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药物性肾损伤:挑战与机遇

Drug-induced kidney injury: challenges and opportunities.

作者信息

Connor Skylar, Roberts Ruth A, Tong Weida

机构信息

National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, United States.

ApconiX Ltd, Alderley Park, Alderley Edge, SK10 4TG, United Kingdom.

出版信息

Toxicol Res (Camb). 2024 Aug 5;13(4):tfae119. doi: 10.1093/toxres/tfae119. eCollection 2024 Aug.

DOI:10.1093/toxres/tfae119
PMID:39105044
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11299199/
Abstract

Drug-induced kidney injury (DIKI) is a frequently reported adverse event, associated with acute kidney injury, chronic kidney disease, and end-stage renal failure. Prospective cohort studies on acute injuries suggest a frequency of around 14%-26% in adult populations and a significant concern in pediatrics with a frequency of 16% being attributed to a drug. In drug discovery and development, renal injury accounts for 8 and 9% of preclinical and clinical failures, respectively, impacting multiple therapeutic areas. Currently, the standard biomarkers for identifying DIKI are serum creatinine and blood urea nitrogen. However, both markers lack the sensitivity and specificity to detect nephrotoxicity prior to a significant loss of renal function. Consequently, there is a pressing need for the development of alternative methods to reliably predict drug-induced kidney injury (DIKI) in early drug discovery. In this article, we discuss various aspects of DIKI and how it is assessed in preclinical models and in the clinical setting, including the challenges posed by translating animal data to humans. We then examine the urinary biomarkers accepted by both the US Food and Drug Administration (FDA) and the European Medicines Agency for monitoring DIKI in preclinical studies and on a case-by-case basis in clinical trials. We also review new approach methodologies (NAMs) and how they may assist in developing novel biomarkers for DIKI that can be used earlier in drug discovery and development.

摘要

药物性肾损伤(DIKI)是一种经常被报道的不良事件,与急性肾损伤、慢性肾病和终末期肾衰竭相关。关于急性损伤的前瞻性队列研究表明,成年人群中的发生率约为14%-26%,而在儿科中这一问题尤为严重,16%的发生率归因于药物。在药物研发过程中,肾损伤分别占临床前和临床失败案例的8%和9%,影响多个治疗领域。目前,用于识别DIKI的标准生物标志物是血清肌酐和血尿素氮。然而,这两种标志物在肾功能显著丧失之前都缺乏检测肾毒性的敏感性和特异性。因此,迫切需要开发替代方法,以便在早期药物发现中可靠地预测药物性肾损伤(DIKI)。在本文中,我们讨论了DIKI的各个方面,以及它在临床前模型和临床环境中的评估方式,包括将动物数据转化为人类数据所面临的挑战。然后,我们研究了美国食品药品监督管理局(FDA)和欧洲药品管理局认可的用于在临床前研究和临床试验中逐案监测DIKI的尿液生物标志物。我们还回顾了新方法学(NAMs),以及它们如何有助于开发可在药物发现和开发早期使用的新型DIKI生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de50/11299199/ea16c3958642/tfae119f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de50/11299199/e08e3727d684/tfae119f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de50/11299199/ea16c3958642/tfae119f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de50/11299199/e08e3727d684/tfae119f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de50/11299199/ea16c3958642/tfae119f2.jpg

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本文引用的文献

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Drug Discov Today. 2024 Apr;29(4):103938. doi: 10.1016/j.drudis.2024.103938. Epub 2024 Mar 1.
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Precision nephrotoxicity testing using 3D in vitro models.使用3D体外模型进行精准肾毒性测试。
Cell Biosci. 2023 Dec 21;13(1):231. doi: 10.1186/s13578-023-01187-0.
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Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR.
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将 QSAR 建模与深度学习整合到药物发现中:深 QSAR 的出现。
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Osteopontin as a Biomarker in Chronic Kidney Disease.骨桥蛋白作为慢性肾脏病的生物标志物
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Novel strategies in nephrology: what to expect from the future?肾脏病学的新策略:未来可期?
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In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity.药物诱导肾毒性结构基础的计算机模拟预测与见解
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