CRISPR-dependent base editing as a therapeutic strategy for rare monogenic disorders
Bibliographic record
Abstract
Rare monogenic disorders are caused by mutations in single genes and have an incidence rate of less than 0.5%. Due to their low prevalence, these diseases often attract limited research and commercial interest, leading to significant unmet medical needs. In a therapeutic landscape where treatments are targeted to manage symptoms, gene editing therapy emerges as a promising approach to craft curative and lasting treatments for these patients, often referred to as "one-and-done" therapeutics. CRISPR-dependent base editing enables the precise correction of genetic mutations by direct modification of DNA bases without creating potentially deleterious DNA double-strand breaks. Base editors combine a nickase version of Cas9 with cytosine or adenine deaminases to convert C·G to T·A and A·T to G·C, respectively. Together, cytosine (CBE) and adenine (ABE) base editors can theoretically correct ∼95% of pathogenic transition mutations cataloged in ClinVar. This mini-review explores the application of base editing as a therapeutic approach for rare monogenic disorders. It provides an overview of the state of gene therapies and a comprehensive compilation of preclinical studies using base editing to treat rare monogenic disorders. Key considerations for designing base editing-driven therapeutics are summarized in a user-friendly guide for researchers interested in applying this technology to a specific rare monogenic disorder. Finally, we discuss the prospects and challenges for bench-to-bedside translation of base editing therapies for rare monogenic disorders.
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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".