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

Dosage optimization of tacrolimus based on the glucocorticoid dose and pharmacogenetics in adult patients with systemic lupus erythematosus

BACKGROUND

The purpose of the study was to develop a genotype-incorporated population pharmacokinetic (PPK) model of tacrolimus (TAC) in adults with systemic lupus erythematosus (SLE) to investigate the factors influencing TAC pharmacokinetics and to develop an individualized dosing regimen based on the model. In addition, a non-genotype-incorporated model was also established to assess its predictive performance compared to the genotype-incorporated model.

METHODS

A total of 365 trough concentrations from 133 adult SLE patients treated with TAC were collected to develop a genotype-incorporated PPK model and a non-genotype-incorporated PPK model of TAC using a nonlinear mixed-effects model (NONMEM). External validation of the two models was performed using data from an additional 29 patients. Goodness-of-fit diagnostic plots, bootstrap method, and normalized predictive distribution error test were used to validate the predictive performance and stability of the final models. The goodness-of-fit of the two final models was compared using the Akaike information criterion (AIC). The dosing regimen was optimized using Monte Carlo simulations based on the developed optimal model.

RESULTS

The typical value of the apparent clearance (CL/F) of TAC estimated in the final genotype-incorporated model was 14.3 L h-1 with inter-individual variability of 27.6%. CYP3A5 polymorphism and coadministered medication were significant factors affecting TAC-CL/F. CYP3A5 rs776746 GG genotype carriers had only 77.3% of the TAC-CL/F of AA or AG genotype carriers. Omeprazole reduced TAC-CL/F by 3.7 L h-1 when combined with TAC, while TAC-CL/F increased nonlinearly as glucocorticoid dose increased. Similar findings were demonstrated in the non-genotype-incorporated PPK model. Comparing these two models, the genotype-incorporated PPK model was superior to the non-genotype-incorporated PPK model (AIC = 643.19 vs. 657.425). Monte Carlo simulation based on the genotype-incorporated PPK model indicated that CYP3A5 rs776746 AA or AG genotype carriers required a 1/2-1 fold higher dose of TAC than GG genotype carriers to achieve the target concentration. And as the daily dose of prednisone increases, the dose of TAC required to reach the target concentration increases appropriately.

CONCLUSIONS

We developed the first pharmacogenetic-based PPK model of TAC in adult patients with SLE and proposed a dosing regimen based on glucocorticoid dose and CYP3A5 genotype according to the model, which could facilitate individualized dosing for TAC.

Individualization of Mycophenolic Acid Therapy through Pharmacogenetic, Pharmacokinetic and Pharmacodynamic Testing

Mycophenolic acid (MPA) is a widely used immunosuppressive agent and exerts its effect by inhibiting inosine 5′-monophosphate dehydrogenase (IMPDH), the main regulating enzyme of purine metabolism. However, significant unexplained differences in the efficacy and tolerability of MPA therapy pose a clinical challenge. Therefore, broad pharmacogenetic, pharmacokinetic, and pharmacodynamic approaches are needed to individualize MPA therapy. In this prospective cohort study including 277 renal transplant recipients, IMPDH2 rs11706052 SNP status was assessed by genetic sequencing, and plasma MPA trough levels were determined by HPLC and IMPDH enzyme activity in peripheral blood mononuclear cells (PBMCs) by liquid chromatography–mass spectrometry. Among the 277 patients, 84 were identified with episodes of biopsy-proven rejection (BPR). No association was found between rs11706052 SNP status and graft rejection (OR 1.808, and 95% CI, 0.939 to 3.479; p = 0.076). Furthermore, there was no association between MPA plasma levels and BPR (p = 0.69). However, the patients with graft rejection had a significantly higher predose IMPDH activity in PBMCs compared to the controls without rejection at the time of biopsy (110.1 ± 50.2 vs. 95.2 ± 45.4 pmol/h; p = 0.001), and relative to the baseline IMPDH activity before transplantation (p = 0.042). Our results suggest that individualization of MPA therapy, particularly through pharmacodynamic monitoring of IMPDH activity in PBMCs, has the potential to improve the clinical outcomes of transplant patients.

Recent lessons learned from population pharmacokinetic studies of mycophenolic acid: physiological, genomic, and drug interactions leading to the prediction of drug effects

INTRODUCTION

Mycophenolic acid (MPA) is a widely used immunosuppressant in transplantation and autoimmune disease. Highly variable pharmacokinetics have been observed with MPA, but the exact mechanisms remain largely unknown.

AREAS COVERED

The current review provided a critical, comprehensive update of recently published population pharmacokinetic/dynamic models of MPA (n=16 papers identified from PubMed and Embase, inclusive from January 2017 to August 2021), with specific emphases on the intrinsic and extrinsic factors influencing the pharmacology of MPA. The significance of the identified covariates, potential mechanisms, and comparisons to historical literature have been provided.

EXPERT OPINION

While select covariates affecting the population pharmacokinetics of MPA are consistently observed and mechanistically supported, some variables have not been regularly reported and/or lacked mechanistic explanation. Very few pharmacodynamic models were available, pointing to the need to extrapolate pharmacokinetic findings. Ideal models of MPA should consist of: i) utilizing optimal sampling points to allow the characterizations of absorption, re-absorption, and elimination phases; ii) characterizing unbound/total MPA, MPA metabolites, plasma/urinary concentrations, and genetic polymorphisms to facilitate mechanistic interpretations; and iii) incorporating actual outcomes and pharmacodynamic data to establish clinical relevance. We anticipate the field will continue to expand in the next 5 to 10 years.

Estimation of Mycophenolic Acid Exposure in Heart Transplant Recipients by Population Pharmacokinetic and Limited Sampling Strategies

Purpose: The aim of this study is i) to establish a strategy to estimate the area under the curve of the dosing interval (AUC0-12h) of mycophenolic acid (MPA) in the heart transplant recipients and ii) to find the covariates that significantly affect the pharmacokinetics of MPA exposure. Methods: This single-center, prospective, open-label, observational study was conducted in 91 adult heart transplant recipients orally taking mycophenolate mofetil dispersible tablets. Samples collected intensively and sparsely were analyzed by the enzyme-multiplied immunoassay technique, and all the data were used in PPK modeling. Potential covariates were tested stepwise. The goodness-of-fit plots, the normalized prediction distribution error, and prediction-corrected visual predictive check were used for model evaluation. Optimal sampling times by ED-optimal strategy and multilinear regression (MLR) were analyzed based on the simulated data by the final PPK model. Moreover, using intensive data from 14 patients, the accuracy of AUC0-12h estimation was evaluated by Passing-Bablok regression analysis and Bland-Alman plots for both the PPK model and MLR equation. Results: A two-compartment model with first-order absorption and elimination with a lag time was chosen as the structure model. Co-medication of proton pump inhibitors (PPIs), estimated glomerular filtration rate (eGFR), and albumin (ALB) were found to significantly affect bioavailability (F), clearance of central compartment (CL/F), and the distribution volume of the central compartment (V2/F), respectively. Co-medication of PPIs decreased F by 27.6%. When eGFR decreased by 30 ml/min/1.73 m2, CL/F decreased by 23.7%. However, the impact of ALB on V2/F was limited to MPA exposure. The final model showed an adequate fitness of the data. The optimal sampling design was pre-dose and 1 and 4 h post-dose for pharmacokinetic estimation. The best-fit linear equation was finally established as follows: AUC0-12h = 3.539 × C0 + 0.288 × C0.5 + 1.349 × C1 + 6.773 × C4.5. Conclusion: A PPK model was established with three covariates in heart transplant patients. Co-medication of PPIs and eGFR had a remarkable impact on AUC0-12h of MPA. A linear equation was also concluded with four time points as an alternative way to estimate AUC0-12h for MPA.

Population Pharmacokinetic Models of Tacrolimus in Adult Transplant Recipients: A Systematic Review

BACKGROUND AND OBJECTIVES

Numerous population pharmacokinetic (PK) models of tacrolimus in adult transplant recipients have been published to characterize tacrolimus PK and facilitate dose individualization. This study aimed to (1) investigate clinical determinants influencing tacrolimus PK, and (2) identify areas requiring additional research to facilitate the use of population PK models to guide tacrolimus dosing decisions.

METHODS

The MEDLINE and EMBASE databases, as well as the reference lists of all articles, were searched to identify population PK models of tacrolimus developed from adult transplant recipients published from the inception of the databases to 29 February 2020.

RESULTS

Of the 69 studies identified, 55% were developed from kidney transplant recipients and 30% from liver transplant recipients. Most studies (91%) investigated the oral immediate-release formulation of tacrolimus. Few studies (17%) explained the effect of drug-drug interactions on tacrolimus PK. Only 35% of the studies performed an external evaluation to assess the generalizability of the models. Studies related variability in tacrolimus whole blood clearance among transplant recipients to either cytochrome P450 (CYP) 3A5 genotype (41%), days post-transplant (30%), or hematocrit (29%). Variability in the central volume of distribution was mainly explained by body weight (20% of studies).

CONCLUSION

The effect of clinically significant drug-drug interactions and different formulations and brands of tacrolimus should be considered for any future tacrolimus population PK model development. Further work is required to assess the generalizability of existing models and identify key factors that influence both initial and maintenance doses of tacrolimus, particularly in heart and lung transplant recipients.

Genetic polymorphisms in metabolic enzymes and transporters have no impact on mycophenolic acid pharmacokinetics in adult kidney transplant patients co-treated with tacrolimus: A population analysis

WHAT IS KNOWN AND OBJECTIVE

Mycophenolate mofetil, an ester prodrug of mycophenolic acid (MPA), is widely used to prevent graft rejection after kidney transplantation. The pharmacokinetic (PK) of MPA has been extensively studied, which revealed a high degree of variability. An integrated population PK (PopPK) model of MPA and its main metabolite mycophenolic acid glucuronide (MPAG) was developed using the adult patients who underwent kidney transplant and were administered oral mycophenolate mofetil combined with tacrolimus.

METHODS

In total, 917 MPA and 740 MPAG concentrations in191 adult patients were analysed via nonlinear mixed-effects modelling. The concentration-time data were adequately described using a chain compartment model, including central and peripheral compartments for MPA and a central compartment for MPAG. Stepwise forward inclusion and backward elimination procedures were used to investigate the effects of genetic polymorphisms, including in UGT1A8, UGT1A9, UGT2B7, ABCB1, ABCC2, ABCG2, SLCO1B1, SLCO1B3, and HNF1α.

RESULTS AND DISCUSSION

These genetic polymorphisms in metabolic enzymes and transporters have no obvious impact on the PK of MPA in adult patients who underwent kidney transplant and were co-treated with tacrolimus. The post-transplant time, serum albumin, and creatinine clearance were identified as significant covariates affecting the PK of MPA and MPAG, which should be considered in the clinical use of mycophenolate mofetil.

WHAT IS NEW AND CONCLUSION

We established a PopPK model of MPA and MPAG in Chinese adult patients who underwent kidney transplant and were co-treated with tacrolimus. Genetic polymorphisms in metabolic enzymes and transporters showed no obvious impact on MMF PK. A model-informed dosing strategy was proposed by the established model, and MMF dose adjustment should be based on ALB levels and the post-transplantation time.

Model-informed precision dosing to optimise immunosuppressive therapy in renal transplantation

Immunosuppressive therapy is pivotal for sustained allograft and patient survival after renal transplantation. However, optimally balanced immunosuppressive therapy is challenged by between-patient and within-patient pharmacokinetic (PK) variability. This could warrant the application of personalised dosing strategies to optimise individual patient outcomes. Pharmacometrics, the science that investigates the xenobiotic-biotic interplay using computer-aided mathematical modelling, provides options to describe and quantify this PK variability and enables identification of patient characteristics affecting immunosuppressant PK and treatment outcomes. Here, we review and critically appraise the available pharmacometric model-informed dosing solutions for the typical immunosuppressants in modern renal transplantation, to guide their initial and subsequent dosing.

Personalized Therapy for Mycophenolate: Consensus Report by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology

ABSTRACT

When mycophenolic acid (MPA) was originally marketed for immunosuppressive therapy, fixed doses were recommended by the manufacturer. Awareness of the potential for a more personalized dosing has led to development of methods to estimate MPA area under the curve based on the measurement of drug concentrations in only a few samples. This approach is feasible in the clinical routine and has proven successful in terms of correlation with outcome. However, the search for superior correlates has continued, and numerous studies in search of biomarkers that could better predict the perfect dosage for the individual patient have been published. As it was considered timely for an updated and comprehensive presentation of consensus on the status for personalized treatment with MPA, this report was prepared following an initiative from members of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT). Topics included are the criteria for analytics, methods to estimate exposure including pharmacometrics, the potential influence of pharmacogenetics, development of biomarkers, and the practical aspects of implementation of target concentration intervention. For selected topics with sufficient evidence, such as the application of limited sampling strategies for MPA area under the curve, graded recommendations on target ranges are presented. To provide a comprehensive review, this report also includes updates on the status of potential biomarkers including those which may be promising but with a low level of evidence. In view of the fact that there are very few new immunosuppressive drugs under development for the transplant field, it is likely that MPA will continue to be prescribed on a large scale in the upcoming years. Discontinuation of therapy due to adverse effects is relatively common, increasing the risk for late rejections, which may contribute to graft loss. Therefore, the continued search for innovative methods to better personalize MPA dosage is warranted.

Population pharmacokinetics of mycophenolic acid in paediatric patients

Mycophenolic acid (MPA) is widely used in paediatric kidney transplant patients and sometimes prescribed for additional indications. Population pharmacokinetic or pharmacodynamic modelling has been frequently used to characterize the fixed, random and covariate effects of MPA in adult patients. However, MPA population pharmacokinetic data in the paediatric population have not been systematically summarized. The objective of this narrative review was to provide an up-to-date critique of currently available paediatric MPA population pharmacokinetic models, with emphases on modelling techniques, pharmacological findings and clinical relevance. PubMed and EMBASE were searched from inception of database to May 2020, where a total of 11 studies have been identified representing kidney transplant (n = 4), liver transplant (n = 1), haematopoietic stem cell transplant (n = 1), idiopathic nephrotic syndrome (n = 2), systemic lupus erythematosus (n = 2), and a combined population consisted of kidney, liver and haematopoietic stem cell transplant patients (n = 1). Critical analyses were provided in the context of MPA absorption, distribution, metabolism, excretion and bioavailability in this paediatric database. Comparisons to adult patients were also provided. With respect to clinical utility, Bayesian estimation models (n = 6) with acceptable accuracy and precision for MPA exposure determination have also been identified and systematically evaluated. Overall, our analyses have identified unique features of MPA clinical pharmacology in the paediatric population, while recognizing several gaps that still warrant further investigations. This review can be used by pharmacologists and clinicians for improving MPA pharmacokinetic-pharmacodynamic modelling and patient care.

Assessment of pharmacokinetic mycophenolic acid clearance models using Monte Carlo numerical analysis

Previously, we performed population pharmacokinetic analysis and indicated age, mycophenolate mofetil (MMF)/mycophenolic acid (MPA) daily dose, and presence of nifedipine in patient therapy as significant predictors of MPA apparent clearance (CL/F) variability. This study aimed to determine the reliability of previously published population pharmacokinetic models derived from similar studies. Furthermore, this study investigated correspondence between chosen population models from the literature.By means of the Monte Carlo simulation method, pharmacokinetic models from different studies are simulated and analysed in the range of standard deviations of measured system parameters as well as the range of observed model parameters taken from the comparison studies.The 1000 numerical simulations were performed for every analysed model in order to calculate the most possible MPA CL/F values according to the expected values from the performed experiment. Fitting our results with other models showed how the presence of nifedipine makes difference in MPA CL/F values.By testing the data from selected studies into our model, a similar range of expected CL/F values was obtained, which may confirm the validity of our model. The results of our population pharmacokinetic study are partially applicable in models by other researchers.

Early prognostic performance of miR155-5p monitoring for the risk of rejection: Logistic regression with a population pharmacokinetic approach in adult kidney transplant patients

Previous results from our group and others have shown that urinary pellet expression of miR155-5p and urinary CXCL-10 production could play a key role in the prognosis and diagnosis of acute rejection (AR) in kidney transplantation patients. Here, a logistic regression model was developed using NONMEM to quantify the relationships of miR155-5p urinary expression, CXCL-10 urinary concentration and tacrolimus and mycophenolic acid (MPA) exposure with the probability of AR in adult kidney transplant patients during the early post-transplant period. Owing to the contribution of therapeutic drug monitoring to achieving target exposure, neither tacrolimus nor MPA cumulative exposure was identified as a predictor of AR in the studied population. Even though CXCL-10 urinary concentration showed a trend, its effect on AR was not significant. In contrast, urinary miR155-5p expression was prognostic of clinical outcome. Monitoring miR155-5p urinary pellet expression together with immunosuppressive drug exposure could be very useful during routine clinical practice to identify patients with a potential high risk of rejection at the early stages of the post-transplant period. This early risk assessment would allow for the optimization of treatment and improved prevention of AR.

Predictors of tacrolimus pharmacokinetic variability: current evidences and future perspectives

INTRODUCTION

In kidney transplantation, tacrolimus (TAC) is at the cornerstone of current immunosuppressive strategies. Though because of its narrow therapeutic index, it is critical to ensure that TAC levels are maintained within this sharp window through reactive adjustments. This would allow maximizing efficiency while limiting drug-associated toxicity. However, TAC high intra- and inter-patient pharmacokinetic (PK) variability makes it more laborious to accurately predict the appropriate dosage required for a given patient.

AREAS COVERED

This review summarizes the state-of-the-art knowledge regarding drug interactions, demographic and pharmacogenetics factors as predictors of TAC PK. We provide a scoring index for each association to grade its relevance and we present practical recommendations, when possible for clinical practice.

EXPERT OPINION

The management of TAC concentration in transplanted kidney patients is as critical as it is challenging. Recommendations based on rigorous scientific evidences are lacking as knowledge of potential predictors remains limited outside of DDIs. Awareness of these limitations should pave the way for studies looking at demographic and pharmacogenetic factors as well as gut microbiota composition in order to promote tailored treatment plans. Therapeutic approaches considering patients' clinical singularities may help allowing to maintain appropriate concentration of TAC.

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.

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.

Population Pharmacokinetics of Mycophenolic Acid: An Update

The most recent comprehensive reviews on the population pharmacokinetics of mycophenolic acid (MPA) were published in 2014. Since then, several population pharmacokinetic studies on MPA have been published. The majority of literature is still focused on the kidney transplant population, although studies have also been conducted in liver and lung transplantation, autoimmune diseases, and hematopoietic stem cell transplant. While the majority of the model building is still based on parametric non-linear mixed-effects modeling, recent studies suggest the suitability of other methodologies. Additionally, instead of just focusing on pharmacokinetic modeling, a trend toward describing the relationships between pharmacokinetic and pharmacodynamic parameters is observed. Given the importance of enterohepatic recirculation (EHR) in the pharmacokinetics of MPA, more authors have attempted to characterize this process in their models. Overall, the recent models have become more sophisticated and incorporate EHR, pharmacodynamic relationships, and metabolites while maintaining many of the population values and covariates identified previously. However, the number of MPA population pharmacokinetic models describing the enteric-coated formulation of MPA (EC-MPA) is still limited. Given the increasing use of EC-MPA, more studies are needed to fill this literature gap. In addition, few studies are yet available characterizing free MPA concentration or MPA metabolites. Given the extensive protein binding, low to intermediate extraction, and intrinsic clearance characteristics of MPA in humans, including these variables would improve the population structural models.

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.

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.

Developmental changes of MPA exposure in children

BACKGROUND

Developmental changes (ontogeny) of drug disposition of Mycophenolate mofetil (MMF) have been understudied.

METHODS

The charts of 37 pediatric renal transplant recipients (median age 7.3 years, median follow-up 7.8 (IQR 6.6, 14.3 years) who had regular mycophenolic acid (MPA) trough level monitoring in combination with tacrolimus (n = 31) or sirolimus (n = 6) therapy were analyzed retrospectively for their dose-normalized MPA exposure, steroid dose, albumin, hematocrit, and cystatin C estimated glomerular filtration rate (eGFR). Using appropriate univariate and multivariate methods, we determined whether MPA exposure was age dependent when controlling for the confounders.

RESULTS

Dose-normalized MPA trough levels could be calculated in 2,128 (median 45/patient) instances. Spearman rank correlation analysis revealed that age correlated with dose-normalized MPA trough level for both body weight and body surface area, as well as serum albumin, hematocrit, steroid dose, and eGFR. In the multivariate analysis, serum albumin and steroid dose were not significant, and hematocrit only being significant when the youngest group of patients < 6 years of age was compared. eGFR was the most important confounder, but age dependency remained significant when controlling for all confounders.

CONCLUSIONS

Small children are at a significantly greater risk for low MPA trough levels than adolescents, highlighting the need for pharmacokinetic monitoring of MPA.

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.

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.

How accurate and precise are limited sampling strategies in estimating exposure to mycophenolic acid in people with autoimmune disease?

Mycophenolic acid (MPA) is a potent immunosuppressant agent, which is increasingly being used in the treatment of patients with various autoimmune diseases. Dosing to achieve a specific target MPA area under the concentration-time curve from 0 to 12 h post-dose (AUC12) is likely to lead to better treatment outcomes in patients with autoimmune disease than a standard fixed-dose strategy. This review summarizes the available published data around concentration monitoring strategies for MPA in patients with autoimmune disease and examines the accuracy and precision of methods reported to date using limited concentration-time points to estimate MPA AUC12. A total of 13 studies were identified that assessed the correlation between single time points and MPA AUC12 and/or examined the predictive performance of limited sampling strategies in estimating MPA AUC12. The majority of studies investigated mycophenolate mofetil (MMF) rather than the enteric-coated mycophenolate sodium (EC-MPS) formulation of MPA. Correlations between MPA trough concentrations and MPA AUC12 estimated by full concentration-time profiling ranged from 0.13 to 0.94 across ten studies, with the highest associations (r (2) = 0.90-0.94) observed in lupus nephritis patients. Correlations were generally higher in autoimmune disease patients compared with renal allograft recipients and higher after MMF compared with EC-MPS intake. Four studies investigated use of a limited sampling strategy to predict MPA AUC12 determined by full concentration-time profiling. Three studies used a limited sampling strategy consisting of a maximum combination of three sampling time points with the latest sample drawn 3-6 h after MMF intake, whereas the remaining study tested all combinations of sampling times. MPA AUC12 was best predicted when three samples were taken at pre-dose and at 1 and 3 h post-dose with a mean bias and imprecision of 0.8 and 22.6 % for multiple linear regression analysis and of -5.5 and 23.0 % for maximum a posteriori (MAP) Bayesian analysis. Although mean bias was less when data were analysed using multiple linear regression, MAP Bayesian analysis is preferable because of its flexibility with respect to sample timing. Estimation of MPA AUC12 following EC-MPS administration using a limited sampling strategy with samples drawn within 3 h post-dose resulted in biased and imprecise results, likely due to a longer time to reach a peak MPA concentration (t max) with this formulation and more variable pharmacokinetic profiles. Inclusion of later sampling time points that capture enterohepatic recirculation and t max improved the predictive performance of strategies to predict EC-MPS exposure. Given the considerable pharmacokinetic variability associated with mycophenolate therapy, limited sampling strategies may potentially help in individualizing patient dosing. However, a compromise needs to be made between the predictive performance of the strategy and its clinical feasibility. An opportunity exists to combine research efforts globally to create an open-source database for MPA (AUC, concentrations and outcomes) that can be used and prospectively evaluated for AUC target-controlled dosing of MPA in autoimmune diseases.

Pharmacology and toxicology of mycophenolate in organ transplant recipients: an update

This review aims to provide an update of the literature on the pharmacology and toxicology of mycophenolate in solid organ transplant recipients. Mycophenolate is now the antimetabolite of choice in immunosuppressant regimens in transplant recipients. The active drug moiety mycophenolic acid (MPA) is available as an ester pro-drug and an enteric-coated sodium salt. MPA is a competitive, selective and reversible inhibitor of inosine-5'-monophosphate dehydrogenase (IMPDH), an important rate-limiting enzyme in purine synthesis. MPA suppresses T and B lymphocyte proliferation; it also decreases expression of glycoproteins and adhesion molecules responsible for recruiting monocytes and lymphocytes to sites of inflammation and graft rejection; and may destroy activated lymphocytes by induction of a necrotic signal. Improved long-term allograft survival has been demonstrated for MPA and may be due to inhibition of monocyte chemoattractant protein 1 or fibroblast proliferation. Recent research also suggested a differential effect of mycophenolate on the regulatory T cell/helper T cell balance which could potentially encourage immune tolerance. Lower exposure to calcineurin inhibitors (renal sparing) appears to be possible with concomitant use of MPA in renal transplant recipients without undue risk of rejection. MPA displays large between- and within-subject pharmacokinetic variability. At least three studies have now reported that MPA exhibits nonlinear pharmacokinetics, with bioavailability decreasing significantly with increasing doses, perhaps due to saturable absorption processes or saturable enterohepatic recirculation. The role of therapeutic drug monitoring (TDM) is still controversial and the ability of routine MPA TDM to improve long-term graft survival and patient outcomes is largely unknown. MPA monitoring may be more important in high-immunological recipients, those on calcineurin-inhibitor-sparing regimens and in whom unexpected rejection or infections have occurred. The majority of pharmacodynamic data on MPA has been obtained in patients receiving MMF therapy in the first year after kidney transplantation. Low MPA area under the concentration time from 0 to 12 h post-dose (AUC0-12) is associated with increased incidence of biopsy-proven acute rejection although AUC0-12 optimal cut-off values vary across study populations. IMPDH monitoring to identify individuals at increased risk of rejection shows some promise but is still in the experimental stage. A relationship between MPA exposure and adverse events was identified in some but not all studies. Genetic variants within genes involved in MPA metabolism (UGT1A9, UGT1A8, UGT2B7), cellular transportation (SLCOB1, SLCO1B3, ABCC2) and targets (IMPDH) have been reported to effect MPA pharmacokinetics and/or response in some studies; however, larger studies across different ethnic groups that take into account genetic linkage and drug interactions that can alter a patient's phenotype are needed before any clinical recommendations based on patient genotype can be formulated. There is little data on the pharmacology and toxicology of MPA in older and paediatric transplant recipients.

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.

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.

Nephro and neurotoxicity of calcineurin inhibitors and mechanisms of rejections: A review on tacrolimus and cyclosporin in organ transplantation

CONTEXT

In the meadow of medical sciences substituting a diseased organ with a healthy one from another individual, dead or alive, to allow a human to stay alive could be consider as the most string event. In this article we review the history of transplantation, mechanisms of rejection, nephro-neurotoxicity of tacrolimus and cyclosporin in organ transplantations.

EVIDENCE ACQUISITIONS

Directory of Open Access Journals (DOAJ), Google Scholar, Pubmed (NLM), LISTA (EBSCO) and Web of Science have been searched.

RESULTS

The first reference to the concept of organ transplantation and replacement for therapeutic purposes appears to be to Hua-To (136 to 208 A.D), who replaced diseased organs with healthy ones in patients under analgesia induced with a mixture of Indian hemp. In 1936, the first human renal transplant performed by Voronoy in Russia. The first liver transplant in humans was performed on March 1, 1963 by Starzl in Denver, USA. Medawar was the first to assert that rejection was an immunological response, with the inflammatory reaction due to lymphocyte infiltration. Consequently, rational immunosuppressive therapies could inhibit deleterious T-cell responses in an antigen specific manner.

CONCLUSIONS

Searching related to the history of organ transplantation from mythic to modern times suggests that, to prevent graft rejection, minimize nephro and neuro toxicity monitoring of immunosupressive concentrations could provide an invaluable and essential aid in adjusting dosage to ensure adequate immunosuppression.