Transdermal microneedle patch for antioxidants release.

Discover Materials Pub Date : 2025-01-01 Epub Date: 2025-03-22 DOI:10.1007/s43939-025-00240-8
Samuel Mugo, Scott Robertson, Weihao Lu
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引用次数: 0

Abstract

Polymer based microneedle transdermal drug delivery system fits the criterion for an efficient patient compliant drug delivery system of the future. Smart and controlled microneedle drug delivery systems remain an emerging research area. One of the objectives of this study was to design an accessible approach of molding microneedles using a beeswax mold. The second objective was to evaluate a polymer-based microneedle drug release transdermal platform fabricated via layer by layer (LbL) assembly of conductive polydimethylsiloxane integrated with carbon nanotubes, cellulose nanocrystal and polyaniline (PDMS@CNT/CNC@PANI). The electrically conductive PDMS@CNT/CNC@PANI microneedle patch provides a platform for drug loading, stabilization and transdermal controlled drug release. The drug loaded PDMS@CNT/CNC@PANI microneedle patch was evaluated for diffusion and voltage mediated transdermal delivery of thymol blue and rutin as model compounds. Chicken skin was used as an analogue of human skin. Both electrochemical and passive release from the rutin loaded PDMS@CNC/CNT microneedle patch resulted in ~ 66%. On the other hand, the PDMS@CNC/CNT@PANI patch loaded with rutin resulted in 66-84% release. The results show the novel microneedle patches could effectively release rutin in a controlled manner, and as such showed promise for potential use in clinical drug release applications.

Graphical abstract:

Supplementary information: The online version contains supplementary material available at 10.1007/s43939-025-00240-8.

抗氧化剂释放透皮微针贴片。
基于聚合物的微针透皮给药系统符合未来高效的患者顺应性给药系统的标准。智能可控微针给药系统仍是一个新兴的研究领域。本研究的目标之一是设计一种使用蜂蜡模具成型微针的简便方法。第二个目标是评估基于聚合物的微针药物释放透皮平台,该平台是由导电聚二甲基硅氧烷与碳纳米管、纳米纤维素晶体和聚苯胺(PDMS@CNT/CNC@PANI)逐层组装而成。导电 PDMS@CNT/CNC@PANI 微针贴片为药物负载、稳定和透皮药物控制释放提供了一个平台。以百里酚蓝和芦丁为模型化合物,对药物负载 PDMS@CNT/CNC@PANI 微针贴片进行了扩散和电压介导透皮给药评估。鸡皮被用作人体皮肤的模拟物。负载 PDMS@CNC/CNT 微针贴片的芦丁的电化学释放和被动释放率均达到约 66%。另一方面,负载芦丁的 PDMS@CNC/CNT@PANI 贴片的释放率为 66-84%。结果表明,新型微针贴片能以可控方式有效释放芦丁,因此有望用于临床药物释放应用:在线版本包含补充材料,可查阅 10.1007/s43939-025-00240-8。
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来源期刊
Discover Materials
Discover Materials materials-
CiteScore
3.30
自引率
0.00%
发文量
10
审稿时长
23 days
期刊介绍: Discover Materials is part of the Discover journal series committed to providing a streamlined submission process, rapid review and publication, and a high level of author service at every stage. It is a broad, open access journal publishing research from across all fields of materials research. Discover Materials covers all areas where materials are activators for innovation and disruption, providing cutting-edge research findings to researchers, academicians, students, and engineers. It considers the whole value chain, ranging from fundamental and applied research to the synthesis, characterisation, modelling and application of materials. Moreover, we especially welcome papers connected to so-called ‘green materials’, which offer unique properties including natural abundance, low toxicity, economically affordable and versatility in terms of physical and chemical properties. They are the activators of an eco-sustainable economy serving all innovation sectors. Indeed, they can be applied in numerous scientific and technological applications including energy, electronics, building, construction and infrastructure, materials science and engineering applications and pollution management and technology. For instance, biomass-based materials can be developed as a source for biodiesel and bioethanol production, and transformed into advanced functionalized materials for applications such as the transformation of chitin into chitosan which can be further used for biomedicine, biomaterials and tissue engineering applications. Green materials for electronics are also a key vector concerning the integration of novel devices on conformable, flexible substrates with free-of-form surfaces for innovative product development. We also welcome new developments grounded on Artificial Intelligence to model, design and simulate materials and to gain new insights into materials by discovering new patterns and relations in the data.
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