2026

Smart thyroid-regulated drug delivery systems: a review of intelligent biosensor-integrated therapeutics for precision thyroxine replacement.

Frontiers in endocrinology Ingle R, Sonwane GM
阅读原文 PDF DOI PubMed

组成图示

示意图生成中

传感器类型

检测对象

检测原理

检测灵敏度

效应效果

传感器的构成

中文摘要

英文摘要

Levothyroxine (L-T4) replacement therapy has remained the standard treatment for post-thyroidectomy hypothyroidism since the mid-twentieth century. The drug works, but the delivery method has a basic flaw: it cannot adjust. A patient swallows the same dose every morning regardless of whether she is pregnant, fighting a fever, fasting, or sleeping. The healthy thyroid gland, by contrast, constantly tunes its output in response to signals from the hypothalamic-pituitary-thyroid (HPT) axis, adjusting on a timescale of minutes to hours. This mismatch between static pills and dynamic physiology leaves most thyroidectomized patients oscillating between mild over- and under-replacement for much of their lives. Recent meta-analyses confirm that only about 34% of these patients hit target euthyroidism at their first post-operative follow-up. The consequences are not trivial: cardiac arrhythmias, bone mineral loss, cognitive fog, metabolic disruption, and impaired fertility all track with even mild thyroid hormone imbalance. This review makes the case that we now have the pieces to build something better. Advances in nanomaterial-based biosensors already permit picomolar-range detection of TSH, free T3, and free T4. Machine learning pharmacokinetic models predict individual L-T4 requirements with R-squared values exceeding 0.85. The artificial pancreas has demonstrated that closed-loop drug delivery can work safely in humans at scale. We propose an Intelligent Medicine Bot (IMB): a wearable or implantable closed-loop platform that continuously senses thyroid hormone levels, computes optimal doses through adaptive AI algorithms, and delivers precisely titrated levothyroxine through a MEMS micro-pump. We review the clinical problem in detail, survey the enabling technologies, present the system architecture, and outline a phased development roadmap toward clinical translation.

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