Viral and non‐viral gene therapy using 3D (bio)printing
Bibliographic record
Abstract
The overall success in launching discovered drugs is tightly restricted to the high rate of late-stage failures, which ultimately inhibits the distribution of medicines in markets. As a result, it is imperative that methods reliably predict the effectiveness and, more critically, the toxicity of medicine early in the drug development process before clinical trials be continuously innovated. We must stay up to date with the fast appearance of new infections and diseases by rapidly developing the requisite vaccinations and medicines. Modern in vitro models of disease may be used as an alternative to traditional disease models, and advanced technology can be used for the creation of pharmaceuticals as well as cells, drugs, and gene delivery systems to expedite the drug discovery procedure. Furthermore, in vitro models that mimic the spatial and chemical characteristics of native tissues, such as a 3D bioprinting system or other technologies, have proven to be more effective for drug screening than traditional 2D models. Viral and non-viral gene delivery vectors are a hopeful tool for combinatorial gene therapy, suggesting a quick way of simultaneously deliver multiple genes. A 3D bioprinting system embraces an excellent potential for gene delivery into the different cells or tissues for different diseases, in tissue engineering and regeneration medicine, in which the precise nucleic acid is located in the 3D printed tissues and scaffolds. Non-viral nanocarriers, in combination with 3D printed scaffolds, are applied to their delivery of genes and controlled release properties. There remains, however, a big obstacle in reaching the full potential of 3D models because of a lack of in vitro manufacturing of live tissues. Bioprinting advancements have made it possible to create biomimetic constructions that may be used in various drug discovery research applications. 3D bioprinting also benefits vaccinations, medicines, and relevant delivery methods because of its flexibility and adaptability. This review discusses the potential of 3D bioprinting technologies for pharmaceutical studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".