Pharmacokinetics, Dynamics, and Metabolism Department, Pfizer Global Research and Development, Groton, CT 06340, USA.
Drug Metab Dispos. 2011 Mar;39(3):433-40. doi: 10.1124/dmd.110.036343. Epub 2010 Nov 22.
In discovery and development, having a qualified metabolite standard is advantageous. Chemical synthesis of metabolite standards is often difficult and expensive. As an alternative, biological generation and isolation of metabolites in the nanomole range are readily feasible. However, without an accurately defined concentration, these isolates have limited utility as standards. There is a significant history of NMR as both a qualitative and a quantitative technique, and these concepts have been merged recently to provide both structural and quantitative information on biologically generated isolates from drug metabolism studies. Previous methodologies relied on either specialized equipment or the use of an internal standard to the isolate. We have developed a technique in which a mathematically generated signal can be inserted into a spectrum postacquisition and used as a quantitative reference: artificial signal insertion for calculation of concentration observed (aSICCO). This technique has several advantages over previous methodologies. Any region in the analyte spectra, free from interference, can be chosen for the reference signal. In addition, the magnitude of the inserted signal can be modified to appropriately match the intensity of the sample resonances. Because this is postacquisition quantification, no special equipment or pulse sequence is needed. Compared with quantitation via the addition of an internal standard (10 mM maleic acid), the signal insertion method produced similar results. For each method, precision and accuracy were within ± 5%, stability of signal response over 8 days was ± 5%, and the dynamic range was more than 3 orders of magnitude: 10 to 0.01 mM.
在发现和开发过程中,拥有合格的代谢物标准是有利的。代谢物标准的化学合成通常既困难又昂贵。作为替代方法,在纳摩尔范围内生物生成和分离代谢物是很容易实现的。然而,如果没有准确定义的浓度,这些分离物作为标准的用途就很有限。NMR 既是一种定性技术,也是一种定量技术,这两个概念最近已经融合在一起,为药物代谢研究中从生物生成的分离物提供了结构和定量信息。以前的方法要么依赖于专门的设备,要么依赖于对分离物的内部标准的使用。我们已经开发了一种技术,其中可以在采集后将数学生成的信号插入到光谱中,并用作定量参考:用于计算观察到的浓度的人工信号插入 (aSICCO)。与以前的方法相比,该技术具有几个优势。可以从分析物光谱的任何没有干扰的区域中选择参考信号。此外,可以修改插入信号的幅度,以适当地匹配样品共振的强度。由于这是采集后的定量,因此不需要特殊的设备或脉冲序列。与通过添加内部标准进行定量(10 mM 马来酸)相比,信号插入方法产生了相似的结果。对于每种方法,精度和准确度都在±5%以内,信号响应的稳定性在 8 天内为±5%,动态范围超过 3 个数量级:10 到 0.01 mM。