N de Rouw, R Beunders, O Hartmann, J Schulte, R J Boosman, H J Derijks, D M Burger, M M van den Heuvel, L B Hilbrands, P Pickkers, R Ter Heine
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Therefore, the aim of this study was to investigate the performance of the kidney function biomarkers serum creatinine, cystatin C and pro-enkephalin in terms of predicting the elimination of pemetrexed.</p><p><strong>Methods: </strong>We performed a population pharmacokinetic analysis using a dataset from two clinical trials containing pharmacokinetic data of pemetrexed and measurements of all three biomarkers. A three-compartment model without covariates was fitted to the data and the obtained individual empirical Bayes estimates for pemetrexed clearance were considered the \"true\" values (Cl<sub>true</sub>). Subsequently, the following algorithms were tested as covariates for pemetrexed clearance: the Chronic Kidney Disease Epidemiology Collaboration equation using creatinine (CKD-EPI<sub>CR</sub>), cystatin C (CKD-EPI<sub>CYS</sub>), a combination of both (CKD-EPI<sub>CR-CYS</sub>), pro-enkephalin as an absolute value or in a combined algorithm with age and serum creatinine, and lastly, a combination of pro-enkephalin with cystatin C.</p><p><strong>Results: </strong>The dataset consisted of 66 subjects with paired observations for all three kidney function biomarkers. Inclusion of CKD-EPI<sub>CR-CYS</sub> as a covariate on pemetrexed clearance resulted in the best model fit, with the largest decrease in objective function (p < 0.00001) and explaining 35% of the total inter-individual variability in clearance. The predictive performance of the model to containing CKD-EPI<sub>CR-CYS</sub> to predict pemetrexed clearance was good with a normalized root mean squared error and mean prediction error of 19.9% and 1.2%, respectively.</p><p><strong>Conclusions: </strong>In conclusion, this study showed that the combined CKD-EPI<sub>CR-CYS</sub> performs best in terms predicting pharmacokinetics of pemetrexed. Despite the hypothesized disadvantages, creatinine remains to be a suitable and readily available marker to predict pemetrexed clearance in clinical practice.</p>","PeriodicalId":9556,"journal":{"name":"Cancer Chemotherapy and Pharmacology","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A comparison of the renal function biomarkers serum creatinine, pro-enkephalin and cystatin C to predict clearance of pemetrexed.\",\"authors\":\"N de Rouw, R Beunders, O Hartmann, J Schulte, R J Boosman, H J Derijks, D M Burger, M M van den Heuvel, L B Hilbrands, P Pickkers, R Ter Heine\",\"doi\":\"10.1007/s00280-024-04717-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>For drugs with a narrow therapeutic window, there is a delicate balance between efficacy and toxicity, thus it is pivotal to administer the right dose from the first administration onwards. Exposure of pemetrexed, a cytotoxic drug used in lung cancer treatment, is dictated by kidney function. To facilitate optimized dosing of pemetrexed, accurate prediction of drug clearance is pivotal. Therefore, the aim of this study was to investigate the performance of the kidney function biomarkers serum creatinine, cystatin C and pro-enkephalin in terms of predicting the elimination of pemetrexed.</p><p><strong>Methods: </strong>We performed a population pharmacokinetic analysis using a dataset from two clinical trials containing pharmacokinetic data of pemetrexed and measurements of all three biomarkers. A three-compartment model without covariates was fitted to the data and the obtained individual empirical Bayes estimates for pemetrexed clearance were considered the \\\"true\\\" values (Cl<sub>true</sub>). Subsequently, the following algorithms were tested as covariates for pemetrexed clearance: the Chronic Kidney Disease Epidemiology Collaboration equation using creatinine (CKD-EPI<sub>CR</sub>), cystatin C (CKD-EPI<sub>CYS</sub>), a combination of both (CKD-EPI<sub>CR-CYS</sub>), pro-enkephalin as an absolute value or in a combined algorithm with age and serum creatinine, and lastly, a combination of pro-enkephalin with cystatin C.</p><p><strong>Results: </strong>The dataset consisted of 66 subjects with paired observations for all three kidney function biomarkers. Inclusion of CKD-EPI<sub>CR-CYS</sub> as a covariate on pemetrexed clearance resulted in the best model fit, with the largest decrease in objective function (p < 0.00001) and explaining 35% of the total inter-individual variability in clearance. 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引用次数: 0
摘要
简介对于治疗窗口较窄的药物来说,疗效和毒性之间存在着微妙的平衡,因此,从首次给药开始就使用正确的剂量至关重要。培美曲塞是一种用于肺癌治疗的细胞毒性药物,其暴露量受肾功能的影响。为了优化培美曲塞的剂量,准确预测药物清除率至关重要。因此,本研究旨在探讨肾功能生物标志物血清肌酐、胱抑素 C 和原烯脑啡肽在预测培美曲塞清除率方面的性能:我们使用两个临床试验的数据集进行了群体药代动力学分析,其中包含培美曲塞的药代动力学数据和所有三种生物标志物的测量值。对数据拟合了不含协变量的三室模型,并将获得的培美曲塞清除率个体经验贝叶斯估计值视为 "真实 "值(Ctrue)。随后,测试了以下作为培美曲塞清除率协变量的算法:使用肌酐的慢性肾脏病流行病学协作方程(CKD-EPICR)、胱抑素 C(CKD-EPICYS)、两者的组合(CKD-EPICR-CYS)、作为绝对值的原脑啡肽或与年龄和血清肌酐的组合算法,以及原脑啡肽与胱抑素 C 的组合:数据集由 66 名受试者组成,对所有三种肾功能生物标志物进行了配对观察。将 CKD-EPICR-CYS 作为培美曲塞清除率的协变量可使模型拟合效果最佳,目标函数(p CR-CYS 预测培美曲塞清除率的归一化均方根误差和平均预测误差分别为 19.9% 和 1.2%)下降幅度最大:总之,本研究表明,CKD-EPICR-CYS 组合在预测培美曲塞的药代动力学方面表现最佳。尽管存在假设的缺点,但在临床实践中,肌酐仍是预测培美曲塞清除率的一个合适且易于使用的指标。
A comparison of the renal function biomarkers serum creatinine, pro-enkephalin and cystatin C to predict clearance of pemetrexed.
Introduction: For drugs with a narrow therapeutic window, there is a delicate balance between efficacy and toxicity, thus it is pivotal to administer the right dose from the first administration onwards. Exposure of pemetrexed, a cytotoxic drug used in lung cancer treatment, is dictated by kidney function. To facilitate optimized dosing of pemetrexed, accurate prediction of drug clearance is pivotal. Therefore, the aim of this study was to investigate the performance of the kidney function biomarkers serum creatinine, cystatin C and pro-enkephalin in terms of predicting the elimination of pemetrexed.
Methods: We performed a population pharmacokinetic analysis using a dataset from two clinical trials containing pharmacokinetic data of pemetrexed and measurements of all three biomarkers. A three-compartment model without covariates was fitted to the data and the obtained individual empirical Bayes estimates for pemetrexed clearance were considered the "true" values (Cltrue). Subsequently, the following algorithms were tested as covariates for pemetrexed clearance: the Chronic Kidney Disease Epidemiology Collaboration equation using creatinine (CKD-EPICR), cystatin C (CKD-EPICYS), a combination of both (CKD-EPICR-CYS), pro-enkephalin as an absolute value or in a combined algorithm with age and serum creatinine, and lastly, a combination of pro-enkephalin with cystatin C.
Results: The dataset consisted of 66 subjects with paired observations for all three kidney function biomarkers. Inclusion of CKD-EPICR-CYS as a covariate on pemetrexed clearance resulted in the best model fit, with the largest decrease in objective function (p < 0.00001) and explaining 35% of the total inter-individual variability in clearance. The predictive performance of the model to containing CKD-EPICR-CYS to predict pemetrexed clearance was good with a normalized root mean squared error and mean prediction error of 19.9% and 1.2%, respectively.
Conclusions: In conclusion, this study showed that the combined CKD-EPICR-CYS performs best in terms predicting pharmacokinetics of pemetrexed. Despite the hypothesized disadvantages, creatinine remains to be a suitable and readily available marker to predict pemetrexed clearance in clinical practice.
期刊介绍:
Addressing a wide range of pharmacologic and oncologic concerns on both experimental and clinical levels, Cancer Chemotherapy and Pharmacology is an eminent journal in the field. The primary focus in this rapid publication medium is on new anticancer agents, their experimental screening, preclinical toxicology and pharmacology, single and combined drug administration modalities, and clinical phase I, II and III trials. It is essential reading for pharmacologists and oncologists giving results recorded in the following areas: clinical toxicology, pharmacokinetics, pharmacodynamics, drug interactions, and indications for chemotherapy in cancer treatment strategy.