimination best described the data. In Advagraf patients, a bimodal distribution was observed for the absorption rate constant (K(tr) ): one group with a K(tr) similar to that of Prograf treated patients and the other group with a slower absorption. A mixture model for K(tr) was tested to describe this bimodal distribution. However, the data were best described by the nonmixture model including covariates (cytochrome P450 3A5, haematocrit and drug formulation). Using this model and tacrolimus concentrations measured at 0, 1 and 3h post-dose, the Bayesian estimator could estimate tacrolimus AUC accurately (bias = 0.1%) and with good precision (8.6%).

CONCLUSIONS

The single Bayesian estimator developed yields good predictive performance for estimation of individual tacrolimus inter-dose AUC in Prograf and Advagraf treated patients and is suitable for clinical practice.

Evaluation of limited sampling methods for estimation of tacrolimus exposure in adult kidney transplant recipients

AIMS

To examine the predictive performance of limited sampling methods for estimation of tacrolimus exposure in adult kidney transplant recipients.

METHODS

Twenty full tacrolimus area under the concentration-time curve from 0 to 12 h post-dose (AUC(0-12)) profiles (AUCf) were collected from 20 subjects. Predicted tacrolimus AUC(0-12) (AUCp) was calculated using the following: (i) 42 multiple regression-derived limited sampling strategies (LSSs); (ii) five population pharmacokinetic (PK) models in the Bayesian forecasting program TCIWorks; and (iii) a Web-based consultancy service. Correlations (r(2)) between C(0) and AUCf and between AUCp and AUCf were examined. Median percentage prediction error (MPPE) and median absolute percentage prediction error (MAPE) were calculated.

RESULTS

Correlation between C(0) and AUCf was 0.53. Using the 42 LSS equations, correlation between AUCp and AUCf ranged from 0.54 to 0.99. The MPPE and MAPE were <15% for 29 of 42 equations (62%), including five of eight equations based on sampling taken ≤2 h post-dose. Using the PK models in TCIWorks, AUCp derived from only C(0) values showed poor correlation with AUCf (r(2)=0.27-0.54) and unacceptable imprecision (MAPE 17.5-31.6%). In most cases, correlation, bias and imprecision estimates progressively improved with inclusion of a greater number of concentration time points. When concentration measurements at 0, 1, 2 and 4 h post-dose were applied, correlation between AUCp and AUCf ranged from 0.75 to 0.93, and MPPE and MAPE were <15% for all models examined. Using the Web-based consultancy service, correlation between AUCp and AUCf was 0.74, and MPPE and MAPE were 6.6 and 9.6%, respectively.

CONCLUSIONS

Limited sampling methods better predict tacrolimus exposure compared with C(0) measurement. Several LSSs based on sampling taken 2 h or less post-dose predicted exposure with acceptable bias and imprecision. Generally, Bayesian forecasting methods required inclusion of a concentration measurement from >2 h post-dose to adequately predict exposure.

Prediction tacrolimus blood levels based on the Bayesian method in adult kidney transplant patients

The use of tacrolimus is complicated by its narrow therapeutic index and wide intra- and interpatient variability. We have previously described a tacrolimus population pharmacokinetics model obtained in an adult kidney transplant cohort. The aims of the present study were (1) to validate that model using an external dataset and (2) to evaluate the prediction using a Bayesian method. Data were retrospectively collected from 34 adult patients receiving kidney transplantation. Trough blood concentrations of tacrolimus were predicted using the empirical Bayesian method with sparse samples obtained during the previous week. The system performance was evaluated by the mean prediction error (ME), mean absolute prediction error (MAE). and root mean square error (RMSE). Patients were administrated oral or intravenous tacrolimus as part of a triple immunosuppressive regimen with mycophenolate mofetil and corticosteroids. Subsequent doses were adjusted on the basis of clinical evidence of efficacy and toxicity and by routine therapeutic drug monitoring. In our previous model, clearance increased with post transplantation days and with prednisone dosage. Concentrations predicted by the population mean pharmacokinetic parameter values match well with observed concentrations during oral therapy. Bayesian prediction using trough concentrations obtained after 21 days of treatment significantly decreased ME, MAE, and RMSE compared with predictions from data including this period. After 21 days of treatment, there was an insignificant bias ME (0.22 ± 2.59 ng/ml), a reasonable precision MAE (1.97 ± 1.69 ng/ml) and RMSE (1.28 ± 0.58 ng/ml). The present study demonstrates the suitability of the Bayesian method for the prediction of trough blood concentrations of tacrolimus using only few samples in adult kidney transplantation recipients.

Lack of tacrolimus circadian pharmacokinetics and CYP3A5 pharmacogenetics in the early and maintenance stages in Japanese renal transplant recipients

AIMS

We investigated whether tacrolimus pharmacokinetics shows circadian variation and the influence of the CYP3A5 A6986G polymorphism on the pharmacokinetics in both the early and maintenance stages after renal transplantation.

METHODS

Tacrolimus was administered twice daily at specified times (09.00 and 21.00 h) throughout the pre- and post-transplant period according to the trough-targeting strategy. Fifty recipients with stable graft function were studied on day 28 and beyond 1-year post transplantation. Whole blood samples were collected prior to and 1, 2, 3, 4, 6, 9 and 12 h after both the morning and evening doses during hospitalization.

RESULTS

Tacrolimus pharmacokinetics did not show circadian variation in either the early or maintenance stage [AUC(0-12) 197.1 (95% confidence interval 182.9, 212.3) in daytime vs. 203.6 ng h ml(-1) (189.8, 217.4) in the night-time at day 28, 102.0 (92.1, 111.9) vs. 107.7 (97.9, 117.5) at 1 year, respectively]. In CYP3A5 *1 allele carriers (CYP3A5 expressers), body weight-adjusted oral clearance was markedly decreased from the early stage to the maintenance stage [0.622 (0.534, 0.709) to 0.369 l h(-1) kg(-1) (0.314, 0425)] compared with a smaller decrease [0.368 (0.305, 0.430) to 0.305 (0.217, 0.393)] in CYP3A5 non-expressers; however, the CYP3A5 genetic variation did not influence tacrolimus chronopharmacokinetics.

CONCLUSION

Equivalent daytime and night-time tacrolimus pharmacokinetics were achieved during both the early and maintenance stages with our specified-time administration strategy. The CYP3A5 polymorphism may be associated with the time-dependent changes in the oral clearance of tacrolimus, suggesting that genotyping of this polymorphism is useful for determining the appropriate dose of tacrolimus in both the early and maintenance stages after renal transplantation.