机器学习辅助万古霉素个体化用药的研究进展-中国药房

机器学习辅助万古霉素个体化用药的研究进展

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篇名：	机器学习辅助万古霉素个体化用药的研究进展
TITLE：	Research progress in machine learning-assisted individualized vancomycin medication
摘要：	万古霉素是一种广泛用于革兰氏阳性菌严重感染的糖肽类抗生素，但因治疗窗窄和个体间药代动力学差异，临床应用受限。其传统给药方案依赖于临床经验和群体药代动力学模型，难以实现精准治疗。机器学习的出现为上述难题提供了新的解决思路，其可通过整合人口统计学、临床指标和联合用药等多维数据，辅助患者个体化用药，有助于提高万古霉素剂量预测、体内暴露量评估和肾毒性风险预警能力。然而，机器学习的应用仍面临数据质量较低、模型泛化能力弱和可解释性不足等多重挑战，其临床价值仍需前瞻性研究和外部验证进一步确证。在未来研究中可考虑将机器学习与群体药代动力学模型进行深度融合，从而更好地辅助万古霉素个体化用药。
ABSTRACT：	Vancomycin is a glycopeptide antibiotic widely used for severe infections caused by gram-positive bacteria. Nevertheless， its clinical application is limited by a narrow therapeutic window and inter-individual differences in pharmacokinetics. Conventional dosing regimens rely on clinical experience and population pharmacokinetic models， which make precise therapy difficult. The emergence of machine learning provides new insights into tackling the above challenges. By integrating multi-dimensional data including demographic characteristics， clinical indicators and combined medications， machine learning can assist individualized medication for patients and improve the capacity for vancomycin dose prediction， in vivo exposure assessment and renal toxicity risk warning. However， the application of machine learning still faces multiple challenges such as poor data quality， weak model generalization ability and insufficient interpretability. Its clinical value needs to be further confirmed by prospective studies and external validation. Future research may consider deep integration of machine learning and population pharmacokinetic models to better support vancomycin individualized medication.
期刊：	2026年第37卷第17期
作者：	陈明月；雷翼菲；宋学武；李晋奇
AUTHORS：	CHEN Mingyue，LEI Yifei，SONG Xuewu，LI Jinqi
关键字：	万古霉素；机器学习；个体化用药；剂量；预测模型
KEYWORDS：	machine learning;individualized medication;dose;prediction model
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