Ming Sun1, Martijn L. Manson1, Tingjie Guo1, Elizabeth CM de Lange1
1Division of Systems Pharmacology and Pharmacy, Leiden Academic Center for Drug Research, Leiden University
I. Introduction/Objectives Central nervous system (CNS) physiologically based pharmacokinetic (PBPK) models are valuable tools for studying CNS drug distribution. LeiCNS-PK3.0 is a validated comprehensive model that accurately predicts drug pharmaockinetics (PK) in multiple CNS compartments for both rats and humans [1]. However, the model does not account for two distinct processes that determine intracellular drug levels: (1) the active transport mechanisms that affect drug entry into brain parenchymal cells and (2) the intracellular drug conversion into their active forms. This is particularly important for antiviral agents such as acyclovir (ACV) and ganciclovir (GCV), whose efficacy depends on both efficient drug entry into brain cells, a process that may be influenced by active transport as they are P-gp substrates [2], and subsequent conversion into active triphosphate metabolites [2]. Therefore, our objective was to extend LeiCNS-PK3.0 by incorporating these mechanisms to enhance the prediction of intracellular PK profiles using ACV and GCV as model drugs. II. Methods LeiCNS-PK3.2 was developed by adding two key modifications to the existing framework: •Active Brain Membrane Transport: An asymmetry factor derived from Kp,uu,cell was integrated to capture active transport processes that facilitate drug entry into brain parenchymal cells [1]. •Intracellular Metabolism: Intracellular conversion of ACV and GCV into their active forms was incorporated into our model by fitting triphosphate metabolite data from an in vitro cell model [3]. The model was validated by comparing predictions with in vivo observations from plasma and brain extracellular fluid (ECF) in rats [4-6] and plasma, cerebrospinal fluid (CSF), and brain ECF in humans [7-9] to the predictions using metrics including the squared Pearson correlation coefficient (Pearson R2), the absolute average fold error (AAFE), and the root mean squared error (RMSE). Simulations were then conducted to compare the ACV and GCV PK profiles in plasma and CNS regions—including brain ECF, brain intracellular fluid (ICF), and the subarachnoid space (SAS)—at clinically relevant dosing schedules (for viral encephalitis: ACV, 10 mg/kg every 8 hours; GCV, 5 mg/kg every 12 hours). The impact of CNS pathophysiological changes induced by viral infection was simulated by varying the values of PZBBB, VSAS and Qcsf within ranges of 100–200%, 100–200% and 100–300% of their original values, respectively [10-14]. All analyses and simulations were performed using R version 4.4.1, employing the rxode2 package version 3.0.4 and ggplot2 package version 3.5.1. III. Results LeiCNS-PK3.2 successfully predicted observed acyclovir concentrations, with most observations falling within a 2-fold error range. The model yielded an R² of 0.99 in both rats and humans, with AAFE values of 1.13 (rats) and 1.23 (humans), and RMSE values of 0.09 (rats) and 0.17 (humans). Predicted unbound exposures of active triphosphate at steady state in brain ICF were comparable to the corresponding prodrug exposures in brain ICF and brain SAS. However, the time required to reach steady state was much longer for active triphosphate in brain ICF than for ACV/GCV in plasma, brain ECF, brain ICF, and SAS. Notably, active triphosphate brain ICF concentration profiles remained unchanged after varying PZBBB, VSAS and Qcsf. IV. Conclusions LeiCNS-PK3.2 demonstrated good performance in predicting observed ACV/GCV concentrations across plasma, brain ECF and CSF. Based on our simulation results, a loading dose may be required to accelerate the attainment of steady state for active triphosphate, given the slow intracellular elimination process in brain ICF. Moreover, pathological changes might have little impact on the brain ICF pharmacokinetic profile of active metabolites, indicating that the original model parameters remain valid for capturing intracellular exposure of active metabolites in patients with CNS viral infections.
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Reference: PAGE 33 (2025) Abstr 11689 [www.page-meeting.org/?abstract=11689]
Poster: Drug/Disease Modelling - CNS