Gaspar Frédéric, Terrier Jean, Jacot-Descombes Celestin, Gosselin Pauline, Ardoino Valentine, Lenoir Camille, Rollason Victoria, Csajka Chantal, Samer Caroline F, Fontana Pierre, Daali Youssef, Reny Jean-Luc
Center for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland.
Br J Clin Pharmacol. 2025 Jul;91(7):2057-2069. doi: 10.1002/bcp.70032. Epub 2025 Mar 4.
In a large cohort of hospitalized patients, previously validated physiologically based pharmacokinetic (PBPK)-based models for apixaban and rivaroxaban are being assessed for their performance in predicting individual pharmacokinetics, aiming to identify patients at high risk of under- or overdosing based on demographic, physiological and CYP-related phenotypic characteristics.
Clinical data were collected from hospitalized patients treated with apixaban (n = 100) or rivaroxaban (n = 100) at the Geneva University Hospitals (HUG). These patients were recruited in the OptimAT trial (NCT03477331). PBPK modelling created virtual twins for each patient, integrating demographic, kidney function, P-glycoprotein (Pgp) and cytochrome P450 (CYP450) 3A phenotyping. Individual PK profiles were simulated for every patient and compared to actual drug exposure, as assessed with LC/MS-MS.
Mean fold error (MFE) (95% CI) for the apixaban and rivaroxaban models integrating demographic and kidney function was within the pre-required bioequivalency criteria with 1.10 (1.04-1.16) and 0.97 (0.93-1.02), respectively. Adding individual Pgp and CYP3A phenotypes led to a slight overprediction 1.25 (1.17-1.33) and 1.30 (1.21-1.39), but patients at risk for bleeding were correctly predicted with MFEs of 0.90 (0.76-1.04) and 1.15 (1.11-1.20).
In a large cohort of hospitalized patients, a PBPK model incorporating demographic characteristics and kidney function can accurately predict, within bioequivalency criteria, an individual's apixaban and rivaroxaban plasma exposure. The added value of individual Pgp and 3A phenotypes on the predictive performance need to be further explored, although patients at higher risk for bleeding may benefit. This innovative approach represents an important step towards the application of PBPK at bedside.
在一大群住院患者中，正在评估先前验证的基于生理药代动力学（PBPK）的阿哌沙班和利伐沙班模型在预测个体药代动力学方面的性能，旨在根据人口统计学、生理学和CYP相关表型特征识别有用药不足或用药过量高风险的患者。
从日内瓦大学医院（HUG）接受阿哌沙班（n = 100）或利伐沙班（n = 100）治疗的住院患者中收集临床数据。这些患者是在OptimAT试验（NCT03477331）中招募的。PBPK建模为每个患者创建虚拟双胞胎，整合人口统计学、肾功能、P-糖蛋白（Pgp）和细胞色素P450（CYP450）3A表型。为每个患者模拟个体药代动力学曲线，并与通过LC/MS-MS评估的实际药物暴露情况进行比较。
整合人口统计学和肾功能的阿哌沙班和利伐沙班模型的平均误差倍数（MFE）（95%CI）在预先要求的生物等效性标准范围内，分别为1.10（1.04 - 1.16）和0.97（0.93 - 1.02）。添加个体Pgp和CYP3A表型导致轻微过度预测，分别为1.25（1.17 - 1.33）和1.30（1.21 - 1.39），但出血风险患者被正确预测，MFE分别为0.90（0.76 - 1.04）和1.15（1.11 - 1.20）。
在一大群住院患者中，纳入人口统计学特征和肾功能的PBPK模型可以在生物等效性标准范围内准确预测个体的阿哌沙班和利伐沙班血浆暴露情况。个体Pgp和3A表型对预测性能的附加价值需要进一步探索，尽管出血风险较高的患者可能会受益。这种创新方法代表了PBPK在床边应用的重要一步。
