Division of Systems Biomedicine and Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands.
PD-Value B.V, Houten, The Netherlands.
J Pharmacokinet Pharmacodyn. 2021 Oct;48(5):725-741. doi: 10.1007/s10928-021-09768-7. Epub 2021 Jun 17.
Predicting brain pharmacokinetics is critical for central nervous system (CNS) drug development yet difficult due to ethical restrictions of human brain sampling. CNS pharmacokinetic (PK) profiles are often altered in CNS diseases due to disease-specific pathophysiology. We previously published a comprehensive CNS physiologically-based PK (PBPK) model that predicted the PK profiles of small drugs at brain and cerebrospinal fluid compartments. Here, we improved this model with brain non-specific binding and pH effect on drug ionization and passive transport. We refer to this improved model as Leiden CNS PBPK predictor V3.0 (LeiCNS-PK3.0). LeiCNS-PK3.0 predicted the unbound drug concentrations of brain ECF and CSF compartments in rats and humans with less than two-fold error. We then applied LeiCNS-PK3.0 to study the effect of altered cerebrospinal fluid (CSF) dynamics, CSF volume and flow, on brain extracellular fluid (ECF) pharmacokinetics. The effect of altered CSF dynamics was simulated using LeiCNS-PK3.0 for six drugs and the resulting drug exposure at brain ECF and lumbar CSF were compared. Simulation results showed that altered CSF dynamics changed the CSF PK profiles, but not the brain ECF profiles, irrespective of the drug's physicochemical properties. Our analysis supports the notion that lumbar CSF drug concentration is not an accurate surrogate of brain ECF, particularly in CNS diseases. Systems approaches account for multiple levels of CNS complexity and are better suited to predict brain PK.
预测脑药代动力学对于中枢神经系统(CNS)药物开发至关重要,但由于人类大脑采样的伦理限制,这一过程具有挑战性。由于特定于疾病的病理生理学,CNS 药代动力学(PK)谱在 CNS 疾病中经常发生改变。我们之前发表了一个全面的中枢神经系统基于生理学的 PK(PBPK)模型,该模型可以预测小分子药物在大脑和脑脊液隔室中的 PK 谱。在这里,我们通过大脑非特异性结合和 pH 对药物离子化和被动转运的影响改进了该模型。我们将这个改进的模型称为莱顿中枢神经系统 PBPK 预测器 V3.0(LeiCNS-PK3.0)。LeiCNS-PK3.0 以小于两倍的误差预测了大鼠和人类大脑细胞外液(ECF)和脑脊液(CSF)隔室中未结合药物的浓度。然后,我们应用 LeiCNS-PK3.0 来研究改变脑脊液(CSF)动力学、CSF 体积和流量对大脑细胞外液(ECF)药代动力学的影响。使用 LeiCNS-PK3.0 模拟了六种药物的改变 CSF 动力学的效果,比较了药物在大脑 ECF 和腰 CSF 中的暴露情况。模拟结果表明,改变 CSF 动力学改变了 CSF PK 谱,但没有改变大脑 ECF 谱,无论药物的物理化学性质如何。我们的分析支持这样一种观点,即腰 CSF 药物浓度不能准确替代大脑 ECF,尤其是在 CNS 疾病中。系统方法考虑了中枢神经系统的多个复杂层次,更适合预测大脑 PK。