Stability, Activity, and Application of Topical Doxycycline Formulations in a Diabetic Wound Case Study.
Bibliographic record
Abstract
INTRODUCTION: Tetracycline molecules comprise a group of broad-spectrum antibiotics whose primary mechanism of action is the inhibition of protein synthesis through the binding of the bacterial ribosome. In addition, tetracyclines inhibit matrix metalloproteases (MMPs), a family of zinc-dependent proteases that contribute to tissue remodeling, inflammation, and angiogenesis and are overexpressed in certain pathophysiologies such as diabetic foot ulcers (DFUs). OBJECTIVE: This study aims to develop a liquid chromatography and mass spectrometry (LC-MS/MS) doxycycline quantification methodology to facilitate the development of a stable topical doxycycline hyclate (DOXY) formulation as well as evaluate the topical DOXY formulation for the efficacy in MMP-9 inhibition in vitro and in a clinical application of diabetic lower extremity wounds. MATERIALS AND METHODS: A simple quantification method utilizing LC-MS/MS was used to develop a topical DOXY formulation, a sample of which was analyzed in stability testing. The formulation was evaluated in vitro for MMP-9 activity using a commercial assay and compared with internal kit controls as well as in a clinical setting for wound healing. RESULTS: Two formulations of 2% (w/w) DOXY demonstrated acceptable stability (±10% target concentration) for 70 days when stored at 4°C. Using an in vitro assay of MMP-9 enzyme activity, the 2% DOXY formulation imparted a ~30% decrease in MMP-9 inhibitory potential as compared with the control drug alone (IC₅₀ values 62.92 µM and 48.27 µM, respectively). This topical product was evaluated for clinical utility in a patient with a DFU, and preliminary data suggest this intervention may promote wound healing. CONCLUSIONS: In summary, novel DOXY formulations may be stable and biologically active tools amenable to complex wound care.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".