Medical and Pharmaceutical Research Unit, Deparment of Mathematics and Statistics, Lancaster University, Lancaster LA14YF, United Kingdom.
J Pharm Biomed Anal. 2011 Jul 15;55(5):1148-56. doi: 10.1016/j.jpba.2011.04.006. Epub 2011 Apr 15.
Biotechnology derived therapeutics may induce an unwanted immune response leading to the formation of anti-drug antibodies (ADA). As a result the efficacy and safety of the therapeutic protein could be impaired. Neutralizing antibodies may, for example, affect pharmacokinetics of the therapeutic protein or induce autoimmunity. Therefore a drug induced immune response is a major concern and needs to be assessed during drug development. It is therefore crucial to have assays available for the detection and characterization of ADAs. These assays are used to classify samples in positive and negative samples based on a cut point. In this manuscript we investigate the performance of established and newly developed methods to determine a cut point in immunoassays such as ELISA through simulation and analysis of real data. The different methods are found to have different advantages and disadvantages. A robust parametric approach generally resulted in very good results and can be recommended for many situations. The newly introduced method based on mixture models yields similar results to the robust parametric approach but offers some additional flexibility at the expense of higher complexity.
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）。因此，治疗蛋白的疗效和安全性可能会受到影响。例如，中和抗体可能会影响治疗蛋白的药代动力学或诱导自身免疫。因此，药物诱导的免疫反应是一个主要关注点，需要在药物开发过程中进行评估。因此，有必要建立检测和鉴定 ADA 的检测方法。这些检测方法用于根据临界点将样本分类为阳性和阴性样本。在本文中，我们通过模拟和分析真实数据，研究了用于确定 ELISA 等免疫检测方法临界点的已建立和新开发方法的性能。发现不同的方法有不同的优缺点。稳健的参数方法通常会产生非常好的结果，可以推荐用于许多情况。新引入的基于混合模型的方法与稳健的参数方法产生相似的结果，但在更高的复杂性的代价下提供了一些额外的灵活性。
