BioPharma Services Inc., Toronto, ON, Canada.
J Chromatogr B Analyt Technol Biomed Life Sci. 2012 Dec 12;911:192-202. doi: 10.1016/j.jchromb.2012.11.008. Epub 2012 Nov 15.
Many different calibration approaches are used for linear calibration in LC-MS bioanalysis, such as different numbers of concentration levels and replicates. However, direct comparison of these approaches is rare, particularly using experimental results. The purpose of this research is to compare different linear calibration approaches (existing and new ones) through simulations and experiments. Both simulation and experimental results demonstrate that linear calibration using two concentrations (two true concentrations, not forced through zero) is as good as or even better than that using multiple concentrations (e.g. 8 or 10) in terms of accuracy. Additionally, two-concentration calibration not only significantly saves time and cost, but is also more robust. Furthermore, it has been demonstrated that the extrapolation of a linear curve at the high concentration end to a linearity-known region is acceptable. When multi-concentration calibration is used, the difference between the two commonly used approaches, i.e. singlet (one curve) or duplicate (two curves) standards per concentration level is small when a method is very precise. Otherwise, one curve approach can result in larger variation at the low concentration end and higher batch failure rate. To reduce the variation and unnecessary reassays due to batch failure or possible rejection of the lowest and/or highest calibration standards, a partially duplicate-standard approach is proposed, which has duplicate-standard-like performance but still saves time and cost as singlet-standard approach does. Finally, the maximum allowable degrees of quadratic (non-linear) response in linear calibration are determined for different scenarios. Because of its multiple advantages and potential application in regulated bioanalysis, recommendations as how to implement two-concentration linear calibration in practice are given and some typical "concerns" regarding linear calibration using only two concentrations are addressed, e.g. how does one know if the response is truly linear over a given range when only two concentrations are used?.
在 LC-MS 生物分析中,有许多不同的校准方法可用于线性校准,例如不同数量的浓度水平和重复次数。然而,很少有直接比较这些方法的研究,特别是使用实验结果进行比较的研究。本研究的目的是通过模拟和实验比较不同的线性校准方法(现有和新方法)。模拟和实验结果均表明,在准确性方面,使用两个浓度(两个真实浓度,而不是强制通过零)进行线性校准与使用多个浓度(例如 8 或 10 个)一样好,甚至更好。此外,两浓度校准不仅显著节省时间和成本,而且更稳健。此外,已经证明在高浓度端将线性曲线外推到线性已知区域是可以接受的。当使用多浓度校准时,当方法非常精确时,两种常用方法(每个浓度水平一个曲线或两个曲线)之间的差异很小。否则,在低浓度端,单曲线方法会导致更大的变化和更高的批次失败率。为了减少由于批次失败或可能拒绝最低和/或最高校准标准而导致的变化和不必要的重新检测,提出了一种部分重复标准方法,该方法具有类似于重复标准的性能,但仍像单标准方法一样节省时间和成本。最后,确定了不同情况下线性校准中最大允许二次(非线性)响应程度。由于其具有多种优势并有可能在监管生物分析中应用,因此给出了如何在实践中实施两浓度线性校准的建议,并解决了一些关于仅使用两个浓度进行线性校准的典型“关注点”,例如当仅使用两个浓度时,如何知道响应在给定范围内是否真正线性?。