Mengxu Zhang1, Ilona Vuist2, Vivi Rottschäfer3,4, Elizabeth CM de Lange1
1. Division of Systems Pharmacology and Pharmacy, Predictive Pharmacology group, Leiden Academic Centre of Drug Research, Leiden University, Leiden, The Netherlands 2. Charles River Laboratories, Groningen, The Netherlands 3. Mathematical Institute, Leiden University, Leiden, The Netherlands 4. Korteweg-de Vries Institute for Mathematics, University of Amsterdam, P.O. Box 94248, 1090 GE Amsterdam, The Netherlands
Objectives: Kp,uu,BBB values are crucial indicators of drug distribution into the brain, representing the steady-state relationship between unbound concentrations in plasma and in brain extracellular fluid (brainECF). Kp,uu,BBB values < 1 are often interpreted as indicators of dominant active efflux transport processes at the blood-brain barrier (BBB). However, the potential impact of brain metabolism on this value is typically not addressed, despite emerging insights into the prevalence of drug-metabolizing enzymes (DMEs) within the brain, such as cytochrome P450 (CYP450) [1] and UDP-glucuronosyltransferase (UGT) [2], and the fact that many CNS-active drugs undergo brain metabolism, like codeine [3], clozapine [4], alprazolam [5] etc. This study investigated the brain distribution of remoxipride, as a paradigm compound for passive BBB transport with yet unexplained brain elimination that was hypothesized to represent brain metabolism.
Methods: The physiologically-based LeiCNS pharmacokinetic predictor (LeiCNS-PK model) [6] was used to compare brain distribution of remoxipride with and without Michaelis-Menten (MM) kinetics at the BBB [7], blood-cerebrospinal fluid barrier (BCSFB) [8] and/or brain cell organelle levels [9, 10]. To accomplish this, the LeiCNS-PK 3.0 model was expanded to include mitochondria (MT) and endoplasmic reticulum (ER), where DMEs are predominantly situated [9, 10]. MM kinetic equations were then incorporated into the membranes of brain cell organelles, considering that DMEs are membrane-bound enzymes deeply embedded within the phospholipid bilayer [10]. To that end, multiple in-house (IV 0.7, 3.5, 4, 5.2, 7, 8, 14 and 16 mg/kg) and external (IV 4 and 8 mg/kg) rat microdialysis studies of plasma and brainECF data were analysed.
Results: The incorporation of active elimination through presumed brain metabolism of remoxipride in the new LeiCNS-PK model significantly improved the prediction accuracy of experimentally observed brainECF profiles of this drug. This improvement is evident not only in the virtual predictive check (VPC) plots but also in the relative accuracy (RAdurg) and average fold error/absolute average fold error (AFE/AAFE) plots. Although the relative accuracy (RAdurg) for one dosage (14 mg/kg) exceeds a 2-fold error, the overall model’s predictive capability is substantially enhanced.
Conclusions: For drugs with Kp,uu,BBB values < 1, not only the current interpretation of dominant BBB efflux transport but also potential brain metabolism needs to be considered, especially because these may be plasma concentration dependent. This will advance the mechanistic understanding of the processes governing brain PK profiles and may hold implications for precision medicine, given the polymorphic nature of enzyme variations among individuals. Furthermore, the development of organelle-level modelling in brain cells within the LeiCNS-PK model opens avenues for investigating cellular responses post-drug administration. However, the current model remains limited to passively transported CYP-metabolized CNS-active drugs, necessitating further inclusion of mechanisms such as active transport.
References:
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[10] Šrejber, M., et al. Journal of Inorganic Biochemistry. 2018. 183: p. 117-136.
Reference: PAGE 32 (2024) Abstr 10997 [www.page-meeting.org/?abstract=10997]
Poster: Drug/Disease Modelling - CNS