Intrauterine enzyme replacement therapies for lysosomal storage disorders: Current developments and promising future prospects
Bibliographic record
Abstract
Lysosomal storage disorders (LSDs) are a group of monogenic condition, with many characterized by an enzyme deficiency leading to the accumulation of an undegraded substrate within the lysosomes. For those LSDs, postnatal enzyme replacement therapy (ERT) represents the standard of care, but this treatment has limitations when administered only postnatally because, at that point, prenatal disease sequelae may be irreversible. Furthermore, most forms of ERT, specifically those administered systemically, are currently unable to access certain tissues, such as the central nervous system (CNS), and furthermore, may initiate an immune response. In utero enzyme replacement therapy (IUERT) is a novel approach to address these challenges evaluated in a first-in-human clinical trial for IUERT in LSDs (NCT04532047). IUERT has numerous advantages: in-utero intervention may prevent early pathology; the CNS can be accessed before the blood-brain barrier forms; and the unique fetal immune system enables exposure to new proteins with the potential to prevent an immune response and may induce sustained tolerance. However, there are challenges and limitations for any fetal procedure that involves two patients. This article reviews the current state of IUERT for LSDs, including its advantages, limitations, and potential future directions for definitive therapies.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
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".