Time of Drug Administration, CYP3A5 and ABCB1 Genotypes, and Analytical Method Influence Tacrolimus Pharmacokinetics: A Population Pharmacokinetic Study

A Comprehensive Mixed-Method Approach to Characterize the Source of Diurnal Tacrolimus Exposure Variability in Children: Systematic Review, Meta-analysis, and Application to an Existing Data Set

Tacrolimus is widely reported to display diurnal variation in pharmacokinetic parameters with twice-daily dosing. However, the contribution of chronopharmacokinetics versus food intake is unclear, with even less evidence in the pediatric population. The objectives of this study were to summarize the existing literature by meta-analysis and evaluate the impact of food composition on 24-hour pharmacokinetics in pediatric kidney transplant recipients. For the meta-analysis, 10 studies involving 253 individuals were included. The pooled effect sizes demonstrated significant differences in area under the concentration-time curve from time 0 to 12 hours (standardized mean difference [SMD], 0.27; 95% confidence interval [CI], 0.03-0.52) and maximum concentration (SMD, 0.75; 95% CI, 0.35-1.15) between morning and evening dose administration. However, there was significant between-study heterogeneity that was explained by food exposure. The effect size for minimum concentration was not significantly different overall (SMD, -0.09; 95% CI, -0.27 to 0.09) or across the food exposure subgroups. A 2-compartment model with a lag time, linear clearance, and first-order absorption best characterized the tacrolimus pharmacokinetics in pediatric participants. As expected, adding the time of administration and food composition covariates reduced the unexplained within-subject variability for the first-order absorption rate constant, but only caloric composition significantly reduced variability for lag time. The available data suggest food intake is the major driver of diurnal variation in tacrolimus exposure, but the associated changes are not reflected by trough concentrations alone.

A Population Pharmacokinetic Model to Predict the Individual Starting Dose of Tacrolimus for Tunisian Adults after Renal Transplantation

BACKGROUND

Tacrolimus is the most frequently used immunosuppressive drug for preventing renal rejection. However, its use is hampered by its narrow therapeutic index and large intra and interpatient variability in pharmacokinetics. The objective of this study was to externally validate a tacrolimus population pharmacokinetic model developed for the Dutch population and adjust the model for the Tunisian population for use in predicting the starting dose requirement after kidney transplantation.

METHODS

Data on tacrolimus exposure were obtained from kidney transplant recipients (KTRs) during the first 3 months post-transplantation. External validation of the Dutch model and its adjustment for the Tunisian population was performed using nonlinear mixed-effects modeling.

RESULTS

In total, 1901 whole-blood predose tacrolimus concentrations from 196 adult KTRs were analyzed. According to a visual predictive check, the Dutch model underestimated the starting dose for the Tunisian adult population. The effects of age, together with the CYP3A5*3 and CYP3A4*22 genotypes on tacrolimus clearance were significantly different in the Tunisian population than in the Dutch population. Based on a bodyweight-based dosing, only 21.9% of tacrolimus concentrations were within the target range, whereas this was estimated to be 54.0% with the newly developed model-based dosing. After adjustment, the model was successfully validated internally in a Tunisian population.

CONCLUSIONS

A starting-dose population pharmacokinetic model of tacrolimus for Tunisian KTRs was developed based on a previously published Dutch model. Using this starting dose could potentially increase the percentage of patients achieving target tacrolimus concentrations after the initial starting dose.

CYP3A5 influences oral tacrolimus pharmacokinetics and timing of acute kidney injury following allogeneic hematopoietic stem cell transplantation

Introduction: Polymorphisms in genes responsible for the metabolism and transport of tacrolimus have been demonstrated to influence clinical outcomes for patients following allogeneic hematologic stem cell transplant (allo-HSCT). However, the clinical impact of germline polymorphisms specifically for oral formulations of tacrolimus is not fully described. Methods: To investigate the clinical impact of genetic polymorphisms in CYP3A4, CYP3A5, and ABCB1 on oral tacrolimus pharmacokinetics and clinical outcomes, we prospectively enrolled 103 adult patients receiving oral tacrolimus for the prevention of graft-versus-host disease (GVHD) following allo-HSCT. Patients were followed in the inpatient and outpatient phase of care for the first 100 days of tacrolimus therapy. Patients were genotyped for CYP3A5 *3 (rs776746), CYP3A4 *1B (rs2740574), ABCB1 exon 12 (rs1128503), ABCB1 exon 21 (rs2032582), ABCB1 exon 26 (rs1045642). Results: Expression of CYP3A5 *1 was highly correlated with tacrolimus pharmacokinetics in the inpatient phase of care (p < 0.001) and throughout the entirety of the study period (p < 0.001). Additionally, Expression of CYP3A5 *1 was associated with decreased risk of developing AKI as an inpatient (p = 0.06). Variants in ABCB1 were not associated with tacrolimus pharmacokinetics in this study. We were unable to discern an independent effect of CYP3A4 *1B or *22 in this population. Conclusion: Expression of CYP3A5 *1 is highly influential on the pharmacokinetics and clinical outcomes for patients receiving oral tacrolimus as GVHD prophylaxis following allo-HSCT.

Fasting Status and Circadian Variation Must be Considered When Performing AUC-based Therapeutic Drug Monitoring of Tacrolimus in Renal Transplant Recipients

Therapeutic drug monitoring (TDM) is mandatory for the immunosuppressive drug tacrolimus (Tac). For clinical applicability, TDM is performed using morning trough concentrations. With recent developments making tacrolimus concentration determination possible in capillary microsamples and Bayesian estimator predicted area under the concentration curve (AUC), AUC‐guided TDM may now be clinically applicable. Tac circadian variation has, however, been reported, with lower systemic exposure following the evening dose. The aim of the present study was to investigate tacrolimus pharmacokinetic (PK) after morning and evening administrations of twice‐daily tacrolimus in a real‐life setting without restrictions regarding food and concomitant drug timing. Two 12 hour tacrolimus investigations were performed; after the morning dose and the following evening dose, respectively, in 31 renal transplant recipients early after transplantation both in a fasting‐state and under real‐life nonfasting conditions (14 patients repeated the investigation). We observed circadian variation under fasting‐conditions: 45% higher peak‐concentration and 20% higher AUC following the morning dose. In the real‐life nonfasting setting, the PK‐profiles were flat but comparable after the morning and evening doses, showing slower absorption rate and lower AUC compared with the fasting‐state. Limited sampling strategies using concentrations at 0, 1, and 3 hours predicted AUC after fasting morning administration, and samples obtained at 1, 3, and 6 hours predicted AUC for the other conditions (evening and real‐life nonfasting). In conclusion, circadian variation of tacrolimus is present when performed in patients who are in the fasting‐state, whereas flatter PK‐profiles and no circadian variation was present in a real‐life, nonfasting setting.

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.

Antiplatelet Effect of a Pulaimab [Anti-GPIIb/IIIa F(ab)2 Injection] Evaluated by a Population Pharmacokinetic-pharmacodynamic Model

BACKGROUND

Cardiovascular disease has one of the highest mortality rates among all the diseases. Platelets play an important role in the pathogenesis of cardiovascular diseases. Platelet membrane glycoprotein GPIIb/IIIa antagonists are the most effective antiplatelet drugs, and pulaimab is one of these. The study aims to promote individual medication of pulaimab [anti-GPIIb/IIIa F(ab)2 injection] by discovering the pharmacological relationship among the dose, concentration, and effects. The goal of this study is to establish a population pharmacokineticpharmacodynamic model to evaluate the antiplatelet effect of intravenous pulaimab injection.

METHODS

Data were collected from 59 healthy subjects who participated in a Phase-I clinical trial. Plasma concentration was used as the pharmacokinetic index, and platelet aggregation inhibition rate was used as the pharmacodynamic index. The basic pharmacokinetics model was a two-compartment model, whereas the basic pharmacodynamics model was a sigmoid-EMAX model with a direct effect. The covariable model was established by a stepwise method. The final model was verified by a goodness-of-fit method, and predictive performance was assessed by a Bootstrap (BS) method.

RESULTS

In the final model, typical population values of the parameters were as follows: central distribution Volume (V1), 183 L; peripheral distribution Volume (V2), 349 L; Central Clearance (CL), 31 L/h; peripheral clearance(Q), 204 L/h; effect compartment concentration reaching half of the maximum effect (EC50), 0.252 mg/L; maximum effect value (EMAX), 54.0%; and shape factor (γ), 0.42. In the covariable model, thrombin time had significant effects on CL and EMAX. Verification by the goodness-of-fit and BS methods showed that the final model was stable and reliable.

CONCLUSION

A model was successfully established to evaluate the antiplatelet effect of intravenous pulaimab injection that could provide support for the clinical therapeutic regimen.

Population pharmacokinetics of immediate- and prolonged-release tacrolimus formulations in liver, kidney and heart transplant recipients

Aims

Develop a population pharmacokinetics model of tacrolimus in organ transplant recipients receiving twice‐daily, immediate‐release (IR‐T; Prograf) and/or once‐daily, prolonged‐release (PR‐T; Advagraf or Astagraf XL) tacrolimus.

Methods

Tacrolimus concentration–time profiles were analysed from 8 Phase II studies in adult and paediatric liver, kidney and heart transplant patients receiving IR‐T and/or PR‐T. A tacrolimus population pharmacokinetic model, including identification of significant covariates, was developed using NONMEM.

Results

Overall, 23,176 tacrolimus concentration records were obtained from 408 patients. A 2‐compartment model with first‐order absorption and elimination described the concentration–time profiles. Tacrolimus absorption rate was 50% slower with PR‐T vs IR‐T. Tacrolimus apparent oral clearance was 44.3 L/h in Whites and 59% higher in Asians. Tacrolimus central volume of distribution was 108 L in males and 55% lower in females; trough concentrations were similar between formulations. Tacrolimus relative bioavailability was similar between formulations (geometric mean ratio PR‐T:IR‐T 95%, 90% confidence intervals: 89%, 101%). Asians had 83% and 51% higher relative bioavailability than Whites and Blacks, respectively, for IR‐T and PR‐T. Whites had 49% and 77% higher relative bioavailability than Blacks for PR‐T and IR‐T, respectively. Blacks had 52% lower relative bioavailability than Whites and Asians for IR‐T and PR‐T. Type of organ transplanted and patient population (adult/paediatric) did not have a significant effect on tacrolimus pharmacokinetics.

Conclusions

This population pharmacokinetic model described data from transplant recipients who received IR‐T and/or PR‐T. Tacrolimus trough concentrations and relative bioavailability were similar between formulations, supporting 1 mg:1 mg conversion from Prograf to Advagraf/Astagraf XL in clinical practice.

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.

A New CYP3A5*3 and CYP3A4*22 Cluster Influencing Tacrolimus Target Concentrations: A Population Approach

BACKGROUND

Single nucleotide polymorphisms (SNPs) in the CYP3A5 and CYP3A4 genes have been reported to be an important cause of variability in the pharmacokinetics of tacrolimus in renal transplant patients. The aim of this study was to merge all of the new genetic information available with tacrolimus pharmacokinetics to generate a more robust population model with data from renal transplant recipients.

METHODS

Tacrolimus exposure data from 304 renal transplant recipients were collected throughout the first year after transplantation and were simultaneously analyzed with a population pharmacokinetic approach using NONMEM^® version 7.2.

RESULTS

The tacrolimus whole-blood concentration versus time data were best described by a two-open-compartment model with inter-occasion variability assigned to plasma clearance. The following factors led to the final model, which significantly decreased the minimum objective function value (p < 0.001): a new genotype cluster variable combining the CYP3A5*3 and CYP3A4*22 SNPs defined as extensive, intermediate, and poor metabolizers; the standardization of tacrolimus whole blood concentrations to a hematocrit value of 45%; and age included as patients <63 years versus patients ≥63 years. External validation confirmed the prediction ability of the model with median bias and precision values of 1.17 ng/mL (95% confidence interval [CI] -3.68 to 4.50) and 1.64 ng/mL (95% CI 0.11-5.50), respectively. Simulations showed that, for a given age and hematocrit at the same fixed dose, extensive metabolizers required the highest doses followed by intermediate metabolizers and then poor metabolizers.

CONCLUSIONS

Tacrolimus disposition in renal transplant recipients was described using a new population pharmacokinetic model that included the CYP3A5*3 and CYP3A4*22 genotype, age, and hematocrit.

Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in Chinese liver transplant patients

WHAT IS KNOWN AND OBJECTIVES

Tacrolimus (TAC) is widely used as part of immunosuppressive regimens. There is great interindividual variation on the disposition of TAC. The aim of this study was to develop a population pharmacokinetic (PPK) model for Chinese liver transplant patients and evaluate genetic polymorphism and other possible factors on the PK parameters. The exposure of TAC is to be estimated through Bayesian modelling.

METHODS

A total of 47 sets of rich-time PK and 1234 conventional therapeutic drug monitoring (TDM) data were collected from 125 Chinese liver transplant patients. The pathophysiological data of these patients were recorded. CYP3A5*3 and ABCB1 genotypes were determined for each patient. The PPK model for TAC was established by nonlinear mixed-effects modelling (nonmem). The impact of pathophysiology and genotype on PPK parameters was evaluated. Bayesian estimators for the area under concentration-time curve (AUC) of TAC were validated.

RESULTS

A two-compartment model with lag time was found to be the most suitable model for the pooled full PK and TDM data for Chinese liver transplant patients. The CL/F, V₂ /F, Q/F, V₃ /F, K_a and lag time were 17.4±0.81 L/h, 165±44.1 L, 54.9±25.8L/h, 594±87.5 L, 0.51±0.095 L/h and 1.57±0.34 h. Post-operative day (POD), creatinine clearance (CLcr) and ABCB1 C3435T genotypes were found to have significant influences on CL/F (P<.01). ABCB1 C3435T genotypes showed a significant correlation with V₂ /F (P<.01). C₀ -C₂ and C₀ -C₂ -C₄ were shown to be suitable for the estimation of AUC in Chinese liver transplant patients.

WHAT IS NEW AND CONCLUSION

A PPK model for TAC was established successfully in Chinese liver transplant patients. POD, CLcr and ABCB1 C3435T genotypes were shown to have significant effects on CL/F. The AUC of TAC in Chinese liver transplant patients could be estimated through Bayesian modelling, based on which individualized immunosuppressive regimens can be designed.

Identifying 24 h variation in the pharmacokinetics of levofloxacin: a population pharmacokinetic approach

AIM

The objective of this study was to investigate whether the pharmacokinetics of orally administered levofloxacin show 24 h variation. Levofloxacin was used as a model compound for solubility and permeability independent absorption and passive renal elimination.

METHODS

In this single centre, crossover, open label study, 12 healthy subjects received an oral dose of 1000 mg levofloxacin at six different time points equally divided over the 24 h period. Population pharmacokinetic modelling was used to identify potential 24 h variation in the pharmacokinetic parameters of this drug.

RESULTS

The pharmacokinetics of levofloxacin could be described by a one compartment model with first order clearance and a transit compartment to describe drug absorption. The fit of the model was significantly improved when the absorption rate constant was described as a cosine function with a fixed period of 24 h, a relative amplitude of 47% and a peak around 08.00 h in the morning. Despite this variation in absorption rate constant, simulations of a once daily dosing regimen showed that tmax , Cmax and the area under the curve at steady-state were not affected by the time of drug administration.

CONCLUSION

The finding that the absorption rate constant showed considerable 24 h variation may be relevant for drugs with similar physicochemical properties as levofloxacin that have a narrower therapeutic index. Levofloxacin, however, can be dosed without taking into account the time of day, at least in terms of its pharmacokinetics.

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.

Multidrug resistance-associated protein 2 (MRP2/ABCC2) haplotypes significantly affect the pharmacokinetics of tacrolimus in kidney transplant recipients

BACKGROUND AND OBJECTIVE

Tacrolimus is an immunosuppressive drug used for the prevention of the allograft rejection in kidney transplant recipients. It exhibits a narrow therapeutic index and large pharmacokinetic variability. Tacrolimus is mainly metabolized by cytochrome P450 (CYP) 3A4 and 3A5 and effluxed via ATP-binding cassette (ABC) transporters such as P-glycoprotein (P-gp), encoded by ABCB1 gene. The influence of CYP3A5*3 on the pharmacokinetics of tacrolimus has been well characterized. On the other hand, the contribution of polymorphisms in other genes is controversial. In addition, the involvement of other efflux transporters than P-gp in tacrolimus disposition is uncertain. The present study was designed to investigate the effects of genetic polymorphisms of CYP3As and efflux transporters on the pharmacokinetics of tacrolimus.

SUBJECTS AND METHODS

A total of 500 blood concentrations of tacrolimus from 102 adult stable kidney transplant recipients were included in the analyses. Genetic polymorphisms in CYP3A4 and CYP3A5 genes were determined. In addition, the genes of efflux transporters including P-gp (ABCB1), multidrug resistance-associated protein (MRP2/ABCC2) and breast cancer resistance protein (BCRP/ABCG2) were genotyped. For ABCC2 gene, haplotypes were determined as follows: H1 (wild type), H2 (1249G>A), H9 (3972C>T) and H12 (-24C>T and 3972C>T). Population pharmacokinetic analysis was performed using nonlinear mixed effects modeling.

RESULTS

Analyses revealed that the CYP3A5 expressers (CYP3A5*1 carriers) and MRP2 high-activity group (ABCC2 H2/H2 and H1/H2) showed a decreased dose-normalized trough concentration of tacrolimus by 2.3-fold (p < 0.001) and 1.5-fold (p = 0.007), respectively. The pharmacokinetics of tacrolimus were best described using a two-compartment model with first order absorption and an absorption lag time. In the population pharmacokinetic analysis, CYP3A5 expressers and MRP2 high-activity groups were identified as the significant covariates for tacrolimus apparent clearance expressed as 20.7 × (age/50)(-0.78) × 2.03 (CYP3A5 expressers) × 1.40 (MRP2 high-activity group). No other CYP3A4, ABCB1 or ABCG2 polymorphisms were associated with the apparent clearance of tacrolimus.

CONCLUSIONS

This is the first report showing that MRP2/ABCC2 has a crucial impact on the pharmacokinetics of tacrolimus in a haplotype-specific manner. Determination of the ABCC2 as well as CYP3A5 genotype may be useful for more accurate tacrolimus dosage adjustment.

The Population Pharmacokinetic Models of Tacrolimus in Chinese Adult Liver Transplantation Patients

Aim. The aim of this study was to establish population pharmacokinetic models of tacrolimus in Chinese adult liver transplantation patients. Methods. Tacrolimus dose and concentration data (n = 435) were obtained from 47 Chinese adult liver transplant recipients, and the data were analyzed using a nonlinear mixed-effect modeling (NONMEM) method. Results. The structural model was a two-compartment model with first-order absorption. The typical population values of tacrolimus for the pharmacokinetic parameters of apparent clearance (CL/F), apparent distribution volume of the central compartment (V₂/F), intercompartmental clearance (Q/F), apparent distribution volume of the peripheral compartment (V₃/F), and absorption rate (k_a) were 11.2 L/h, 406 L, 57.3 L/h, 503 L, and 0.723 h⁻¹, respectively. The interindividual variabilities of these parameters were 16.2%, 163%, 19.7%, 199%, and 74.3%, respectively, and the intraindividual variability of observed concentration was 26.54%. The covariates retained in the final models were postoperative days (POD) and dosage per day (DOSE) on CL/F. Conclusion. Population pharmacokinetic models of tacrolimus were developed in Chinese adult liver transplant patients. These results could provide the interpretation of the outcome of pharmacokinetics modeling and the impact of covariate tested on individualized tacrolimus therapy.

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.

Effects of CYP3A4 and CYP3A5 polymorphisms on tacrolimus pharmacokinetics in Chinese adult renal transplant recipients: a population pharmacokinetic analysis

OBJECTIVE

Tacrolimus is used clinically for the long-term treatment of antirejection of transplanted organs in liver and kidney transplant recipients, although dose optimization is poorly managed. The aim of this study was to examine the association between tacrolimus pharmacokinetic variability and CYP3A4 and CYP3A5 genotypes by a population pharmacokinetic analysis based on routine drug monitoring data in adult renal transplant recipients.

MATERIALS AND METHODS

Trough tacrolimus concentrations were obtained from 161 adult kidney transplant recipients after transplantation. The population pharmacokinetic analysis was carried out using the nonlinear mixed-effect modeling software NONMEM version 7.2. The CYP3A4*1G and CYP3A5*3 genetic polymorphisms from the patients studied were determined by direct sequencing using a validated automated genetic analyzer.

RESULTS

A one-compartment model with first-order absorption and elimination adequately described the pharmacokinetics of tacrolimus. Covariates including CYP3A5*3 and CYP3A4*1G alleles and hematocrit were retained in the final model. The apparent clearance of tacrolimus was about two-fold higher in kidney transplant patients with higher enzymatic activity of CYP3A5*1 and CYP3A4*1G (with the CYP3A5*1/*1 or *1/*3 and CYP3A4*1/*1G or CYP3A4*1G/*1G) compared with those with lower enzymatic activity (CYP3A5*3/*3 and CYP3A4*1/*1).

CONCLUSION

This is the first study to extensively determine the effect of CYP3A4*1G and CYP3A5*3 genetic polymorphisms and hematocrit value on tacrolimus pharmacokinetics in Chinese renal transplant recipients. The findings suggest that CYP3A5*3 and CYP3A4*1G polymorphisms and hematocrit are determinant factors in the apparent clearance of tacrolimus. The initial dose design is mainly based on CYP3A5 and CYP3A4 genotypes as well as hematocrit. This result may also be useful for maintenance tacrolimus dose optimization and may help to avoid fluctuating tacrolimus levels and improve the efficacy and tolerability of tacrolimus in kidney transplant recipients.

A simultaneous d-optimal designed study for population pharmacokinetic analyses of mycophenolic Acid and tacrolimus early after renal transplantation

Mycophenolic acid (MPA) and tacrolimus (TAC) are immunosuppressive agents used in combination with corticosteroids for the prevention of acute rejection after solid organ transplantation. Their pharmacokinetics (PK) show considerable unexplained intraindividual and interindividual variability, particularly in the early period after transplantation. The main objective of the present work was to design a study based on D-optimality to describe the PK of the 2 drugs with good precision and accuracy and to explain their variability by means of patients' demographics, biochemical test results, and physiological characteristics. Pharmacokinetic profiles of MPA and TAC were obtained from 65 stable adult renal allograft recipients on a single occasion (ie, day 15 after transplantation). A sampling schedule was estimated based on the D-optimality criterion with the POPED software, using parameter values from previously published studies on MPA and TAC modeling early after transplantation. Subsequently, a population PK model describing MPA and TAC concentrations was developed using nonlinear mixed-effects modeling. Optimal blood-sampling times for determination of MPA and TAC concentrations were estimated to be at 0 (predose) and at 0.24, 0.64, 0.98, 1.37, 2.38, and 11 hours after oral intake of mycophenolate and TAC. The PK of MPA and TAC were best described by a 2-compartment model with first-order elimination. For MPA, the absorption was best described by a transit compartment model, whereas first-order absorption with a lag time best described TAC transfer from the gastrointestinal tract. Parameters were estimated with good precision and accuracy. While hematocrit levels and CYP3A5 genetic polymorphism significantly influenced TAC clearance, the pharmaceutical formulation and MRP2 genetic polymorphism were retained as significant covariates on MPA absorption and elimination, respectively. The prospective use of the simultaneous D-optimal design approach for MPA and TAC has allowed good estimation of MPA and TAC PK parameters in the early period after transplantation characterized by a very high unexplained variability. The influence of some relevant covariates could be shown.

Bottom-up modeling and simulation of tacrolimus clearance: prospective investigation of blood cell distribution, sex and CYP3A5 expression as covariates and assessment of study power

The objectives were to investigate the ability of population-based in vitro-in vivo extrapolation (IVIVE) to reproduce the influence of haematocrit on the clearance of tacrolimus, observed previously, and to assess the power of clinical studies to detect the effects of covariates on the clearance of tacrolimus. A population-based pharmacokinetic simulator (Simcyp) was used to simulate tacrolimus clearance from in vitro metabolism data and demographic characteristics of Japanese liver transplant patients (JLTs). The relationship between haematocrit and dose-to-concentration (D/C) ratio was validated using seven JLTs, whose highly variable haematocrit and D/C ratio were previously analysed. This validation was used as a surrogate for establishing 'interindividual' variability and to assess the power of clinical studies to discern the effect of haematocrit, sex and CYP3A5 genotype on tacrolimus clearance in a virtual JLT population. The relationship between haematocrit and D/C ratio was reproducible by Simcyp and corresponded well to those observed in seven JLTs. The number of JLTs required to detect the influence of CYP3A5 genotype and sex were estimated to be about 50 and > 600, respectively, which was consistent with the results of previous population pharmacokinetic studies for tacrolimus. In conclusion, population-based IVIVE is considered to be a useful approach to assess the influence of covariates a priori before conducting clinical studies. This is also helpful with study design and assessment of the statistical power of clinical studies involving population-based pharmacokinetics to detect the effects of covariates.

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.

CYP3A5 *1 allele: impacts on early acute rejection and graft function in tacrolimus-based renal transplant recipients

BACKGROUND

Tacrolimus is a major immunosuppressant, which has a narrow therapeutic range and wide interindividual variation. The effects of genetic polymorphisms of cytochrome P450 3A (CYP3A) 5 and Adenosine triphosphate-binding cassette subfamily B member 1 (ABCB1) genes on the achievement of target tacrolimus trough levels and clinical outcomes in renal transplants were evaluated.

METHODS

A total of 62 patients participated in this prospective study. After an initial fixed oral dose (0.08 mg/kg two times per day), tacrolimus doses were adjusted to a target range based on daily measurement of blood trough concentration. Every patient underwent 10-day scheduled biopsy. Both the patients and investigators were blinded for the genetic polymorphisms.

RESULTS

Those subjects expressing CYP3A5 (n=29) evidenced significantly lower tacrolimus trough levels between days 1 and 5 after transplantation, when compared with nonexpressers (n=33). Significantly higher overall incidences of early T-cell-mediated rejection (TCR) of at least Banff grade 1 in severity (P=0.017), including clinical rejection within 10 days and subclinical rejection in biopsies at postoperative day 10 were detected in CYP3A5 expressers. The severity of TCR according to Banff '07 classification was associated with CYP3A5 genotypes (P=0.012). Moreover, multivariate analysis identified CYP3A5 expression as an independent risk factor for TCR (odds ratio: 2.79; P=0.043). Significantly lower estimated glomerular filtration rates until 1 month and numerically lower estimated glomerular filtration rates by 12 months were noted in the CYP3A5 expressers. The genetic polymorphisms of the ABCB1 genes exerted no significant effects.

CONCLUSION

We confirmed the significant effects of CYP3A5 polymorphism on the achievement of target tacrolimus trough levels and the development of acute rejection in early period after transplantation and consequent renal allograft function.

Simultaneous detection of CYP3A5 and MDR1 polymorphisms based on the SNaPshot assay

BACKGROUND

The 6986A>G polymorphism for CYP3A5 and the -129T>C, 1236C>T, 2677G>T/A, and 3435C>T polymorphisms for MDR1 are considered the major genetic factors affecting a range of drugs' metabolism and transport. Simultaneous genotyping of these five polymorphisms would be useful for estimating the therapeutic effects of their related drugs.

SUBJECTS AND METHODS

We have described a SNaPshot assay that can simultaneously detect all the five polymorphisms based on multiplex PCR and minisequencing reaction. A total of 168 unrelated Chinese DNA samples were used to establish and evaluate the assay.

RESULTS

The different genotypes of the five polymorphisms could be determined by peak retention time and colors. DNA sequencing was performed on samples randomly selected from each of the genotype groups detected by SNaPshot assay, and the results indicated 100% concordance.

CONCLUSION

The SNaPshot assay for the CYP3A5 and MDR1 five polymorphisms detection was accurate, automated, and cost-effective.