Wu Yujie, Kong Weijie, Li Jiayu, Xiang Xiaoqiang, Liang Hao, Liu Dongyang
Department of Nephrology, Peking University Third Hospital, Beijing 100191, China.
Drug Clinical Trial Center, Peking University Third Hospital, Beijing 100191, China.
Pharmaceutics. 2025 Aug 20;17(8):1078. doi: 10.3390/pharmaceutics17081078.
Physiologically based pharmacokinetic (PBPK) modeling is a powerful tool for predicting pharmacokinetics (PK) to support drug development and precision medicine. However, it has not been established for non-renal clearance pathways in patients with end-stage renal disease (ESRD), a population that bears heavy medication burden and is thereby at high risk for drug-drug-disease interactions (DDDIs). Furthermore, the pronounced inter-individual variability in PK observed in ESRD patients highlights the urgent need for individualized PBPK models. In this study, we developed a PBPK population model for ESRD patients, incorporating functional changes in key drug-metabolizing enzymes and transporters (DMETs), including CYP3A4, OATP1B1/3, P-gp, and BCRP. The model was initially constructed using the recalibrated demographic and physiological parameters of ESRD patients. Then, we used five well-validated substrates (midazolam, dabigatran etexilate, pitavastatin, rosuvastatin, and atorvastatin) and their corresponding PK profiles from ESRD patients taking a microdose cocktail regimen to simultaneously estimate the abundance of all these DMETs. Lastly, machine learning was employed to identify potential factors influencing individual clearance. Our study suggested a significant reduction in hepatic OATP1B1/3 (75%) and intestinal P-gp abundance (34%) in ESRD patients. Ileum BCRP abundance was estimated to increase by 100%, while change in hepatic CYP3A4 abundance is minimal. Notably, simulations of drug combinations revealed potential DDDI risks that were not observed in healthy volunteers. Machine learning further identified Clostridium XVIII and Escherichia genus abundances as significant factors influencing dabigatran clearance. For rosuvastatin, aspartate aminotransferase, total bilirubin, , and genus abundances were key influencers. No significant factors were identified for midazolam, pitavastatin, or atorvastatin. Our study proposes a feasible strategy for individualized PK prediction by integrating PBPK modeling with machine learning to support the development and precise use of the aforementioned DMET substrates in ESRD patients.
基于生理的药代动力学（PBPK）模型是预测药代动力学（PK）以支持药物开发和精准医学的有力工具。然而，对于终末期肾病（ESRD）患者的非肾清除途径，尚未建立相关模型。ESRD患者承受着沉重的药物负担，因此存在药物 - 药物 - 疾病相互作用（DDDI）的高风险。此外，在ESRD患者中观察到的药代动力学明显的个体间差异凸显了对个体化PBPK模型的迫切需求。在本研究中，我们为ESRD患者开发了一个PBPK群体模型，纳入了关键药物代谢酶和转运蛋白（DMETs）的功能变化，包括CYP3A4、OATP1B1/3、P - gp和BCRP。该模型最初使用重新校准的ESRD患者人口统计学和生理学参数构建。然后，我们使用五种经过充分验证的底物（咪达唑仑、达比加群酯、匹伐他汀、瑞舒伐他汀和阿托伐他汀）以及来自接受微剂量鸡尾酒疗法的ESRD患者的相应PK谱，同时估计所有这些DMETs的丰度。最后，采用机器学习来识别影响个体清除率的潜在因素。我们的研究表明，ESRD患者肝脏中的OATP1B1/3（75%）和肠道中的P - gp丰度（34%）显著降低。回肠BCRP丰度估计增加100%，而肝脏CYP3A4丰度变化最小。值得注意的是，药物组合模拟揭示了在健康志愿者中未观察到的潜在DDDI风险。机器学习进一步确定了 XVIII 梭菌属和大肠杆菌属丰度是影响达比加群清除率的重要因素。对于瑞舒伐他汀，天冬氨酸转氨酶、总胆红素和 属丰度是关键影响因素。对于咪达唑仑、匹伐他汀或阿托伐他汀，未发现显著因素。我们的研究提出了一种可行的策略，通过将PBPK模型与机器学习相结合进行个体化PK预测，以支持上述DMET底物在ESRD患者中的开发和精确使用。
