Cell and Gene Therapies for Mucopolysaccharidoses: Base Editing and Therapeutic Delivery to the CNS
Bibliographic record
Abstract
Although individually uncommon, rare diseases collectively account for a considerable proportion of disease impact worldwide. A group of rare genetic diseases called the mucopolysaccharidoses (MPSs) are characterized by accumulation of partially degraded glycosaminoglycans cellularly. MPS results in varied systemic symptoms and in some forms of the disease, neurodegeneration. Lack of treatment options for MPS with neurological involvement necessitates new avenues of therapeutic investigation. Cell and gene therapies provide putative alternatives and when coupled with genome editing technologies may provide long term or curative treatment. Clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing technology and, more recently, advances in genome editing research, have allowed for the addition of base editors to the repertoire of CRISPR-based editing tools. The latest versions of base editors are highly efficient on-targeting deoxyribonucleic acid (DNA) editors. Here, we describe a number of putative guide ribonucleic acid (RNA) designs for precision correction of known causative mutations for 10 of the MPSs. In this review, we discuss advances in base editing technologies and current techniques for delivery of cell and gene therapies to the site of global degeneration in patients with severe neurological forms of MPS, the central nervous system, including ultrasound-mediated blood-brain barrier disruption.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".