Current Sickle Cell Disease Gene Therapy Treatments: Literature Review
Bibliographic record
Abstract
Sickle cell disease (SCD) consists of haemoglobin-mutation related blood disorders caused by mutations of the HBB gene. Current treatments for SCD are symptom-based or preventive treatments. The only curative treatment for SCD- an allogeneic hematopoietic stem cell transplant- is inaccessible to the majority of SCD patients. The transplant required a donor graft is unavailable for most individuals. Currently, research in gene therapy treatment for SCD attempts to provide long-lasting treatments in two distinct techniques. The first technique is to change the mutation-containing genotype to produce a normal or functional haemoglobin protein. The second technique is to bypass the production of the mutated adult haemoglobin and product fetal haemoglobin instead. This literature review compares three gene-editing methods; Lentiviral Vectors, CRISPR/Cas9, and Base Editors. A review of previously published research papers was conducted and compared over a 2 month period during a summer student research program to determine the progression of each of the three gene-editing methods in the two techniques of SCD gene therapy treatment. While all three were successful in both genotype correction and fetal haemoglobin induction, only the Lentiviral Vector and CRISPR/Cas9 treatments for fetal haemoglobin induction have published data on human trials. However, the Base Editor shows promise in its ability to surpass many issues faced with both viral vectors and CRISPR/Cas9 such as off-target DNA breaks. Progression in SCD gene therapy can provide a treatment option for all affected individuals and can even provide a basis for gene therapy for other blood 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".