Progress in Pseudotyping Lentiviral Vectors Towards Cell-Specific Gene Delivery In Vivo
Bibliographic record
Abstract
Lentiviral vectors (LVs) have become a fundamental tool in gene therapy due to their unique ability to transduce both dividing and non-dividing cells, transfer large genes of up to 10 kb, and facilitate stable, long-term expression of therapeutic genes into target cells. A key application of LVs is the ex vivo genetic modification of patient-derived cells, such as the production of CAR-T cells by transducing isolated T cells with LVs to express the CAR gene, enabling them to target and destroy cancer cells once infused back into the patient. However, these ex vivo gene therapy drugs are often dismally unaffordable due to the complex procedures involved, including cell isolation, genetic modification, and expansion, along with the significant risks associated with immune conditioning to ensure successful engraftment. To overcome these barriers, direct in vivo transgene delivery to physiologically relevant cells has been explored, bypassing the need for ex vivo manipulations and reducing costs. Yet, a major challenge in this approach is engineering LV cell tropism to ensure the precise targeting of specific cells while avoiding off-target effects. Recent advances in modifying LV surface proteins have shown promise, including the successful in vivo generation of CAR T cells and ensuing clinical trials. This review is aimed at providing an up-to-date account of the progress in engineering LV tropism, covering the utility of different heterologous viral envelopes and their engineering to achieve cell-type-specific delivery and host immune evasion, and highlighting the potential of in vivo gene therapy to improve the affordability and accessibility of life-saving treatments.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".