Advances in controlled release hormonal technologies for contraception: A review of existing devices, underlying mechanisms, and future directions

透皮 医学 剂型 生物医学工程 控制释放 计算机科学 纳米技术 材料科学 药理学
作者
Sowmya Sivasankaran,Sriramakamal Jonnalagadda
出处
期刊:Journal of Controlled Release [Elsevier BV]
卷期号:330: 797-811 被引量:64
标识
DOI:10.1016/j.jconrel.2020.12.044
摘要

This article presents a comprehensive review on controlled release hormonal contraceptive systems that include transdermal patches, intravaginal rings (IVRs), intrauterine devices (IUDs), injectables and subdermal implants. These systems represent a substantial advance from traditional oral contraceptive pills, to improve upon safety, efficiency, and compliance among women. The widespread use of controlled release systems is hindered by limitations, which are discussed in this review. Biodegradable polymers such as poly (lactic-co-glycolic acid) and polycaprolactone have been used to formulate subdermal implants and injectable microspheres to eliminate the need for implant removal and reduce provider intervention. To address low permeability in transdermal patches, permeation enhancers such as alkanols, fatty acids, prodrugs, and vesicular delivery for steroids have been investigated. Local anesthetics in the form of creams, gels and sprays have been evaluated to alleviate the pain associated with IUD insertion. Among methods for device fabrication, 3D printing has emerged as a potential approach for fabricating customizable IVRs and IUDs. Several other modified delivery systems such as transdermal microneedle patches, in situ forming injectable implants, microspheres embedded in implants and IVRs addressing multiple clinical conditions have been investigated for controlled release of contraceptive hormones. To ensure drug release at zero-order rates, empirical and theoretical modelling have been extensively employed and evaluated. The limitations of low predictive power associated with empirical modelling may be overcome through theoretical modelling and simulation that consider underlying mechanisms. Newer approaches such as Monte Carlo based simulation and deep learning models based on artificial neural networks can prove highly beneficial in developing precise contraceptive delivery system, to enhance the quality of life for women worldwide.
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