他克莫司对9例肾移植病人的药物动力学

目的 :研究单剂量口服他克莫司在肾移植病人体内药物动力学。方法 :9名肾移植病人单剂量口服他克莫司 2～ 3mg后 ,于 0 ,0 .3 3 ,0 .66,1,1.5 ,2 ,3 ,4,6,8,10 ,12h分别取外周静脉血 ,用微粒子酶免分析法 (MEIA)测定全血药物浓度 ,3P97模拟药物动力学模型 ,计算有关药物动力学参数。结果 :他克莫司符合二室模型 ,T12 α(0 .6± 0 .5 )h ;T12 β(13± 2 1)h ;Cmax(15± 7) μg·L- 1;Tmax(1.6±1.4)h ;V/F(15 6± 95 )L ;AUC0→∞(14 0± 166) μg·h·L- 1;Tlog(0 .3 0± 0 .0 5 )h ;Cssmin(10± 7) μg·L- 1。结论 :9例肾移植病人单剂量口服他克莫司后药物动力学参数与国外文献报道基本相符。     

肾移植患者全血和外周血单个核细胞内他克莫司的药动学

目的 分析中国肾移植患者他克莫司(TAC)在全血(WB)和外周血单个核细胞(PBMC)中的药动学,并探索二者之间的关系。方法采集27例接受TAC在内的三联免疫疗法肾移植患者在给予TAC后0、0.5、1、1.5、2、4、6、8、10、12 h的WB标本,提取PBMC,采用LC-MS/MS法测定WB与PBMC中TAC浓度,Win Nonlin软件以非房室法计算WB和PBMC中的TAC药动学参数。结果肾移植患者WB与PBMC内的AUC_{0-12 h}分别为125.05(40.32～306.6)ng·h·m L^-1和6.25(0.31～30.64)ng·h·nmol^-1,C_max分别为21.30(0.25～34.50)ng·m L^-1和1.28(0.07～6.49)ng·nmol^-1。WB与PBMC中AUC_{0-12 h}(r²=0.422 6)、C₀(r²=0.585 4)和C     

霉酚酸在肾移植患者中有限采样法监测方案的探讨

目的：研究临床应用的霉酚酸（MPA）类药物霉酚酸酯（MMF）和霉酚酸钠肠溶片（EC-MPS）在肾移植患者中的药代动力学特征,确定两者浓度曲线下面积的有限采样监测方案。方法：76例患者均接受MPA、他克莫司、激素的三联免疫抑制方案。其中33例接受MMF治疗,43例接受EC-MPS治疗。采用EMIT法测定服药前及服药后0.5、1、1.5、2、4、6、8、10、12 h的血浆MPA浓度,梯形法计算MPA的AUC0-12h,多元线性回归分析用于计算简化 AUC 拟合方程,以SPSS 21软件进行临床资料的统计分析。结果：MMF组和EC-MPS组个体间C0、Cmax、Tmax变异度大,且两组的Cmax、Tmax有显著性差异。多元线性回归分析得出4点估算 AUCMMF=1.133+6.643C0+1.130C1+1.540C2+2.689C4,r2 = 0.947;4点估算 AUCEC-MPS=-1.360+2.164C2+2.315C4+2.569C6+4.324C8,r2 = 0.944。结论：MMF和 EC-MPS 药代动力学参数个体差异大,研究确定的有限采样监测方案适用于临床肾移植患者配伍他克莫司时AUC的监测。但考虑到影响MPA暴露量和药效的因素众多且不易控制,因此考察应继续加大样本量,探索准确的临床监测指标,从而为临床精准使用MPA提供新策略。     

比较他克莫司缓释制剂与普通制剂在中国肾移植患者的药代动力学

目的比较中国肾移植患者重复剂量口服他克莫司(免疫抑制剂)缓释制剂和普通制剂的药代动力学。方法用多中心、随机、开放、平行对照的临床试验。受试者为18～70岁成年初次肾移植患者,按1∶1随机分成他克莫司缓释制剂组,每天1次;普通制剂组,每天2次。患者于术后分别口服他克莫司缓释胶囊,在连续给药后(56±5)d进行24 h的药代动力学比较研究,用液相色谱串联质谱法测定全血中他克莫司浓度。结果术后56 d,他克莫司缓释制剂组(n=17)和普通制剂组(n=15)给药剂量分别为(0.12±0.05)和(0.10±0.04)mg.kg-1.d-1,药代动力学参数tmax分别为(3.10±3.00),(1.80±0.90)h,Cmax分别为(13.20±4.20),(11.80±3.80)ng.mL-1,C24分别为(4.40±1.30),(4.50±1.20)ng.mL-1,AUC0-24分别为(171.70±49.90),(145.10±29.30)ng.mL-1.h。AUC0-24与C24的相关系数,缓释制剂为0.87,普通制剂为0.62。结论初次肾移植患者术后56 d,他克莫司缓释制剂的日剂量比普通制剂高23%,体内暴...     

药物相互作用对肾移植受者霉酚酸体内暴露量的影响

目的 分析合并用药对肾移植患者霉酚酸体内暴露量的影响,为吗替麦考酚酯(MMF)的临床合理应用提供参考。方法 采用回顾性研究方法,对郑州市第七人民医院肾移植科使用MMF治疗并测定浓度的住院患者进行信息归纳,从浓度分布情况,抑制胃酸分泌药物、抗病毒药物、CNI类药物对霉酚酸暴露量的影响等方面进行分析探讨。结果 肾移植科霉酚酸AUC_{0～12 h}在30～60μg(h/mL)范围内占58.39%;谷浓度与霉酚酸AUC_{0～12 h}呈正相关的趋势(r=0.804,P=0.000);合用抑制胃酸分泌药物未见对霉酚酸AUC_{0～12 h}产生明显影响;更昔洛韦可以降低霉酚酸AUC_{0～12 h}(U=12936.5,Z=-2.442,P=0.015);合用他克莫司组的霉酚酸AUC_{0～12 h}显著高于合用环孢素组(U=3916.0,Z=-4.698,P=0.000)。结论 霉酚酸体内暴露量与谷浓度有一定的相关性,但是不能通过谷浓度估算出来;合用抑制胃酸分泌药物不影响霉酚酸体内暴露量;合用更昔洛韦降低霉酚酸...     

他克莫司浓度在移植患者全血、血浆与血细胞中的相关性研究

目的:研究移植患者全血、血浆与血细胞中他克莫司浓度的相关性,并分析移植类型及年龄对三者间他克莫司浓度相关性的影响,为临床合理用药提供参考。方法:随机选取我院20例移植术后使用他克莫司抗排异治疗并进行血药浓度监测的患者,根据移植类型分为肾移植组和肺移植组(各10例),根据年龄分为20～40岁组、41～60岁组和61～80岁组(分别有4、9、7例)。收集患者血药浓度监测的残血,采用化学发光微粒子免疫分析法(CMIA)检测全血中他克莫司谷浓度,并采用超高效液相色谱-串联质谱(UPLC-MS/MS)法同时检测血浆及血细胞中他克莫司浓度。应用散点图矩阵和Spearman秩相关性分析全血与血浆、全血与血细胞、血浆与血细胞中他克莫司浓度的相关性,以及移植类型、年龄对三者间他克莫司浓度相关性的影响。结果:全血与血浆中他克莫司浓度相关性(r=0.623,P<0.01)略强于全血与血细胞中他克莫司浓度的相关性(r=0.591,P<0.01),血浆与血细胞中他克莫司浓度相关性相对较弱(r=0.497,P<0.05)。移植类型、年龄对全血、血浆、血细胞三者间他克莫司浓度相关性均有影响,肾移植组患者全血、血细胞、血浆三者间他克莫司浓度相关性均较弱(r均<0.5),且弱于肺移植组患者;20～40岁组患者全血、血浆、血细胞三者间他克莫司浓度相关性也均较弱(r均<0.3),且均弱于41～60岁组、61～80岁组患者。结论:移植术后患者全血、血浆与血细胞三者间他克莫司浓度的相关性均不强,尤其是肾移植患者和20～40岁年龄段患者,应加强对其排斥反应和不良反应的监测。     

LSS法估算肾移植患者口服环孢素的药时曲线下面积

目的:应用有限采样法(Limited Sampling Strategy,LSS)估算肾移植患者口服环孢素后药时曲线下面积(AUC)。方法:12名肾移植患者单剂量口服CsA微乳制剂,荧光免疫偏振法测定各采样时间点CsA的血药浓度,以多元线性逐步回归法建立数学模型。结果:单个血药浓度-时间点预测AUC0-12的回归模型,线性关系较差,2点和3点预测AUC0-12的回归模型较单点预测好(r2>0．9,P<0．05)。其中C2,C6预测AUC0-12的回归模型(AUC0-12=1017．029 3．047 C2 3.121 C6,r2=0．960, P<0．05)线性关系佳且准确性好。结论:以LSS法估算口服CsA微乳制剂AUC0-12准确性好,临床上可用作CsA治疗药物监测的手段。     

80例肾移植患者他克莫司血药浓度数据分析

目的探讨肾移植患者他克莫司血药浓度与效应关系,定性分析影响他克莫司血药浓度的各种因素。方法收集我院2000年～2001年80例肾移植患者他克莫司血药浓度达稳态后谷浓度数据,并作回顾性分析。结果与结论他克莫司有效血药浓度与疗程有关,肾移植术后2wk内,他克莫司血药浓度宜为12～15ng/ml,2wk以后应为8～15ng/ml。     

盐酸小檗碱对大鼠体内他克莫司药动学的影响

目的:研究盐酸小檗碱单次、多次给药对大鼠体内他克莫司药动学的影响,为两者联合用药提供参考。方法:将30只大鼠随机分为5组,每组6只,第1组大鼠单次灌胃他克莫司;第2组大鼠每日灌胃他克莫司2次,连续给药1周;第3组大鼠先单次灌胃盐酸小檗碱,5 min后单次灌胃他克莫司;第4组大鼠先每日灌胃他克莫司2次,连续给药1周,第8天时先灌胃盐酸小檗碱5 min后再灌胃他克莫司1次;第5组大鼠每日灌胃盐酸小檗碱2次,每次间隔5 min后相应灌胃他克莫司1次,连续给药8 d。盐酸小檗碱给药剂量均为200 mg/kg,他克莫司给药剂量均为0.945 mg/kg。末次灌胃他克莫司后0、5、15、30 min和1、2、3、4、6、8、12 h,分别从各组大鼠的眼眶后静脉丛取血约0.3 m L,采用液相色谱-串联质谱法(LC-MS/MS)测定大鼠全血中他克莫司的浓度,应用DAS2.0软件进行药动学研究。结果:与第1组比较,第3组大鼠体内的他克莫司药动学参数AUC_(0-12) h、AUC0-∞和MRT0-12 h显著降低(P<0.05),第4组大鼠体内他克莫司所有药动学参数差异均无统计学意义(P>0.05);与第2组比较,第4组大鼠体内他克莫司药动学参数AUC_(0-12) h显著降低、CLz显著升高(P<0.05),第5组大鼠体内他克莫司所有药动学参数差异均无统计学意义(P>0.05)。结论:单次、多次灌胃盐酸小檗碱均对大鼠体内他克莫司的药动学有影响,表现在血药浓度有下降的趋势,需谨慎联用。     

维吾尔族与汉族肾移植术后患者他克莫司全血谷浓度的对比分析

目的对比分析维吾尔族与汉族肾移植术后患者不同时期他克莫司全血谷浓度特征,为他克莫司的临床个体化合理使用提供依据。方法应用Emit2000法测定21例维吾尔族与25例汉族肾移植术后患者共1142例次他克莫司全血谷浓度值,按族别、术后时间分组并进行统计分析。结果他克莫司全血谷浓度平均值在术后≤1、1—3、4～6、7～12及〉12个月时,在维吾尔族中为（13．494-4．16）、（11．75±3．87）、（8．09-1-2．19）、（7．17±3．41）及（5．74±2．93）μg·L～,在汉族中为（11．95±3．32）、（10．66±3．33）、（8．094-2．19）、（6．31±2．43）及（4．91±2．15）μg·L^-1。维吾尔族肾移植术后患者在不同时期内他克莫司平均谷浓度值与汉族比较差异均具有统计学意义（P〈0．05）。结论维吾尔族肾移植术后患者他克莫司全血谷浓度值高于汉族肾移植术后患者。     

Defining algorithms for efficient therapeutic drug monitoring of mycophenolate mofetil in heart transplant recipients

Pharmacokinetics of mycophenolate mofetil (MMF) show large interindividual variability. Concentration-controlled dosing of MMF based on routine therapeutic drug monitoring, which requires area under the concentration-time curve (mycophenolic acid [MPA]-AUC0-12h) determinations, is uncommon. Dose adjustments are based on predose concentrations (C0h) or side effects. The aim of this study was to compare C0h with postdose concentrations (C0.5h-C12h) and to develop practical methods for estimation of MPA-AUCs on the basis of a limited sampling strategy (LSS) in heart transplant recipients under MMF and tacrolimus maintenance immunosuppression. Full MPA-AUC0-12h profiles were generated by high-performance liquid chromatography in 28 patients. Statistical analysis for MPA-AUC0-12h was performed by a case resampling bootstrap method. Bland and Altmann analysis was performed to test agreement between "predicted AUC" and "measured AUC." C1h provided the highest coefficient of determination (r2 = 0.57) among the concentrations determined during the 12-hour interval, which were correlated with AUC. All other MPA levels were better surrogates of the MPA-AUC0-12h when compared with C0h (r2 = 0.14). The best estimation of MPA-AUC0-12h was achieved with four sampling points with the algorithm AUC = 1.25*C1h + 5.29*C4h + 2.90*C8h + 3.61*C10h (r2 = 0.95). Since LSS with four time points appeared unpractical, the authors prefer models with three or two points. To optimize practicability, LSS with sample points within the first 2 hours were evaluated resulting in the algorithms: AUC = 1.09*C0.5h + 1.19*C1h + 3.60*C2h (r2 = 0.84) and AUC = 1.65*C0.5h + 4.74*C2h (r2 = 0.75) for three and two sample points, respectively. The results provide strong evidence for the use of either LSS or the use of time points other than C0h for therapeutic drug monitoring of MMF. Using the algorithms for the estimation of MPA-AUC0-12h based on LSS within the first 2 hours after MMF dosing may help to optimize treatment with MMF by individualization of dosing.     

Limited-sampling strategy for the prediction of boosted hard-gel saquinavir exposure at a dosage of 1000/100 mg twice daily in human immunodeficiency virus-infected individuals

Area under the concentration time curve (AUC) over a dosing interval is considered to be the best estimate of drug exposure in a patient. However, determination of this parameter is costly and often impractical, requiring multiple samples and a great deal of time and resources. A limited-sampling strategy (LSS) may overcome some of these issues, making pharmacokinetic studies easier to perform, particularly in a limited-resource setting. The aim of this work was to develop and validate a pragmatic LSS for the accurate and precise prediction of boosted saquinavir AUC0-12 (AUC over the 12-hour dosing interval) at a dosage of 1000/100 mg twice daily. Pharmacokinetic data were obtained from 34 human immunodeficiency virus (HIV)-infected individuals stable on saquinavir/ritonavir-containing therapy, randomly split into two sets (n = 17 per set). One set was used to construct prediction models using univariate and multivariate analysis (development set), and the second was used to determine the predictive performance of the models (validation set). For single samples, 6- and 10-hour concentrations correlated best with saquinavir AUC0-12 (r2: 0.913 and 0.911, respectively), yet all single samples failed to produce precise and unbiased predictions. However, combinations at 2, 6; 0, 2, 6; 0, 4, 10; 0, 4, 12; and 2, 4, 6 hours achieved good predictive performances, and both precise [root mean squared relative prediction error (%RMSE): 6.4% to 11.9%] and unbiased [mean relative prediction error (%MPE), 95% CI: -2.7%, (-0.8)-2.7 to 1.6%, (-1.8)-4.7] estimations of saquinavir AUC0-12. Of these models, concentrations obtained at 0, 2, 6 and 2, 4, 6 hours are more practical in a clinical setting and are therefore the LSS with most potential. Provided that the technique is validated in specific patient populations, an LSS approach is a potentially useful tool to evaluate the AUC0-12 of saquinavir in resource-limited settings, reducing both costs and volumes of blood taken. It may also aid the choice of sampling times for population analysis.     

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.

     

Investigation on pharmacokinetics of mycophenolic acid in Chinese adult renal transplant patients

AIMS: To characterize the pharmacokinetics of mycophenolic acid (MPA) in Chinese renal transplant patients. METHODS: Thirty-one renal transplant patients (17 male, 14 female) receiving mycophenolate mofetil (MMF) 1.0 g twice daily were included in this study. A pharmacokinetic study was performed during an interval in dosing after steady state had been reached within 2 months after transplantation. The plasma MPA concentration were measured by high-performance liquid chromatography (HPLC) at 0.5, 1, 1.5, 2, 4, 6, 8, 10 and 12 h after the administration of a single dose. Pharmacokinetic parameters were calculated with 3P97 software. SAS software was used for statistical analysis. Multiple linear regression analysis was used to determine limited sampling approaches. RESULTS: The mean peak plasma concentration (C(max)) and area under the concentration-time curve (AUC(0-12)) were 19.67 +/- 8.21 microg ml(-1) and 52.16 +/- 12.50 microg h ml(-1), but there was large variability in these pharmacokinetic parameters. Regression analysis between each plasma concentration and AUC for the limited sampling strategy of MMF therapeutic drug monitoring demonstrated that each of the concentrations at 0.5, 1, 4 and 10 h was positively correlated with AUC (r = 0.60, P = 0.0004; r = 0.60, P = 0.0003; r = 0.61, P = 0.0003; r = 0.64, P = 0.0001, respectively). The combined use of these four samples explained over 90% of the variance in the total (nine-point) AUC(0-12). A formula was obtained for the assessment of MPA AUC based on four samples: MPA AUC = 12.61 + 0.37 x C(0.5) + 0.49 x C(1) + 3.22 x C(4) + 8.17 x C(10). CONCLUSIONS: Chinese renal transplant patients had higher median AUCs than caucasians and African-Americans. As in other studies, there was large interindividual variability. A limited four-point AUC was in good agreement with the 12-h AUC and provided the basis of a predictive formula.     

Carbamazepine: a bioequivalence study and limited sampling modeling

OBJECTIVES: To assess the bioequivalence of 2 formulations of carbamazepine and to develop and validate limited sampling strategy (LSS) models for estimating the area under the plasma concentration-time curve (AUC0-infinity) and the peak plasma concentration (Cmax) of carbamazepine. METHODS: Twenty-four (12 men, 12 women) healthy volunteers received single oral doses (400 mg) of carbamazepine, as reference and test conventional-release formulations, in a standard 2-sequence, 2-period crossover design. Bioequivalence assessment was based on the individual ratios of log-transformed values of AUC0-infinity and Cmax LSS modeling was developed in a training set of 12 randomly assigned volunteers and was validated on the other 12 subjects (validation set). RESULTS: Carbamazepine AUC0-infinity and Cmax can be accurately predicted (R2 = 0.89 - 0.95, precision = 2.6 - 7.2%) by single-point (72 h) and 2-point LSS models (6, 32 h), respectively. Bioequivalence assessments based on LSS-derived AUC0-infinity and Cmax provided results similar to those obtained using all the concentration-in-plasma data points, and indicated that the 2 formulations are bioequivalent. CONCLUSION: One-and 2-point LSS models provided accurate estimates of carbamazepine's AUC0-infinity and Cmax, and allowed correct assessment of bioequivalence between the formulations studied.     

Flexible limited sampling model for monitoring tacrolimus in stable patients having undergone liver transplantation with samples 4 to 6 hours after dosing is superior to trough concentration

Trough (C0) monitoring is not optimal for therapeutic drug monitoring of tacrolimus. To better estimate systemic exposure of tacrolimus and achieve clinical benefit, an improved therapeutic drug monitoring strategy should be developed. The authors examined which single and combination of time points best estimated the empiric "gold standard" AUC0-12h and developed and validated a new, flexible, and accurate limited sampling model for monitoring tacrolimus in patients having undergone liver transplantation. Twenty-three stable patients with full AUC0-12h were divided into two groups based on area under the concentration-time curve/dose. With multiple regression analysis, limited sampling formulae were derived and population-pharmacokinetic-based limited sampling models were developed and validated. A regression analysis was performed between either area under the concentration-time curves calculated with formulae or models with the reference trapezoidal AUC0-12h. Both formulae and models based on single samples C4-C6 (r2 = 0.94 [MPE/MAPE 0/7]-0.90 [2/8] and 0.97 [0/7]-0.97 [1/5]) showed excellent performance. The calculated area under the concentration-time curve target range for tacrolimus was 90 to 130 h*microg/L. Multiple point sampling performed better, especially when using models (r2 > 0.94). C0 was a less precise predictor of AUC0-12h compared with both formulae and models (r2's 0.68 [5/17] and 0.87 [2/14]). In conclusion, trough concentration monitoring is not an accurate method for assessing systemic exposure to tacrolimus in stable patients having undergone liver transplantation. This new limited sampling model, based on single time points C4-C6, shows excellent performance in estimating the AUC0-12h.     

Evaluation of a limited sampling strategy to estimate area under the curve of tacrolimus in adult renal transplant patients

Limited sampling strategies have been developed to predict full AUCs. The goal of this study was to develop a limited sampling strategy to estimate the AUC of tacrolimus in adult renal transplant patients and to evaluate its predictive performance in an independent patient population. A total of 27 tacrolimus pharmacokinetic profiles were studied. Blood samples were collected before the dose (0) and at 0.5, 1, 2, 4, 6, 8, and 12 hours postdose. The study was divided into 2 phases. In phase 1, the goal was to obtain a sampling strategy from 14 pharmacokinetic profiles. In phase 2, the bias and precision of the model were evaluated in another 13 pharmacokinetic profiles. The best correlation was achieved at 4 hours after dose (r(2) = 0.790). Stepwise multiple regression analysis determined that the abbreviated AUC at 0, 1, and 4 hours could accurately predict total AUC (r(2) = 0.965). The following formula was developed: AUC = 8.90 + 4.0C0h+ 1.77C1h + 5.47C4h. No significant differences were found between calculated and estimated AUC (165.6 +/- 41.1 and 166.7 +/- 43.2 ng.h/mL, respectively). The mean prediction error (MPE), the relative prediction error (PE), and the mean squared error (MSE) were 0.48 ng.h/mL, 0.16%, and 40.0 ng.h/mL, respectively. The limited sampling with use of the 3 levels at 0, 1, and 4 hours postdose provides accurate, reliable determination of tacrolimus AUC in renal transplant patients.     

Chronopharmacokinetics of tacrolimus in kidney transplant recipients: occurrence of acute rejection

The circadian variation of clinical pharmacokinetics of tacrolimus was studied using 16 adult renal transplant recipients 1 month after the operation. The recipients were administered tacrolimus twice a day (9 a.m. and 9 p.m.), and whole-blood samples were obtained just prior to and 1, 2, 3, 6, 9, and 12 hours after oral administration. Histological specimens of transplant kidney were collected by an allograft core biopsy on day 28 after the transplantation. There were no circadian changes in the area under the concentration-time curve (AUC0-12) (214 ng.h/mL during daytime vs. 223 ng.h/mL during nighttime) resulting from morning and night doses. A slight delay in mean residence time (MRT0-12) and time to the peak concentration (tmax) was found after night doses, but there was no statistical significance. Three patients (18.8%) had a clinical acute rejection (AR) episode 4 to 6 weeks after transplantation, and AUC0-12 at nighttime was significantly lower (18.4% on average) in patients with AR in comparison to those without AR. There was no statistical significance in maximum concentration (Cmax) or morning/night trough levels between patients with and without AR. In regard to the correlation between tacrolimus concentrations in each sampling time and AUC0-12, the morning trough concentrations were less predictable for daytime AUC0-12 (r2 = 0.125), but there was a weak correlation to nighttime AUC0-12 (r2 = 0.424). Tacrolimus concentrations at 2, 3, and 6 hours after the morning dose (C2, C3, and C6) had a good correlation against daytime AUC. The results of this study indicate that the variance on the clinical pharmacokinetics of tacrolimus between daytime and nighttime in renal transplant patients is not significant, while the lower nighttime AUC corresponded to the occurrence of AR.     

Cyclosporin A absorption profiles in pediatric renal transplant recipients predict the risk of acute rejection

The current focus of cyclosporin A (CsA) monitoring in adult transplantation for optimized immunosuppression is on the early portion of the CsA area under the concentration-time curve (AUC), particularly in the first 4 hours postdose, designated as AUC(0-4), and on the blood concentration 2 hours postdose (C2) as a highly predictive marker for AUC(0-4). Because data in pediatric patients are scarce, full-time (12 hours) and absorption profiles of CsA were analyzed in relation to CsA effectiveness in 61 pediatric renal transplant recipients aged 3.2 to 17.4 years on an immunosuppressive triple regimen with CsA, mycophenolate mofetil, and methylprednisolone. CsA dosing was based on body surface area and adjusted to CsA trough levels. Pharmacokinetic (PK) profiles were obtained 1 and 3 weeks (initial period) and 3 and 6 months posttransplant (stable period). Patients with an AUC(0-4) < 4400 microg x h/L at both PK sampling periods in the first 3 weeks posttransplant had an adjusted relative risk of 48.4% to suffer an acute rejection episode (ARE), whereas in patients with at least 1 AUC0-4 above this threshold, the adjusted relative risk for an ARE was only 13.1% (P < 0.02). The single PK parameters C0 or C2 did not discriminate between patients with and without acute rejection. The PK parameters C1.25 (r2 = 0.64) or C2 (r2 = 0.60) showed a stronger relationship to the absorption profile (AUC(0-4)) than C0 (r2 = 0.15). An abbreviated profile consisting of the PK variables C(0.5;2) or C(0;0.5;2) showed the closest correlation to the absorption profile (r2 = 0.89) and the lowest percentage prediction error. These data indicate that absorption profiling in pediatric renal transplant recipients has the potential to optimize immunosuppressive therapy with CsA.