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Record W7133107379

Targeting the Sodium Potassium ATPase Pump for the Treatment of Prion Diseases

2024· dissertation· W7133107379 on OpenAlexaboutno aff
Shehab Eid

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicPrion Diseases and Protein Misfolding
Canadian institutionsnot available
Fundersnot available
KeywordsPrion proteinDrugDiseaseDrug discoveryLysosomal storage disordersADMEBovine spongiform encephalopathyATPase
DOInot available

Abstract

fetched live from OpenAlex

Prion diseases are fatal neurodegenerative diseases of humans and animals. An inverse correlation exists between the levels of the cellular prion protein (PrPC) and the survival time in prion diseases [1]. Consequently, if we were able to reduce the levels of PrPC, we would be able to extend prion disease survival. We know that it would be safe to do so because individuals whose cells produce half the levels of PrPC can reach old age in good health and animals who produce no PrPC are similarly unaffected [2].So far, efforts to identify PrPC-lowering drugs through screens of compound libraries have largely failed, with some of the best lead compounds either requiring relatively high concentrations to exert their effect, or lacking favorable ADME drug characteristics, including the ability to pass the blood-brain barrier (BBB). Recent results from a study that targeted PrPC transcripts with antisense oligonucleotides (ASOs) provided proof-of-principle validation of the premise that lowering steady-state PrPC levels can extend survival of prion-infected mice. Adapting this approach to humans has challenges: the observation that there is limited ASO delivery to deep brain structures, a caveat that is exacerbated in human adults due to relatively large brain size and the high costs associated with this treatment approach [3]. We recently discovered that PrPC binds to sodium-potassium ATPases (NKAs). We hypothesized that targeting NKAs with their natural inhibitors, cardiac glycosides (CGs), may cause cells to internalize and degrade NKAs, and that PrPC, on account of it residing next to NKAs, may get co-internalized and co-degraded. Since 2019, these efforts culminated in the discovery of CGs as compounds that exhibit exquisite potency for reducing PrPC levels. In collaboration with researchers in Connecticut, Edmonton, Toronto, and Ann Arbor, and in partnership with drug and biotech companies, Charles River and Cyclica, these efforts have led to KDC203 as a promising lead CG for the treatment of prion diseases. This thesis summarizes this research program. After introducing the topic (Chapter 1), it describes the original discovery of NKAs in proximity to PrPC (Chapter 2), documents early work that validated the hypothesis of PrPC getting co-degraded with NKAs upon CG exposure (Chapter 3), presents the in silico screen for brain-penetrant CGs and the validation of KDC203 as a potent lead compound that exhibits low toxicity (Chapter 4), describes efforts aimed at revealing the ability of KDC203 to work in intact brains (Chapter 5), and finally, takes stock, looks at what we have learned, and presents possible future directions (Chapter 6).

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.319
Teacher spread0.305 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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