Population pharmacokinetics of carbamazepine in Chinese epilepsy patients

AIM: To investigate the pharmacokinetic profile of carbamazepine (CBZ) in Chinese epilepsy patients. MATERIALS AND METHODS: Serum samples through concentrations at steady state (n = 687) were collected prospectively from 585 patients during routine clinical care. Data were analyzed by the non-linear mixed-effect modeling (NONMEM) technique with a one-compartment model of first-order absorption and elimination. RESULTS: The important determinants of clearance (CL) were total body weight (TBW); dose; patient age over 65 years (E); and comedication with phenytoin (PHT), phenobarbital (PB), or valproic acid (VPA) when VPA daily dose was greater than 18 mg/kg. The final pharmacokinetic model for relative CL and apparent distribution volume (V) were: Equation CONCLUSION: A population pharmacokinetic model was proposed to estimate the individual CL for Chinese patients receiving CBZ in terms of patient's dose, TBW, and comedications to establish a priori dosage regimens.     

Population pharmacokinetics of digoxin in Japanese patients: a 2-compartment pharmacokinetic model.

OBJECTIVE: To clarify the observed variability of digoxin disposition by performing a population pharmacokinetic analysis in a Japanese population. DESIGN: Retrospective analysis of clinical pharmacokinetic data. PATIENTS AND PARTICIPANTS: Data were obtained from 106 patients with heart failure and atrial fibrillation (43 males and 63 females). METHODS: Digoxin concentrations in serum were measured by fluorescence polarisation immunoassay. Population pharmacokinetic analysis was performed using a 2-compartment open pharmacokinetic model with the computer program NONMEM. RESULTS: 246 serum concentrations were obtained. Final pharmacokinetic parameters were: CL (L/h) = (0.036 x TBW + 0.112 x CL(CR)) x 0.77SPI x 0.784CCB, V1 = 1.83 L/kg, V2 = 22.6 L/kg and Q = 0.629 L/h/kg, where CL is total body clearance, V1 and V2 are the apparent volumes of distribution in the central and peripheral compartments, Q is intercompartmental clearance, TBW is total bodyweight (in kg), CL(CR) is creatinine clearance (in ml/min), SPI = 1 for concomitant administration of spironolactone (and zero otherwise) and CCB = 1 for concomitant administration of calcium antagonists (and zero otherwise). Concomitant administration of digoxin and spironolactone resulted in a 23% decrease in digoxin clearance. Concomitant administration of digoxin and calcium antagonists (diltiazem, nicardipine, nifedipine or verapamil) resulted in a 21.6% decrease in digoxin clearance. CONCLUSIONS: The estimated population parameter values may assist clinicians in the individualisation of digoxin dosage regimens.     

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.     

Population pharmacokinetics of tobramycin in hospitalized patients receiving once-daily dosing regimen

The population pharmacokinetics of tobramycin was investigated in a group of 327 adult hospitalized patients receiving once-daily administration of tobramycin at a dose of 7 mg kg(-1). The patients had an average age of 57+/-18 y and an average weight of 65+/-14 kg; 153 of the patients were female. Data, comprised of 575 serum concentrations, were analyzed using a nonlinear mixed-effect model (NONMEM) with a first-order conditional estimation method and were best described with a one-compartment model. The patient covariates including body weight, gender, age and creatinine clearance (CL(CR)) were added in a stepwise fashion to identify their potential influences on tobramycin pharmacokinetics. Results showed that tobramycin clearance (CL) was linearly correlated with CL(CR) (proportionality constant: 0.066+/-0.002 x CL(CR) (ml min(-1))) and the volume of distribution (Vd) was linearly related to body weight (proportionality constant: 0.40+/-0.024 x body weight (1 kg(-1))). The mean population estimates for CL and Vd were 4.53 l h(-1) and 27.3 l, respectively. The half-life of tobramycin was estimated to be 4.2 h. The inter-individual variability in CL and Vd were 37.0 and 28.5%, respectively. The residual error was 1.2 mg l(-1). Based on the results, optimal dosing intervals for renal impaired patients were calculated and were comparable with the intervals derived from the previous established nomogram.     

Population pharmacokinetics of haloperidol using routine clinical pharmacokinetic data in Japanese patients

OBJECTIVE: To clarify the observed variability of haloperidol disposition in patients with psychiatric disorders. DESIGN: Retrospective population pharmacokinetic study. PARTICIPANTS: 218 Japanese patients aged 16 to 82 years who provided 391 serum haloperidol concentrations. METHODS: Routine clinical pharmacokinetic data gathered from patients receiving haloperidol were analysed to estimate population pharmacokinetic parameters with the nonlinear mixed effects model (NONMEM) computer program. RESULTS: The final pharmacokinetic model was CL = 42.4 * (TBW/60)(0.655) * 0.814(AGE> or = 55) * (DOSE/200)(0.236) * 1.32(ANTIEP) and Vd = 34.4 * TBW * 0.336( AGE> or = 65), where CL is total body clearance (L/h), Vd is apparent volume of distribution (L), TBW is total bodyweight (kg), DOSE is daily dosage (microg/kg/day), ANTIEP = 1 for concomitant administration of antiepileptic drugs (phenobarbital, phenytoin or carbamazepine) and 0 otherwise, AGE > or = 55 = 1 for patient aged 55 years or over and 0 otherwise, and AGE > or = 65 = 1 for patient aged 65 years or over and 0 otherwise. Concomitant administration of haloperidol and antiepileptic drugs resulted in a 32% increase in haloperidol clearance. Patients aged 55 years or over showed an 18.6% reduction in clearance, and elderly patients aged 65 years or over showed a 66.4% reduction in apparent volume of distribution. Inclusion of terms for the concomitant administration of haloperidol and antiparkinsonian drugs (amantadine, bromocriptine, biperiden, trihexyphenidyl or mazaticol) or cytochrome P450 (CYP) 2D6 substrates (levomepromazine, perphenazine, thioridazine, amitriptyline or clomipramine) did not significantly improve the estimate of haloperidol clearance. CONCLUSION: Application of the findings in this study to patient care may permit selection of an appropriate initial maintenance dosage to achieve target haloperidol serum concentrations, thus enabling the clinician to achieve the desired therapeutic effect.     

A population pharmacokinetic approach to ceftazidime use in burn patients: influence of glomerular filtration, gender and mechanical ventilation

AIMS: The aim of this study was to evaluate the disposition of ceftazidime in burn patients using a population pharmacokinetic approach, and to identify the clinical and biological parameters influencing its pharmacokinetics. METHODS: The development of the pharmacokinetic model was based on 237 serum ceftazidime concentrations from 50 burn patients. The determination of the pharmacokinetic parameters and the selection of covariates were performed using a nonlinear mixed-effect modelling method. RESULTS: A two-compartment model with first order elimination incorporating a proportional error model best fitted the data. Ceftazidime clearance (CL, l h(-1)) was significantly correlated with creatinine clearance (CL(CR)), and the distribution volume of the peripheral compartment (V2, l) was correlated with gender, mechanical ventilation and the CL(CR). The final model was defined by the following equations: Ceftazidime clearance was 6.1 and 5.7 l h(-1) for mechanically ventilated males and females, respectively, and 7.2 and 6.6 l h(-1) for nonventilated patients. The total volume of distribution was 31.6 and 49.4 l for mechanically ventilated males and females, respectively, and 22.8 and 28.1 l h (-1)for nonventilated patients. CONCLUSIONS: We have shown that gender, mechanical ventilation and CL(CR) significantly influence the disposition of ceftazidime in burn patients. Interindividual variability in the pharmacokinetics of ceftazidime was significant and emphasizes the need for therapeutic monitoring.     

Clinical pharmacokinetics of phenobarbital in neonates.

Demographic and clinical pharmacokinetic data collected from term and preterm neonates who were treated with intravenous phenobarbital have been analysed to evaluate the role of patient characteristics in pharmacokinetic parameters. Significant relationships between total body weight (TBW) or body surface area (BSA) and volume of distribution (Vd) and total body clearance (CL) were found. Coefficients of determination were: 0.55 and 0.59 for Vd, and 0.76 and 0.72 for CL against TBW and BSA, respectively. In addition, significant relationships between height of the infants and volume of distribution of phenobarbital and total body clearance were observed. Coefficients of determination were 0.58 for Vd and 0.56 for CL. A weaker but significant correlation existed between gestational age and Vd or CL of phenobarbital. Coefficients of determination were 0.43 and 0.64, respectively. There was no correlation between volume of distribution per kg body weight or total body clearance per kg body weight and any patient parameter investigated. However, these latter pharmacokinetic parameters tended to decrease with increasing gestational age and height of the neonates. The results obtained were used to develop new loading and maintenance doses for phenobarbital in neonates based on total body weight and body surface area and based on height and gestational age for cases that weight is not available.     

Covariate effects on the apparent clearance of tacrolimus in paediatric liver transplant patients undergoing conversion therapy

OBJECTIVE: To analyse the influence of covariates on the apparent clearance (CL) of tacrolimus in paediatric liver transplant recipients being converted from cyclosporin to tacrolimus. DESIGN: Retrospective modelling study. PATIENTS and PARTICIPANTS: 18 children, 13 girls and 5 boys, aged 4 months to 16 years (median 9.1 years) who required conversion to tacrolimus because of acute or chronic rejection or cyclosporin toxicity. METHODS: 287 whole-blood tacrolimus concentrations from therapeutic drug monitoring were used to build a nonlinear mixed-effects population model (NONMEM program) for the apparent clearance of tacrolimus. Variables considered were age, total bodyweight (TBW), body surface area (BSA), time after initiation of treatment (T), gender, haematocrit (Hct), albumin (Alb), aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (gammaGT), alkaline phosphatase (ALP), bilirubin (BIL), creatinine clearance (CL(CR)) and dosage of concomitant corticosteroids (EST). RESUTLS: TBW, T, BIL and ALT were the covariates that displayed a significant influence on CL according to the final regression model: CL (L/h) = 10.4(TBW/70)3/4 x e(-0.00032 T) x e(-0.057 BIL) x (1 - 0.079 ALT). With this model, the estimates of the coefficients of variation were 24.3% and 29.5% for interpatient variability in CL and residual variability, respectively. CONCLUSIONS: The proposed model for tacrolimus CL can be applied for a priori dosage calculations, although the results should be used with caution because of the unexplained variability in the CL. We therefore recommended close monitoring of tacrolimus whole blood concentrations, especially within the first months of treatment. The best use of the model would be its application in dosage adjustment based on therapeutic drug monitoring and the Bayesian approach.     

Population pharmacokinetics of doxorubicin in Indian cancer patients using NONMEM

The population pharmacokinetics of doxorubicin were evaluated based on a mixed-effect model using the NONMEM (VI) program. Doxorubicin in plasma was measured using high-performance liquid chromatography. Plasma concentration measurements (85 plasma samples) of doxorubicin from 28 patients with cancer receiving doxorubicin (with other co-medication) ranging from 20–120?mg by infusion over 1–2?h were analyzed according to a two-compartment model both in FO and FOCE methods. Additive proportional error model was used to describe inter-individual and residual variability. The influence of covariates such as age, body surface area, gender, and clinical laboratory values (SGOT, SGPT) on total body clearance (CL) and volume of distribution (Vd) were examined. No covariate was found to affect the CL and Vd of unchanged doxorubicin. The CL and Vd estimated by FO method were 1.42?L/h and 51.1?L, respectively, and FOCE method are 1.43?L/h and 51.4?L, respectively. The inter-individual variability for CL and Vd and residual variability were 45.8%, 36%, and 12.6%, respectively. The population means and inter-individual and residual variability of pharmacokinetics of doxorubicin were evaluated using the NONMEM program. The results of this study show that the population pharmacokinetic approach could be useful to manage doxorubicin cardio toxicity using sparse data in a clinical setting.     

NONMEM法估算中国癫痫患者卡马西平的清除率

目的　考察中国癫痫患者卡马西平的群体药动学参数。方法　癫痫病例来自上海、北京两地 4所医院 ,采集服用卡马西平的 5 92例患者的稳态血药浓度 (n =70 3)。NONMEM程序估算分析时 ,采用一级吸收和消除的药动学模型并固定吸收速率、生物利用度和表观分布体积参数。结果　体重 (TBW )、剂量 (Dose)、合用丙戊酸钠 (VPA)且其日剂量大于 2 0mg·kg-1·d-1、苯妥英 (PHT)、苯巴比妥 (PB)和年龄大于 6 5岁的老年人 (ELDER)均为卡马西平清除率(CL)的影响因素。性别、合用氯硝西泮、妥吡酯不改变卡马西平的清除率。最终模型为 :CL(CL/F) (L/h) =1 32·Dose(g·kg-1·d-1) 0 42 1·TBW (kg) 0 .691·1 2 0 VPA·1 4 3PHT·1 14 PB·0 836 ELDER。讨论　根据中国癫痫患者的群体药动学模型 ,结合患者服用的剂量、体重和合并用药可估算其清除率 ,制定给药方案     

阿米卡星在严重败血症患者体内的药代动力学研究

目的：初步探讨25 mg·kg －1的阿米卡星在重症监护室（ICU）患者体内的药代动力学。方法纳入符合条件的30例革兰阴性（G －）败血症患者进行阿米卡星药物治疗研究,通过非房室模型计算每名患者的阿米卡星的药代动力学。结果阿米卡星在 G －败血症患者体内平均药物分布为（0．36±0．07）L·kg －1,平均血液清除率为（3．88±0．97）mL·min －1·kg －1。肌酐清除率与血清肌酸酐（SCr）相关性具有统计学意义。结论对 ICU 患者应用高剂量阿米卡星（≥25 mg·kg －1）需要考虑败血症对血液动力学的影响,需要密切监测败血症血液药物浓度变化,关键要考虑到重症患者体内药代动力学与普通人群是不同的。     

A population approach to the forecasting of amikacin plasma and urinary levels using a prescribed dosage regimen.

We retrospectively analyzed amikacin pharmacokinetics in 19 critically ill patients who received amikacin intravenously. Fourteen subjects (577 serum amikacin concentrations, 167 urine measurements) were studied to obtain data for population modeling, while 5 patients (267 serum amikacin concentrations, 68 urine measurements) were studied for the assessment of predictive performance. The population analysis was performed using serum and urine amikacin measurements; the renal clearance of amikacin was expressed as a function of creatinine clearance. A two-compartment model was fitted to the population data by using NONMEM. The population characteristics of the pharmacokinetic parameters (fixed and random effects) were estimated using the FOCE method. The population pharmacokinetic parameters with the interindividual variability (CV%) were as follows: slope (0.254, 126%) and intercept (3 l/h, 59.6%) of the linear model which relate the amikacin renal clearance to the creatinine clearance, initial volume of distribution (17.1 l, 22.2%), intercompartment clearance (5.22 l/h, 104%), steady state volume of distribution (55.2 l, 64.1%) and urinary elimination (67.5%, 36.3%). The Bayesian approach developed in this study accurately predicts amikacin concentrations in serum and urine and allows for the estimation of amikacin pharmacokinetic parameters, minimizing the risk of bias in the prediction.     

Evaluation of population pharmacokinetic models for amikacin dosage individualization in critically ill patients

Objectives The aim of this study was to evaluate the reliability for dosage individualization and Bayesian adaptive control of several literature‐retrieved amikacin population pharmacokinetic models in patients who were critically ill. Methods Four population pharmacokinetic models, three of them customized for critically‐ill patients, were applied using pharmacokinetic software to fifty‐one adult patients on conventional amikacin therapy admitted to the intensive care unit. An estimation of patient‐specific pharmacokinetic parameters for each model was obtained by retrospective analysis of the amikacin serum concentrations measured (n = 162) and different clinical covariates. The model performance for a priori estimation of the area under the serum concentration‐time curve (AUC) and maximum serum drug concentration (C_max) targets was obtained. Key findings Our results provided valuable confirmation of the clinical importance of the choice of population pharmacokinetic models when selecting amikacin dosages for patients who are critically ill. Significant differences in model performance were especially evident when only information concerning clinical covariates was used for dosage individualization and over the two most critical determinants of clinical efficacy of amikacin i.e. the AUC and C_max values. Conclusions Only a single amikacin serum level seemed necessary to diminish the influence of population model on dosage individualization.     

Population pharmacokinetics of meropenem in critically ill patients undergoing continuous renal replacement therapy

BACKGROUND AND OBJECTIVE: Meropenem is a carbapenem antibacterial frequently prescribed for the treatment of severe infections in critically ill patients, including those receiving continuous renal replacement therapy (CRRT). The objective of this study was to develop a population pharmacokinetic model of meropenem in critically ill patients undergoing CRRT. PATIENTS AND METHODS: A prospective, open-label study was conducted in 20 patients undergoing CRRT. Blood and dialysate-ultrafiltrate samples were obtained after administration of 500 mg, 1000 mg or 2000 mg of meropenem every 6 or 8 hours by intravenous infusion. The data were analysed under the population approach using NONMEM version V software. Age, bodyweight, dialysate plus ultrafiltrate flow, creatinine clearance (CL(CR)), the unbound drug fraction in plasma, the type of membrane, CRRT and the patient type (whether septic or severely polytraumatized) were the covariates studied. RESULTS: The pharmacokinetics of meropenem in plasma were best described by a two-compartment model. CL(CR) was found to have a significant correlation with the apparent total clearance (CL) of the drug during the development of the covariate model. However, the influence of CL(CR) on CL differed between septic and polytraumatized patients (CL = 6.63 + 0.064 x CL(CR) for septic patients and CL = 6.63 + 0.72 x CL(CR) for polytraumatized patients). The volume of distribution of the central compartment (V(1)) was also dependent on the patient type, with values of 15.7 L for septic patients and 69.5 L for polytraumatized patients. The population clearance was 15 L/h, and the population apparent volume of distribution of the peripheral compartment was 19.8 L. From the base to the final model, the interindividual variabilities in CL and the V(1) were significantly reduced. When computer simulations were carried out and efficacy indexes were calculated, it was shown that polytraumatized patients and septic patients with conserved renal function may not achieve adequate efficacy indexes to deal with specific infections. Continuous infusion of meropenem is recommended for critically septic patients and polytraumatized patients when pathogens with a minimum inhibitory concentration (MIC) of > or =4 mg/L are isolated. Infections caused by pathogens with an MIC of > or =8 mg/L should not be treated with meropenem in polytraumatized patients without or with moderate renal failure because excessive doses of meropenem would be necessary. CONCLUSION: A population pharmacokinetic model of meropenem in intensive care patients undergoing CRRT was developed and validated. CL(CR) and the patient type (whether septic or polytraumatized) were identified as significant covariates. The population pharmacokinetic model developed in the present study has been employed to recommend continuous infusion protocols in patients treated with CRRT.     

Population pharmacokinetic modelling and estimation of dosing strategy for NXY-059, a nitrone being developed for stroke

BACKGROUND AND OBJECTIVES: NXY-059 (disufenton sodium, Cerovive, a nitrone with neuroprotective and free radical trapping properties (in experimental stroke) is under development for the treatment of acute stroke. The objectives of this study were to develop a population pharmacokinetic model for NXY-059 in acute stroke patients and to estimate individualised dosing strategies for NXY-059 using preclinical pharmacological and clinical pharmacokinetic information and knowledge of characteristics of the patient population. METHODS: NXY-059 was given as a continuous intravenous infusion for 72 hours, including a 1-hour loading infusion. Maintenance infusion rates were individualised based on creatinine clearance (CL(CR)). Population pharmacokinetic models were derived using NONMEM software. Optimal dosing strategies, individualised based on CL(CR) or bodyweight, were estimated using the population pharmacokinetic models, empirical covariate distributions relevant for the target population, and a target definition. Dosing strategies were selected based on target fulfillment criteria and parsimony. PATIENTS: Pharmacokinetic data from 179 patients with acute ischaemic or haemorrhagic stroke, included in two clinical studies, were used for the analyses. Patients were aged 34-92 years with varying degrees of renal impairment (estimated CL(CR) 20-143 mL/min). MAIN OUTCOME MEASURES AND RESULTS: The final population model based on data from both studies comprised a two-compartment model with unexplained interpatient variability for clearance (23% coefficient of variation [CV]) and central volume of distribution (40% CV). Part of the variability in clearance and volume of distribution was explained by CL(CR) and bodyweight, respectively. Typical clearance was estimated to 4.54 L/h in a patient with CL(CR) of 70 mL/min. The preferred dosing strategy for NXY-059 comprised an initial loading infusion (the same for all patients) followed by an individualised maintenance infusion on the basis of CL(CR) (three dosing categories) with cut-off values (at which infusion rates are incremented or decremented) of 50 and 80 mL/min. CONCLUSION: The results illustrate how an individualised dosing strategy, given a pharmacokinetic target, for NXY-059 was successfully optimised through estimation using the increasing pharmacokinetic and pharmacodynamic knowledge during a clinical drug development programme. The chosen dosing strategy of NXY-059 provides an easily adapted treatment regimen for acute stroke, resulting in early achievement of target plasma concentrations.     

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.     

Pharmacokinetics of ceftazidime and cefepime in burn patients: the importance of age and creatinine clearance

AIM: The standard dosage recommendations for beta-lactam antibiotics can result in very low drug levels in intensive care (IC) patients and burn patients in the absence of renal dysfunction. We studied the pharmacokinetic parameters and serum concentrations of ceftazidime (CF) and cefepime (CE) in burn patients and analyzed the modifications according to clinical and biological parameters and in particular age and creatinine clearance. MATERIAL AND METHODS: Two pharmacokinetic studies were carried out with daily doses of 1 g x 6 for CF (n = 17) and 2 g x 3 for CE (n = 13). Creatinine clearance (CL(CR)) was both estimated and measured. Blood was sampled at steady state after an initial and a subsequent antibiotic dose. C(max) (maximal) and C(min) (minimal) concentrations were measured by HPLC. The influence of clinical and biological data was analyzed using ANOVA, ANCOVA and stepwise multiple linear regression. RESULTS: The ratio of C(min) to the low MIC break point (4 mg/l) was lower than 4 in 52% of subjects receiving CF and in 80% of subjects receiving CE. The C(min) of CF was correlated with measured CL(CR) and was higher in mechanically ventilated patients than in non-ventilated patients. The clearance of CF was correlated with age. The C(min) of CE was correlated with age and drug clearance with measured CL(CR). Therefore dosage adjustment of these drugs in burn patients needs to take into account age, measured creatinine clearance and the danger of low concentrations occurring when the creatinine clearance is greater than 120 ml x min(-1). CONCLUSION: In burn patients, the pharmacokinetic disposition of CF and CE was much more variable than in healthy subjects. Age and CL(CR) were predictors of the disposition of these antibiotics. Shortening the dosage interval or using continuous infusions will prevent low serum levels and keep trough levels above the MIC for longer periods of time. In view of the lack of a bedside measurement technique for ceftazidime and cefepime levels, we suggest a more frequent use of measured CL(CR) in order to attain efficacious clinical concentrations.     

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。     

Population Pharmacokinetics of Amikacin in Indian Pediatric Patients

The aim of this study was to describe population pharmacokinetics of amikacin in Indian pediatric population. Dosage adjustment based on individual pharmacokinetic parameters is of considerable importance for effective and safe use of drugs. Extensive work on amikacin and other aminoglycosides was carried out in different pediatric patient populations but no data are available in Indian pediatric patients. In the present study 74 steady state concentrations of amikacin were analyzed from 42 patients. Pharmacostatistical work was done by using NONMEM. The covariates evaluated in this study were age, body weight, height, and sex and creatinine clearance. The model found to best describe the data following FO method was: Clearance (CL)?=?θ1*(wt/14.2)*exp. (η1) and volume (V)?=?θ2*exp (η2) and following FOCE method was: Clearance (CL) = θ1*(age/5.38) + θ3*(wt/14.2)*exp.(η1) and volume(V) = θ2*exp(η2). The final model estimates of CL and V estimated by FO method were1.02 L/h and 4.55L respectively and by FOCE method were1.07L/hr and 4.91L respectively. These parameters are utilized for individualizing the loading and maintenance doses in pediatric patients.     

Pharmacokinetics of gentamicin in 957 patients with varying renal function dosed once daily

AIMS: 1. To determine the population pharmacokinetics of gentamicin in 957 patients with varying renal function dosed once daily. 2. To see if current starting doses for once daily aminoglycoside dosing are appropriate. 3. To test whether calculating creatinine clearance using an adjusted Cockcroft and Gault method (CLCr,adjusted ) was a better predictor of gentamicin clearance than the standard Cockcroft and Gault method (CLCr,unadjusted ). METHODS: Nine hundred and fifty-seven patients were dose-individualized for gentamicin using SeBA-GEN, a Bayesian dosing method. This method returns estimates of the values of gentamicin CL and V d from which the 24 h AUC can be estimated. The goal of therapy was to attain an AUC of 70-100 mg l-1 h depending on the severity of the infection. The population was divided into four groups of differing renal function. Linear regression analysis was performed to determine the relationship between V d and various indices of weight, and gentamicin CL and either CLCr,adjusted or CLCr,unadjusted. RESULTS: The mean V d (+/-s. d.) and CL (+/-s.d.) of gentamicin in our total population were 17.4 (+/-4.1) l and 4.0 (+/-1.8) l h-1, respectively. There was a decrease in V d with reducing renal function when comparing patients with normal renal function and patients with poor renal function. The lower of total body weight (TBW) and lean body weight (LBW), termed dosing weight (DWT), was a slightly better predictor of V d (r2=0.28) than either TBW (r2=0.21) or LBW (r2=0.21). CLCr,adjusted (r2=0.80) was a better predictor of gentamicin CL than CLCr, unadjusted (r2=0.57). CONCLUSIONS: The mean population values of V d and CL of gentamicin dosed once daily are similar to those described by others in relation to multiple daily dosing. Given that previous methods have been based on population values of V d and CL from multiple daily dosing, the currently recommended starting doses for once daily aminoglycoside dosing would seem appropriate. The V d reduced with decreasing renal function, with a maximum of 23% difference between patients with normal and poor renal function. The Cockcroft and Gault method of calculating creatinine clearance does not appear to perform well at low values of serum creatinine concentration. An adjustment of the Cockcroft and Gault method is proposed to allow for this.