Department of Laboratory Medicine, Semmelweis University, Budapest, Hungary.
Laboratory of Applied Pharmacokinetics and Bioinformatics, Children's Hospital of Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America.
PLoS One. 2020 Mar 5;15(3):e0229873. doi: 10.1371/journal.pone.0229873. eCollection 2020.
The clinical value of therapeutic drug monitoring can be increased most significantly by integrating assay results into clinical pharmacokinetic models for optimal dosing. The correct weighting in the modeling process is 1/variance, therefore, knowledge of the standard deviations (SD) of each measured concentration is important. Because bioanalytical methods are heteroscedastic, the concentration-SD relationship must be modeled using assay error equations (AEE). We describe a methodology of establishing AEE's for liquid chromatography-tandem mass spectrometry (LC-MS/MS) drug assays using carbamazepine, fluconazole, lamotrigine and levetiracetam as model analytes.
Following method validation, three independent experiments were conducted to develop AEE's using various least squares linear or nonlinear, and median-based linear regression techniques. SD's were determined from zero concentration to the high end of the assayed range. In each experiment, precision profiles of 6 ("small" sample sets) or 20 ("large" sample sets) out of 24 independent, spiked specimens were evaluated. Combinatorial calculations were performed to attain the most suitable regression approach. The final AEE's were developed by combining the SD's of the assay results, established in 24 specimens/spiking level and using all spiking levels, into a single precision profile. The effects of gross hyperbilirubinemia, hemolysis and lipemia as laboratory interferences were investigated.
Precision profiles were best characterized by linear regression when 20 spiking levels, each having 24 specimens and obtained by performing 3 independent experiments, were combined. Theil's regression with the Siegel estimator was the most consistent and robust in providing acceptable agreement between measured and predicted SD's, including SD's below the lower limit of quantification.
In the framework of precision pharmacotherapy, establishing the AEE of assayed drugs is the responsibility of the therapeutic drug monitoring service. This permits optimal dosages by providing the correct weighting factor of assay results in the development of population and individual pharmacokinetic models.
通过将分析结果整合到临床药代动力学模型中以实现最佳给药，可最大程度提高治疗药物监测的临床价值。建模过程中的正确加权为 1/方差，因此，了解每个测量浓度的标准偏差（SD）很重要。由于生物分析方法是异方差的，因此必须使用分析误差方程（AEE）对浓度-SD 关系进行建模。我们描述了一种使用卡马西平、氟康唑、拉莫三嗪和左乙拉西坦作为模型分析物为液相色谱-串联质谱（LC-MS/MS）药物分析建立 AEE 的方法。
在方法验证后，使用各种最小二乘线性或非线性以及基于中位数的线性回归技术进行了三项独立的实验来开发 AEE。SD 是从零浓度到测定范围的高端确定的。在每个实验中，评估了 24 个独立、加标样本中的 6 个（“小”样本集）或 20 个（“大”样本集）的精密度曲线。进行组合计算以获得最合适的回归方法。最终的 AEE 是通过将在 24 个样本/加标水平中建立的分析结果的 SD 以及使用所有加标水平的 SD 组合到单个精密度曲线中而开发的。研究了总胆红素血症、溶血和脂血等实验室干扰的影响。
当将 20 个加标水平（每个水平有 24 个样本）结合在一起，并且通过进行 3 次独立实验获得时，精密度曲线最好通过线性回归来描述。Theil 回归与 Siegel 估计器相结合，在提供测量和预测 SD 之间可接受的一致性方面最为一致和稳健，包括低于定量下限的 SD。
在精准药物治疗的框架内，建立被测药物的 AEE 是治疗药物监测服务的责任。这允许通过在开发群体和个体药代动力学模型中提供分析结果的正确加权因子来实现最佳剂量。
