人工智能驱动的经皮给药皮肤动力学创新:克服障碍,提高精度。

IF 5.5 3区 医学 Q1 PHARMACOLOGY & PHARMACY Pharmaceutics Pub Date : 2025-02-02 DOI:10.3390/pharmaceutics17020188
Nubul Albayati, Sesha Rajeswari Talluri, Nirali Dholaria, Bozena Michniak-Kohn
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引用次数: 0

摘要

透皮给药系统(TDDS)通过绕过胃肠道和肝脏代谢,提高生物利用度,并最大限度地减少全身副作用,为传统的口服和注射给药提供了一种替代方案。然而,TDDS的广泛采用受到皮肤渗透性屏障(特别是角质层)和优化配方需求等挑战的限制。皮肤类型、水合水平和年龄等因素进一步使普遍有效的解决方案的开发复杂化。人工智能(AI)的进步通过预测建模和个性化医疗方法解决了这些挑战。在广泛的分子数据集上训练的机器学习模型预测皮肤渗透性并加速合适候选药物的选择。人工智能驱动的算法优化配方,包括渗透增强剂和微针、脂质体等先进的给药技术,同时确保安全性和有效性。个性化TDDS设计可根据患者个体情况量身定制药物递送,提高治疗精度。创新系统,如传感器集成贴片,根据实时反馈动态调整药物释放,确保最佳结果。人工智能还简化了制药过程,从疾病诊断到药物在皮肤层中的分布预测,从而实现了高效的配方开发。本文重点介绍了人工智能在TDDS中的变革作用,包括深度神经网络(DNN)、人工神经网络(ANN)、BioSIM、COMSOL、k -近邻(KNN)和集合覆盖机(SVM)等模型的应用。这些技术彻底改变了皮肤和非皮肤疾病的TDDS,表明人工智能有潜力克服现有障碍,并通过创新的给药解决方案改善患者护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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AI-Driven Innovation in Skin Kinetics for Transdermal Drug Delivery: Overcoming Barriers and Enhancing Precision.

Transdermal drug delivery systems (TDDS) offer an alternative to conventional oral and injectable drug administration by bypassing the gastrointestinal tract and liver metabolism, improving bioavailability, and minimizing systemic side effects. However, widespread adoption of TDDS is limited by challenges such as the skin's permeability barrier, particularly the stratum corneum, and the need for optimized formulations. Factors like skin type, hydration levels, and age further complicate the development of universally effective solutions. Advances in artificial intelligence (AI) address these challenges through predictive modeling and personalized medicine approaches. Machine learning models trained on extensive molecular datasets predict skin permeability and accelerate the selection of suitable drug candidates. AI-driven algorithms optimize formulations, including penetration enhancers and advanced delivery technologies like microneedles and liposomes, while ensuring safety and efficacy. Personalized TDDS design tailors drug delivery to individual patient profiles, enhancing therapeutic precision. Innovative systems, such as sensor-integrated patches, dynamically adjust drug release based on real-time feedback, ensuring optimal outcomes. AI also streamlines the pharmaceutical process, from disease diagnosis to the prediction of drug distribution in skin layers, enabling efficient formulation development. This review highlights AI's transformative role in TDDS, including applications of models such as Deep Neural Networks (DNN), Artificial Neural Networks (ANN), BioSIM, COMSOL, K-Nearest Neighbors (KNN), and Set Covering Machine (SVM). These technologies revolutionize TDDS for both skin and non-skin diseases, demonstrating AI's potential to overcome existing barriers and improve patient care through innovative drug delivery solutions.

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来源期刊
Pharmaceutics
Pharmaceutics Pharmacology, Toxicology and Pharmaceutics-Pharmaceutical Science
CiteScore
7.90
自引率
11.10%
发文量
2379
审稿时长
16.41 days
期刊介绍: Pharmaceutics (ISSN 1999-4923) is an open access journal which provides an advanced forum for the science and technology of pharmaceutics and biopharmaceutics. It publishes reviews, regular research papers, communications,  and short notes. Covered topics include pharmacokinetics, toxicokinetics, pharmacodynamics, pharmacogenetics and pharmacogenomics, and pharmaceutical formulation. Our aim is to encourage scientists to publish their experimental and theoretical details in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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