基于非线性混合效应模型法建立的拉莫三嗪群体药动学模型研究进展

目的 探讨拉莫三嗪在不同群体中的群体药动学特征及其影响因素,为建立更精准的群体药动学模型提供参考。方法 系统检索PubMed、Embase、Web of Science、Science Direct及Cochrane Library数据库中采用非线性混合效应模型(NONMEM)法在人体内进行的拉莫三嗪群体药动学的研究,时间为1995年1月—2021年7月。结果 共纳入研究20项,其中17项研究将拉莫三嗪的药动学特征描述为一室模型结构。伴随抗癫痫药物、体质量和基因多态性被认为是影响拉莫三嗪清除的3个最常见显著协变量,其他显著协变量研究较少。结论 在建立新的拉莫三嗪群体药动学模型时,应考虑合用其他抗癫痫药物、体质量、基因型等重要协变量因素;对于妊娠妇女等特殊人群,应纳入特殊人群的生理病理特征作为协变量因素,同时进行内部验证和外部验证增加模型的普适性。     

非线性混合效应模型法估算癫痫患者卡马西平的群体药动学参数

目的:建立癫痫患者卡马西平(CBZ)的群体药动学(PPK)模型。方法:采集我院服用CBZ的270例门诊癫痫患者的稳态血药浓度数据(共316个样本)以及患者相关资料数据。应用非线性混合效应模型(NONMEM)法估算癫痫患者CBZ的PPK参数值,建立PPK模型。并运用自举法(Bootstrap)验证模型的可靠性。结果:年龄(AGE)、每日服药剂量(DKG)、体质量(BW)均为CBZ清除率(CL)的影响因素。最终模型:当AGE≤14岁时,CL(L/h)=[2.55+0.013×(AGE-15)]×(DKG/0.011)0.443×(BW/40)0.392;AGE>14岁时,CL(L/h)=2.55×(DKG/0.011)0.443×(BW/40)0.392。表观分布容积(Vd)=85L。经Bootstrap法验证,本模型稳定、可靠。结论:用NONMEM软件成功建立我院癫痫患者服用CBZ的PPK模型。根据本院癫痫患者的PPK模型,结合患者DKG、BW和合并用药可估算其CL,优化临床个体化用药方案。     

非线性混合效应模型法研究卡马西平在癫痫儿童中的群体药动学

目的:考察卡马西平(CBZ)在癫痫儿童中的群体药动学参数。方法:采集我院的866例儿童癫痫患者服用CBZ常规治疗及监测的资料数据,利用Michaelis-Menten一级消除药物动力学模型,非线性混合效应模型程序估算癫痫儿童服用CBZ的群体药动学参数。结果:癫痫儿童卡马西平群体药动学主要参数Ke、Vd、CL在单用CBZ组分别为0.091h-1、0.502L.kg-1和0.046L.h-1.kg-1;性别、身高以及合并氯硝西泮、妥吡酯对CBZ清除率未见明显影响;儿童年龄、体质量、肝肾功能异常以及合并丙戊酸、苯巴比妥、苯妥因为CBZ清除率影响的重要因素,并且均增加CBZ的清除率。结论:根据癫痫儿童的群体药动学模型,结合患儿的年龄、体质量、肝肾功能、服药剂量以及合并用药等资料,估算其清除率,预测患儿体内的药物浓度,制定个体化给药方案。     

非线性混合效应模型法在卡马西平治疗儿童癫痫中的群体药动学应用

目的探讨非线性混合效应模型法在卡马西平治疗儿童癫痫中的群体药动学应用。方法选取2010年1月至2011年6月我院收治的癫痫患儿共180例,采用非线性混合效应模型法估算儿童癫痫卡马西平的群体药动学参数,并建立群体药动学模型。结果年龄、体质量及每日服药剂量均为卡马西平清除率的影响因素。经自举法验证,本模型可靠、稳定。结论用NONMEM软件成功建立我院癫痫患者服用卡马西平的PPK模型。根据我院癫痫患者的PPK模型,结合患者年龄、体质重和合并用药可估算其清除率,优化临床个体化用药方案。     

基于NONMEN法建立万古霉素新生儿群体药动学模型

目的:建立新生儿万古霉素群体药动学模型,为临床个体化给药方案提供参考。方法:回顾性收集80例静脉使用万古霉素新生儿的170个稳态血药浓度数据及临床资料,运用非线性混合效应模型(NONMEM),建立新生儿万古霉素群体药动学(PPK)模型;考察各项协变量对药动学参数的影响,对最终模型进行拟合优度、自举法(Bootstrap)及正态预测分布误差法(NPDE)验证。利用蒙特卡洛法评估患儿在不同给药方案下的血药浓度范围。结果:一室模型能较好地拟合万古霉素体内过程,清除率(CL)和表观分布容积(V)的群体典型值分别为0.297L·h-1和2.230L,表观分布容积对CL有显著影响。拟合优度、Bootstrap和NPDE表明最终模型稳定、预测结果可靠。建立不同体质量范围新生儿万古霉素初始剂量推荐表。结论:本研究建立的新生儿万古霉素PPK模型稳定可靠,可为优化新生儿给药方案提供依据。     

基于NONMEM法建立紫杉醇群体药动学模型

目的：建立国人紫杉醇（paclitaxel,PTX）群体药动学（population pharmacokinetic,PPK）模型,为制定个体化给药方案提供理论支持。方法：收集138例接受紫杉醇治疗的肿瘤患者（建模组105例,验证组33例）210个血样,HPLC法测定紫杉醇血药浓度,PCR-RFLP法检测MDR1 C3435T。应用非线性混合效应模型（NONMEM）法,考察MDR1 C3435T基因多态性、合并用药及病理生理因素对紫杉醇药动学参数的影响,建立紫杉醇PPK模型。对模型进行拟合优度诊断、自举法（Bootstrap）内部验证,正态预测分布误差法（NPDE）及外部验证考察模型预测能力。结果：紫杉醇清除率（CL）和表观分布容积（V_d）的群体典型值分别为64.7 L·h^-1和1 240 L,患者内生肌酐清除率（CLcr）和给药速率（RATE）显著影响紫杉醇清除率。最终模型Bootstrap法验证结果与模型计算值相符,拟合优度、准确度及精密度均优于最简模型。结论：紫杉醇PPK最终模型稳定、有效,可结合Bayesian反馈法为临床优化给药方案提供科学依据。     

中国健康人群西布曲明的群体药动学模型的建立

目的建立中国人群中西布曲明的群体药动学模型。方法 20例男性健康志愿者口服10 mg西布曲明,于服药后0～24 h采集13个采样点采血,采用已验证的HPLC法测定血药浓度。采用非线性混合效应模型（NONMEM）进行群体药动学分析,估算药动学参数。以直观预测检验（Visual predictive check,VPC）和正态预测分布误差（Normalized predictive distribution error,NPDE）,Bootstrap法进行模型性能评估。结果以有吸收时滞的一级吸收和消除的二房室模型为西布曲明的基础药动学模型。协变量筛选未见体重、年龄可显著影响模型参数。残差模型选择指数模型。西布曲明群体药动学参数V1,V2,CL,Q,Ka,Tlag的典型值分别为：7.85 L、2.03 L、1.08 L/h、0.289 L/h、1.95/h、0.187 h;个体间变异分别为42.8%、48.2%、38.5%、27.1%、56.8%和17.8%。Bootstrape、拟合优度、VPC和NPDE的评价结果均表明模型稳定,预测结果可靠。结论用非线性混合效应模型法建立的中国人群中西布曲明的群体药动学模型,结果稳定。     

新型肺靶向多西他赛脂质体在原位肺癌模型兔中的药动学研究

目的 研究新型肺靶向多西他赛脂质体(DTX-LP)在原位肺癌模型兔中的药动学行为。方法 采用超高效液相色谱-二级串联质谱(UPLC-MS/MS)法测定DTX在兔血浆中的含量,并进行方法学考察。采用胸腔微创穿刺术制备原位肺癌模型兔,然后随机分为多西他赛注射液(DTX-IN)组和DTX-LP组,耳缘静脉注射给予相应药物,给药剂量均为1.0 mg/kg(以DTX计),然后于5、15、30、60、90、120、240、480 min时取血,测定血浆中DTX浓度。采用DAS 3.3软件进行拟合与分析,并计算药动学参数。结果本研究所用UPLC-MS/MS法的准确度、精密度良好,符合生物样品分析要求。与DTX-IN组比较,DTX-LP组的药-时曲线趋势较平缓,各时间点的血药浓度更低,c_max、t_1/2、AUC_{0→480 min}、AUC_0→∞均显著降低(P<0.05)。结论 DTX-LP在血浆中的暴露量较DTX-IN降低,提示其能快速地从体循环中分布到肺靶器官。     

NONMEM法建立大鼠外周和中枢左旋多巴群体药动学模型

目的:建立大鼠左旋多巴(levodopa, LD)群体药动学模型,考察LD药动学参数的影响因素。方法:14只大鼠随机分为高、低两个剂量组,单次灌胃给予多巴丝肼片。采用脑微透析技术收集大鼠纹状体细胞外液透析液,同时采集外周血;高效液相色谱-电化学法测定透析液及血浆LD浓度,并利用非线性混合效应模型(Nonlinear mixed effect model, NONMEM)进行群体药动学数据分析。结果:建立了包含大鼠个体间变异、个体自身变异及体质量、给药剂量等固定效应参数的统计学模型,原始数据估算的参数值均位于Bootstrap估算参数值的2.5%~97.5%范围内,视觉预测评估法显示建模大鼠外周血和中枢纹状体LD浓度基本位于90%百分位数范围之内,所建立的最终模型稳定、有效、且有较强的预测能力。体质量可影响LD药动参数K₃₂。结论:建立的群体药动学模型能较好地描述LD在大鼠中枢及外周血的药动学特点。大鼠给药剂量对LD药动参数无影响,体质量可影响LD药动参数。     

丙戊酸清除率的群体药动学模型的建立

目的:建立中国癫痫病患者丙戊酸清除率的群体药动学模型.方法:前瞻性收集上海、北京两地4所医院服用丙戊酸的350名患者的稳态血药浓度(n=435).数据分析采用非线性混合效应模型.结果:最终模型为:CL(L·h-1)=0.0422·Dose·BSA0.269·1.41(如果合用卡马西平,否则为1)·1.37(如果合用苯妥因,否则为1)·(0.00735·PBS 0.807)(如果合用苯巴比妥,否则为1)·(Dose/950)(如果Dose大于950 mg·m-2·d-1,否则为1)·1.21(如果BSA大于1.7 m2,否则为1)·1.24(如果年龄小于6,否则为1).上式中Dose为日剂量(mg·m-2·d-1);BSA为体表面积(m2);PBS为苯巴比妥的日剂量(mg·m-2·d-1).结论:根据患者的生理用药资料,结合上述模型,可估算其清除率,为制定给药方案提供依据.     

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.     

Population pharmacokinetics of gentamicin in neonates using a nonlinear, mixed-effects model.

The population pharmacokinetics of gentamicin in neonates was determined using a nonlinear, mixed-effects model (NONMEM). The final regression equations derived to estimate clearance (Cl) and volume of distribution (Vd) were Cl = 0.120 * (WT/2.4)1.36 L/hr and Vd = 0.429 * (WT) L. The interindividual variability (% CV) for clearance was 26.2% and for volume of distribution 15.9%. Intraindividual variability was 11.0%. In a separate group of 30 neonates, the predictive ability of the NONMEM-generated population variables was compared to the predictions from a standard two-stage population analysis. The trough concentrations predicted using NONMEM-generated parameters were significantly less biased and more precise; there were no significant differences between the methods in predicting peaks. NONMEM is a useful tool for determining population pharmacokinetics and appears to be consistent across populations using routine clinical data and limited observation.     

基于非线性混合效应模型的比较药动学分析方法研究

比较药动学研究贯穿药物研发的整个阶段,通过求算个体药动学参数,推测各处理因素间AUC、Cmax比值的90%置信区间,然后与事先设定的等效区间进行比较,最终判断各处理因素间是否等效,为用药剂量的合理调整提供依据。然而,很多比较药动学研究为稀疏采样设计,传统的统计矩法很难对个体药动学参数进行估计,此时需要借助群体药动学的计算方法,利用非线性混合效应模型进行计算。本研究在密集采样设计比较药动学研究实例基础之上,模拟稀疏采样过程,对稀疏数据采用非线性混合效应模型分析,原密集数据采用统计矩法分析,通过Bootstrap法1 000次重复抽样,最终比较两种方法所得参数的90%置信区间。结果表明非线性混合效应模型对稀疏数据处理结果可靠,与统计矩法计算结果一致,为此类比较药动学研究提供了参考。     

Clinical pharmacokinetics of docetaxel

Docetaxel (Taxotere), a semi-synthetic analog of paclitaxel (Taxol), is a promoter of microtubule polymerization leading to cell cycle arrest at G2/M, apoptosis and cytotoxicity. Docetaxel has significant activity in breast, non-small-cell lung, ovarian and head and neck cancers. Docetaxel has undergone phase I study in a number of schedules, including different infusion durations and various treatment cycles. Doses studied in adults have ranged from 5 to 145 mg/m2 and those in children from 55 to 235 mg/m2. The most frequently used regimen in adults is 100 mg/m2 every 3 weeks. A 1-hour infusion every 3 weeks has been favoured in phase II and III studies, and the disposition of docetaxel after such treatment is best described by a 3 compartment model with alpha, beta and gamma half-lives of 4.5 minutes, 38.3 minutes and 12.2 hours, respectively. The disposition of docetaxel appears to be linear, the area under the plasma concentration-time curve (AUC) increasing proportionately with dose. Docetaxel is widely distributed in tissues with a mean volume of distribution of 74 L/m2 after 100 mg/m2, every 3 weeks. The mean total body clearance after this schedule is approximately 22 L/h/m2, principally because of hepatic metabolism by the cytochrome P450 (CYP)3A4 system and biliary excretion into the faeces. Renal excretion is minimal (< 5%). Docetaxel is > 90% bound in plasma. Population pharmacokinetic studies of docetaxel have demonstrated that clearance is significantly decreased with age, decreased body surface area, increased concentrations of alpha 1-acid glycoproteinand albumin. Importantly, patients with elevated plasma levels of bilirubin and/or transaminases have a 12 to 27% decrease in docetaxel clearance and should receive reduced doses. Although docetaxel is metabolised by CYP3A4, phase I combination studies have not shown major evidence of significant interaction between docetaxel and other drugs metabolised by the same pathway. Nevertheless, care should be taken with the use of known CYP3A4 inhibitors such as erythromycin, ketoconazole and cyclosporin. Conversely, increased doses may be required for patients receiving therapy known to induce this cytochrome (e.g. anticonvulsants). Perliminary data suggest the erythromycin breath test, an indicator of CYP3A4 function, is a predictor of toxicity after treatment with docetaxel. Such methodologies may eventually enable clinicians to individualise doses of docetaxel for patients with cancer.     

The effect of Echinacea purpurea on the pharmacokinetics of docetaxel

Aims

The herbal medicine Echinacea purpurea (E. purpurea) has been shown to induce cytochrome P450 3A4 (CYP3A4) both in vitro and in humans. This study explored whether E. purpurea affects the pharmacokinetics of the CYP3A4 substrate docetaxel in cancer patients.

Methods

Ten evaluable cancer patients received docetaxel (135 mg, 60 min IV infusion) before intake of a commercially available E. purpurea extract (20 oral drops three times daily) and 3 weeks later after a 14 day supplementation period with E. purpurea. In both cycles, pharmacokinetic parameters of docetaxel were determined.

Results

Before and after supplementation with E. purpurea, the mean area under the plasma concentration–time curve of docetaxel was 3278 ± 1086 and 3480 ± 1285 ng ml⁻¹ h, respectively. This result was statistically not significant. Nonsignificant alterations were also observed for the elimination half-life (from 30.8 ± 19.7 to 25.6 ± 5.9 h, P = 0.56) and maximum plasma concentration of docetaxel (from 2224 ± 609 to 2097 ± 925 ng ml⁻¹, P = 0.30).

Conclusions

The multiple treatment of E. purpurea did not significantly alter the pharmacokinetics of docetaxel in this study. The applied E. purpurea product at the recommended dose may be combined safely with docetaxel in cancer patients.     

非线性混合效应模型估算环孢素在人体相对生物利用度和药动学参数

目的:用非线性混合效应模型(NONMEM)估算环孢素2种制剂在人体的相对生物利用度和药动学参数。方法: 20名男性志愿者随机、交叉单次口服环孢素微乳剂和普通乳剂500mg。HPLC法测定血药浓度。经典药动学方法和NONMEM法估算相对生物利用度和药动学参数。结果:用NONMEM法估算环孢素微乳剂生物利用度是普通乳剂的(209±s60) %;普通乳剂和微乳剂的V/F分别是(0. 30±0. 10), (0. 14±0. 06)L;Ka分别是0. 40±0. 11, 0. 9±0. 5;Ke分别是0. 16±0. 18, 0. 32±0. 13;K12分别是0. 23±0. 17, 0. 20±0. 17;K21分别是0. 021±0. 021, 0. 17±0. 08, 与传统方法相比基本一致。结论:NONMEM法为药物生物利用度评价和药动学参数计算提供一种简捷和快速的数据分析途径。     

权重配方模型的非线性混合效应分析:一个药物相互作用研究实例

目的:基于权重配方模型,采用非线性混合效应分析(NONMEM),对尿囊素、甲硝唑及地塞米松联合抗炎作用的组方合理性进行定量评价,结果与原算法比较。方法:实验数据为昆明种小鼠组织纤溶酶原激活物(t-PA)样品于405nm处的吸光度值,以权重配方模型为基本模型,组方间相互作用为固定效应,并考虑随机效应,通过NONMEM法建立最终模型,定量评价尿囊素、甲硝唑和地塞米松的联合抗炎作用及其交互作用。结果:基于整体效应的权重配方模型建立成功(P<0.001),X1X2(X1尿囊素,X2地塞米松)作为固定效应加入模型,最终模型中甲硝唑对整体药效作用不明显,尿囊素与地塞米松存在较强协同作用,都是主要起效组分,最优剂量配比尿囊素∶甲硝唑∶地塞米松=400∶131∶8.0(mg/kg,i.p.)。结论:采用NONMEM法,可全面考虑各组分间交互作用及个体间和个体内误差等随机效应,使权重配方模型更加严谨,与原算法比较,提供的信息量更大。     

在本院患者中建立伏立康唑群体药物代谢动力学模型的初步探索

目的利用非线性混合效应模型(NONMEM)法建立伏立康唑的群体药物代谢动力学(Pop PK)模型,探索影响伏立康唑体内处置的生理病理因素,为伏立康唑的临床个体化用药提供依据。方法回顾性地收集2014年5月到2015年4月中南大学湘雅二医院药学部治疗药物监测室监测的患者资料,从中挑选出55名患者,收集86个血药浓度数据点及其生理病理数据(包括人口学数据、实验室数据与合并用药情况),收集的数据使用Phoenix软件构建模型。结果 Pop PK研究最终得到的模型是:CL(m L·min-1)=1.63×[1+(Age-47)×(-0.01)]×[(1+(ALT-42)×0.001]×exp(0.05),V(L)=6.93×exp(1.37×10-7)。结论本研究得到的模型提示老年患者比年轻成年患者有着更低的伏立康唑清除率,丙氨酸转氨酶浓度较高的患者清除率较高,这一结果还需进一步验证。     

用NONMEM法建立西酞普兰群体药代动力学模型

目的建立中国人西酞普兰(抗抑郁药)的群体药代动力学(PPK)模型,为临床个体化给药提供参考。方法用群体药代动力学方法,对西酞普兰生物等效性研究中23例受试者的血药浓度和临床资料进行分析,用NON-MEM软件求算西酞普兰的PPK参数值,建立西酞普兰的PPK模型,并进行模型验证。结果经NONMEM法处理,所有因素中,年龄、体重以及CYP2C19基因型对中央隔室清除率有显著性的影响;体重对分布容积有显著性的影响。年龄和体重的增加对清除率影响分别为-0.39L·h-1.a-1和0.18L.h-1·kg-1。结论用NONMEM软件拟合获得的西酞普兰群体药代动力学最终模型,经验证稳定可靠。     

Population pharmacokinetics

The major strength of the population analysis approach is that useful information can be extracted from sparse data using blood samples and pharmacologic monitoring during routine safety and efficacy studies conducted during the development of a drug product. The results of these analyses may lead to integrated pharmacokinetic-pharmacodynamic models that can aid the clinician during the initiation and adjustment of therapeutic regimens. In some cases it may be possible to develop closed-loop control systems that monitor a drug concentration or a response and automatically adjust the drug administration rate. Overall, an increase in the safety and efficiency of drug use can be anticipated.