Bahnasawy Salma M, Parrott Neil J, Gijsen Matthias, Spriet Isabel, Friberg Lena E, Nielsen Elisabet I
Department of Pharmacy, Uppsala University, Uppsala, Sweden.
Pharmaceutical Sciences, Roche Pharma Research and Early Development, Roche Innovation Centre Basel, Basel, Switzerland.
Int J Antimicrob Agents. 2024 Dec;64(6):107352. doi: 10.1016/j.ijantimicag.2024.107352. Epub 2024 Sep 28.
Applying physiologically-based pharmacokinetic (PBPK) modelling in sepsis could help to better understand how PK changes are influenced by drug- and patient-related factors. We aimed to elucidate the influence of sepsis pathophysiology on the PK of meropenem by applying PBPK modelling.
A whole-body meropenem PBPK model was developed and evaluated in healthy individuals, and renally impaired non-septic patients. Sepsis-induced physiological changes in body composition, organ blood flow, kidney function, albumin, and haematocrit were implemented according to a previously proposed PBPK sepsis model. Model performance was evaluated, and a local sensitivity analysis was conducted.
The model-predicted PK metrics (AUC, C, CL, V) were within 1.33-fold-error margin of published data for 87.5% of the simulated profiles in healthy individuals. In sepsis, the model provided good predictions for literature-digitised average plasma and tissue exposure data, where the model-predicted AUC was within 1.33-fold-error margin for 9 out 11 simulated study profiles. Furthermore, the model was applied to individual plasma concentration data from 52 septic patients, where the model-predicted AUC, C, and CL had a fold-error ratio range of 0.98-1.12, with alignment of the predicted and observed variability. For V, the fold-error ratio was 0.81, and the model underpredicted the population variability. CL was sensitive to renal plasma clearance, and kidney volume, whereas V was sensitive to the unbound fraction, organ volume fraction of the interstitial compartment, and the organ volume.
These findings may be extended to more diverse drug types and support a more mechanistic understanding of the effect of sepsis on drug exposure.
将基于生理学的药代动力学（PBPK）模型应用于脓毒症有助于更好地理解药代动力学变化是如何受到药物和患者相关因素影响的。我们旨在通过应用PBPK模型阐明脓毒症病理生理学对美罗培南药代动力学的影响。
建立了一个全身美罗培南PBPK模型，并在健康个体和肾功能受损的非脓毒症患者中进行了评估。根据先前提出的PBPK脓毒症模型，纳入了脓毒症引起的身体组成、器官血流、肾功能、白蛋白和血细胞比容的生理变化。对模型性能进行了评估，并进行了局部敏感性分析。
在健康个体中，87.5%的模拟曲线中，模型预测的药代动力学指标（AUC、C、CL、V）在已发表数据的1.33倍误差范围内。在脓毒症中，该模型对文献数字化的平均血浆和组织暴露数据提供了良好的预测，在11个模拟研究曲线中的9个中，模型预测的AUC在1.33倍误差范围内。此外，该模型应用于52例脓毒症患者的个体血浆浓度数据，模型预测的AUC、C和CL的误差比范围为0.98 - 1.12，预测值与观察到的变异性一致。对于V，误差比为0.81，模型低估了总体变异性。CL对肾血浆清除率和肾脏体积敏感，而V对未结合分数、间质隔室的器官体积分数和器官体积敏感。
这些发现可能扩展到更多种类的药物，并支持对脓毒症对药物暴露影响的更具机制性的理解。
