Ionis Pharmaceuticals, 2855 Gazelle Rd., Carlsbad, California, 92010, USA.
Regeneron, Tarrytown, New York, USA.
AAPS J. 2020 Jan 29;22(2):38. doi: 10.1208/s12248-020-0412-0.
Blood-based soluble protein biomarkers provide invaluable clinical information about patients and are used as diagnostic, prognostic, and pharmacodynamic markers. The most commonly used blood sample matrices are serum and different types of plasma. In drug development research, the impact of sample matrix selection on successful protein biomarker quantification is sometimes overlooked. The sample matrix for a specific analyte is often chosen based on prior experience or literature searches, without good understanding of the possible effects on analyte quantification. Using a data set of 32 different soluble protein markers measured in matched serum and plasma samples, we examined the differences between serum and plasma and discussed how platelet or immune cell activation can change the quantified concentration of the analyte. We have also reviewed the effect of anticoagulant on analyte quantification. Finally, we provide specific recommendations for biomarker sample matrix selection and propose a systematic and data-driven approach for sample matrix selection. This review is intended to raise awareness of the impact and considerations of sample matrix selection on biomarker quantification.
基于血液的可溶性蛋白生物标志物为患者提供了非常有价值的临床信息,可用作诊断、预后和药效学标志物。最常用的血液样本基质是血清和不同类型的血浆。在药物研发研究中,样本基质选择对成功进行蛋白生物标志物定量的影响有时被忽视。特定分析物的样本基质通常是基于先前的经验或文献检索选择的,而对可能对分析物定量产生的影响了解不足。我们使用了 32 种不同可溶性蛋白标志物在匹配的血清和血浆样本中的测量数据集,研究了血清和血浆之间的差异,并讨论了血小板或免疫细胞激活如何改变分析物的定量浓度。我们还回顾了抗凝剂对分析物定量的影响。最后,我们为生物标志物样本基质选择提供了具体建议,并提出了一种系统的数据驱动的样本基质选择方法。本综述旨在提高对样本基质选择对生物标志物定量的影响和注意事项的认识。