The Therapeutic Potential of TGF-β as a Target for Multiple Chronic Diseases: A Comprehensive Review of Pharmacologically Approved Drugs and Investigational Agents
Bibliographic record
Abstract
Background: Noncommunicable chronic diseases account for the highest number of mortalities across the globe and are responsible as the greatest contributor to medical healthcare expenses. To create new medicines to fight these diseases, we need to fully understand the pathological mechanisms behind them in addition to the usual therapeutic targets. TGF-β has become a promising target for therapy to help a number of fatal diseases, and it may be possible to challenge it therapeutically by either increasing its activity or decreasing it. Objective: The present review aims to highlight the therapeutic importance of TGF-β as a potent target to cure multiple chronic diseases, such as cardiovascular disorders and malignant tumors. It also mentions pharmacologically approved drugs as well as drugs that are currently under investigation. Methods: Our approach entailed a comprehensive literature review employing keywords such as “TGF-β signaling pathway,” “myocardial fibrosis,” and “neurological disorders.” We sourced pertinent information from reputable databases, including PubMed, Scopus, and Elsevier. The TGF-β signaling pathway is what makes cancer grow and spread, as well as fibrotic proliferation in many organs, including the lungs, heart, kidneys, and liver. In such diseases, enhanced signaling is implicated in the progression. However, in the case of nervous disorders such as Parkinson’s disease and Alzheimer’s disease, TGF-β signaling is found to be hampered. Both augmentation and inhibition of TGF-β are proven to be useful as therapeutic targets to counter these diseases. Conclusion: This review aims to provide an in-depth analysis of the historical development of research on TGF-β and the molecular mechanisms that underlie its biosynthesis, activation, and signaling transmission. Our objective is to offer a comprehensive and systematic understanding of TGF-β signaling, building on previous knowledge and recent updates, and to encourage further research in this area.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".