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Record W3201224866 · doi:10.1016/j.ebiom.2021.103503

Reversing pathology in a preclinical model of Alzheimer's disease by hacking cerebrovascular neoangiogenesis with advanced cancer therapeutics

2021· article· en· W3201224866 on OpenAlexafffund
Chaahat S. B. Singh, Kyung Bok Choi, Lonna Munro, Hong Yue Wang, Cheryl G. Pfeifer, Wilfred A. Jefferies

Bibliographic record

VenueEBioMedicine · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsCanada's Michael Smith Genome Sciences CentreVancouver General HospitalUniversity of British Columbia
FundersCanadian Institutes of Health ResearchCentre for Blood Research, University of British ColumbiaVictoria UniversityW. Garfield Weston Foundation
KeywordsHypervascularityMedicineCognitive declineDementiaNeuroinflammationBlood–brain barrierPathologyCerebral amyloid angiopathyAlzheimer's diseaseVascular dementiaAmyloid betaNeuroscienceCancer researchDiseaseInternal medicineBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Cognitive decline leading to dementia, accompanied by the accumulation of amyloid-beta (Aβ) in neuritic plaques together with the appearance of neurofibrillary tangles (NFT) composed of hyperphosphorylated tau protein (tau), are previously noted hallmarks of Alzheimer's disease (AD). We previously discovered hypervascularity in brain specimens from AD patients and consistent with this observation, we demonstrated that overexpression of Aβ drives cerebrovascular neoangiogenesis leading to hypervascularity and coincident tight-junction disruption and blood-brain barrier (BBB) leakiness in animal models of AD. We subsequently demonstrated that amyloid plaque burden and cerebrovascular pathogenesis subside when pro-angiogenic Aβ levels are reduced. Based on these data, we propose a paradigm of AD etiology where, as a compensatory response to impaired cerebral blood flow (CBF), Aβ triggers pathogenic cerebrovascular neoangiogenesis that underlies the conventional hallmarks of AD. Consequently, here we present evidence that repurposing anti-cancer drugs to modulate cerebrovascular neoangiogenesis, rather than directly targeting the amyloid cascade, may provide an effective treatment for AD and related vascular diseases of the brain. METHODS: We explored whether the anti-cancer drug, Axitinib, a small molecule tyrosine kinase inhibitor that targets vascular endothelial growth factor receptors (VEGFR) can inhibit aberrant cerebrovascular neoangiogenic changes, reduce Aβ deposits and reverse cognitive decline in an animal model of AD. One month post-treatment with Axitinib, we employed a battery of tests to assess cognition and memory in aged Tg2576 AD mice and used molecular analysis to demonstrate reduction of amyloid plaques, BBB leakage, hypervascularity and associated disease pathology. FINDINGS: Targeting the pro-angiogenic pathway in AD using the cancer drug, Axitinib, dramatically reduced cerebrovascular neoangiogenesis, restored BBB integrity, resolved tight-junction pathogenesis, diminishes Aβ depositions in plaques and effectively restores memory and cognitive performance in a preclinical mouse model of AD. INTERPRETATION: Modulation of neoangiogenesis, in an analogous approach to those used to treat aberrant vascularization in cancer and also in the wet form of age-related macular degeneration (AMD), provides an alternative therapeutic strategy for intervention in AD that warrants clinical investigation. FUNDING: None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.344
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations35
Published2021
Admission routes2
Has abstractyes

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