School of Chemistry, Manchester Institute of Biotechnology, University of Manchester , 131 Princess Street, Manchester M1 7DN, United Kingdom.
School of Chemical Engineering and Analytical Science, Manchester Institute of Biotechnology, University of Manchester , 131 Princess Street, Manchester M1 7DN, United Kingdom.
Anal Chem. 2016 Nov 15;88(22):10884-10892. doi: 10.1021/acs.analchem.6b02041. Epub 2016 Oct 25.
There has been an increasing demand for rapid and sensitive techniques for the identification and quantification of pharmaceutical compounds in human biofluids during the past few decades, and surface-enhanced Raman scattering (SERS) is one of a number of physicochemical techniques with the potential to meet these demands. In this study we have developed a SERS-based analytical approach for the assessment of human biofluids in combination with chemometrics. This novel approach has enabled the detection and quantification of the β-blocker propranolol spiked into human serum, plasma, and urine at physiologically relevant concentrations. A range of multivariate statistical analysis techniques, including principal component analysis (PCA), principal component-discriminant function analysis (PC-DFA) and partial least-squares regression (PLSR) were employed to investigate the relationship between the full SERS spectral data and the level of propranolol. The SERS spectra when combined with PCA and PC-DFA demonstrated clear differentiation of neat biofluids and biofluids spiked with varying concentrations of propranolol ranging from 0 to 120 μM, and clear trends in ordination scores space could be correlated with the level of propranolol. Since PCA and PC-DFA are categorical classifiers, PLSR modeling was subsequently used to provide accurate propranolol quantification within all biofluids with high prediction accuracy (expressed as root-mean-square error of predictions) of 0.58, 9.68, and 1.69 for serum, plasma, and urine respectively, and these models also had excellent linearity for the training and test sets between 0 and 120 μM. The limit of detection as calculated from the area under the naphthalene ring vibration from propranolol was 133.1 ng/mL (0.45 μM), 156.8 ng/mL (0.53 μM), and 168.6 ng/mL (0.57 μM) for serum, plasma, and urine, respectively. This result shows a consistent signal irrespective of biofluid, and all are well within the expected physiological level of this drug during therapy. The results of this study demonstrate the potential of SERS application as a diagnostic screening method, following further validation and optimization to improve detection of pharmaceutical compounds and quantification in human biofluids, which may open up new exciting opportunities for future use in various biomedical and forensic applications.
在过去的几十年中，人们对快速、灵敏的技术的需求不断增加，以便在人体生物流体中识别和定量药物化合物，表面增强拉曼散射（SERS）是许多具有潜在应用前景的物理化学技术之一。在这项研究中，我们开发了一种基于 SERS 的分析方法，结合化学计量学用于评估人体生物流体。这种新方法使我们能够在生理相关浓度下检测和定量检测到掺入人血清、血浆和尿液中的β受体阻滞剂普萘洛尔。我们采用了多种多元统计分析技术，包括主成分分析（PCA）、主成分判别分析（PC-DFA）和偏最小二乘回归（PLSR），以研究全 SERS 光谱数据与普萘洛尔水平之间的关系。SERS 光谱与 PCA 和 PC-DFA 相结合，能够清楚地区分未掺杂的生物流体和掺杂了不同浓度普萘洛尔（0 至 120 μM）的生物流体，并且在排序得分空间中可以清楚地看出与普萘洛尔水平相关的趋势。由于 PCA 和 PC-DFA 是分类分类器，因此随后使用 PLSR 模型在所有生物流体中进行了准确的普萘洛尔定量，血清、血浆和尿液的预测精度（表示为预测均方根误差）分别为 0.58、9.68 和 1.69，并且这些模型在 0 至 120 μM 之间对于训练集和测试集也具有出色的线性关系。根据普萘洛尔萘环振动面积计算的检测限为血清 133.1ng/mL（0.45 μM），血浆 156.8ng/mL（0.53 μM），尿液 168.6ng/mL（0.57 μM）。该结果表明，无论生物流体如何，信号都是一致的，并且都在治疗过程中该药物的预期生理水平之内。该研究结果表明，SERS 应用具有作为诊断筛选方法的潜力，进一步验证和优化可以提高对人体生物流体中药物化合物的检测和定量，这可能为未来在各种生物医学和法医学应用中开辟新的令人兴奋的机会。
