Development and multi-center validation of a fully automated digital immunoassay for neurofilament light chain: toward a clinical blood test for neuronal injury

CLINICAL CHEMISTRY AND LABORATORY MEDICINE(2024)

引用 0|浏览46
暂无评分
摘要
Objectives Neurofilament light chain (NfL) has emerged as a promising biomarker for detecting and monitoring axonal injury. Until recently, NfL could only be reliably measured in cerebrospinal fluid, but digital single molecule array (Simoa) technology has enabled its precise measurement in blood samples where it is typically 50-100 times less abundant. We report development and multi-center validation of a novel fully automated digital immunoassay for NfL in serum for informing axonal injury status.Methods A 45-min immunoassay for serum NfL was developed for use on an automated digital analyzer based on Simoa technology. The analytical performance (sensitivity, precision, reproducibility, linearity, sample type) was characterized and then cross validated across 17 laboratories in 10 countries. Analytical performance for clinical NfL measurement was examined in individual patients with relapsing remitting multiple sclerosis (RRMS) after 3 months of disease modifying treatment (DMT) with fingolimod.Results The assay exhibited a lower limit of detection (LLoD) of 0.05 ng/L, a lower limit of quantification (LLoQ) of 0.8 ng/L, and between-laboratory imprecision <10 % across 17 validation sites. All tested samples had measurable NfL concentrations well above the LLoQ. In matched pre-post treatment samples, decreases in NfL were observed in 26/29 RRMS patients three months after DMT start, with significant decreases detected in a majority of patients.Conclusions The sensitivity characteristics and reproducible performance across laboratories combined with full automation make this assay suitable for clinical use for NfL assessment, monitoring in individual patients, and cross-comparisons of results across multiple sites.
更多
查看译文
关键词
digital immunoassay,neuronal injury,multiple sclerosis,validation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要