Delivery of Therapeutics Using Bacteriophage Vectors
Bibliographic record
Abstract
Bacteriophages are viruses obligately infecting bacteria. They constitute the most numerous categories of biological forms populating our biosphere and are highly diverse and capable of infecting almost all bacteria. Phages make use of their host cell molecular machinery to express their own genes as they lack the ability to independently reproduce themselves. The inimitable characteristics of bacteriophages have enabled them to become propitious tools in biotechnology and genetic engineering. Phages show no tropism for mammalian cells but however, can be easily modified to present targeting ligands on their surface as coat protein fusions without any negative impacts on phage structure. These displayed ligands thereupon guide the recognition, interaction, and internalization of the phage into cells wherein efficiency of transfection is directly influenced by the copy number of the ligands used for targeting. Engineered phages are more efficacious for transgene delivery and gene expression in cancer cells when compared to other non-viral gene transfer strategies and are therefore being employed in developing cancer vaccines. The high level of stability as well as resistance of bacteriophages to various environmental conditions have enabled the development of virus-like particles (VLPs) capable of successful deliverance of several therapeutic drug cargos into tumors by selective targeting. Phage display technology has been used in therapy of Alzheimer’s disease and drug delivery into the brain. Exogenous peptides fused into the coat protein of phages enables the display of these peptides on the phage surface to generate combinatorial phage that facilitates their rapid separation using their ability to bind to a specific molecular target. Phage therapy has been shown to be safe in clinical settings when compared to antibiotics as it shows no adverse anaphylaxis nor adverse effects such as the emergence of multi-drug resistant bacteria. This review provides intriguing details of the use of natural and engineered phages in the therapy of diseases such as cancer, bacterial infections, bovine mastitis and dementia in addition to the use of CRISPR-Cas9 technology in generating genetically engineered phages. Further, the use of phage display technology in generating monoclonal antibodies against various human diseases is elucidated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".