Immuno-Isolation in Cancer Gene Therapy
Bibliographic record
Abstract
The implantation of genetically-modified non-autologous cells in immuno-protected microcapsules is an alternative to ex vivo gene therapy. Such cells delivering a recombinant therapeutic product are isolated from the host's immune system by being encapsulated within permselective microcapsules. This approach has been successful in pre-clinical animal studies involving delivery of hormone or enzymes to treat dwarfism, lysosomal storage disease, or hemophilia B. Recently, this platform technology has shown promise in the treatment for more complex diseases such as cancer. One of the earliest strategy was to augment the chemotherapeutic effect of a prodrug by implanting encapsulated cells that can metabolise prodrugs into cytotoxic products in close proximity to the cancer cells. More recent approaches include enhancing tumor cell death through immunotherapy, or suppressing tumor cell proliferation through anti-angiogenesis. These can be achieved by delivering single molecules of cytokines or angiostatin, respectively, by implanting microencapsulated cells engineered to secrete these recombinant products. Recent refinements of these approaches include genetic fusion of cytokines or angiostatin to additional functional groups with tumor targeting or tumor cell killing properties, thus enhancing the potency of the recombinant products. Furthermore, a COMBO strategy of implanting microencapsulated cells to deliver multiple products targeted to diverse pathways in tumor suppression also showed much promise. This review will summarise the application of microencapsulation of genetically-modified cells to cancer treatment in animal models, the efficacy of such approaches, and how these studies have led to better understanding of the biology of cancer treatment. The flexibility of this modular system involving molecular engineering, cellular genetic modification, and polymer chemistry provides potentially a huge range of application modalities, and a tremendous multi-disciplinary challenge for the future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.004 | 0.003 |
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".