Deng Guoliang, Yang Fan, Sun Ning, Liang Danhong, Cen Anfen, Zhang Chen, Ni Suiqin
School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China.
Department of Pharmacy, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.
Front Pharmacol. 2023 Mar 7;14:1126714. doi: 10.3389/fphar.2023.1126714. eCollection 2023.
Chronic kidney disease (CKD) has significant effects on renal clearance of drugs. The application of antibiotics in CKD patients to achieve the desired therapeutic effect is challenging. This study aims to determine meropenem plasma exposure in the CKD population and further investigate optimal dosing regimens. A healthy adult PBPK model was established using the meropenem's physicochemical parameters, pharmacokinetic parameters, and available clinical data, and it was scaled to the populations with CKD and dialysis. The differences between the predicted concentration, C, and AUC predicted and observed model values were assessed by mean relative deviations (MRD) and geometric mean fold errors (GMFE) values and plotting the goodness of fit plot to evaluate the model's performance. Finally, dose recommendations for CKD and hemodialysis populations were performed by Monte Carlo simulations. The PBPK models of meropenem in healthy, CKD, and hemodialysis populations were successfully established. The MRD values of the predicted concentration and the GMFE values of C and AUC were within 0.5-2.0-fold of the observed data. The simulation results of the PBPK model showed the increase in meropenem exposure with declining kidney function in CKD populations. The dosing regimen of meropenem needs to be further adjusted according to the renal function of CKD patients. In patients receiving hemodialysis, since meropenem declined more rapidly during the on-dialysis session than the off-dialysis session, pharmacodynamic evaluations were performed for two periods separately, and respective optimal dosing regimens were determined. The established PBPK model successfully predicted meropenem pharmacokinetics in patients with CKD and hemodialysis and could further be used to optimize dosing recommendations, providing a reference for personalized clinical medication.
慢性肾脏病(CKD)对药物的肾脏清除率有显著影响。在CKD患者中应用抗生素以达到理想的治疗效果具有挑战性。本研究旨在确定美罗培南在CKD人群中的血浆暴露量,并进一步研究最佳给药方案。利用美罗培南的理化参数、药代动力学参数和可用的临床数据建立了一个健康成人的生理药代动力学(PBPK)模型,并将其扩展到CKD和透析人群。通过平均相对偏差(MRD)和几何平均倍数误差(GMFE)值评估预测浓度(C)以及AUC的预测值与观察到的模型值之间的差异,并绘制拟合优度图来评估模型的性能。最后,通过蒙特卡洛模拟对CKD和血液透析人群进行剂量推荐。成功建立了美罗培南在健康、CKD和血液透析人群中的PBPK模型。预测浓度的MRD值以及C和AUC的GMFE值在观察数据的0.5 - 2.0倍范围内。PBPK模型的模拟结果表明,在CKD人群中,随着肾功能下降,美罗培南的暴露量增加。美罗培南的给药方案需要根据CKD患者的肾功能进一步调整。在接受血液透析的患者中,由于美罗培南在透析期间比非透析期间下降得更快,因此分别对两个时间段进行了药效学评估,并确定了各自的最佳给药方案。所建立的PBPK模型成功预测了CKD和血液透析患者中美罗培南的药代动力学,并且可进一步用于优化给药推荐,为个性化临床用药提供参考。