GSK-3 Inhibition as a Therapeutic Approach Against SARs CoV2: Dual Benefit of Inhibiting Viral Replication While Potentiating the Immune Response
Bibliographic record
Abstract
The SARS-CoV2 (COVID-19) pandemic and uncertainties in developing a vaccine have created an urgent need for new therapeutic approaches. A key question is whether it is possible to make rational predictions of new therapies based on the presently available scientific and medical information. In this regard, I have noticed an omission in the present analysis in the literature related to the exploitation of glycogen synthase kinase 3 (GSK-3) as a therapeutic approach. This is based on two key observations, that GSK-3 inhibitors can simultaneously block SARs viral replication, while boosting CD8+ adaptive T-cell and innate natural killer (NK) responses. Firstly, it is already clear that GSK-3 phosphorylation of SARs CoV1 N protein on key serine residues is needed for viral replication such that small molecule inhibitors (SMIs) of GSK-3 can inhibit viral replication. In comparing protein sequences, I show here that the key sites in the N protein of SARs CoV1 N for replication are conserved in SARs CoV2. This strongly suggests that GSK-3 SMIs will also inhibit SARs Cov2 replication. Secondly, we and others have previously documented that GSK-3 SMIs markedly enhance CD8+ cytolytic T-cell (CTL) and NK cell anti-viral effector functions leading to a reduction in both acute and chronic viral infections in mice. My hypothesis is that the repurposing of low-cost inhibitors of GSK-3 such as lithium will limit SARS-CoV2 infections by both reducing viral replication and potentiating the immune response against the virus. To date, there has been no mention of this dual connection between GSK-3 and SARs CoV2 in the literature. To my knowledge, no other drugs exist with the potential to simultaneously target both viral replication and immune response against SARs CoV2.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".