Teicoplanin population pharmacokinetic analysis in hospitalized patients

The goal of this study was to build a population pharmacokinetic (PK) model to characterize the population PK parameters in our hospitalized patients. Teicoplanin serum concentrations from clinical routine were used. Antibiotic dose history and blood collection times were recorded and analyzed with NONMEM-V. Demographic and biologic data creatinine clearance (CLcr), weight (WT), and albumin (Alb) were tested for inclusion as covariates in the basic model. Intraindividual and residual variability were modeled. One hundred seven sparse samples (mainly trough levels), from 79 patients, were included. A 2-compartment PK model characterized by clearance (CL), central compartment volume of distribution (Vc), intercompartment clearance, and steady-state volume of distribution (VSS) with first-order elimination adequately described the data. CLcr and WT significantly influenced teicoplanin CL (CL = 0.57[0.15]*(1+0.0048[0.39]*(CLcr - averageCLcr)*WT) L/h). VSS was not affected by any covariate (VSS = 50.2[0.13]L). A negative trend between Alb and individual VSS estimates was observed without statistical significance. In a new data set, bias and precision resulted in mean values of -3.24% and 9.42%, respectively. In conclusion, CLcr and WT are significant covariates on teicoplanin CL. Results from predictive accuracy and precision show the usefulness of this model for implementation in a therapeutic drug monitoring program in the near future.     

Population pharmacokinetics of total and unbound plasma cisplatin in adult patients

AIMS: To investigate the pharmacokinetics of unbound (ultrafilterable) and total plasma platinum using a population approach and to identify patient characteristics that may influence the disposition of the drug. METHODS: Pharmacokinetic and demographic data were collected from adult patients treated with 30-min daily infusions of cisplatin for various malignancies. Unbound and total platinum concentration-time data were analysed using a nonlinear mixed effects model. RESULTS: Data from 43 patients were available for analysis. A linear two-compartment model best described total and unbound platinum plasma concentration-time data. The mean population estimates for total and unbound drug were, respectively, 0.68 and 35.5 l h(-1) for clearance and 21.1 and 23.4 l for central distribution volume (V(1)). Unbound clearance (CL) was dependent on body surface area (BSA) and creatinine clearance, and V(1) was dependent on BSA. The elimination rate constant for plasma-bound platinum (modelled as metabolite formation) was 0.014 h(-1). The pharmacokinetic parameter, f(m)/V(m), a measure of the clearance of unbound platinum due to irreversible plasma binding, was related to serum protein concentration and to the inverse of dose per m(2). The covariate modelling of CL, V(1) and f(m)/V(m) improved the intersubject variabilities associated with these parameters. The final pharmacokinetic models were validated using 200 bootstrap samples from the original datasets. CONCLUSIONS: The results support the conventional dose adjustment of cisplatin based on BSA. They also support the need for a dose reduction in case of renal insufficiency.     

Population pharmacokinetics of raltitrexed in patients with advanced solid tumours

AIMS: To investigate the population pharmacokinetics of raltitrexed in patients with advanced solid tumours and to identify patient covariates contributing to the interpatient variability in the pharmacokinetics of raltitrexed. METHODS: Patient covariate and concentration-time data were collected from patients receiving 0.1-4.5 mg m(-2) raltitrexed during the early clinical trials of raltitrexed. Data were fitted using nonlinear mixed effects modelling to generate population mean estimates for clearance (CL) and central volume of distribution (V). The relationship between individual estimates of the pharmacokinetic parameters and patient covariates was examined and the influence of significant covariates on the population parameter estimates and their variance was investigated using stepwise multiple linear regression. The performance of the developed model was tested using an independent validation dataset. All patient data were pooled in the total cohort to refine the population pharmacokinetic model for raltitrexed. RESULTS: three-compartment pharmacokinetic model was used to fit the concentration-time data of raltitrexed. Estimated creatinine clearance (CL(CR)) was found to influence significantly the CL of raltitrexed and explained 35% of variability in this parameter, whilst body weight (WT) and serum albumin concentrations (ALB) accounted for 56% of the variability in V. Satisfactory prediction (mean prediction error 0.17 micro g l(-1) and root mean square prediction error 4.99 micro g l(-1)) of the observed raltitrexed concentrations was obtained in the model validation step. The final population mean estimates were 2.17 l h(-1)[95% confidence interval (CI) 2.06, 2.28] and 6.36 l (95% CI 6.02, 6.70) for CL and V, respectively. Interpatient variability in the pharmacokinetic parameters was reduced (CL 28%, V 25%) when influential covariates were included in the final model. The following covariate relationships with raltitrexed parameters were described by the final population model: CL (l h(-1)) = 0.54 + 0.02 CL(CR) (ml min(-1)) and V (l) = 6.64 + 0.08 WT (kg) - 0.16 ALB (g l(-1)). CONCLUSIONS: A population pharmacokinetic model has been developed for raltitrexed in patients with advanced cancer. Pharmacokinetic parameters of raltitrexed are markedly influenced by the patient's renal function, body weight and serum albumin levels, which may be taken into account in dose individualization. The use of influential covariates to guide anticancer dosage selection may result in less variability in drug exposure and potentially a better clinical outcome.     

Pharmacokinetic variability of routinely administered bisoprolol in middle-aged and elderly Japanese patients

The nonlinear mixed effects model (NONMEM) was used to analyze the pharmacokinetics of routinely administered bisoprolol in middle-aged and elderly Japanese patients. The subjects consisted of 29 males and 11 females with a mean age of 63.5+/-10.1. Data on the plasma concentration of bisoprolol from 94 blood samples obtained at steady-state following repetitive oral administration were analyzed using the NONMEM program, where a one-compartment model with repetitive bolus dosing was parameterized in terms of oral clearance (CL/F) and apparent volume of distribution (V/F). Individual CL/F values were correlated with body weight (WT) and creatinine clearance (CLcr). The relation between CLcr and the CL/F of bisoprolol was not altered by the CYP2D6 and CYP2C19 genotypes, gender, or age. The mean CL/F value estimated with NONMEM was 0.0612.WT+1.15.CLcr (l/h), and the mean V/F value was 2.61.WT (l). The residual interindividual variability of CL/F and V/F were 22.0% and 12.6%, respectively. The pharmacokinetic variability of bisoprolol is small even in routinely treated Japanese patients, provided that both body weight and renal function are taken into account for the prediction of oral clearance of the drug.     

Population pharmacokinetics of higher-dose mizoribine in healthy male volunteers

The population pharmacokinetic parameters of mizoribine in healthy subjects were estimated using a nonlinear mixed effects model (NONMEM) program. Pharmacokinetic data for population analysis were obtained in the previous study, in which 24 healthy Caucasian male subjects participated in a single-dose (3, 6, 9, 12 mg/kg) study, and 12 subjects participated in a multiple-dose (6, 12 mg/kg/d) study. The mean value of the absorption lag time, absorption rate constant (KA), and apparent distribution volume (V/F) was estimated to be 0.349 h, 0.869 h-1, and 0.834 l/kg, respectively. Oral clearance (CL/F) was modeled with creatinine clearance (CLcr), and the mean value was estimated to be 1.93.CLcr l/h. In addition, pharmacokinetic parameters in individual 36 subjects were obtained from population estimates according to Bayes' theorem. Pharmacokinetic parameters (KA, V/F, and CL/F) in the single-dose study were almost constant at a dose range of 3-12 mg/kg, and were similar to those in the multiple-dose study. These findings indicated that the pharmacokinetics of mizoribine is well described by a simple one-compartment model with first-order absorption.     

肾移植患者西罗莫司的群体药动学研究

目的:建立中国肾移植患者西罗莫司的群体药动学模型,为实施个体化用药提供理论支持。方法:选择47名肾移植术后采用西罗莫司+泼尼松+环孢素或他克莫司或霉酚酸酯(MMF)三联免疫抑制治疗的患者为研究对象,回顾性收集47名患者服药后的101个西罗莫司稳态血药浓度及相应的试验室检查数据,运用Winnonmix药动学软件,采用非线性混合效应模型(NONMEM)分析体重、年龄、性别、给药剂量、合并用药、肌酐清除率等对药动学参数的影响。最终模型的验证采用Jackknife法进行内部验证。结果:西罗莫司符合无滞后时间的一级消除动力学一室模型。固定效应结果量子,合用MMF和体重可影响药物清除率。最终模型公式为:CL/F(L·h-1)=11.01×0.14MMF+0.089×W。CL/F和Vd/F的群体典型值分别是11.01L·h-1和3616L,个体间变异分别为62.82%和85.07%。观测值和预测值间的残差(SD)和相关系数(r)分别是1.0ng·mL-1和0.94。结论:所建立的群体药动学模型能较好地估算服用西罗莫司的肾移植患者的个体及群体药动学参数,对指导临床个体化用药具有重要意义。     

A population pharmacokinetic model for cefuroxime using cystatin C as a marker of renal function

AIMS: Since cefuroxime mainly is excreted by renal filtration, dosing is currently based on serum creatinine (Scr) or creatinine clearance (CLcr). However, it has been suggested that cystatin C (CysC) is superior to Scr as a marker of renal function. The aim of this prospective study was to develop a population model that describes the pharmacokinetics of cefuroxime and to investigate the usefulness of CysC as a covariate of the model parameters. METHODS: Ninety-seven patients were studied (CLcr range 6.5-115 ml min(-1)). Blood samples (n = 407) for the determination of cefuroxime were withdrawn according to a sparse data sampling schedule and analysed by liquid chromatography mass spectrometry. The population analysis was performed in NONMEM. RESULTS: A two-compartment model described the data well. The biomarkers Scr, CLcr and CysC were evaluated as covariates on clearance (CL). The model that included CysC generated the best fit. In the final population model CL was a function of CysC and body weight, whereas V(1) was only a function of body weight. Final parameter estimates (relative standard errors) were 6.00 (3.2%) l h(-1), 11.4 (5.3%) l and 5.11 (11%) l for CL, V(1) and V(2), respectively. CONCLUSIONS: Based on the results of the present study, and because CysC is practical to use in the clinic, it is suggested that individual dosing of cefuroxime may be based on CysC rather than on Scr or CLcr. Furthermore, our final population model may be useful as a tool when designing new dosing schedules for cefuroxime.     

Population pharmacokinetics of oxaliplatin in patients with metastatic cancer

Our aim was to develop a population pharmacokinetic model of ultrafilterable oxaliplatin in metastatic cancer patients. Oxaliplatin was administered by 2- or 4-h infusions, 50, 65, 75, 85, 100 or 130 mg/m2 to 56 patients. Blood samples were collected over 28 h. Plasma concentrations of ultrafilterable oxaliplatin were determined by flameless atomic absorption spectrophotometry. Population pharmacokinetic analysis was performed using a non-linear mixed-effects modeling method. Ultrafilterable oxaliplatin concentration-time profiles showed a secondary peak or a shoulder aspect post-infusion, attributed to the existence of an enterohepatic recirculation (EHR). They were best described by a two-compartment model incorporating an EHR component. Plasma clearance (CL) was related positively to body weight (BW) and negatively to serum creatinine (SCr), and was greater in male patients than in female patients. This covariate modeling resulted in a decrease in the interindividual variability for CL from 104 to 62%. The central distribution volume (V1) and inter-compartmental clearance (Q) were related to BW. Typical population estimates of CL, central distribution volume (V1), input rate constant into gallbladder (k1B) and lag time for drug reabsorption (TLAG) were 14.1 or 8.5 l/h (male or female patients), 24.9 l, 1.8 h-1 and 2.0 h, respectively. The final pharmacokinetic model was validated using 200 bootstrap samples of the original data. We conclude that a two-compartment with EHR model adequately described ultrafilterable oxaliplatin pharmacokinetics, explaining a secondary transient increase in concentration. This study identified combined-covariate-effects ultrafilterable oxaliplatin clearance, supporting dose adjustment of oxaliplatin based on BW, gender and corrected for SCr level, if drug exposure is thought to be related to therapeutic or toxic issues.     

Population pharmacokinetics of weekly docetaxel in patients with advanced cancer

AIMS: Previous pharmacokinetic studies of the 3-weekly regimen (100 mg m(-2) every 3 weeks) of docetaxel have shown that docetaxel clearance is affected by liver function, body surface area, age, serum alpha1-acid glycoprotein and cytochrome P450 3A4 (CYP3A4) activity. However, the pharmacokinetics of a weekly docetaxel (40 mg m(-2) week(-1)) schedule are not well characterized. The aims of this study were (a) to investigate the pharmacokinetics of docetaxel (40 mg m(-2) week(-1)) using sparse concentration-time data collected from patients with advanced cancer and (b) to utilize a population pharmacokinetic approach to identify patient covariates that significantly influence the clearance of docetaxel when administered according to this regimen. METHODS: A two-compartment pharmacokinetic model was used to describe the docetaxel concentration-time data from 54 patients with advanced cancer. The mean population and individual posterior Bayesian estimates of docetaxel clearance were estimated using P-PHARM. The relationships between docetaxel clearance and 21 covariates were investigated. This included estimates of CYP3A4 function in each patient using the erythromycin breath test (1/tmax). Significant covariates were included into the final population pharmacokinetic model. Pharmacokinetic models were validated using a data splitting approach with a dataset consisting of 16 patients. RESULTS: Significant relationships were found between docetaxel clearance and 1/tmax (erythromycin breath test parameter) and several of the liver function enzymes and CL was best described by the equation; CL = 21.51 + 217 (1/tmax) - 0.13 (ALT). This final population pharmacokinetic model provided both precise and unbiased predictions of docetaxel concentrations in a validation group of patients and an estimate of the population mean (95% confidence interval) clearance of docetaxel was 30.13 l h(-1) (12.54, 46.04 l h(-1)) with an intersubject variability 30%. CONCLUSIONS: A population pharmacokinetic model has been developed and validated for weekly docetaxel (40 mg m(-2)) in patients with advanced cancer. These results indicate that CYP3A4 activity and hepatic function have an impact on the pharmacokinetics of docetaxel when administered weekly.     

奈达铂（Nedaplatin）在大鼠体内的肾毒性和骨髓抑制的时辰毒理学研究

目的:以大鼠为实验对象,通过测定给药时间与奈达铂(Nedaplatin)诱发的肾毒性和骨髓抑制的关系,研究铂(Pt)衍生物奈达铂的时辰毒理.方法:于8:00或20:00通过尾静脉给S-D大鼠(n=8)注射奈达铂(5 mg/kg体重)或空白溶媒,给药间隔为7天.定期采血、采尿测定血清肌苷清除率和周边血中的中性粒细胞.最后一次给药后24小时,处死动物,采集肾脏和大腿骨用于Pt浓度测定和组织学检查.共给药6次.结果:20:00给药组的体重抑制明显高于8:00给药组,实验结束时,两实验组均有2只动物死亡.奈达铂诱发的骨髓抑制没有明显的给药时间相关性,但20:00给药组的肾毒性明显大于8:00给药组.肌苷清除率和肾组织损伤积分均与肾皮质中n的含量有很好的相关性.结论:奈达铂诱发的肾毒性和药物在组织中的蓄积与给药时间有很好的相关性,提示该类药物在临床使用过程中应注意给药时间的选择.     

Mixed-effects modelling of the interspecies pharmacokinetic scaling of pegylated human erythropoietin

The aim of this study was to develop a population pharmacokinetic model for interspecies allometric scaling of pegylated r-HuEPO (PEG-EPO) pharmacokinetics to man. A total of 927 serum concentrations from 193 rats, 6 rabbits, 34 monkeys, and 9 dogs obtained after a single dose of PEG-EPO, administered by the i.v. (dose range: 12.5–550 μg/kg) and s.c. (dose range: 12.5–500 μg/kg) routes, were pooled in this analysis. An open two-compartment model with first-order absorption and lag time (Tlag) and linear elimination from the central compartment was fitted to the data using the NONMEM V software. Body weight (WT) was used as a scaling factor and the effect of brain weight (BW), sex, and pregnancy status on the pharmacokinetic parameters was investigated. The final model was evaluated by means of a non-parametric bootstrap analysis and used to predict the PEG-EPO pharmacokinetic parameters in healthy male subjects. The systemic clearance (CL) in males was estimated to be 4.08WT1.030 × BW−0.345 ml/h. In females, the CL was 90.7% of the CL in males. The volumes of the central (Vc) and the peripheral (Vp) compartment were characterized as 57.8WT0.959 ml, and 48.1WT1.150 ml, respectively. Intercompartmental flow was estimated at 2.32WT0.930 ml/h. Absorption rate constant (Ka) was estimated at 0.0538WT−0.149. The absolute s.c. bioavailability F was calculated at 52.5, 80.2, and 49.4% in rat, monkey, and dog, respectively. The interindividual variability in the population pharmacokinetic parameters was fairly low (<35%). Non-parametric bootstrap confirmed the accuracy of the NONMEM estimates. The mean model predicted pharmacokinetic parameters in healthy male subjects of 70 kg were estimated at: CL: 26.2 ml/h; Vc: 3.6 l; Q: 286 l/h; Vp: 6.9 l, and Ka: 0.031 h⁻¹. The population pharmacokinetic model developed was appropriate to describe the time course of PEG-EPO serum concentrations and their variability in different species. The model predicted pharmacokinetics of PEG-EPO in humans suggest a less frequent dosing regimen relative to erythropoietin and darbepoetin, potentially leading to a simplification of anemia management.     

Pharmacokinetic modelling of total and unbound plasma carboplatin--a population study in 75 children

AIMS: A compartmental open model was developed to describe the relationship between plasma unbound (C.) and bound (CT) carboplatin concentrations. A population pharmacokinetic study was then undertaken to investigate the effect of demographic covariates on unbound and bound carboplatin clearance and volume parameters. METHODS: Carboplatin and demographic data were collected from 75 children (1-17 years old, 10 children with unilateral nephrectomy) treated using 1-hour daily infusions for various malignancies. Concentration-time data, C(U) and C(T), from children with rich data were used to develop the model. The data from all children were then simultaneously analyzed using a population approach. RESULTS: The average population values for total unbound carboplatin clearance, CL(U), and distribution volume of unbound carboplatin, VI, were 3.87 l/h and 6.26 l/h, respectively. The clearance of plasma-bound carboplatin was comparatively low, 0.11 l/h. CL(U) was dependent on weight, nephrectomy status and serum creatinine. A constant fraction of CL(U), 0.17 l/h, included the disappearance of unbound compound due to irreversible plasma binding. V1 was dependent on body weight. The unbound plasma carboplatin fraction (fu) was simulated and rapidly decreased with post-infusion time. CONCLUSIONS: The body weight was a better predictor for unbound carboplatin clearance than body surface area, and UNP and SCr caused a reduction in clearance of unbound carboplatin, as previously reported. The rate ofcarboplatin plasma binding was low and not dependent on demographic patient characteristics. The f(U) of plasma carboplatin could be predicted as a function of time, infusion rate and covariates affecting CL(U), weight, UNP and SCr.     

Pharmacokinetic modeling of valproate from clinical data in Serbian epileptic patients

The aim of this study was to define a population pharmacokinetic model that could estimate the clearance of valproate (VPA) in a Serbian epileptic population. For the analysis, 97 steady-state concentrations of VPA were used, collected from 93 patients with epilepsy during routine clinical care in our hospital. To build a final model, we selected the ADVAN1 program subroutine from the NONMEM library for estimating the drug clearance (CL) and determining the influence of different covariates. This is a one-compartment model with first-order elimination and without absorption. Estimation of the predictive performances of the final model was performed on a validation set consisting of 20 epileptic patients. Typical mean value of CL of VPA estimated by the base model in our population was 0.368 l h(-1). The results of the final model show that the CL of VPA increased linearly with total body weight (TBW) and patients' age, while carbamazepine (CBZ) comedication did not affect it significantly. Interindividual variability (coefficient of variation) for CL was 27.2%. Residual error, including intraindividual variability, was 29.68%. The results of the validation process and the analysis of prediction errors suggest a good predictive performance of the final population model. The defined model describes CL of VPA in terms of specific Serbian patient characteristics, using serum concentration data obtained from routine therapeutic drug monitoring.     

Pharmacokinetics of fluconazole in people with HIV infection: a population analysis

1 The population pharmacokinetics of fluconazole have been investigated in 113 male subjects with HIV infection and AIDS. Plasma concentration–time data (between 1 and 17 observations per dose) were collected from individuals as part of a pharmacokinetic investigation (13 subjects) or during routine fluconazole therapy (100 subjects) for the treatment or prophylaxis of fungal infection. 2 A one-compartment pharmacokinetic model was used to describe the disposition of fluconazole after oral and intravenous infusion doses. Population pharmacokinetic parameters were generated using the NONMEM and P-PHARM computer programs. 3 The population estimates (calculated using NONMEM) of fluconazole clearance and volume of distribution were 0.78 l h⁻¹ and 47.6 l, respectively. The intersubject variability for these parameters was 41% and 8%, respectively. The model-dependent estimate of the extent of absorption was 0.99 with an intersubject variability of 6%. Mean population estimates generated by NONMEM and P-PHARM were in close agreement. 4 Examination of the relationship between patient covariates and pharmacokinetic parameters indicated that intersubject variability in fluconazole clearance could in part be explained by the severity of disease (as indicated by CD4+T-lymphocyte count) and renal function (indicated by estimated creatinine clearance). Other pharmacokinetic parameters were unaffected by these covariates. 5 Fluconazole clearance (estimated using NONMEM) in subjects with a CD4+T-lymphocyte count less than and greater than 200 cells mm³ was 0.73 l h⁻¹ (95% CI ; 0.64–0.82 l h⁻¹) and 0.99 l h⁻¹ (95% CI ; 0.86–1.12 l h⁻¹), respectively. The regression model for fluconazole clearance that accounted for changes in renal function and disease severity was CL (l h⁻¹)=0.25 (33%)+0.0057 (32%)×CLcr (in ml min⁻¹)+0.00068 (10%)×CD4 cell count (in cells mm⁻³) where intersubject variability (expressed as %CV) is shown in brackets. 6 Based on pharmacokinetic considerations a reduction in the dose of fluconazole would appear to be warranted in people with HIV infection who are seriously ill or who have compromised renal function. However, the emergence of resistance to fluconazole must also be considered when thinking of dosage adjustments.     

Population pharmacokinetic estimation of tacrolimus apparent clearance in adult liver transplant recipients

The goal was to study the factors affecting tacrolimus apparent clearance (CL/F) in adult liver transplant recipients. Tacrolimus dose and concentration data (n = 694) were obtained from 67 liver transplant recipients (22 female and 45 male), and the data were analyzed using a nonlinear mixed-effect modeling (NONMEM) method. A 1-compartment pharmacokinetic model with first-order elimination, an absorption rate constant fixed at 4.5 hours, and first-order conditional estimation was used to describe tacrolimus disposition. The predictive performance of the final model was evaluated using data splitting and assessing bias and precision of the estimates. The population estimate of tacrolimus CL/F and apparent volume of distribution (V/F) were found to be 21.3 L/h (95% confidence interval, CI, 18.0-24.6 L/h) and 316.1 L (95% CI 133-495 L), respectively. Neither patient's age, weight, gender, nor markers of liver function influenced tacrolimus CL/F. The final model was TVCL = 21.3 + 9.8 x (1 - HEM) + 3.4 x (1 - ALB) - 2.1 x (1 - DIL) - 7.4 x (1 - FLU), where TVCL, typical estimate of apparent clearance, HEM = 0 if hematocrit <35%, otherwise 1; ALB = 0 if albumin <3.5 g/dL, otherwise 1; DIL = 0 if diltiazem is coadministered, otherwise 1; FLU = 0 if fluconazole is coadministered, otherwise 1. This study identified the factors that significantly affect tacrolimus disposition in adult liver transplant recipients during the early posttransplantation period. This information will be helpful to clinicians for dose individualization of tacrolimus in liver transplant recipients with different clinical conditions including anemia or hypoalbuminemia or in those patients receiving diltiazem or fluconazole.     

Non steady state and steady state PKS Bayesian forecasting and vancomycin pharmacokinetics in ICU adult patients.

The pharmacokinetics of vancomycin was investigated in adult ICU patients after the first administration and at steady state. Then the predictive performance of a two-compartment Bayesian forecasting program was assessed in these patients by using population-based parameters and three non steady state vancomycin concentrations as feedback information. Finally a prospective investigation was carried out to search potential covariates. At steady state, a significant decrease (around 30%) in clearance (CL) was observed, while creatinine clearance (CLcr) was stable and a significant increase (around 30%) in volume of distribution (V(SS)) was observed. A two-fold increase in elimination half-life was found. CL was weakly correlated with CLcr at onset of therapy and at steady state. The Bayesian program tended to overpredict vancomycin peak and trough concentrations. A larger mean prediction error and a poorer precision were observed when population-based parameter estimates were used (no feedback) compared to feedback prediction, but the differences were not significant. Mechanical ventilation and concurrent opioid therapy may be pertinent covariates of vancomycin pharmacokinetics. The current work has shown that vancomycin pharmacokinetics in ICU patients displayed a significant variability and a significant change in both clearance and distribution during the course of therapy. Further investigation is necessary to clarify these findings. Moreover, the use of the Bayesian forecasting PKS program in our patients led to a prediction with low bias but rather poor precision. This outcome highlights the need to implement a population modeling approach, to determine the vancomycin pharmacokinetic parameters and covariates in our ICU patients, and to apply this information to provide more accurate concentration predictions.     

Population pharmacokinetics of high-dose methotrexate in children with acute lymphoblastic leukaemia

OBJECTIVE: To develop and a priori validate a methotrexate population pharmacokinetic model in children with acute lymphoblastic leukaemia (ALL), receiving high-dose methotrexate followed by folinic acid rescue, identifying the covariates that could explain part of the pharmacokinetic variability of methotrexate. METHODS: The study was carried out in 49 children (aged 6 months to 17 years) who received high-dose methotrexate (3 g/m(2) per course) in long-term treatment. In an index group (37 individuals; 1236 methotrexate plasma concentrations), a population pharmacokinetic model was developed using a nonlinear mixed-effects model. The remaining patients' data (12 individuals; 278 methotrexate plasma concentrations) were used for model validation. Age, sex, total bodyweight (TBW), height, body surface area, lowest urine pH during infusion, serum creatinine, ALT, AST, folinic acid dose and length of rescue were analysed as possible covariates. The final predictive performance of the pharmacokinetic model was tested using standardised mean prediction errors. RESULTS: The final population pharmacokinetic model (two-compartmental) included only age and total bodyweight as influencing clearance (CL) and volume of distribution of central compartment (V(1)). For children aged < or =10 years: CL (L/h) = 0.287 . TBW(0.876); V(1) (L) = 0.465 . TBW, and for children aged >10 years: CL (L/h) = 0.149 . TBW; V(1) (L) = 0.437 . TBW. From the base to the final model, the inter-individual variabilities for CL and V(1) were significantly reduced in both age groups (30-50%). The coefficients of variation of the pharmacokinetic parameters were <30%, while residual and inter-occasional coefficients maintained values close to 40%. Validation of the proposed model revealed the suitability of the model. CONCLUSION: A methotrexate population pharmacokinetic model has been developed for ALL children. The proposed model could be used in Bayesian algorithms with a limited sampling strategy to estimate the systemic exposure of individual patients to methotrexate and adapt both folinic acid rescue and methotrexate dosing accordingly.     

左旋多巴中国人群药动学模型的建立

目的:建立中国人群左旋多巴/苄丝肼复合制剂中左旋多巴的群体药动学模型。方法:前瞻性收集服用多巴丝肼片的帕金森病(PD)门诊患者稳态谷浓度97例102个血样和健康志愿者13例153个密集血样,高效液相色谱-电化学(HPLC-ECD)法测定左旋多巴(LD)血药浓度。应用NONMEM软件进行群体药动学数据分析,Bootstrap重复抽样用于模型的内部验证。另收集20例PD患者22个血样点作为验证组进行模型外部验证,计算最简模型和最终模型对验证组的平均预测误差(MPE)和平均绝对误差(MAE)对模型进行外部验证。结果:数据采用一房室模型拟合,年龄(AGE)对LD清除率有显著影响,性别(SEX)、体质量(WT)、给药剂量(TAMT)、合并用药不影响LD的药动学参数。LD的基础模型为:CL(CL/F)(L.h-1)=18.2×EXP[ETA(1)],V(V/F)(L)=48.4,ka(h-1)=2.13×EXP[ETA(2)];最终模型为:CL(CL/F)(L.h-1)=17.9×(55/AGE)0.59×(EXP[ETA(1)],V(V/F)(L)=47.5,ka(h-1)=2.14×EXP[ETA(2)]。CL、V、ka的群体典型值分别为17.9 L.h-1、47.5 L、2.14 h-1。Bootstrap重复抽样显示所建立的最终模型稳定、可靠,最终模型对验证组的MPE和MAE较最简模型有显著改善,显示模型有效,且有一定代表性。结论:根据患者的生理用药资料,结合上述模型,可估算个体药动学参数,为临床个体化给药提供参考。     

Population pharmacokinetics of netilmicin in short-term prophylactic treatment

AIMS: To characterize the population pharmacokinetics of netilmicin, an aminoglycoside antibiotic, in adult urology patients and to develop a covariate model for improved dose titration. METHODS: Data from 62 adult patients (55 male, seven female), undergoing urological surgery and treated with netilmicin for short-term prophylaxis, were evaluated retrospectively. The group had (median, range) ages 68, 31-92 years, weights 72, 43-106 kg and heights 167, 148-182 cm. No patient showed renal impairment before netilmicin treatment (serum creatinine 相似文献   

Memantine pharmacotherapy: a naturalistic study using a population pharmacokinetic approach

BACKGROUND AND OBJECTIVE: Memantine plasma concentrations show considerable interindividual variability. High memantine plasma concentrations are associated with the occurrence of neuropsychiatric adverse effects such as confusion. The objective of the present study was, therefore, to investigate the reasons for the observed variability of the pharmacokinetics of memantine in a representative patient population and to explore patient covariates on drug disposition. SUBJECTS: Fifty-six ambulatory Western European patients aged 50-91 years. METHODS: This prospective study used a full population pharmacokinetic sampling design. After at least 11 days of continuous memantine intake, the patients provided pharmacokinetic profiles, with six measurements each over a 12-hour period, with a total of 335 serum memantine concentrations. Covariates considered for inclusion in the models were: subject demographic factors (age, total bodyweight, gender), laboratory tests (urinary pH), total daily dose of memantine, memantine formulation type, comedication eliminated via tubular secretion and smoking history. The model development was conducted in three sequential steps. First, an adequate basic structural model was chosen (e.g. a one-, two- or three-compartment pharmacokinetic model). The data were analysed to estimate population pharmacokinetic parameters with the nonlinear mixed-effects model computer program NONMEM. Second, the effects of covariates were investigated on post hoc estimates using multivariate statistics. Third, the covariates with significant effects in the second step were used to build a final covariate pharmacokinetic model, again using NONMEM. RESULTS: A two-compartment model with first-order absorption satisfactorily described memantine pharmacokinetics. In the final regression model, total bodyweight, memantine formulation type (solution vs tablets) and concomitant medication eliminated via tubular secretion were all important determinants of the apparent clearance (CL/F). The final regression model was: CL/F (L/h) = (1.92 + 0.048 x BW (kg)) x 0.530(FRM) x 0.769(CMD) where FRM = 1 for patients receiving memantine solution, otherwise FRM = 0; CMD = 1 for patients receiving a comedication eliminated via tubular secretion, otherwise CMD = 0; and BW is bodyweight. Compared with the basic model, the final population pharmacokinetic model explained 61% of the interindividual variance of the apparent clearance. CONCLUSIONS: The population pharmacokinetic model that was developed identified a set of sources of variability in the apparent clearance of memantine, which can be used as a reference in order to optimise memantine therapy in Western European patients.