有限采样策略估算心脏移植受者霉酚酸体内暴露的研究

目的 用有限采样策略(LSS)建立心脏移植受者霉酚酸(MPA)血药浓度-时间曲线下面积(AUC)的简化预测模型.方法 共收集20例心脏移植受者,术后按相同剂量连续服用吗替麦考酚酯片或其分散片至少7d后,采集不同时间的全血样本,用LC-MS/MS法测定血浆MPA浓度.用WinNonlin软件以非房室模型计算其药代动力学参...     

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

AIMS

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

METHODS

Twenty full tacrolimus area under the concentration–time curve from 0 to 12 h post-dose (AUC_0–12) profiles (AUCf) were collected from 20 subjects. Predicted tacrolimus AUC_0–12 (AUCp) was calculated using the following: (i) 42 multiple regression-derived limited sampling strategies (LSSs); (ii) five population pharmacokinetic (PK) models in the Bayesian forecasting program TCIWorks; and (iii) a Web-based consultancy service. Correlations (r²) between C₀ and AUCf and between AUCp and AUCf were examined. Median percentage prediction error (MPPE) and median absolute percentage prediction error (MAPE) were calculated.

RESULTS

Correlation between C₀ and AUCf was 0.53. Using the 42 LSS equations, correlation between AUCp and AUCf ranged from 0.54 to 0.99. The MPPE and MAPE were <15% for 29 of 42 equations (62%), including five of eight equations based on sampling taken ≤2 h post-dose. Using the PK models in TCIWorks, AUCp derived from only C₀ values showed poor correlation with AUCf (r² = 0.27–0.54) and unacceptable imprecision (MAPE 17.5–31.6%). In most cases, correlation, bias and imprecision estimates progressively improved with inclusion of a greater number of concentration time points. When concentration measurements at 0, 1, 2 and 4 h post-dose were applied, correlation between AUCp and AUCf ranged from 0.75 to 0.93, and MPPE and MAPE were <15% for all models examined. Using the Web-based consultancy service, correlation between AUCp and AUCf was 0.74, and MPPE and MAPE were 6.6 and 9.6%, respectively.

CONCLUSIONS

Limited sampling methods better predict tacrolimus exposure compared with C₀ measurement. Several LSSs based on sampling taken 2 h or less post-dose predicted exposure with acceptable bias and imprecision. Generally, Bayesian forecasting methods required inclusion of a concentration measurement from <2 h post-dose to adequately predict exposure.     

A limited sampling strategy to estimate exposure of once-daily modified release tacrolimus in renal transplant recipients using linear regression analysis and comparison with Bayesian population pharmacokinetics in different cohorts

European Journal of Clinical Pharmacology - Tacrolimus has a narrow therapeutic window. Measuring trough level (C0) as surrogate for drug exposure (AUC) in renal transplant recipients has...     

Population pharmacokinetics and pharmacogenetics of once daily prolonged-release formulation of tacrolimus in pediatric and adolescent kidney transplant recipients

Background and Objectives

Tacrolimus^PR is a new prolonged-release once-daily formulation of the calcineurin inhibitor tacrolimus, currently used in adult transplant patients. As there are no pharmacokinetic data available in pediatric kidney transplant recipients, the aims of this study were to develop a population pharmacokinetic model of tacrolimus^PR in pediatric and adolescent kidney transplant recipients and to identify covariates that have a significant impacts on tacrolimus^PR pharmacokinetics, including CYP3A5 polymorphism.

Methods

Pharmacokinetic samples were collected from 22 pediatric kidney transplant patients. Population pharmacokinetic analysis was performed using NONMEM. Pharmacogenetic analysis was performed on the CYP3A5 gene.

Results

The pharmacokinetic data were best described by a one-compartment model with first order absorption and lag-time. The weight normalized oral clearance CL/F [CL/F/ (weight/70)^0.75] was lower in patients with CYP3A5*3/*3 as compared to patients with the CYP3A5*1/*3 (32.2?±?10.1 vs. 53.5?±?20.2 L/h, p?=?0.01).

Conclusions

The population pharmacokinetic model of tacrolimus^PR was developed and validated in pediatric and adolescent kidney transplant patients. Body weight and CYP3A5 polymorphism were identified as significant factors influencing pharmacokinetics. The developed model could be useful to optimize individual pediatric tacrolimus ^PR dosing regimen in routine clinical practice.     

Limited sampling strategy for simultaneous estimation of the area under the concentration-time curve of tacrolimus and mycophenolic acid in adult renal transplant recipients

The aim of this study was to develop a limited sampling strategy to allow the simultaneous estimation of the area under the concentration-time curves (AUCs) of tacrolimus and mycophenolic acid (MPA), the active metabolite of the prodrug mycophenolate mofetil, using a small number of samples from patients undergoing renal transplantation. Fifty Japanese patients were enrolled. On day 28 after transplantation, samples were collected just before and 1, 2, 3, 4, 6, 9, and 12 hours after tacrolimus and mycophenolate mofetil administration at 9:00 am and 9:00 pm. The full pharmacokinetic profiles obtained from these timed concentration data were used to choose the best sampling times. Three error indices (percent mean error, percent mean absolute error, and percent relative mean square error) were used to evaluate the predictive bias, accuracy, and precision. The predicted AUC0-12 of MPA calculated at the three time points of C2h-C4h-C9h best approximated the actual AUC0-12 of MPA (r = 0.877), and the AUC0-12 of tacrolimus calculated at the same time points predicted a good correlation with the actual AUC (r = 0.928). When the three sampling times of trough level (C0h) and two other points within 4 hours after administration were used, the three points of C0h-C2h-C4h were the best points for estimation of the AUC0-12 tacrolimus and MPA (AUC0-12 = 7.04.C0 + 1.71.C2 + 3.23.C4 + 15.19, r = 0.799, P < 0.001 and AUC0-12 = 0.26.C0 + 2.06.C2 + 3.82.C4 + 20.38, r = 0.693, P < 0.001, respectively). The percent mean error, percent mean absolute error, and percent relative mean square error of the prediction formula using the three time points of C0h-C2h-C4h were -0.3%, 8.8%, and 13.5% for tacrolimus and 2.9%, 17.1%, and 21.5% for MPA, respectively. A limited sampling strategy using C2h-C4h-C9h provides the most reliable and accurate simultaneous estimation of the AUC0-12 of tacrolimus and MPA in patients undergoing renal transplantation. In addition, a limited sampling strategy using C0h-C2h-C4h is recommended for the simultaneous estimation of the AUC0-12 of tacrolimus and MPA when focused on samples collected within 4 hours after administration for clinical expediency.     

Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in paediatric liver transplant recipients

Aims

The objectives of this study were to develop a population pharmacokinetic (PopPK) model for tacrolimus in paediatric liver transplant patients and determine optimal sampling strategies to estimate tacrolimus exposure accurately.

Methods

Twelve hour intensive pharmacokinetic profiles from 30 patients (age 0.4–18.4 years) receiving tacrolimus orally were analysed. The PopPK model explored the following covariates: weight, age, sex, type of transplant, age of liver donor, liver function tests, albumin, haematocrit, drug interactions, drug formulation and time post-transplantation. Optimal sampling strategies were developed and validated with jackknife.

Results

A two-compartment model with first-order absorption and elimination and lag time described the data. Weight was included on all pharmacokinetic parameters. Typical apparent clearance and central volume of distribution were 12.1 l h⁻¹ and 31.3 l, respectively. The PopPK approach led to the development of optimal sampling strategies, which allowed estimation of tacrolimus pharmacokinetics and area under the concentration–time curve (AUC) on the basis of practical sampling schedules (three or four sampling times within 4 h) with clinically acceptable prediction error limit. The mean bias and precision of the Bayesian vs. reference (trapezoidal) AUCs ranged from −2.8 to −1.9% and from 7.4 to 12.5%, respectively.

Conclusions

The PopPK of tacrolimus and empirical Bayesian estimates represent an accurate and convenient method to predict tacrolimus AUC_(0–12) in paediatric liver transplant recipients, despite high between-subject variability in pharmacokinetics and patient demographics. The developed optimal sampling strategies will allow the undertaking of prospective trials to define the tacrolimus AUC-based therapeutic window and dosing guidelines in this population.     

Limited sampling strategy for the estimation of systemic exposure to the protease inhibitor nelfinavir

Therapeutic drug monitoring (TDM) of antiretroviral drugs has been proposed as a means of optimizing response to highly active antiretroviral therapy (HAART) in HIV infection because suboptimal exposure to these agents may lead to the development of resistant viral strains and subsequent therapeutic failure. The area under the curve (AUC), though considered to make the best estimate of total drug exposure, requires repeated blood sampling. The authors investigated the predictability of individual nelfinavir (NFV) concentrations at different time points for the AUC and tried to find the best sampling time for the abbreviated AUC to predict NFV total body exposure. A total of 99 NFV AUC0-12h values were measured in 99 patients receiving a 1250-mg oral dose twice a day. Venous blood samples were collected at baseline (predose, 0) and 1, 2, 3, 4, 5, 6, 8, and 12 hours postdose. A stepwise forward-selection, multiple-regression technique was chosen to assess the relative importance of single and combination concentration time points to predict the AUC calculated from the entire pharmacokinetic profile. Data were split into a development set and a validation set. The development set contained 49 randomly selected HIV patients. Of these, 22 HIV patients were coinfected with HCV, 7 with and 15 without cirrhosis. One-point predictors provided the lowest prediction precision, but predictive performance improved after the first 2 hours postdose. Plasma concentrations at 0 and 4 hours after the oral dose were most predictive if 2 variables were used in the regression equation. The AUC could be estimated from data for these 2 samples by using the following equation: AUC0-12 = 3.0 + 2.7 (C0) + 6.4 (C4), r = 92. The predictive performance of 2-point predictors at 0 and 4 hours (C0 + C4) was validated by comparing their ability to predict the full AUC in a validation set representative of HIV/HCV patients (n = 28) and HIV/HCV patients, with (n = 8) and without (n = 14) cirrhosis. The results showed a mean bias ranging from +2.7% in HIV/HCV patients to -6.0% in HCV coinfection with cirrhosis. The authors conclude that this result is clinically significant. The limited sampling strategy (LSS) described could be used in clinical practice for the easy assessment of the total exposure to NFV in HIV/HCV patients, both with and without cirrhosis.     

A limited sampling strategy for tacrolimus in renal transplant patients

AIMS

To develop and validate limited sampling strategy (LSS) equations to estimate area under the curve (AUC_0–12) in renal transplant patients.

METHODS

Twenty-nine renal transplant patients (3–6 months post transplant) who were at steady state with respect to tacrolimus kinetics were included in this study. The blood samples starting with the predose (trough) and collected at fixed time points for 12 h were analysed by microparticle enzyme immunoassay. Linear regression analysis estimated the correlations of tacrolimus concentrations at different sampling time points with the total measured AUC_0–12. By applying multiple stepwise linear regression analysis, LSS equations with acceptable correlation coefficients (R²), bias and precision were identified. The predictive performance of these models was validated by the jackknife technique.

RESULTS

Three models were identified, all with R² ≥ 0.907. Two point models included one with trough (C₀) and 1.5 h postdose (C_1.5), another with trough and 4 h postdose. Increasing the number of sampling time points to more than two increased R² marginally (0.951 to 0.990). After jackknife validation, the two sampling time point (trough and 1.5 h postdose) model accurately predicted AUC_0–12. Regression coefficient R² = 0.951, intraclass correlation = 0.976, bias [95% confidence interval (CI)] 0.53% (−2.63, 3.69) and precision (95% CI) 6.35% (4.36, 8.35).

CONCLUSION

The two-point LSS equation [AUC_0–12 = 19.16 + (6.75.C₀) + (3.33.C1.5)] can be used as a predictable and accurate measure of AUC_0–12 in stable renal transplant patients prescribed prednisolone and mycophenolate.

WHAT IS ALREADY KNOWN ABOUT THE SUBJECT

• Tacrolimus trough concentration is being currently used for dose individualization.
• Limited sampling strategies (LSS) have been developed and validated for renal transplant patients.
• Earlier literature has suggested that measurement of tacrolimus AUC is more reliable than trough with respect to both rejection and nephrotoxicity.

WHAT THIS STUDY ADDS

• Four thousand renal transplants take place annually in India, with many patients prescribed tacrolimus in combination with mycophenolate and steroid.
• In this study a LSS with two points, i.e. trough and 1.5 h postdose was developed and validated to estimate AUC_0–12.
• The added benefit of only a single additional sample with completion of blood collection in 1.5 h and minimum additional cost makes this a viable LSS algorithm in renal transplant patients.
• In patients having tacrolimus trough concentrations outside the recommended range (<3 and >10 ng ml⁻¹ in the treatment protocol in our institution) or having side-effects in spite of trough concentrations in the desired range, we can estimate AUC using this LSS for a better prediction of exposure.

     

Pharmacokinetics of tacrolimus converted from twice-daily formulation to once-daily formulation in Chinese stable liver transplant recipients

Aim:

To evaluate the pharmacokinetics of tacrolimus in Chinese stable liver transplant recipients converted from immediate release (IR) tacrolimus-based immunosuppression to modified release (MR) tacrolimus-based immunosuppression.

Methods:

Open-label, multi-center study with a one-way conversion design was conducted. Eighty-three stable liver recipients (6–24 months post-transplant) with normal renal and stable hepatic function were converted from IR tacrolimus twice-daily treatment to MR tacrolimus once-daily treatment on a 1:1 (mg: mg) total daily dose basis. Twenty-four hour pharmacokinetic studies were carried out on d 0 (pre-conversion), d 1, and d 84 (post-conversion).

Results:

The area under the blood concentration–time curve of MR tacrolimus from 0 to 24 h (AUC_0–24) on d 1 was comparable to that of IR tacrolimus on d 0, with a 90% confidence interval (CI) for MR/IR tacrolimus of 92%–97%. The AUC_0–24 value for MR tacrolimus on d 84 with the daily dose increased by 14% was approximately 17% lower than that for IR tacrolimus. The 90% CI was 77%–90%, outside the bioequivalence range of 80%–125%. There was a good correlation between AUC_0–24 and concentration at 24 h (C₂₄) for IR tacrolimus (d 0, r=0.930) and MR tacrolimus (d 1, r=0.936; d 84, r=0.903).

Conclusion:

The exposure to tacrolimus when administered MR tacrolimus once daily is not equivalent to that for IR tacrolimus twice daily after an 84-day conversion in Chinese stable liver transplant recipients. The dose should be adjusted on the basis of trough levels. The therapeutic drug monitoring for patients treated with IR tacrolimus is considered to be applicable to MR tacrolimus.     

Bayesian estimation of cyclosporin exposure for routine therapeutic drug monitoring in kidney transplant patients

AIMS: AUC-based monitoring of cyclosporin A (CsA) is useful to optimize dose adaptation in difficult cases. We developed a population pharmacokinetic model to describe dose-exposure relationships for CsA in renal transplant patients and applied it to the Bayesian estimation of AUCs using three blood concentrations. METHODS: A total of 84 renal graft recipients treated with CsA microemulsion were included in this study. Population pharmacokinetic analysis was conducted using NONMEM. A two-compartment model with zero-order absorption and a lag time best described the data. Bayesian estimation was based on CsA blood concentrations measured before dosing and 1 h and 2 h post dose. Predictive performance was evaluated using a cross-validation approach. Estimated AUCs were compared with AUCs calculated by the trapezoidal method. The Bayesian approach was also applied to an independent group of eight patients exhibiting unusual pharmacokinetic profiles. RESULTS: Mean population pharmacokinetic parameters were apparent clearance 30 l h(-1), apparent volume of distribution 79.8 l, duration of absorption 52 min, absorption lag time 7 min. No significant relationships were found between any of the pharmacokinetic parameters and individual characteristics. A good correlation was obtained between Bayesian-estimated and experimental AUCs, with a mean prediction error of 2.8% (95% CI [-0.6, 6.2]) and an accuracy of 13.1% (95% CI [7.5, 17.2]). A good correlation was also obtained in the eight patients with unusual pharmacokinetic profiles (r(2) = 0.96, P < 0.01). CONCLUSIONS: Our Bayesian approach enabled a good estimation of CsA exposure in a population of patients with variable pharmacokinetic profiles, showing its usefulness for routine AUC-based therapeutic drug monitoring.     

有限采样策略应用于中国器官移植患者霉酚酸暴露量估算的研究进展

霉酚酸(mycophenolic acid,MPA)是一种抗代谢免疫抑制药,广泛应用于实体器官移植术后,其具有治疗窗窄、药动学个体差异大等特点,常常需要治疗药物监测(therapeutic drug monitoring,TDM)测定药-时曲线下面积(area under the concentration-time ...     

Limited sampling model for the estimation of pharmacokinetic parameters in children

A limited sampling model (LSM) is proposed for the first-time assessment of pharmacokinetic parameters (area under the concentration-time curve (AUC), Cmax, and T1/2) in children after a single oral dose of drug. Three drugs were evaluated in this study. The LSM was developed for each drug from the data of 10 healthy adult volunteers. The relationship at selected time points between plasma concentration and the AUC or Cmax was evaluated by multiple linear regression. The multiple linear regression that gave the best correlation coefficient (r) for 3 sampling times versus AUC or Cmax was chosen as the LSM. Pharmacokinetic parameters generated using sparse sampling (3 blood samples) were compared with pharmacokinetic parameters generated using extensive sampling (>7 blood samples). The results indicated that a limited sampling model can be developed from adult data to estimate pharmacokinetic parameters in children with fair degree of accuracy.     

Limited sampling strategies for the estimation of raltegravir daily exposure in HIV-infected patients

Stepwise multiple regression analyses were applied to 50 raltegravir pharmacokinetic profiles from 50 HIV patients with the goal to identify limited sampling strategies for the prediction of drug area under the time-concentration curve (AUC(0-12)). Raltegravir single sampling point-based equations failed to reliably predict daily drug exposure. Conversely, different algorithms based on few samples and associated with good correlation, acceptable bias, and imprecision with the measured raltegravir AUC(0-12) were identified. These models could used to predict raltegravir exposure for clinic or research purposes.     

Limited sampling strategy for the estimation of mycophenolic acid area under the plasma concentration-time curve in adult patients undergoing liver transplant

Mycophenolate mofetil (MMF), the oral prodrug of mycophenolic acid (MPA), is increasingly used in liver transplantation and plays a central role in the immunosuppressive regimen in liver transplantation. To study pharmacokinetic-pharmacodynamic relationships and therapeutic drug monitoring of MPA in the clinical setting, limited sampling strategies have been investigated for the estimation of MPA areas under the curves (AUCs). Thirty-eight adult patients undergoing liver transplant (31 males, seven females) receiving 1.0 g MMF twice daily and concomitant tacrolimus provided a total of 72 pharmacokinetic profiles. Multiple stepwise regression analysis was used to determine the algorithms for limited sampling strategies. Twenty-eight one-, two-, three-, and four-sampling estimation models were fitted (r = 0.288-0.964) to all the profiles using linear regression and were used to estimate MPA AUC0-12h comparing those estimates with the corresponding AUC0-12h values calculated with the linear trapezoidal rule, including all 10 timed MPA concentrations. The four-point estimates at C1h, C2h, C6h, and C8h resulted in the best correlation between estimated AUC and true AUC when using the formula AUC = 6.03 + 0.89C1h + 1.94C2h + 2.24C6h + 4.64 C8h (r = 0.911). Bland and Altman analysis revealed good agreement between estimated AUC and AUC from the full profile. This limited sampling strategy provides an effective approach for estimation of full MPA AUC0-12h in patients undergoing liver transplant receiving concomitant tacrolimus therapy.     

Population pharmacokinetic modeling and optimal sampling strategy for Bayesian estimation of amikacin exposure in critically ill septic patients

Because the sepsis-induced pharmacokinetic (PK) modifications need to be considered in aminoglycoside dosing, the present study aimed to develop a population PK model for amikacin (AMK) in severe sepsis and to subsequently propose an optimal sampling strategy suitable for Bayesian estimation of the drug PK parameters. Concentration-time profiles for AMK were obtained from 88 critically ill septic patients during the first 24 hours of antibiotic treatment. The population PK model was developed using a nonlinear mixed effects modeling approach. Covariate analysis included demographic data, pathophysiological characteristics, and comedication. Optimal sampling times were selected based on a robust Bayesian design criterion. Taking into account clinical constraints, a two-point sampling approach was investigated. A two-compartment model with first-order elimination best fitted the AMK concentrations. Population PK estimates were 19.2 and 9.34 L for the central and peripheral volume of distribution and 4.31 and 2.21 L/h for the intercompartmental and total body clearance. Creatinine clearance estimated using the Cockcroft-Gault equation was retained in the final model. The two optimal sampling times were 1 hour and 6 hours after onset of the drug infusion. Predictive performance of individual Bayes estimates computed using the proposed optimal sampling strategy was reported: mean prediction errors were less than 5% and root mean square errors were less than 30%. The present study confirmed the significant influence of the creatinine clearance on the PK disposition of AMK during the first hours of treatment in critically ill septic patients. Based on the population estimates, an optimal sampling strategy suitable for Bayesian estimation of the drug PK parameters was developed, meeting the need of clinical practice.