Targeted Protein Degradation for the Prevention of Pathological Glycogen Accumulation
Bibliographic record
Abstract
A peculiar feature common to certain rare diseases and cancers is the overaccumulation of glycogen. Glycogen Storage Diseases (GSDs) are a group of rare genetic disorders defined by an overaccumulation of glycogen in various tissues that often proves fatal. This overaccumulation can be attributed to malformed, insoluble glycogen particles or deficiencies in glycogen digesting enzymes. Knockdown of Glycogen Synthase 1 (GYS1), the rate-limiting enzyme of glycogenesis, in preclinical models of GSDs reduced pathological glycogen accumulation and has validated GYS1 as a potential therapeutic entry point for these indications. The genetic knockdown of GYS1 in certain cancer xenografts reduces tumor volume, but GYS1 as a therapeutic target in cancer, including glycogen’s role in tumorigenesis beyond an energy source, requires further investigation. Despite the significance of this metabolic pathway, there is currently no targeted, small molecule chemical probe of GYS1 to interrogate the inhibition of glycogenosis in these indications. Meanwhile, there has been a recent surge in the development of bifunctional molecules known as proteolysis-targeting chimeras (PROTACs) as therapeutics and chemical probes. Targeted protein degradation has revolutionized drug discovery and provides a new approach to drug the “undruggable”. Despite the first demonstration over twenty years ago, designing and discovering PROTACs for therapeutic targets have recently become a bona fide strategy in small-molecule drug development. Herein, this dissertation discloses efforts to discover PROTACs that induce GYS1 degradation to further examine the physiological role of glycogen and potentially add to the toolkit of therapeutics for targeted glycogen depletion.
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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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".