数字健康中的智能计算精确剂量管理。

Hong Lu, Sara Rosenbaum, Wei Lu
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引用次数: 0

摘要

儿科给药不仅对药物开发中成功的儿科试验至关重要,而且对床边的安全性和有效治疗至关重要。由于儿童与成人相比具有复杂的药代动力学,在药物开发期间和临床医生批准药物后,在精确管理给药方面存在一些挑战。特别是,考虑到现实世界的实践,了解发育对剂量-暴露-反应关系的影响对于优化不同年龄儿童的剂量至关重要。在本文中,我们提出了一种新的智能计算框架来研究生长和成熟如何在药代动力学和药效学中产生大小和年龄依赖性的变异性,并总结了基于建模的方法在儿科药物开发中的剂量发现应用,使临床医生能够预测可能的治疗效果,并有更高的可能性尽早实现最佳剂量方案,同时减少药物开发周期时间和成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Precision Dosing Management with Intelligent Computing in Digital Health.

Pediatric dosing is not only critical for successful pediatric trials in drug development but also paramount to safety and effective treatment at bedside. Due to the complex pharmacokinetic of children compared to adults, several challenges are posed in managing dosing precisely during drug development and after drug approval to clinicians. In particular, given the real-world practice, understanding the impact of development on the dose-exposure-response relationship is essential in optimizing the dosing to children of different ages. In this paper we propose a novel intelligent computing framework to examine how the growth and maturation create size- and age-dependent variability in pharmacokinetics and pharmacodynamics, and summarize the use of modeling-based approaches for dose finding in pediatric drug development, allowing clinicians to anticipate probable treatment effects and to have a higher likelihood of achieving optimal dose regimens early, as well as reducing the drug development cycling time and cost.

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