Chauzy Alexia, Bouchène Salim, Aranzana-Climent Vincent, Clarhaut Jonathan, Adier Christophe, Grégoire Nicolas, Couet William, Dahyot-Fizelier Claire, Marchand Sandrine
UFR de Médecine-Pharmacie, Université de Poitiers, Inserm U1070, 86073 Poitiers, France.
Clinical Pharmacology Department, Menarini Stemline, 50131 Florence, Italy.
Antibiotics (Basel). 2022 Sep 22;11(10):1293. doi: 10.3390/antibiotics11101293.
Understanding antibiotic concentration-time profiles in the central nervous system (CNS) is crucial to treat severe life-threatening CNS infections, such as nosocomial ventriculitis or meningitis. Yet CNS distribution is likely to be altered in patients with brain damage and infection/inflammation. Our objective was to develop a physiologically based pharmacokinetic (PBPK) model to predict brain concentration-time profiles of antibiotics and to simulate the impact of pathophysiological changes on CNS profiles. A minimal PBPK model consisting of three physiological brain compartments was developed from metronidazole concentrations previously measured in plasma, brain extracellular fluid (ECF) and cerebrospinal fluid (CSF) of eight brain-injured patients. Volumes and blood flows were fixed to their physiological value obtained from the literature. Diffusion clearances characterizing transport across the blood-brain barrier and blood-CSF barrier were estimated from system- and drug-specific parameters and were confirmed from a Caco-2 model. The model described well unbound metronidazole pharmacokinetic profiles in plasma, ECF and CSF. Simulations showed that with metronidazole, an antibiotic with extensive CNS distribution simply governed by passive diffusion, pathophysiological alterations of membrane permeability, brain ECF volume or cerebral blood flow would have no effect on ECF or CSF pharmacokinetic profiles. This work will serve as a starting point for the development of a new PBPK model to describe the CNS distribution of antibiotics with more limited permeability for which pathophysiological conditions are expected to have a greater effect.
了解中枢神经系统(CNS)中的抗生素浓度-时间曲线对于治疗严重危及生命的中枢神经系统感染至关重要,如医院获得性脑室炎或脑膜炎。然而,脑损伤和感染/炎症患者的中枢神经系统分布可能会发生改变。我们的目标是建立一个基于生理的药代动力学(PBPK)模型,以预测抗生素的脑浓度-时间曲线,并模拟病理生理变化对中枢神经系统曲线的影响。从8名脑损伤患者的血浆、脑细胞外液(ECF)和脑脊液(CSF)中先前测量的甲硝唑浓度建立了一个由三个生理性脑区室组成的最小PBPK模型。体积和血流量固定为从文献中获得的生理值。根据系统和药物特异性参数估计表征跨血脑屏障和血脑脊液屏障转运的扩散清除率,并通过Caco-2模型进行确认。该模型很好地描述了血浆、ECF和CSF中游离甲硝唑的药代动力学曲线。模拟结果表明,对于一种仅通过被动扩散广泛分布于中枢神经系统的抗生素甲硝唑,膜通透性、脑ECF体积或脑血流量的病理生理改变对ECF或CSF药代动力学曲线没有影响。这项工作将作为开发新的PBPK模型的起点,以描述通透性更有限的抗生素在中枢神经系统中的分布,预计病理生理条件对此类抗生素的影响更大。