Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom.
J Pharm Biomed Anal. 2014 Jan;88:27-35. doi: 10.1016/j.jpba.2013.08.013. Epub 2013 Aug 21.
Biotechnology-derived therapeutics may induce an unwanted immune response leading to the formation of anti-drug antibodies (ADAs) which can result in altered efficacy and safety of the therapeutic protein. Anti-drug antibodies may, for example, affect pharmacokinetics of the therapeutic protein or induce autoimmunity. It is therefore crucial to have assays available for the detection and characterization of ADAs. Commonly, a screening assay is initially used to classify samples as either ADA positive or negative. A confirmatory assay, typically based on antigen competition, is subsequently employed to separate false positive samples from truly positive samples. In this manuscript we investigate the performance of different statistical methods classifying samples in competition assays through simulation and analysis of real data. In our evaluations we do not find a uniformly best method although a simple t-test does provide good results throughout. More crucially we find that very large differences between uninhibited and inhibited measurements relative to the assay variability are required in order to obtain useful classification results questioning the usefulness of competition assays with high variability.
生物技术衍生的治疗药物可能会引起不必要的免疫反应，导致产生抗药物抗体 (ADA)，从而改变治疗蛋白的疗效和安全性。抗药物抗体例如可能会影响治疗蛋白的药代动力学或引起自身免疫。因此，拥有用于检测和表征 ADA 的检测方法至关重要。通常，最初使用筛选检测方法将样品分类为 ADA 阳性或阴性。随后使用基于抗原竞争的确认检测方法将假阳性样品与真正的阳性样品分开。在本文中，我们通过模拟和分析真实数据来研究不同统计方法在竞争检测中的分类性能。在我们的评估中，我们没有发现一种统一的最佳方法，尽管简单的 t 检验在整个过程中都提供了很好的结果。更重要的是，我们发现相对于检测变异性，未受抑制和受抑制测量之间的差异非常大是获得有用分类结果所必需的，这质疑了具有高变异性的竞争检测的有用性。
