Targeting the Sodium Potassium ATPase Pump for the Treatment of Prion Diseases
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".