Lending a helping hand, screening chemical libraries for compounds that enhance β‐hexosaminidase A activity in GM2 gangliosidosis cells
Bibliographic record
Abstract
Enzyme enhancement therapy is an emerging therapeutic approach that has the potential to treat many genetic diseases. Candidate diseases are those associated with a mutant protein that has difficulty folding and/or assembling into active oligomers in the endoplasmic reticulum. Many lysosomal storage diseases are candidates for enzyme enhancement therapy and have the additional advantage of requiring only 5-10% of normal enzyme levels to reduce and/or prevent substrate accumulation. Our long experience in working with the beta-hexosaminidase (EC 3.2.1.52) isozymes system and its associated deficiencies (Tay-Sachs and Sandhoff disease) lead us to search for possible enzyme enhancement therapy-agents that could treat the chronic forms of these diseases which express 2-5% residual activity. Pharmacological chaperones are enzyme enhancement therapy-agents that are competitive inhibitors of the target enzyme. Each of the known beta-hexosaminidase inhibitors (low microm IC50) increased mutant enzyme levels to >or= 10% in chronic Tay-Sachs fibroblasts and also attenuated the thermo-denaturation of beta-hexosaminidase. To expand the repertoire of pharmacological chaperones to more 'drug-like' compounds, we screened the Maybridge library of 50,000 compounds using a real-time assay for noncarbohydrate-based beta-hexosaminidase inhibitors and identified several that functioned as pharmacological chaperones in patient cells. Two of these inhibitors had derivatives that had been tested in humans for other purposes. These observations lead us to screen the NINDS library of 1040 Food and Drug Administration approved compounds for pharmacological chaperones. Pyrimethamine, an antimalarial drug with well documented pharmacokinetics, was confirmed as a beta-hexosaminidase pharmacological chaperone and compared favorably with our best carbohydrate-based pharmacological chaperone in patient cells with various mutant genotypes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".