Levetiracetam Modulates Brain Metabolic Networks and Transcriptomic Signatures in the 5XFAD Mouse Model of Alzheimer's disease.

Autor: Burton CP; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis IN 46202 USA., Chumin EJ; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis IN 46202 USA.; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis IN 46202., Collins AY; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis IN 46202 USA., Persohn SA; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis IN 46202 USA., Onos KD; The Jackson Laboratory, Bar Harbor, ME 04609., Pandey RS; The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032., Quinney SK; Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis IN 46202 USA., Territo PR; Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis IN 46202 USA.; Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis IN 46202 USA.
Jazyk: angličtina
Zdroj: BioRxiv : the preprint server for biology [bioRxiv] 2023 Dec 07. Date of Electronic Publication: 2023 Dec 07.
DOI: 10.1101/2023.11.10.566574
Abstrakt: Introduction: Subcritical epileptiform activity is associated with impaired cognitive function and is commonly seen in patients with Alzheimer's disease (AD). The anti-convulsant, levetiracetam (LEV), is currently being evaluated in clinical trials for its ability to reduce epileptiform activity and improve cognitive function in AD. The purpose of the current study was to apply pharmacokinetics (PK), network analysis of medical imaging, gene transcriptomics, and PK/PD modeling to a cohort of amyloidogenic mice to establish how LEV restores or drives alterations in the brain networks of mice in a dose-dependent basis using the rigorous preclinical pipeline of the MODEL-AD Preclinical Testing Core.
Methods: Chronic LEV was administered to 5XFAD mice of both sexes for 3 months based on allometrically scaled clinical dose levels from PK models. Data collection and analysis consisted of a multi-modal approach utilizing 18 F-FDG PET/MRI imaging and analysis, transcriptomic analyses, and PK/PD modeling.
Results: Pharmacokinetics of LEV showed a sex and dose dependence in C max , CL/F, and AUC 0-∞ , with simulations used to estimate dose regimens. Chronic dosing at 10, 30, and 56 mg/kg, showed 18 F-FDG specific regional differences in brain uptake, and in whole brain covariance measures such as clustering coefficient, degree, network density, and connection strength (i.e. positive and negative). In addition, transcriptomic analysis via nanoString showed dose-dependent changes in gene expression in pathways consistent 18 F-FDG uptake and network changes, and PK/PD modeling showed a concentration dependence for key genes, but not for network covariance modeling.
Discussion: This study represents the first report detailing the relationships of metabolic covariance and transcriptomic network changes resulting from LEV administration in 5XFAD mice. Overall, our results highlight non-linear kinetics based on dose and sex, where gene expression analysis demonstrated LEV dose- and concentration- dependent changes, along with cerebral metabolism, and/or cerebral homeostatic mechanisms relevant to human AD, which aligned closely with network covariance analysis of 18 F-FDG images. Collectively, this study show cases the value of a multimodal connectomic, transcriptomic, and pharmacokinetic approach to further investigate dose dependent relationships in preclinical studies, with translational value towards informing clinical study design.
Competing Interests: Competing Interests: The authors do not report any competing interests.
Databáze: MEDLINE
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