Visualization of the degradation of long-acting microneedles and correlation of drug release in vivo based on FRET mechanism
Source abstract
Abstract
This study introduces a live imaging technique for real-time, non-invasive monitoring of drug release from long-acting microneedles using FRET (Fluorescence Resonance Energy Transfer). Employing Cy5.5 and Cy7 as FRET pairs and levonorgestrel as the model drug, we fabricated microneedles with varying PLGA molecular weights, demonstrating distinct release profiles. The FRET-PLGA-10-MN demonstrated a rapid drug release profile, reaching nearly complete release within a two-day period, while FRET-PLGA-30-MN showed a sustained release over four days. Sensitized Emission FRET (SE-FRET) optimized the imaging process, providing a robust correlation between FRET signals and drug absorption. This method surpasses traditional pharmacokinetic studies by offering a more efficient and comprehensive analysis of microneedle release dynamics in vivo, paving the way for enhanced long-acting microneedle design and therapeutic outcomes. STATEMENT OF SIGNIFICANCE: 1. FRET technology was applied to microneedle drug delivery system for the first time, which realized real-time, quantitative and non-invasive monitoring of drug release process. 2. The long-term microneedle technique was combined with sensitized emission method, and the FRET remaining ratio was innovatively used to investigate the FRET characteristics of microneedles, and the fluorescence ratio of FRET and donor double-channel was quantitatively calculated. 3. The correlation between visual fluorescence images of FRET effect and semi-quantitative calculation results based on fluorescence intensity and drug release in vivo with drug-loaded microneedles was analyzed.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advancements in Transdermal Drug Delivery · Lipid Membrane Structure and Behavior · Ocular Surface and Contact Lens
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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