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

Evaluation of Published Population Pharmacokinetic Models to Inform Tacrolimus Therapy in Adult Lung Transplant Recipients

BACKGROUND

The applicability of currently available tacrolimus population pharmacokinetic models in guiding dosing for lung transplant recipients is unclear. In this study, the predictive performance of relevant tacrolimus population pharmacokinetic models was evaluated for adult lung transplant recipients.

METHODS

Data from 43 lung transplant recipients (1021 tacrolimus concentrations) administered an immediate-release oral formulation of tacrolimus were used to evaluate the predictive performance of 17 published population pharmacokinetic models for tacrolimus. Data were collected from immediately after transplantation up to 90 days after transplantation. Model performance was evaluated using (1) prediction-based assessments (bias and imprecision) of individual predicted tacrolimus concentrations at the fourth dosing based on 1 to 3 previous dosings and (2) simulation-based assessment (prediction-corrected visual predictive check; pcVPC). Both assessments were stratified based on concomitant azole antifungal use. Model performance was clinically acceptable if the bias was within ±20%, imprecision was ≤20%, and the 95% confidence interval of bias crossed zero.

RESULTS

In the presence of concomitant antifungal therapy, no model showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33), and pcVPC plots displayed poor model fit to the data set. However, this fit slightly improved in the absence of azole antifungal use, where 4 models showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33).

CONCLUSIONS

Although none of the evaluated models were appropriate in guiding tacrolimus dosing in lung transplant recipients receiving concomitant azole antifungal therapy, 4 of these models displayed potential applicability in guiding dosing in recipients not receiving concomitant azole antifungal therapy. However, further model refinement is required before the widespread implementation of such models in clinical practice.

Tools for a personalized tacrolimus dose adjustment in the follow-up of renal transplant recipients. Metabolizing phenotype according to CYP3A genetic polymorphisms versus concentration-dose ratio

BACKGROUND AND JUSTIFICATION

The strategy of the concentration-dose (C/D) approach and the different profiles of tacrolimus (Tac) according to the cytochrome P450 polymorphisms (CYPs) focus on the metabolism of Tac and are proposed as tools for the follow-up of transplant patients. The objective of this study is to analyse both strategies to confirm whether the stratification of patients according to the pharmacokinetic behaviour of C/D corresponds to the classification according to their CYP3A4/5 cluster metabolizer profile.

MATERIALS AND METHODS

425 kidney transplant patients who received Tac as immunosuppressive treatment have been included. The concentration/dose ratio (C/D) was used to divide patients in terciles and classify them according to their Tac metabolism rate (fast, intermediate, and slow). Based on CYP3A4 and A5 polymorphisms, patients were classified into 3 metabolizer groups: fast (CYP3A5*1 carriers and CYP34A*1/*1), intermediate (CYP3A5*3/3 and CYP3A4*1/*1) and slow (CYP3A5*3/*3 and CYP3A4*22 carriers).

RESULTS

When comparing patients included in each metabolizer group according to C/D ratio, 47% (65/139) of the fast metabolizers, 85% (125/146) of the intermediate and only 12% (17/140) of the slow also fitted in the homonym genotype group. Statistically lower Tac concentrations were observed in the fast metabolizers group and higher Tac concentrations in the slow metabolizers when compared with the intermediate group both in C/D ratio and polymorphisms criteria. High metabolizers required approximately 60% more Tac doses than intermediates throughout follow-up, while poor metabolizers required approximately 20% fewer doses than intermediates. Fast metabolizers classified by both criteria presented a higher percentage of times with sub-therapeutic blood Tac concentration values.

CONCLUSION

Determination of the metabolizer phenotype according to CYP polymorphisms or the C/D ratio allows patients to be distinguished according to their exposure to Tac. Probably the combination of both classification criteria would be a good tool for managing Tac dosage for transplant patients.

A prospective validation of a population pharmacokinetic model of tacrolimus in Tunisian kidney transplant patients

BACKGROUND

A prospective validation of pharmacokinetic population (Pk pop) of Tacrolimus (Tac) for dose adjustment in kidney transplant patients was assessed in only one study. The present study was aimed at prospectively evaluating the performance of our previously developed Tac- Pk pop model in predicting trough concentration (C0) in Tunisian kidney transplant patients.

PATIENTS AND METHOD

It was a prospective study including patients who had undergone kidney transplantation at Monastir-Nephrology Department. The population study was divided into adherence and control groups.

RESULTS

A total of 198 C0 (30 patients) were analyzed. The proportion of C0 within TR was 63.9% and 38.0% in the adhesion and control group, respectively. The percentage of C0 within TR was significantly higher in the adherence group during both early and late post-transplant period (p = 0.03 and 0.04, respectively). This percentage was found to be significantly higher during the third C0 monitoring and thereafter in the adherence group compared with the control group (65.8% vs 41%, respectively).

CONCLUSION

Tac dose proposal based on this model could be helpful to improve clinical outcomes in our population by reducing the risk of acute rejection and this immunosuppressant's toxic side effects.

A prospective controlled, randomized clinical trial of kidney transplant recipients developed personalized tacrolimus dosing using model-based Bayesian Prediction

For three decades, tacrolimus (Tac) dose adjustment in clinical practice has been calculated empirically according to the manufacturer's labeling based on a patient's body weight. Here, we developed and validated a Population pharmacokinetic (PPK) model including pharmacogenetics (cluster CYP3A4/CYP3A5), age, and hematocrit. Our study aimed to assess the clinical applicability of this PPK model in the achievement of Tac Co (therapeutic trough Tac concentration) compared to the manufacturer's labelling dosage. A prospective two-arm, randomized, clinical trial was conducted to determine Tac starting and subsequent dose adjustments in 90 kidney transplant recipients. Patients were randomized to a control group with Tac adjustment according to the manufacturer's labeling or the PPK group adjusted to reach target Co (6-10 ng/ml) after the first steady state (primary endpoint) using a Bayesian prediction model (NONMEM). A significantly higher percentage of patients from the PPK group (54.8%) compared with the control group (20.8%) achieved the therapeutic target fulfilling 30% of the established superiority margin defined. Patients receiving PPK showed significantly less intra-patient variability compared to the control group, reached the Tac Co target sooner (5 days vs 10 days), and required significantly fewer Tac dose modifications compared to the control group within 90 days following kidney transplant. No statistically significant differences occurred in clinical outcomes. Thus, PPK-based Tac dosing offers significant superiority for starting Tac prescription over classical labeling-based dosing according to the body weight, which may optimize Tac-based therapy in the first days following transplantation.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Model-Based Tacrolimus Follow-up Dosing in Adult Renal Transplant Recipients: A Simulation Trial

BACKGROUND

Initial algorithm-based dosing appears to be effective in predicting tacrolimus dose requirement. However, achieving and maintaining the target concentrations is challenging. Model-based follow-up dosing, which considers patient characteristics and pharmacological data, may further personalize treatment. This study investigated whether model-based follow-up dosing could lead to more accurate tacrolimus exposure than standard therapeutic drug monitoring (TDM) in kidney transplant recipients after an initial algorithm-based dose.

METHODS

This simulation trial included patients from a prospective trial that received an algorithm-based tacrolimus starting dose followed by TDM. For every measured tacrolimus predose concentration (C 0,obs ), model-based dosing advice was simulated using the InsightRX software. Based on previous tacrolimus doses and C 0 , age, body surface area, CYP3A4 and CYP3A5 genotypes, hematocrit, albumin, and creatinine, the optimal next dose, and corresponding tacrolimus concentration (C 0,pred ) were predicted.

RESULTS

Of 190 tacrolimus C 0 values measured in 59 patients, 121 (63.7%; 95% CI 56.8-70.5) C 0,obs were within the therapeutic range (7.5-12.5 ng/mL) versus 126 (66.3%, 95% CI 59.6-73.0) for C 0,pred ( P = 0.89). The median absolute difference between the tacrolimus C 0 and the target tacrolimus concentration (10.0 ng/mL) was 1.9 ng/mL for C 0,obs versus 1.6 ng/mL for C 0,pred . In a historical cohort of 114 kidney transplant recipients who received a body weight-based starting dose followed by TDM, 172 of 335 tacrolimus C 0 (51.3%) were within the therapeutic range (10.0-15.0 ng/mL).

CONCLUSIONS

The combination of an algorithm-based tacrolimus starting dose with model-based follow-up dosing has the potential to minimize under- and overexposure to tacrolimus in the early posttransplant phase, although the additional effect of model-based follow-up dosing on initial algorithm-based dosing seems small.

Efficacy of Tacrolimus in Uveitis, and the Usefulness of Serum Tacrolimus Levels in Predicting Disease Control. Results from a Single Large Center

AIMS

To evaluate the efficacy of tacrolimus in patients with noninfectious uveitis, as well as the usefulness of serum tacrolimus concentration measurements in predicting disease control.

METHODS

A retrospective review was carried out on 71 eligible patients from a single specialist uveitis center for minimum 1-year follow-up. Analysis was carried out on disease activity, visual acuity, and trough serum tacrolimus concentrations (STC).

RESULTS

At 1-year follow-up, disease control was achieved in 49 patients (69.0%), this was significantly more likely in patients with trough STC levels above 5 ng/mL (88% vs 53%, p = .002). There was a significant reduction in oral prednisolone (dose ≥7.5 mg, 86% vs 54%, p < .0001). Tacrolimus was discontinued in 12 patients (17%) due to side effects.

DISCUSSION

In this study cohort, oral tacrolimus was effective and well tolerated in the treatment of noninfectious uveitis. Trough STC between 5 ng/mL and 10 ng/ml was associated with better disease control at 1-year follow-up.

Body composition is associated with tacrolimus pharmacokinetics in kidney transplant recipients

PURPOSE

A population pharmacokinetic (popPK) model may be used to improve tacrolimus dosing and minimize under- and overexposure in kidney transplant recipients. It is unknown how body composition parameters relate to tacrolimus pharmacokinetics and which parameter correlates best with tacrolimus exposure. The aims of this study were to investigate which body composition parameter has the best association with the pharmacokinetics of tacrolimus and to describe this relationship in a popPK model.

METHODS

Body composition was assessed using bio-impedance spectroscopy (BIS). Pharmacokinetic analysis was performed using nonlinear mixed effects modeling (NONMEM). Lean tissue mass, adipose tissue mass, over-hydration, and phase angle were measured with BIS and then evaluated as covariates. The final popPK model was evaluated using goodness-of-fit plots, visual predictive checks, and a bootstrap analysis.

RESULTS

In 46 kidney transplant recipients, 284 tacrolimus concentrations were measured. The base model without body composition parameters included age, plasma albumin, plasma creatinine, CYP3A4 and CYP3A5 genotypes, and hematocrit as covariates. After full forward inclusion and backward elimination, only the effect of the phase angle on clearance (dOFV =  - 13.406; p < 0.01) was included in the final model. Phase angle was positively correlated with tacrolimus clearance. The inter-individual variability decreased from 41.7% in the base model to 34.2% in the final model. The model was successfully validated.

CONCLUSION

The phase angle is the bio-impedance spectroscopic parameter that correlates best with tacrolimus pharmacokinetics. Incorporation of the phase angle in a popPK model can improve the prediction of an individual's tacrolimus dose requirement after transplantation.

A Prediction Model for Tacrolimus Daily Dose in Kidney Transplant Recipients With Machine Learning and Deep Learning Techniques

Tacrolimus is a major immunosuppressor against post-transplant rejection in kidney transplant recipients. However, the narrow therapeutic index of tacrolimus and considerable variability among individuals are challenges for therapeutic outcomes. The aim of this study was to compare different machine learning and deep learning algorithms and establish individualized dose prediction models by using the best performing algorithm. Therefore, among the 10 commonly used algorithms we compared, the TabNet algorithm outperformed other algorithms with the highest R² (0.824), the lowest prediction error [mean absolute error (MAE) 0.468, mean square error (MSE) 0.558, and root mean square error (RMSE) 0.745], and good performance of overestimated (5.29%) or underestimated dose percentage (8.52%). In the final prediction model, the last tacrolimus daily dose, the last tacrolimus therapeutic drug monitoring value, time after transplantation, hematocrit, serum creatinine, aspartate aminotransferase, weight, CYP3A5, body mass index, and uric acid were the most influential variables on tacrolimus daily dose. Our study provides a reference for the application of deep learning technique in tacrolimus dose estimation, and the TabNet model with desirable predictive performance is expected to be expanded and applied in future clinical practice.

The Relationship between Initial Tacrolimus Metabolism Rate and Recipients Body Composition in Kidney Transplantation

There are several premises that the body composition of kidney transplant recipients may play a role in tacrolimus metabolism early after transplantation. The present study aimed at analyzing the relationship between the body composition parameters assessed by bioimpedance analysis (BIA) and initial tacrolimus metabolism. Immediately prior to transplantation, BIA using InBody 770 device was performed in 122 subjects. Tacrolimus concentration-to-dose (C/D) ratio was calculated based on the first blood trough level measurement. There was no difference in phase angle, visceral fat area, lean body mass index (LBMI) and the proportion of lean mass as a percentage of total body mass between the subgroups of slow and fast metabolizers. However, subjects with LBMI ≥ median value of 18.7 kg/m², despite similar initial tacrolimus dose per kg of body weight, were characterized by a significantly lower tacrolimus C/D ratio (median 1.39 vs. 1.67, respectively; p < 0.05) in comparison with the subgroup of lower LBMI. Multivariate regression analysis confirmed that age (r_partial = 0.322; p < 0.001) and LBMI (r_partial = −0.254; p < 0.01) independently influenced the tacrolimus C/D ratio. A LBMI assessed by BIA may influence the tacrolimus metabolism in the early post-transplant period and can be a useful in the optimization of initial tacrolimus dosing.

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.

A Population Pharmacokinetic Model Does Not Predict the Optimal Starting Dose of Tacrolimus in Pediatric Renal Transplant Recipients in a Prospective Study: Lessons Learned and Model Improvement

Background and Objective

Bodyweight-based dosing of tacrolimus is considered standard care. Currently, at first steady state, a third of pediatric kidney transplant recipients has a tacrolimus pre-dose concentration within the target range. We investigated whether adaptation of the starting dose according to a validated dosing algorithm could increase this proportion.

Methods

This was a multi-center, single-arm, prospective trial with a planned interim analysis after 16 patients, in which the tacrolimus starting dose was based on bodyweight, cytochrome P450 3A5 genotype, and donor status (living vs. deceased donor).

Results

At the interim analysis, 31% of children had a tacrolimus pre-dose concentration within the target range. As the original dosing algorithm was poorly predictive of tacrolimus exposure, the clinical trial was terminated prematurely. Next, the original model was improved by including the data of the children included in this trial, thereby doubling the number of children in the model building cohort. Data were best described with a two-compartment model with inter-individual variability, allometric scaling, and inter-occasion variability on clearance. Cytochrome P450 3A5 genotype, hematocrit, and creatinine influenced the tacrolimus clearance. A new starting dose model was developed in which the cytochrome P450 3A5 genotype was incorporated. Both models were successfully internally and externally validated.

Conclusions

The weight-normalized starting dose of tacrolimus should be higher in patients with a lower bodyweight and in those who are cytochrome P450 3A5 expressers.

Electronic supplementary material

The online version of this article (10.1007/s40262-019-00831-8) contains supplementary material, which is available to authorized users.

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

Purpose

In this study, we aimed to establish a tacrolimus population pharmacokinetic model and better understand the drug-drug interaction between Wuzhi capsule and tacrolimus in Chinese renal transplant recipients.

Patients and Methods

We performed a population pharmacokinetic analysis using a non-linear mixed-effects model to determine the suitable Wuzhi capsule dose in combination with tacrolimus. Data on 1378 tacrolimus steady-state concentrations were obtained from 142 patients who received kidney transplant in Changhai Hospital and Huashan Hospital. Demographic characteristics, laboratory tests, genetic polymorphisms, and co-medications were evaluated.

Results

The one-compartment model best described data. Our final model identified creatinine clearance rate, hematocrit, Wuzhi capsule dose, CYP3A5*3 genetic polymorphisms, and tacrolimus daily dose as significant covariates for tacrolimus clearance, with the value of 14.4 L h^-1, and the between-subject variability (BSV) was 25.4%. The Wuzhi capsule showed a dose-dependent effect on tacrolimus pharmacokinetics, demonstrating a stronger inhibitory effect than inductive effect.

Conclusion

Our model can accurately describe population pharmacokinetics of tacrolimus when combined with different doses of Wuzhi capsule. Additionally, this model can be used for individualizing tacrolimus dose following kidney transplantation.

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.

Tacrolimus Area Under the Concentration Versus Time Curve Monitoring, Using Home-Based Volumetric Absorptive Capillary Microsampling

BACKGROUND

Therapeutic drug monitoring (TDM) of tacrolimus (Tac) is mandatory in renal transplant recipients (RTxR). Area under the concentration versus time curve (AUC) is the preferred measure for Tac exposure; however, for practical purposes, most centers use trough concentrations as a clinical surrogate. Limited sampling strategies in combination with population pharmacokinetic model-derived Bayesian estimators (popPK-BE) may accurately predict individual AUC. The use of self-collected capillary microsamples could simplify this strategy. This study aimed to investigate the potential of AUC-targeted Tac TDM using capillary microsamples in combination with popPK-BE.

METHODS

A single-center prospective pharmacokinetic study was conducted in standard-risk RTxR (n = 27) receiving Tac twice daily. Both venous and capillary microsamples (Mitra; Neoteryx, Torrance, CA) were obtained across 2 separate 12-hour Tac dosing intervals (n = 13 samples/AUC). Using popPK-BE, reference AUC (AUCref) was determined for each patient using all venous samples. Different limited sampling strategies were tested for AUC predictions: (1) the empiric sampling scheme; 0, 1, and 3 hours after dose and (2) 3 sampling times determined by the multiple model optimal sampling time function in Pmetrics. Agreement between the predicted AUCs and AUCref were evaluated using C-statistics. Accepted agreement was defined as a total deviation index ≤±15%.

RESULTS

The AUC from capillary microsamples revealed high accuracy and precision compared with venous AUCref, and 85% of the AUCs had an error within ±11.9%. Applying microsamples at 0, 1, and 3 hours after dose predicted venous AUCref with acceptable agreement. Patients performed self-sampling with acceptable accuracy.

CONCLUSIONS

Capillary microsampling is patient-centered, making AUC-targeted TDM of Tac feasible without extended hospital stays. Samples obtained 0, 1, and 3 hours after dose, combined with popPK-BE, accurately predict venous Tac AUC.

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

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

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.

An Algorithm for Nonparametric Estimation of a Multivariate Mixing Distribution with Applications to Population Pharmacokinetics

Population pharmacokinetic (PK) modeling has become a cornerstone of drug development and optimal patient dosing. This approach offers great benefits for datasets with sparse sampling, such as in pediatric patients, and can describe between-patient variability. While most current algorithms assume normal or log-normal distributions for PK parameters, we present a mathematically consistent nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions without any assumption about the shape of the distribution. This approach can handle distributions with any shape for all PK parameters. It is shown in convexity theory that the NPML estimator is discrete, meaning that it has finite number of points with nonzero probability. In fact, there are at most N points where N is the number of observed subjects. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. In the simplest case, each point essentially represents the set of PK parameters for one patient. The probability of the points is found by a primal-dual interior-point method; the location of the support points is found by an adaptive grid method. Our method is able to handle high-dimensional and complex multivariate mixture models. An important application is discussed for the problem of population pharmacokinetics and a nontrivial example is treated. Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics. Thereby, this approach presents an important addition to the pharmacometric toolbox for drug development and optimal patient dosing.

Population pharmacokinetics of tacrolimus in umbilical cord blood transplant patients focusing on the variation in red blood cell counts

WHAT IS KNOWN AND OBJECTIVE

The distribution of tacrolimus (TAC), an immunosuppressant used during cord blood transplantation (CBT)-one of the haematopoietic stem cell transplantations, to red blood cell (RBC) is approximately 90% in whole blood. In CBT patients, the total RBC count shows dramatic fluctuation due to conditioning before transplantation, including anticancer agents and total body irradiation, as well as RBC transfusions during the treatment period. Therefore, the amount of TAC in whole blood may show wide variation. However, therapeutic drug monitoring (TDM) of TAC has been performed based on the whole blood concentration. In this study, to contribute to TDM of TAC in CBT, we performed the population pharmacokinetic (PPK) analysis of TAC in 56 CBT patients and investigated the factors that affected the concentration of TAC, focusing the variation of RBC count.

METHOD

A one-compartment model was applied to the observed whole blood TAC concentrations, and a PPK analysis was conducted with a non-linear mixed effect model.

RESULTS AND DISCUSSION

Our final PPK model indicated good robustness and accuracy. In addition, haemoglobin (Hb) level was an influential covariate on Vd, which was expressed as Vd(L) = 91.4 × (Hb/8.2)^(-1.07) .

WHAT IS NEW AND CONCLUSION

In this study, our results showed the necessity for the Hb level monitoring during TDM of TAC in CBT patients and provided useful information for improving TDM strategy of TAC.

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.

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

BACKGROUND AND OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

Aims

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

Methods

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

Results

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

Conclusions

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

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

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

High tacrolimus clearance - a risk factor for development of interstitial fibrosis and tubular atrophy in the transplanted kidney: a retrospective single-center cohort study

Patients with high tacrolimus clearance are more likely to experience transient under-immunosuppression in case of a missed or delayed dose. We wanted to investigate the association between estimated tacrolimus clearance and development of graft interstitial fibrosis and tubular atrophy (IFTA) in kidney transplant recipients. Associations between estimated tacrolimus clearance [daily tacrolimus dose (mg)/trough concentration (μg/l)] and changes in IFTA biopsy scores from week 7 to 1-year post-transplantation were investigated. Data from 504 patients transplanted between 2009 and 2013 with paired protocol biopsies (7 weeks + 1-year post-transplant) were included. There were no differences in baseline biopsy scores (7 weeks) in patients with different estimated tacrolimus clearance. Increasing tacrolimus clearance was significantly associated with increased ci + ct score of ≥2 at 1 year, odds ratio of 1.67 (95% CI; 1.11-2.51). In patients without fibrosis (ci + ct ≤ 1) at 7 weeks (n = 233), increasing tacrolimus clearance was associated with development of de novo IFTA (i + t ≤ 1 and ci + ct ≥ 2) at 1 year, odds ratio of 2.01 (95% CI; 1.18-3.50) after adjusting for confounders. High tacrolimus clearance was significantly associated with development of IFTA the first year following renal transplantation.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

CYP3A5*3 Genetic Polymorphism and Tacrolimus Concentration in Myanmar Renal Transplant Patients

BACKGROUND

Genetic polymorphism is an important factor that influences tacrolimus concentrations and has the potential to predict the optimal dosage of tacrolimus in personalized medicine. Tacrolimus, a drug of narrow therapeutic index, is used in renal transplant recipients as an immunosuppressant agent. It is a substrate of cytochrome P450 3A (CYP3A) and has highly variable pharmacokinetic parameters.

OBJECTIVE

The aim of this study was to identify the proportion of CYP3A5 gene polymorphism in Myanmar kidney transplant recipients and to determine the impact of CYP3A5 gene polymorphisms on tacrolimus level in CYP3A5 expressors and nonexpressors.

METHODS

This study included 41 adult Myanmar post-renal transplant patients. Tacrolimus trough blood levels were determined and CYP3A5 genotype analysis was conducted by using polymerase chain reaction amplification of target followed by detection by restriction fragment length polymorphism analysis.

RESULTS

The CYP3A5 nonexpressors and expressors were detected in 25 (60.97%) and 16 (39.02%) of the 41 renal transplant recipients, respectively. The tacrolimus concentration/dose ratio in the CYP3A5 expressor group was lower than in the CYP3A5 nonexpressor group (1.49 ± 0.69 vs 3.49 ± 3.08 [P = .003] at 1 month; and 1.54 ± 0.9 vs 7.88 ± 8.25 [P = .0001] at 3 months).

CONCLUSIONS

The findings of the present study revealed that more than one half of the study population were carrying the mutant allele CYP3A5*3(A6986G). CYP3A5 genetic polymorphism is one of the important factors in determining daily requirements for tacrolimus and in adjusting tacrolimus trough concentrations.

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.

Tacrolimus Updated Guidelines through popPK Modeling: How to Benefit More from CYP3A Pre-emptive Genotyping Prior to Kidney Transplantation

Tacrolimus (Tac) is a profoundly effective immunosuppressant that reduces the risk of rejection after solid organ transplantation. However, its use is hampered by its narrow therapeutic window along with its highly variable pharmacological (pharmacokinetic [PK] and pharmacodynamic [PD]) profile. Part of this variability is explained by genetic polymorphisms affecting the metabolic pathway. The integration of CYP3A4 and CY3A5 genotype in tacrolimus population-based PK (PopPK) modeling approaches has been proven to accurately predict the dose requirement to reach the therapeutic window. The objective of the present study was to develop an accurate PopPK model in a cohort of 59 kidney transplant patients to deliver this information to clinicians in a clear and actionable manner. We conducted a non-parametric non-linear effects PopPK modeling analysis in Pmetrics^®. Patients were genotyped for the CYP3A4^∗22 and CYP3A5^∗3 alleles and were classified into 3 different categories [poor-metabolizers (PM), Intermediate-metabolizers (IM) or extensive-metabolizers (EM)]. A one-compartment model with double gamma absorption route described very accurately the tacrolimus PK. In covariate analysis, only CYP3A genotype was retained in the final model (Δ-2LL = -73). Our model estimated that tacrolimus concentrations were 33% IC_95%[20–26%], 41% IC_95%[36–45%] lower in CYP3A IM and EM when compared to PM, respectively. Virtually, we proved that defining different starting doses for PM, IM and EM would be beneficial by ensuring better probability of target concentrations attainment allowing us to define new dosage recommendations according to patient CYP3A genetic profile.

In vitro and in silico characterisation of Tacrolimus released under biorelevant conditions

This work aims to better understand the in vivo behaviour of modified release (MR) formulations (Envarsus^® tablets and Advagraf^® capsules) using in vitro properties of tacrolimus and in silico simulations. The in silico concentration profiles of tacrolimus released from the MR formulations were predicted after building a three compartments PK model with GastroPlus™, and using the experimentally determined in vitro physico-chemical properties as input parameters. In vitro-in vivo correlations (IVIVC) were obtained after deconvolution of in vivo data from a clinical trial. The IVIVC showed that the in vitro dissolution was faster than the in vivo deconvoluted dissolution for Advagraf^®, while the in vitro dissolution was slightly slower than the in vivo deconvoluted dissolution for Envarsus^®. Population PK simulation showed that variability in the simulation was lower for Envarsus^® compared to Advagraf^®. The in silico predicted preferential absorption sites were the proximal and distal tract for Advagraf^® and Envarsus^®, respectively. The integration of experimental in vitro solubility, permeability and biorelevant dissolution data allowed to generate in silico tacrolimus concentrations for two different MR formulations. This permitted to compare the two formulations in a single PK profile, in a simulated population PK study and with respect to their absorption sites.

Prediction of Fat-Free Mass in Kidney Transplant Recipients

BACKGROUND

Individualization of drug doses is essential in kidney transplant recipients. For many drugs, the individual dose is better predicted when using fat-free mass (FFM) as a scaling factor. Multiple equations have been developed to predict FFM based on healthy subjects. These equations have not been evaluated in kidney transplant recipients. The objectives of this study were to develop a kidney transplant specific equation for FFM prediction and to evaluate its predictive performance compared with previously published equations.

METHODS

Ten weeks after transplantation, FFM was measured by dual-energy X-ray absorptiometry. Data from a consecutive cohort of 369 kidney transplant recipients were randomly assigned to an equation development data set (n = 245) or an evaluation data set (n = 124). Prediction equations were developed using linear and nonlinear regression analysis. The predictive performance of the developed equation and previously published equations in the evaluation data set was assessed.

RESULTS

The following equation was developed: FFM (kg) = {FFMmax × body weight (kg)/[81.3 + body weight (kg)]} × [1 + height (cm) × 0.052] × [1-age (years) × 0.0007], where FFMmax was estimated to be 11.4 in males and 10.2 in females. This equation provided an unbiased, precise prediction of FFM in the evaluation data set: mean error (ME) (95% CI), -0.71 kg (-1.60 to 0.19 kg) in males and -0.36 kg (-1.52 to 0.80 kg) in females, root mean squared error 4.21 kg (1.65-6.77 kg) in males and 3.49 kg (1.15-5.84 kg) in females. Using previously published equations, FFM was systematically overpredicted in kidney-transplanted males [ME +1.33 kg (0.40-2.25 kg) to +5.01 kg (4.06-5.95 kg)], but not in females [ME -2.99 kg (-4.07 to -1.90 kg) to +3.45 kg (2.29-4.61) kg].

CONCLUSIONS

A new equation for FFM prediction in kidney transplant recipients has been developed. The equation may be used for population pharmacokinetic modeling and clinical dose selection in kidney transplant recipients.

Improved Tacrolimus Target Concentration Achievement Using Computerized Dosing in Renal Transplant Recipients--A Prospective, Randomized Study

BACKGROUND

Early after renal transplantation, it is often challenging to achieve and maintain tacrolimus concentrations within the target range. Computerized dose individualization using population pharmacokinetic models may be helpful. The objective of this study was to prospectively evaluate the target concentration achievement of tacrolimus using computerized dosing compared with conventional dosing performed by experienced transplant physicians.

METHODS

A single-center, prospective study was conducted. Renal transplant recipients were randomized to receive either computerized or conventional tacrolimus dosing during the first 8 weeks after transplantation. The median proportion of tacrolimus trough concentrations within the target range was compared between the groups. Standard risk (target, 3-7 μg/L) and high-risk (8-12 μg/L) recipients were analyzed separately.

RESULTS

Eighty renal transplant recipients were randomized, and 78 were included in the analysis (computerized dosing (n = 39): 32 standard risk/7 high-risk, conventional dosing (n = 39): 35 standard risk/4 high-risk). A total of 1711 tacrolimus whole blood concentrations were evaluated. The proportion of concentrations per patient within the target range was significantly higher with computerized dosing than with conventional dosing, both in standard risk patients (medians, 90% [95% confidence interval {95% CI}, 84-95%] vs 78% [95% CI, 76-82%], respectively, P < 0.001) and in high-risk patients (medians, 77% [95% CI, 71-80%] vs 59% [95% CI, 40-74%], respectively, P = 0.04).

CONCLUSIONS

Computerized dose individualization improves target concentration achievement of tacrolimus after renal transplantation. The computer software is applicable as a clinical dosing tool to optimize tacrolimus exposure and may potentially improve long-term outcome.