Recombinant Platelet-Derived Growth Factor in Tissue Repair: A Review Exploring Frontiers in Regenerative Medicine
Bibliographic record
Abstract
SUMMARY: Pure platelet-derived growth factor BB (PDGF-BB) enhances healing in multiple tissues and is the most thoroughly studied growth factor in medicine. Beyond the thousands of nonclinical studies that define its mechanism of action in tissue repair, more than 140 published human clinical studies have examined the safety and effectiveness of PDGF-BB therapeutics. Manufactured pure PDGF-BB mimics the biologic activity of the endogenous protein and is the active ingredient in 4 US Food and Drug Administration (FDA)-approved products used for the regeneration of skin, bone, mucosa, and gingiva, a diverse array of tissues important to reconstructive surgeons. The recent introduction of an FDA-registered product for use in medical aesthetics demonstrates interest in using this beneficial growth factor more broadly for skin rejuvenation and scar reduction. The authors review the history of clinical development of recombinant pure PDGF-BB, summarize the evidence from more than 30 years of translational research on the applications of PDGF-BB, and propose areas of unmet needs where pure PDGF-BB could potentially enhance patient outcomes. For this review, a literature search was conducted searching PubMed, Web of Science, Google, and clinicaltrials.gov . Evidence-based data from phase 1, 2, 3, and 4 FDA trials including more than 15,000 patients were extracted from systematic reviews and additional primary studies not referenced in systematic reviews. This summary of evidence supports the safety and efficacy of pure PDGF-BB therapeutics in wound healing and tissue repair, can help guide health care providers on the beneficial use of PDGF-BB applications, and provides a framework for future studies to further investigate PDGF-BB in regenerative medicine.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".