Immunoassays have become invaluable in the generation of data for preclinical and clinical studies, process development/optimization and manufacture of biotherapeutics, and also for new applications such as determining viral titer in cell and gene therapy. Having confidence in the data based on the accurate and precise determination of analytes in complex matrices means overcoming many challenges. These can include interference that reduces robustness, selectivity and specificity, the poor performance of cross-reacting antibody reagents, and time-consuming methods that threaten time plans. Such challenges can be overcome by choosing the right reagents, using the right approach on the right immunoassay platform to deliver reliable data as quickly as possible.
Matrix interference can be a major challenge in the development of immunoassays to study a number of disease states. Kathi Williams and her colleagues at Genentech Inc., USA, experienced considerable problems with matrix interference when developing an ELISA for Phase III studies of a humanized monoclonal antibody when a new patient group was introduced. The solution was to transfer the assay to the Gyrolab platform that, thanks to its flow-through technology helped minimizing matrix effects by reducing the incubation times with the capture reagent antibody.
It is not unusual for a pharmacokinetics assay used early on in a study proves to lack the robustness needed to see the drug candidate through to more advanced clinical studies. A team at Genentech experienced just that and they solved the problem by transferring their ELISA to the Gyrolab platform.
While many preclinical and clinical studies require analysis of plasma, serum or blood, some involve more challenging matrices, such as vitreous and aqueous humor, tears, synovial fluid, and bronchoalveolar lavage. These matrices often have high viscosity, high protein concentration and low analyte concentration. Added to that, the samples are almost invariably very small, expensive and difficult to source. Analytical methods must therefore match the matrix’s complex qualities and also the small volumes available. 
