Substrate reduction therapy for inborn errors of metabolism
Bibliographic record
Abstract
Inborn errors of metabolism (IEM) represent a growing group of monogenic disorders each associated with inherited defects in a metabolic enzyme or regulatory protein, leading to biochemical abnormalities arising from a metabolic block. Despite the well-established genetic linkage, pathophysiology and clinical manifestations for many IEMs, there remains a lack of transformative therapy. The available treatment and management options for a few IEMs are often ineffective or expensive, incurring a significant burden to individual, family, and society. The lack of IEM therapies, in large part, relates to the conceptual challenge that IEMs are loss-of-function defects arising from the defective enzyme, rendering pharmacologic rescue difficult. An emerging approach that holds promise and is the subject of a flurry of pre-/clinical applications, is substrate reduction therapy (SRT). SRT addresses a common IEM phenotype associated with toxic accumulation of substrate from the defective enzyme, by inhibiting the formation of the substrate instead of directly repairing the defective enzyme. This minireview will summarize recent highlights towards the development of emerging SRT, with focussed attention towards repurposing of currently approved drugs, approaches to validate novel targets and screen for hit molecules, as well as emerging advances in gene silencing as a therapeutic modality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".