A formal comparison of different methods for establishing cut points to distinguish positive and negative samples in immunoassays.

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.