Performance qualification for reproducible Surface Plasmon Resonance analysis.

Surface Plasmon Resonance Biosensors (SPR) are one of the most powerful tools to characterize protein binding, e.g. for drug discovery, like target identification, ligand fishing, assay development, lead selection and manufacturing quality control. However, there is increasing concern about its reproducibility in the light of the reproducibility crisis. Therefore an appropriate analytical instrument qualification (AIQ) is required for quality assurance of SPR instruments. AIQ is a prerequisite for analytical method validation and it is consisting of four parts, Design Qualification (DQ), Installation Qualification (IQ), Operational Qualification (OQ) and Performance Qualification (PQ). PQ regularly executed is supposed to continuously control the performance of the instrument under actual running conditions. In this work a performance qualification method was developed for the SPR instrument Biacore X100. This method is suitable for the routinely control of the instrument performance for antibody-antigen binding measurements. Control charts were designed to get a clearly representable and easy implementable tool to check the critical parameters. These control charts and a straightforward protocol now allow the design and application of an individual performance qualification procedure that can be used in the laboratory routine. They serve as reference for individual standard operation procedures (SOPs).

Determination of cross-reactivity of poly- and monoclonal antibodies for synthetic cannabinoids by direct SPR and ELISA.

One of the main reasons for the rise in popularity of synthetic cannabinoids (SCs) is their ability to remain unrecognized in conventional drug screenings. Due to their structural diversity, caused by the constant introduction of new substances to circumvent legal regulation, antibodies with a wide range of cross-reactivity are necessary for the establishment of a reliable immunological based drug test. Therefore, high-quality binding data are needed to select promising antibody candidates for further development. In this study, we carried out a direct surface plasmon resonance (SPR) method and evaluated its suitability for the characterization of antibody-SC interactions. The cross-reactivity of 22 SCs with three polyclonal antibodies, raised against JWH-018 haptens with different attachment positions of the linker, and two commercial available monoclonal antibodies were determined. These results were compared with the commonly used competitive enzyme-linked immunosorbent assay (ELISA). It could be demonstrated, that direct SPR and competitive ELISA show comparable specificity results for the majority of the measured compounds. However, the reduced manual labor, the real-time analysis and the high information content about the binding events of SPR compared to ELISA, showed that SPR is a valuable tool during the development of antibodies against synthetic cannabinoids, currently the largest group of new psychoactive substances.

Long term kinetic measurements revealing precision and general performance of surface plasmon resonance biosensors.

This work presents an extensive parameter list that facilitates a survey of biosensor performance using Biacore instruments for kinetic binding studies. Six long term measurements were performed using a strongly interacting antigen-antibody (ß2 microglobulin) system. Both Single Cycle Kinetic (SCK) and Multi Cycle Kinetic (MCK) were executed each with five different analyte concentrations. The overall comparison of the long term monitored parameters, like the dissociation constant (KD with approximately 3-6% relative percental standard deviation), the association and dissociation rate constants (ka, kd), the analyte binding capacity (Rmax), chi2 and the sum of the absolute values of the residuals, revealed the delicate factors that make the system performance vulnerable. The main influential factors on kinetic performance were the regeneration conditions, the quality of the sensor surface, the usage time and alteration of the sensor surface, the dilution series and the number of run cycles (about 250-600 per chip). Moreover the direct comparison of MCK and SCK uncovered distinct differences in the accuracy of the KD values. The study of sensor chips from two manufacturers showed distinct differences in the precision of the data. Using control charts for the surveillance of these parameters contributes to an overall better system performance.

Data quality in drug discovery: the role of analytical performance in ligand binding assays.

Despite its importance and all the considerable efforts made, the progress in drug discovery is limited. One main reason for this is the partly questionable data quality. Models relating biological activity and structures and in silico predictions rely on precisely and accurately measured binding data. However, these data vary so strongly, such that only variations by orders of magnitude are considered as unreliable. This can certainly be improved considering the high analytical performance in pharmaceutical quality control. Thus the principles, properties and performances of biochemical and cell-based assays are revisited and evaluated. In the part of biochemical assays immunoassays, fluorescence assays, surface plasmon resonance, isothermal calorimetry, nuclear magnetic resonance and affinity capillary electrophoresis are discussed in details, in addition radiation-based ligand binding assays, mass spectrometry, atomic force microscopy and microscale thermophoresis are briefly evaluated. In addition, general sources of error, such as solvent, dilution, sample pretreatment and the quality of reagents and reference materials are discussed. Biochemical assays can be optimized to provide good accuracy and precision (e.g. percental relative standard deviation <10 %). Cell-based assays are often considered superior related to the biological significance, however, typically they cannot still be considered as really quantitative, in particular when results are compared over longer periods of time or between laboratories. A very careful choice of assays is therefore recommended. Strategies to further optimize assays are outlined, considering the evaluation and the decrease of the relevant error sources. Analytical performance and data quality are still advancing and will further advance the progress in drug development.