Polyurethanes: Preparation, Properties, and Applications Volume 3: Emerging Applications

doi:10.1021/bk-2023-1454

Cited by 1 publication

(1 citation statement)

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“…PUs have been utilized in various pharmaceutical formulations as more and more monomers became available. Recent literature and reviews mainly focus on the potential of PUs in drug delivery systems with emphasis on general properties and drug administration studies ( Gupta, 2023 ; Nadaf et al, 2023 ). The drug delivery systems based on PUs exhibit noteworthy promise owing to their straightforward preparation, elevated pharmacological effectiveness, minimal toxicity, and precise control over drug release.…”

Section: Introductionmentioning

confidence: 99%

The state-of-art polyurethane nanoparticles for drug delivery applications

Song,

Muhammad,

Dang

et al. 2024

Front. Chem.

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Nowadays, polyurethanes (PUs) stand out as a promising option for drug delivery owing to their versatile properties. PUs have garnered significant attention in the biomedical sector and are extensively employed in diverse forms, including bulk devices, coatings, particles, and micelles. PUs are crucial in delivering various therapeutic agents such as antibiotics, anti-cancer medications, dermal treatments, and intravaginal rings. Effective drug release management is essential to ensure the intended therapeutic impact of PUs. Commercially available PU-based drug delivery products exemplify the adaptability of PUs in drug delivery, enabling researchers to tailor the polymer properties for specific drug release patterns. This review primarily focuses on the preparation of PU nanoparticles and their physiochemical properties for drug delivery applications, emphasizing how the formation of PUs affects the efficiency of drug delivery systems. Additionally, cutting-edge applications in drug delivery using PU nanoparticle systems, micelles, targeted, activatable, and fluorescence imaging-guided drug delivery applications are explored. Finally, the role of artificial intelligence and machine learning in drug design and delivery is discussed. The review concludes by addressing the challenges and providing perspectives on the future of PUs in drug delivery, aiming to inspire the design of more innovative solutions in this field.

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Section: Introductionmentioning

confidence: 99%