Department of Pharmacy, Radboudumc, Nijmegen, The Netherlands.
Department of Pharmacy, Amphia Hospital, Breda, The Netherlands.
Cancer Chemother Pharmacol. 2024 Dec;94(6):799-806. doi: 10.1007/s00280-024-04717-w. Epub 2024 Oct 4.
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.
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). Subsequently, the following algorithms were tested as covariates for pemetrexed clearance: the Chronic Kidney Disease Epidemiology Collaboration equation using creatinine (CKD-EPI), cystatin C (CKD-EPI), a combination of both (CKD-EPI), 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.
The dataset consisted of 66 subjects with paired observations for all three kidney function biomarkers. Inclusion of CKD-EPI 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 to predict pemetrexed clearance was good with a normalized root mean squared error and mean prediction error of 19.9% and 1.2%, respectively.
In conclusion, this study showed that the combined CKD-EPI 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.
对于治疗窗较窄的药物，在疗效和毒性之间存在微妙的平衡，因此从首次给药开始就准确给予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，其暴露量取决于肾功能。为了促进培美曲塞的优化剂量，准确预测药物清除率至关重要。因此，本研究旨在研究肾功能生物标志物血清肌酐、胱抑素 C 和 pro-enkephalin 在预测培美曲塞消除方面的性能。
我们使用包含培美曲塞药代动力学数据和所有三种生物标志物测量值的两项临床试验数据集进行群体药代动力学分析。未纳入协变量的三房室模型拟合数据，获得的培美曲塞清除率个体经验贝叶斯估计值被视为“真实”值（Cl）。随后，测试了以下算法作为培美曲塞清除率的协变量：基于肌酐（CKD-EPI）、胱抑素 C（CKD-EPI）、两者组合（CKD-EPI）的慢性肾脏病流行病学合作方程、pro-enkephalin 的绝对值或与年龄和血清肌酐的组合算法，最后是 pro-enkephalin 与胱抑素 C 的组合。
数据集包含 66 名受试者，所有三种肾功能生物标志物均具有配对观察值。纳入 CKD-EPI 作为培美曲塞清除率的协变量可使模型拟合度最佳，客观函数的降幅最大（p<0.00001），并解释了清除率总个体间变异性的 35%。该模型包含 CKD-EPI 来预测培美曲塞清除率的预测性能良好，归一化均方根误差和平均预测误差分别为 19.9%和 1.2%。
总之，本研究表明，联合 CKD-EPI 在预测培美曲塞药代动力学方面表现最佳。尽管存在假设的缺点，但肌酐仍然是预测临床实践中培美曲塞清除率的合适且易于获得的标志物。
