University of Würzburg, Institute for Pharmacy and Food Chemistry, 97074 Würzburg, Germany.
Thermo Fisher Scientific, 82110 Germering, Germany.
J Chromatogr A. 2021 Jan 25;1637:461844. doi: 10.1016/j.chroma.2020.461844. Epub 2020 Dec 25.
Charged aerosol detection (CAD) is an universal technique in liquid chromatography that is increasingly used for the quality control of drugs. Consequently, it has found its way into compendial monographs promoted by its simple and robust application. However, the response of CAD is inherently nonlinear due to its principle of function. Thus, easy and rapid linearization procedures, in particular regarding compendial applications, are highly desirable. One effective approach to linearize the detector's signal makes use of the built-in power function value (PFV) setting of the instrument. The PFV is basically a multiplication factor to the power law exponent of the equation describing the CAD's response, thereby altering the detector's signal output to optimize the quasi-linear range of the response curve. The experimental optimization of the PFV for a series of analytes is a time-consuming process, limiting the practicability of this approach. Here, two independent approaches for the determination of the optimal PFV based on an empirical model and a mathematical transformation in each case, are evaluated. Both approaches can be utilized to predict the optimal PFV for each analyte solely based on the experimental results of a series of calibration standards obtained at a single PFV. The approaches were applied to the HPLC-UV-CAD impurity analysis of the drug gabapentin to improve the observed nonlinear response of the impurities in the range of interest. The predicted optimal PFV of both approaches were in good agreement with the experimentally obtained optimal PFV of the analytes. As a result, the accuracy of the method was significantly improved when using the optimal PFV (90 - 105% versus 81 - 115% recovery rate for quantitation by either single-point calibration or linear regression) for the majority of the analytes. The final method with a PFV adjusted to 1.30 was validated with respect to ICH guideline Q2(R1).
荷电气溶胶检测(CAD)是一种在液相色谱中普遍使用的技术,越来越多地用于药物的质量控制。因此,由于其简单而强大的应用,它已经在药典专论中找到了自己的位置。然而,由于其功能原理,CAD 的响应本质上是非线性的。因此,特别是对于药典应用,非常需要简单快速的线性化程序。使检测器信号线性化的一种有效方法是利用仪器的内置幂函数值(PFV)设置。PFV 基本上是描述 CAD 响应的幂律指数的乘法因子,从而改变检测器的信号输出,以优化响应曲线的准线性范围。针对一系列分析物对 PFV 的实验优化是一个耗时的过程,限制了该方法的实用性。在这里,基于经验模型和每种情况下的数学变换,评估了两种独立的方法来确定最佳 PFV。这两种方法都可以用于仅基于在单个 PFV 下获得的一系列校准标准的实验结果来预测每个分析物的最佳 PFV。该方法应用于药物加巴喷丁的 HPLC-UV-CAD 杂质分析中,以改善感兴趣范围内杂质的观察到的非线性响应。这两种方法预测的最佳 PFV 与分析物的实验获得的最佳 PFV 吻合良好。结果,当使用最佳 PFV(对于定量分析,使用单点校准或线性回归时,对于大多数分析物为 90-105%与 81-115%的回收率)时,方法的准确性显著提高。针对 ICH 指南 Q2(R1),对调整到 PFV 1.30 的最终方法进行了验证。