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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".